diff --git a/.gitignore b/.gitignore
index 99c0f9693..8ae62ea94 100644
--- a/.gitignore
+++ b/.gitignore
@@ -92,3 +92,10 @@ ENV/
 
 # PyCharm project setting
 .idea
+.DS_Store
+debug/
+
+.mypy_cache
+.vscode
+.vscode/settings.json
+notebooks/models/*
diff --git a/.travis.yml b/.travis.yml
index 0f853c496..0d5a83cd4 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,17 +1,13 @@
 language: python
 sudo: required
 python:
-  - "2.7"
-  - "3.5"
-# command to install dependencies
+  - '3.6'
 install:
-# numpy not using wheel to avoid problem described in 
-#  https://github.com/tensorflow/tensorflow/issues/6968
   - pip install --no-binary numpy --upgrade numpy
   - pip install -r requirements.txt
-# command to run tests
 script:
-  - export PYTHONPATH=./src:./src/models:./src/align
+  - >-
+    export
+    PYTHONPATH=./facenet_sandberg:./facenet_sandberg/models:./facenet_sandberg/align
   - python -m unittest discover -s test --pattern=*.py 1>&2
 dist: trusty
-
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 000000000..c4336d672
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1 @@
+include facenet_sandberg/align/*.npy
\ No newline at end of file
diff --git a/README.md b/README.md
index 9220d20e4..7b4a7f7e7 100644
--- a/README.md
+++ b/README.md
@@ -1,55 +1,101 @@
-# Face Recognition using Tensorflow [![Build Status][travis-image]][travis]
+# Facial Recognition and Alignment
+## What's this?
 
-[travis-image]: http://travis-ci.org/davidsandberg/facenet.svg?branch=master
-[travis]: http://travis-ci.org/davidsandberg/facenet
+This repository contains a refactored implementation of David Sandberg's [FaceNet](https://github.com/davidsandberg/facenet) and [InsightFace](https://github.com/deepinsight/insightface) for facial recognition. It also contains an implementation of [MTCNN](https://github.com/ipazc/mtcnn) and [Faceboxes](https://github.com/TropComplique/FaceBoxes-tensorflow) for face cropping and alignment. What is in the refactor:
 
-This is a TensorFlow implementation of the face recognizer described in the paper
-["FaceNet: A Unified Embedding for Face Recognition and Clustering"](http://arxiv.org/abs/1503.03832). The project also uses ideas from the paper ["Deep Face Recognition"](http://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf) from the [Visual Geometry Group](http://www.robots.ox.ac.uk/~vgg/) at Oxford.
+- Made algorithms easily and efficiently usable with [convenience classes](https://github.com/armanrahman22/facenet/tree/master/facenet_sandberg/inference). 
+- Added much more efficient methods of batch processing face recognition and alignment
+- Added true face alignment (with affine transformation) to align face to bottom-center of image: [code](https://github.com/armanrahman22/facenet/blob/f6cb32a193925002da41fb491c52bb85384bee55/facenet_sandberg/utils.py#L187)
+- Added proportional margin to alignment as per this [issue](https://github.com/davidsandberg/facenet/issues/283)
+- Ability to easily switch between [insightface](https://github.com/armanrahman22/facenet/blob/master/facenet_sandberg/inference/insightface_encoder.py) and [facenet](https://github.com/armanrahman22/facenet/blob/master/facenet_sandberg/inference/facenet_encoder.py) at [inference time](https://github.com/armanrahman22/facenet/blob/master/facenet_sandberg/inference/identifier.py)
 
-## Compatibility
-The code is tested using Tensorflow r1.7 under Ubuntu 14.04 with Python 2.7 and Python 3.5. The test cases can be found [here](https://github.com/davidsandberg/facenet/tree/master/test) and the results can be found [here](http://travis-ci.org/davidsandberg/facenet).
+More information on customizing and implementing new face detection algorithms can be found [here](./algorithms/README.md).
 
-## News
-| Date     | Update |
-|----------|--------|
-| 2018-04-10 | Added new models trained on Casia-WebFace and VGGFace2 (see below). Note that the models uses fixed image standardization (see [wiki](https://github.com/davidsandberg/facenet/wiki/Training-using-the-VGGFace2-dataset)). |
-| 2018-03-31 | Added a new, more flexible input pipeline as well as a bunch of minor updates. |
-| 2017-05-13 | Removed a bunch of older non-slim models. Moved the last bottleneck layer into the respective models. Corrected normalization of Center Loss. |
-| 2017-05-06 | Added code to [train a classifier on your own images](https://github.com/davidsandberg/facenet/wiki/Train-a-classifier-on-own-images). Renamed facenet_train.py to train_tripletloss.py and facenet_train_classifier.py to train_softmax.py. |
-| 2017-03-02 | Added pretrained models that generate 128-dimensional embeddings.|
-| 2017-02-22 | Updated to Tensorflow r1.0. Added Continuous Integration using Travis-CI.|
-| 2017-02-03 | Added models where only trainable variables has been stored in the checkpoint. These are therefore significantly smaller. |
-| 2017-01-27 | Added a model trained on a subset of the MS-Celeb-1M dataset. The LFW accuracy of this model is around 0.994. |
-| 2017‑01‑02 | Updated to run with Tensorflow r0.12. Not sure if it runs with older versions of Tensorflow though.   |
+## Installation
+To use in other projects, this implementation can be pip installed as follows:
+```
+pip install facenet_sandberg
+```
 
-## Pre-trained models
-| Model name      | LFW accuracy | Training dataset | Architecture |
-|-----------------|--------------|------------------|-------------|
-| [20180408-102900](https://drive.google.com/open?id=1R77HmFADxe87GmoLwzfgMu_HY0IhcyBz) | 0.9905        | CASIA-WebFace    | [Inception ResNet v1](https://github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py) |
-| [20180402-114759](https://drive.google.com/open?id=1EXPBSXwTaqrSC0OhUdXNmKSh9qJUQ55-) | 0.9965        | VGGFace2      | [Inception ResNet v1](https://github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py) |
+To use locally:
+1. Clone repo
+2. cd to base directory with setup.py 
+3. run:
+```
+pip install -e .
+```
+^(installs package in [development mode](https://setuptools.readthedocs.io/en/latest/setuptools.html#development-mode))
 
-NOTE: If you use any of the models, please do not forget to give proper credit to those providing the training dataset as well.
+## Important Requirements
+1. Python 3.5 
+2. Tensorflow==1.7
+3. Tensorlayer==1.7
+The rest is specified in [requirements.txt](https://github.com/armanrahman22/facenet/blob/master/requirements.txt)
 
-## Inspiration
-The code is heavily inspired by the [OpenFace](https://github.com/cmusatyalab/openface) implementation.
+## Models
+Links to pretrained models: 
 
-## Training data
-The [CASIA-WebFace](http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html) dataset has been used for training. This training set consists of total of 453 453 images over 10 575 identities after face detection. Some performance improvement has been seen if the dataset has been filtered before training. Some more information about how this was done will come later.
-The best performing model has been trained on the [VGGFace2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) dataset consisting of ~3.3M faces and ~9000 classes.
+- [Facenet](https://redcrossstorage.blob.core.windows.net/images/facenet_model.pb)
+  - Uses RGB images of size 160x160
+- [Insightface.zip](https://redcrossstorage.blob.core.windows.net/images/insightface_ckpt.zip)
+  - Uses BGR images of size 112x112
 
-## Pre-processing
+## Datasets
+Links to download training datasets (!big files!):
 
-### Face alignment using MTCNN
-One problem with the above approach seems to be that the Dlib face detector misses some of the hard examples (partial occlusion, silhouettes, etc). This makes the training set too "easy" which causes the model to perform worse on other benchmarks.
-To solve this, other face landmark detectors has been tested. One face landmark detector that has proven to work very well in this setting is the
-[Multi-task CNN](https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html). A Matlab/Caffe implementation can be found [here](https://github.com/kpzhang93/MTCNN_face_detection_alignment) and this has been used for face alignment with very good results. A Python/Tensorflow implementation of MTCNN can be found [here](https://github.com/davidsandberg/facenet/tree/master/src/align). This implementation does not give identical results to the Matlab/Caffe implementation but the performance is very similar.
+- [Emore](https://redcrossstorage.blob.core.windows.net/datasets/faces_emore.zip) 
+- [MSM_refined_112x112](https://redcrossstorage.blob.core.windows.net/datasets/faces_ms1m-refine-v2_112x112.zip)
+- [VGG2_112x112](https://redcrossstorage.blob.core.windows.net/datasets/faces_vgg2_112x112.zip)
 
-## Running training
-Currently, the best results are achieved by training the model using softmax loss. Details on how to train a model using softmax loss on the CASIA-WebFace dataset can be found on the page [Classifier training of Inception-ResNet-v1](https://github.com/davidsandberg/facenet/wiki/Classifier-training-of-inception-resnet-v1) and .
+## Image directory structure
+This repo assumes images are in [LFW format](http://vis-www.cs.umass.edu/lfw/README.txt):
+```
+-/base_images_folder
+  -/person_1
+    -person_1_0001.jpg
+    -person_1_0002.jpg
+    -person_1_0003.jpg
+  -/person_2
+    -person_2_0001.jpg
+    -person_2_0002.jpg
+  ...
+```
+
+If your dataset is not like this you can use [lfw.py](https://github.com/armanrahman22/facenet/blob/master/facenet_sandberg/lfw.py) to put your images into the right format like so (from facenet_sandberg/facenet_sandberg):
+```
+python lfw.py --image_directory PATH_TO_YOUR_BASE_IMAGE_DIRECTORY
+```
+
+## Alignment
+Alignment is done with a combination of Faceboxes and MTCNN. While Faceboxes is more accurate and works with more images than MTCNN, it does not return [facial landmarks](https://raw.githubusercontent.com/ipazc/mtcnn/master/result.jpg). Whichever algorithm returns more results is used.
+
+Use the [align_dataset.py](https://github.com/armanrahman22/facenet/blob/master/facenet_sandberg/align_dataset.py) script to align an entire image directory:
+```
+python align_dataset.py --input_dir PATH_TO_YOUR_BASE_IMAGE_DIRECTORY \
+                        --output_dir PATH_TO_OUTPUT_ALIGNED_IMAGES \
+                        --facenet_model_checkpoint PATH_TO_PRETRAINED_FACENET_MODEL \
+                        --image_height DESIRED_IMAGE_HEIGHT \
+                        --image_width DESIRED_IMAGE_WIDTH \
+                        --margin DESIRED_PROPORTIONAL_MARGIN \
+                        --scale_factor DESIRED_SCALE_FACTOR \
+                        --steps_threshold DESIRED_STEPS \
+                        --detect_multiple_faces \
+                        --use_faceboxes \
+                        --use_affine \
+                        --num_processes NUM_PROCESSES_TO_USE
+```
+* Default values for most arguments are provided [here](https://github.com/armanrahman22/facenet/blob/f6cb32a193925002da41fb491c52bb85384bee55/facenet_sandberg/align_dataset.py#L262) 
+
+## Generate Pairs.txt
+A pairs.txt file is used in training and testing. It follows this [format](http://vis-www.cs.umass.edu/lfw/README.txt). In order to generate your own pairs.txt run:
+```
+python align_dataset.py --image_dir PATH_TO_YOUR_BASE_IMAGE_DIRECTORY \
+                        --pairs_file_name OUTPUT_NAME_OF_PAIRS_FILE \
+                        --num_folds NUMBER_OF_FOLDS_FOR_CROSS_VALIDATION \
+                        --num_matches_mismatches NUMBER_OF_MATCHES_AND_MISMATCHES
+```
+
+## Copyright
+MIT License from original repo https://github.com/davidsandberg/facenet/blob/master/LICENSE.md
 
-## Pre-trained models
-### Inception-ResNet-v1 model
-A couple of pretrained models are provided. They are trained using softmax loss with the Inception-Resnet-v1 model. The datasets has been aligned using [MTCNN](https://github.com/davidsandberg/facenet/tree/master/src/align).
 
-## Performance
-The accuracy on LFW for the model [20180402-114759](https://drive.google.com/open?id=1EXPBSXwTaqrSC0OhUdXNmKSh9qJUQ55-) is 0.99650+-0.00252. A description of how to run the test can be found on the page [Validate on LFW](https://github.com/davidsandberg/facenet/wiki/Validate-on-lfw). Note that the input images to the model need to be standardized using fixed image standardization (use the option `--use_fixed_image_standardization` when running e.g. `validate_on_lfw.py`).
diff --git a/facenet_sandberg/__init__.py b/facenet_sandberg/__init__.py
new file mode 100644
index 000000000..752dcbb2b
--- /dev/null
+++ b/facenet_sandberg/__init__.py
@@ -0,0 +1,5 @@
+from .align_dataset import align_dataset
+from .common_types import *
+from .generate_pairs import generate_pairs
+from .inference import *
+from .utils import *
diff --git a/facenet_sandberg/align_dataset.py b/facenet_sandberg/align_dataset.py
new file mode 100644
index 000000000..8d14386d5
--- /dev/null
+++ b/facenet_sandberg/align_dataset.py
@@ -0,0 +1,217 @@
+"""Performs face alignment and stores face thumbnails in the output directory."""
+import argparse
+import os
+import sys
+import warnings
+from ctypes import c_int
+from multiprocessing import Lock, Value
+from typing import List, Optional, Tuple, cast
+
+import cv2
+import progressbar as pb
+import tensorflow as tf
+from pathos.multiprocessing import ProcessPool
+
+from facenet_sandberg.common_types import Face, FacesGenerator, PersonClass
+from facenet_sandberg.inference import align, facenet_encoder
+from facenet_sandberg.utils import (get_dataset, get_image_from_path_rgb,
+                                    transform_to_lfw_format)
+
+# pylint: disable=no-member
+from .config import AlignConfig
+
+# pylint: disable=no-member
+
+
+warnings.filterwarnings("ignore", message="numpy.dtype size changed")
+warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+tf.logging.set_verbosity(tf.logging.ERROR)
+
+WIDGETS = ['Aligning Dataset: ', pb.Percentage(), ' ',
+           pb.Bar(), ' ', pb.ETA()]
+TIMER = pb.ProgressBar(widgets=WIDGETS)
+NUM_SUCESSFUL = Value(c_int)  # defaults to 0
+NUM_SUCESSFUL_LOCK = Lock()
+NUM_UNSECESSFUL = Value(c_int)
+NUM_UNSUCESSFUL_LOCK = Lock()
+NUM_IMAGES_TOTAL = Value(c_int)
+NUM_IMAGES_TOTAL_LOCK = Lock()
+
+
+def align_dataset(config_file: str):
+    """Aligns an image dataset
+    """
+    config = AlignConfig(config_file)
+    output_dir = os.path.expanduser(config.output_dir)
+    os.makedirs(output_dir, exist_ok=True)
+
+    dataset = get_dataset(config.input_dir)
+
+    num_images = sum(len(i) for i in dataset)
+    TIMER.max_value = num_images
+    TIMER.start()
+
+    num_processes = cast(int, min(config.num_processes, os.cpu_count()))
+    if num_processes == -1:
+        num_processes = os.cpu_count()
+    if num_processes > 1:
+        process_pool = ProcessPool(num_processes)
+        process_pool.imap(
+            align_person, zip(
+                dataset, [config] * len(dataset)))
+        process_pool.close()
+        process_pool.join()
+    else:
+        for person in dataset:
+            align_person((person, config))
+
+    transform_to_lfw_format(output_dir, num_processes)
+
+    TIMER.finish()
+    print('Total number of images: %d' % int(NUM_IMAGES_TOTAL.value))
+    print('Number of faces found and aligned: %d' %
+          int(NUM_SUCESSFUL.value))
+    print('Number of unsuccessful: %d' %
+          int(NUM_UNSECESSFUL.value))
+
+
+def align_person(data: Tuple[PersonClass, AlignConfig]) -> None:
+    person, config = data
+    output_class_dir = os.path.join(config.output_dir, person.name)
+    if already_done(person, output_class_dir):
+        increment_total(len(person.image_paths))
+        TIMER.update(int(NUM_IMAGES_TOTAL.value))
+        return None
+    detector = align.Detector(
+        face_crop_height=config.face_crop_height,
+        face_crop_width=config.face_crop_width,
+        face_crop_margin=config.face_crop_margin,
+        scale_factor=config.scale_factor,
+        steps_threshold=config.scale_factor,
+        detect_multiple_faces=config.detect_multiple_faces,
+        use_affine=config.use_affine,
+        use_faceboxes=config.use_faceboxes)
+
+    if not os.path.exists(output_class_dir):
+        os.makedirs(output_class_dir)
+
+    all_faces = gen_all_faces(person, output_class_dir, detector)
+    if config.detect_multiple_faces and config.facenet_model_checkpoint and all_faces:
+        encoder = None
+        anchor = get_anchor(person, output_class_dir, detector)
+        if anchor:
+            for faces in all_faces:
+                if not faces:
+                    pass
+                elif len(faces) > 1:
+                    if not encoder:
+                        encoder = facenet_encoder.Facenet(
+                            model_path=config.facenet_model_checkpoint)
+                    best_face = encoder.get_best_match(anchor, faces)
+                    if best_face:
+                        cv2.imwrite(
+                            best_face.name, cv2.cvtColor(
+                                best_face.image, cv2.COLOR_RGB2BGR))
+                elif len(faces) == 1:
+                    cv2.imwrite(
+                        faces[0].name, cv2.cvtColor(
+                            faces[0].image, cv2.COLOR_RGB2BGR))
+        if encoder:
+            encoder.tear_down()
+            del encoder
+    else:
+        for faces in all_faces:
+            if faces:
+                for face in faces:
+                    cv2.imwrite(
+                        face.name, cv2.cvtColor(
+                            face.image, cv2.COLOR_RGB2BGR))
+    del detector
+    TIMER.update(int(NUM_IMAGES_TOTAL.value))
+
+
+def gen_all_faces(person: PersonClass, output_class_dir: str,
+                  detector: align.Detector) -> FacesGenerator:
+    for image_path in person.image_paths:
+        increment_total()
+        output_filename = get_file_name(image_path, output_class_dir)
+        if not os.path.exists(output_filename):
+            faces = process_image(detector, image_path, output_filename)
+            if faces:
+                yield faces
+
+
+def already_done(person: PersonClass, output_class_dir: str):
+    total = sum(os.path.exists(get_file_name(image_path, output_class_dir))
+                for image_path in person.image_paths)
+    return total == len(person.image_paths)
+
+
+def get_anchor(person: PersonClass, output_class_dir: str,
+               detector: align.Detector) -> Optional[Face]:
+    first_face = None
+    for image_path in person.image_paths:
+        output_filename = get_file_name(image_path, output_class_dir)
+        faces = process_image(detector, image_path, output_filename)
+        if faces and not first_face:
+            first_face = faces[0]
+        if len(faces) == 1:
+            return faces[0]
+    return first_face
+
+
+def process_image(detector: align.Detector,
+                  image_path: str, output_filename: str) -> List[Face]:
+    image = get_image_from_path_rgb(image_path)
+    if image is not None:
+        faces = detector.find_faces(image)
+        if not faces:
+            increment_unsucessful()
+        for person in faces:
+            increment_sucessful()
+            filename_base, file_extension = os.path.splitext(
+                output_filename)
+            output_filename_n = "{}{}".format(
+                filename_base, file_extension)
+            person.name = output_filename_n
+        return faces
+    return []
+
+
+def increment_sucessful(add_amount: int=1):
+    with NUM_SUCESSFUL_LOCK:
+        NUM_SUCESSFUL.value += add_amount
+
+
+def increment_unsucessful(add_amount: int=1):
+    with NUM_UNSUCESSFUL_LOCK:
+        NUM_UNSECESSFUL.value += add_amount
+
+
+def increment_total(add_amount: int=1):
+    with NUM_IMAGES_TOTAL_LOCK:
+        NUM_IMAGES_TOTAL.value += add_amount
+
+
+def get_file_name(image_path: str, output_class_dir: str) -> str:
+    filename = os.path.splitext(os.path.split(image_path)[1])[0]
+    output_filename = os.path.join(
+        output_class_dir, filename + '.png')
+    return output_filename
+
+
+def parse_arguments(argv):
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--config_file',
+        type=str,
+        help='Path to align config file',
+        default='facenet_config.json')
+    return parser.parse_args(argv)
+
+
+if __name__ == '__main__':
+    ARGS = parse_arguments(sys.argv[1:])
+    if ARGS:
+        align_dataset(ARGS.config_file)
diff --git a/src/calculate_filtering_metrics.py b/facenet_sandberg/calculate_filtering_metrics.py
similarity index 70%
rename from src/calculate_filtering_metrics.py
rename to facenet_sandberg/calculate_filtering_metrics.py
index f60b9ae4d..34e0e0fe2 100644
--- a/src/calculate_filtering_metrics.py
+++ b/facenet_sandberg/calculate_filtering_metrics.py
@@ -1,19 +1,19 @@
 """Calculate filtering metrics for a dataset and store in a .hdf file.
 """
 # MIT License
-# 
+#
 # Copyright (c) 2016 David Sandberg
-# 
+#
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
 # in the Software without restriction, including without limitation the rights
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
 # furnished to do so, subject to the following conditions:
-# 
+#
 # The above copyright notice and this permission notice shall be included in all
 # copies or substantial portions of the Software.
-# 
+#
 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
@@ -22,58 +22,58 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
-import tensorflow as tf
-import numpy as np
 import argparse
-import facenet
+import math
 import os
 import sys
 import time
+
 import h5py
-import math
-from tensorflow.python.platform import gfile
+import numpy as np
+import tensorflow as tf
+from facenet_sandberg import facenet
 from six import iteritems
+from tensorflow.python.platform import gfile
+
 
 def main(args):
     dataset = facenet.get_dataset(args.dataset_dir)
-  
+
     with tf.Graph().as_default():
-      
+
         # Get a list of image paths and their labels
         image_list, label_list = facenet.get_image_paths_and_labels(dataset)
         nrof_images = len(image_list)
         image_indices = range(nrof_images)
 
         image_batch, label_batch = facenet.read_and_augment_data(image_list,
-            image_indices, args.image_size, args.batch_size, None, 
-            False, False, False, nrof_preprocess_threads=4, shuffle=False)
-        
+                                                                 image_indices, args.image_size, args.batch_size, None,
+                                                                 False, False, False, nrof_preprocess_threads=4, shuffle=False)
+
         model_exp = os.path.expanduser(args.model_file)
-        with gfile.FastGFile(model_exp,'rb') as f:
+        with gfile.FastGFile(model_exp, 'rb') as f:
             graph_def = tf.GraphDef()
             graph_def.ParseFromString(f.read())
-            input_map={'input':image_batch, 'phase_train':False}
+            input_map = {'input': image_batch, 'phase_train': False}
             tf.import_graph_def(graph_def, input_map=input_map, name='net')
-        
+
         embeddings = tf.get_default_graph().get_tensor_by_name("net/embeddings:0")
 
         with tf.Session() as sess:
             tf.train.start_queue_runners(sess=sess)
-                
+
             embedding_size = int(embeddings.get_shape()[1])
             nrof_batches = int(math.ceil(nrof_images / args.batch_size))
             nrof_classes = len(dataset)
             label_array = np.array(label_list)
             class_names = [cls.name for cls in dataset]
-            nrof_examples_per_class = [ len(cls.image_paths) for cls in dataset ]
+            nrof_examples_per_class = [len(cls.image_paths) for cls in dataset]
             class_variance = np.zeros((nrof_classes,))
-            class_center = np.zeros((nrof_classes,embedding_size))
-            distance_to_center = np.ones((len(label_list),))*np.NaN
-            emb_array = np.zeros((0,embedding_size))
+            class_center = np.zeros((nrof_classes, embedding_size))
+            distance_to_center = np.ones((len(label_list),)) * np.NaN
+            emb_array = np.zeros((0, embedding_size))
             idx_array = np.zeros((0,), dtype=np.int32)
             lab_array = np.zeros((0,), dtype=np.int32)
             index_arr = np.append(0, np.cumsum(nrof_examples_per_class))
@@ -84,45 +84,60 @@ def main(args):
                 idx_array = np.append(idx_array, idx, axis=0)
                 lab_array = np.append(lab_array, label_array[idx], axis=0)
                 for cls in set(lab_array):
-                    cls_idx = np.where(lab_array==cls)[0]
-                    if cls_idx.shape[0]==nrof_examples_per_class[cls]:
+                    cls_idx = np.where(lab_array == cls)[0]
+                    if cls_idx.shape[0] == nrof_examples_per_class[cls]:
                         # We have calculated all the embeddings for this class
                         i2 = np.argsort(idx_array[cls_idx])
-                        emb_class = emb_array[cls_idx,:]
-                        emb_sort = emb_class[i2,:]
+                        emb_class = emb_array[cls_idx, :]
+                        emb_sort = emb_class[i2, :]
                         center = np.mean(emb_sort, axis=0)
                         diffs = emb_sort - center
                         dists_sqr = np.sum(np.square(diffs), axis=1)
                         class_variance[cls] = np.mean(dists_sqr)
-                        class_center[cls,:] = center
-                        distance_to_center[index_arr[cls]:index_arr[cls+1]] = np.sqrt(dists_sqr)
+                        class_center[cls, :] = center
+                        distance_to_center[index_arr[cls]
+                            : index_arr[cls + 1]] = np.sqrt(dists_sqr)
                         emb_array = np.delete(emb_array, cls_idx, axis=0)
                         idx_array = np.delete(idx_array, cls_idx, axis=0)
                         lab_array = np.delete(lab_array, cls_idx, axis=0)
 
-                        
-                print('Batch %d in %.3f seconds' % (i, time.time()-t))
-                
+                print('Batch %d in %.3f seconds' % (i, time.time() - t))
+
             print('Writing filtering data to %s' % args.data_file_name)
-            mdict = {'class_names':class_names, 'image_list':image_list, 'label_list':label_list, 'distance_to_center':distance_to_center }
+            mdict = {
+                'class_names': class_names,
+                'image_list': image_list,
+                'label_list': label_list,
+                'distance_to_center': distance_to_center}
             with h5py.File(args.data_file_name, 'w') as f:
                 for key, value in iteritems(mdict):
                     f.create_dataset(key, data=value)
-                        
+
+
 def parse_arguments(argv):
     parser = argparse.ArgumentParser()
-    
-    parser.add_argument('dataset_dir', type=str,
+
+    parser.add_argument(
+        'dataset_dir',
+        type=str,
         help='Path to the directory containing aligned dataset.')
-    parser.add_argument('model_file', type=str,
+    parser.add_argument(
+        'model_file',
+        type=str,
         help='File containing the frozen model in protobuf (.pb) format to use for feature extraction.')
-    parser.add_argument('data_file_name', type=str,
+    parser.add_argument(
+        'data_file_name',
+        type=str,
         help='The name of the file to store filtering data in.')
     parser.add_argument('--image_size', type=int,
-        help='Image size.', default=160)
-    parser.add_argument('--batch_size', type=int,
-        help='Number of images to process in a batch.', default=90)
+                        help='Image size.', default=160)
+    parser.add_argument(
+        '--batch_size',
+        type=int,
+        help='Number of images to process in a batch.',
+        default=90)
     return parser.parse_args(argv)
 
+
 if __name__ == '__main__':
     main(parse_arguments(sys.argv[1:]))
diff --git a/facenet_sandberg/common_types.py b/facenet_sandberg/common_types.py
new file mode 100644
index 000000000..8b6d2e19a
--- /dev/null
+++ b/facenet_sandberg/common_types.py
@@ -0,0 +1,119 @@
+from enum import Enum
+from typing import Dict, Generator, List, Tuple, Union
+
+import numpy as np
+
+Landmarks = Dict[str, Tuple[int, int]]
+
+
+class DistanceMetric(Enum):
+    ANGULAR_DISTANCE = 1
+    EUCLIDEAN_SQUARED = 0
+
+    @staticmethod
+    def from_str(label: str):
+        if label == 'ANGULAR_DISTANCE':
+            return DistanceMetric.ANGULAR_DISTANCE
+        elif label == 'EUCLIDEAN_SQUARED':
+            return DistanceMetric.EUCLIDEAN_SQUARED
+        else:
+            raise NotImplementedError(
+                "Distance metric must be either ANGULAR_DISTANCE or EUCLIDEAN_SQUARED")
+
+
+class ThresholdMetric(Enum):
+    ACCURACY = 0
+    PRECISION = 1
+    RECALL = 2
+
+    @staticmethod
+    def from_str(label: str):
+        if label == 'ACCURACY':
+            return ThresholdMetric.ACCURACY
+        elif label == 'PRECISION':
+            return ThresholdMetric.PRECISION
+        elif label == 'RECALL':
+            return ThresholdMetric.RECALL
+        else:
+            raise NotImplementedError(
+                "Threshold metric must be either ACCURACY or PRECISION or RECALL")
+
+
+class ImageExtensions(Enum):
+    PNG = 'png'
+    JPG = 'jpg'
+    JPEG = 'jpeg'
+
+
+class PersonClass:
+    "Stores the paths to images for a given person"
+
+    def __init__(self, name: str, image_paths: List[str]) -> None:
+        self.name = name
+        self.image_paths = image_paths
+
+    def __str__(self):
+        return self.name + ', ' + str(len(self.image_paths)) + ' images'
+
+    def __len__(self):
+        return len(self.image_paths)
+
+
+class AlignResult:
+    def __init__(
+            self, bounding_box: List[int], landmarks: Landmarks=None) -> None:
+        # Bounding Box: [x1, y2, x2, y2]
+        self.bounding_box = bounding_box
+        self.landmarks = landmarks
+
+
+class Face:
+    """Class representing a single face
+
+    Attributes:
+        name {str} -- Name of person
+        bounding_box {Float[]} -- box around their face in container_image
+        image {Image} -- Image cropped around face
+        container_image {Image} -- Original image
+        embedding {Float} -- Face embedding
+        matches {Matches[]} -- List of matches to the face
+        url {str} -- Url where image came from
+    """
+
+    def __init__(self):
+        self.name = None
+        self.bounding_box = None
+        self.image = None
+        self.container_image = None
+        self.embedding = None
+        self.matches = []
+        self.url = None
+
+
+class Match:
+    """Class representing a match between two faces
+
+    Attributes:
+        face_1 {Face} -- Face object for person 1
+        face_2 {Face} -- Face object for person 2
+        score {Float} -- Distance between two face embeddings
+        is_match {bool} -- whether is match between faces
+    """
+
+    def __init__(self):
+        self.face_1 = Face()
+        self.face_2 = Face()
+        self.score = float("inf")
+        self.is_match = False
+
+
+Image = np.ndarray
+Embedding = np.ndarray
+EmbeddingsGenerator = Generator[List[np.ndarray], None, None]
+ImageGenerator = Generator[np.ndarray, None, None]
+FaceGenerator = Generator[Face, None, None]
+FacesGenerator = Generator[List[Face], None, None]
+Match = Tuple[str, int, int]
+Mismatch = Tuple[str, int, str, int]
+Pair = Union[Match, Mismatch]
+Label = bool
diff --git a/facenet_sandberg/config.py b/facenet_sandberg/config.py
new file mode 100644
index 000000000..8625e5458
--- /dev/null
+++ b/facenet_sandberg/config.py
@@ -0,0 +1,61 @@
+import json
+
+from facenet_sandberg.common_types import DistanceMetric, ThresholdMetric
+
+
+def get_config(config_file: str):
+    with open(config_file, 'r') as json_file:
+        config = json.load(json_file)
+    return config
+
+
+class AlignConfig:
+    def __init__(self, config_file: str):
+        self.config = get_config(config_file)
+        self.is_rgb = self.config['IS_RGB']
+        self.face_crop_height = self.config['FACE_CROP_HEIGHT']
+        self.face_crop_width = self.config['FACE_CROP_WIDTH']
+        self.face_crop_margin = self.config['FACE_CROP_MARGIN']
+        self.scale_factor = self.config['SCALE_FACTOR']
+        self.steps_threshold = self.config['STEPS_THRESHOLD']
+        self.detect_multiple_faces = self.config['DETECT_MULTIPLE_FACES']
+        self.use_affine = self.config['USE_AFFINE']
+        self.use_faceboxes = self.config['USE_FACEBOXES']
+        self.num_processes = self.config['NUM_PROCESSES']
+        self.facenet_model_checkpoint = self.config['FACENET_MODEL_CHECKPOINT']
+        self.input_dir = self.config['INPUT_DIR']
+        self.output_dir = self.config['OUTPUT_DIR']
+
+
+class ValidateConfig:
+    def __init__(self, config_file: str):
+        self.config = get_config(config_file)
+        # Path to the image directory
+        self.image_dir = self.config['IMAGE_DIR']
+        # Filename of pairs.txt
+        self.pairs_file_name = self.config['PAIRS_FILE_NAME']
+        # Number of cross validation folds
+        self.num_folds = self.config['NUM_FOLDS']
+        # Distance metric for face verification
+        self.distance_metric = DistanceMetric.from_str(
+            self.config['DISTANCE_METRIC'])
+        # Start value for distance threshold.
+        self.threshold_start = self.config['THRESHOLD_START']
+        # End value for distance threshold
+        self.threshold_end = self.config['THRESHOLD_END']
+        # Step size for iterating in cross validation search
+        self.threshold_step = self.config['THRESHOLD_STEP']
+        # metric for calculating threshold automatically
+        self.threshold_metric = ThresholdMetric.from_str(
+            self.config['THRESHOLD_METRIC'])
+        # Size of face vectors
+        self.embedding_size = self.config['EMBEDDING_SIZE']
+        # Instead of a default encoding for images where faces are not
+        # detected, remove them
+        self.remove_empty_embeddings = self.config['REMOVE_EMPTY_EMBEDDINGS']
+        # Subtract mean of embeddings before distance calculation.
+        self.subtract_mean = self.config['SUBTRACT_MEAN']
+        # Divide embeddings by stddev before distance calculation.
+        self.divide_stddev = self.config['DIVIDE_STDDEV']
+        # Specify if the images have already been aligned.
+        self.prealigned = self.config['PREALIGNED']
diff --git a/data/images/Anthony_Hopkins_0001.jpg b/facenet_sandberg/data/images/Anthony_Hopkins_0001.jpg
similarity index 100%
rename from data/images/Anthony_Hopkins_0001.jpg
rename to facenet_sandberg/data/images/Anthony_Hopkins_0001.jpg
diff --git a/data/images/Anthony_Hopkins_0002.jpg b/facenet_sandberg/data/images/Anthony_Hopkins_0002.jpg
similarity index 100%
rename from data/images/Anthony_Hopkins_0002.jpg
rename to facenet_sandberg/data/images/Anthony_Hopkins_0002.jpg
diff --git a/data/learning_rate_retrain_tripletloss.txt b/facenet_sandberg/data/learning_rate_retrain_tripletloss.txt
similarity index 100%
rename from data/learning_rate_retrain_tripletloss.txt
rename to facenet_sandberg/data/learning_rate_retrain_tripletloss.txt
diff --git a/data/learning_rate_schedule_classifier_casia.txt b/facenet_sandberg/data/learning_rate_schedule_classifier_casia.txt
similarity index 100%
rename from data/learning_rate_schedule_classifier_casia.txt
rename to facenet_sandberg/data/learning_rate_schedule_classifier_casia.txt
diff --git a/data/learning_rate_schedule_classifier_msceleb.txt b/facenet_sandberg/data/learning_rate_schedule_classifier_msceleb.txt
similarity index 100%
rename from data/learning_rate_schedule_classifier_msceleb.txt
rename to facenet_sandberg/data/learning_rate_schedule_classifier_msceleb.txt
diff --git a/data/learning_rate_schedule_classifier_vggface2.txt b/facenet_sandberg/data/learning_rate_schedule_classifier_vggface2.txt
similarity index 100%
rename from data/learning_rate_schedule_classifier_vggface2.txt
rename to facenet_sandberg/data/learning_rate_schedule_classifier_vggface2.txt
diff --git a/data/pairs.txt b/facenet_sandberg/data/pairs.txt
similarity index 100%
rename from data/pairs.txt
rename to facenet_sandberg/data/pairs.txt
diff --git a/src/decode_msceleb_dataset.py b/facenet_sandberg/decode_msceleb_dataset.py
similarity index 69%
rename from src/decode_msceleb_dataset.py
rename to facenet_sandberg/decode_msceleb_dataset.py
index 4556bfa6c..188f18bd5 100644
--- a/src/decode_msceleb_dataset.py
+++ b/facenet_sandberg/decode_msceleb_dataset.py
@@ -2,19 +2,19 @@
 https://www.microsoft.com/en-us/research/project/ms-celeb-1m-challenge-recognizing-one-million-celebrities-real-world/
 """
 # MIT License
-# 
+#
 # Copyright (c) 2016 David Sandberg
-# 
+#
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
 # in the Software without restriction, including without limitation the rights
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
 # furnished to do so, subject to the following conditions:
-# 
+#
 # The above copyright notice and this permission notice shall be included in all
 # copies or substantial portions of the Software.
-# 
+#
 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
@@ -23,19 +23,17 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
-from scipy import misc
-import numpy as np
+import argparse
 import base64
-import sys
 import os
-import cv2
-import argparse
-import facenet
+import sys
 
+import cv2
+import numpy as np
+from facenet_sandberg import facenet
+from scipy import misc
 
 # File format: text files, each line is an image record containing 6 columns, delimited by TAB.
 # Column1: Freebase MID
@@ -45,16 +43,17 @@
 # Column5: PageURL
 # Column6: ImageData_Base64Encoded
 
+
 def main(args):
     output_dir = os.path.expanduser(args.output_dir)
-  
+
     if not os.path.exists(output_dir):
         os.mkdir(output_dir)
-  
+
     # Store some git revision info in a text file in the output directory
-    src_path,_ = os.path.split(os.path.realpath(__file__))
+    src_path, _ = os.path.split(os.path.realpath(__file__))
     facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
-    
+
     i = 0
     for f in args.tsv_files:
         for line in f:
@@ -64,24 +63,45 @@ def main(args):
             img_string = fields[5]
             img_dec_string = base64.b64decode(img_string)
             img_data = np.fromstring(img_dec_string, dtype=np.uint8)
-            img = cv2.imdecode(img_data, cv2.IMREAD_COLOR) #pylint: disable=maybe-no-member
+            img = cv2.imdecode(
+                img_data, cv2.IMREAD_COLOR)  # pylint: disable=maybe-no-member
             if args.size:
-                img = misc.imresize(img, (args.size, args.size), interp='bilinear')
+                img = misc.imresize(
+                    img, (args.size, args.size), interp='bilinear')
             full_class_dir = os.path.join(output_dir, class_dir)
             if not os.path.exists(full_class_dir):
                 os.mkdir(full_class_dir)
-            full_path = os.path.join(full_class_dir, img_name.replace('/','_'))
-            cv2.imwrite(full_path, img) #pylint: disable=maybe-no-member
+            full_path = os.path.join(
+                full_class_dir, img_name.replace(
+                    '/', '_'))
+            cv2.imwrite(full_path, img)  # pylint: disable=maybe-no-member
             print('%8d: %s' % (i, full_path))
             i += 1
-  
+
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
 
-    parser.add_argument('output_dir', type=str, help='Output base directory for the image dataset')
-    parser.add_argument('tsv_files', type=argparse.FileType('r'), nargs='+', help='Input TSV file name(s)')
-    parser.add_argument('--size', type=int, help='Images are resized to the given size')
-    parser.add_argument('--output_format', type=str, help='Format of the output images', default='png', choices=['png', 'jpg'])
+    parser.add_argument(
+        'output_dir',
+        type=str,
+        help='Output base directory for the image dataset')
+    parser.add_argument(
+        'tsv_files',
+        type=argparse.FileType('r'),
+        nargs='+',
+        help='Input TSV file name(s)')
+    parser.add_argument(
+        '--size',
+        type=int,
+        help='Images are resized to the given size')
+    parser.add_argument(
+        '--output_format',
+        type=str,
+        help='Format of the output images',
+        default='png',
+        choices=[
+            'png',
+            'jpg'])
 
     main(parser.parse_args())
-
diff --git a/src/classifier.py b/facenet_sandberg/examples/classifier.py
similarity index 58%
rename from src/classifier.py
rename to facenet_sandberg/examples/classifier.py
index 749db4d6b..076856cbb 100644
--- a/src/classifier.py
+++ b/facenet_sandberg/examples/classifier.py
@@ -1,19 +1,19 @@
 """An example of how to use your own dataset to train a classifier that recognizes people.
 """
 # MIT License
-# 
+#
 # Copyright (c) 2016 David Sandberg
-# 
+#
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
 # in the Software without restriction, including without limitation the rights
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
 # furnished to do so, subject to the following conditions:
-# 
+#
 # The above copyright notice and this permission notice shall be included in all
 # copies or substantial portions of the Software.
-# 
+#
 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
@@ -22,149 +22,196 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
-import tensorflow as tf
-import numpy as np
 import argparse
-import facenet
-import os
-import sys
 import math
+import os
 import pickle
+import sys
+
+import numpy as np
+import tensorflow as tf
+from facenet_sandberg import facenet
 from sklearn.svm import SVC
 
+
 def main(args):
-  
+
     with tf.Graph().as_default():
-      
+
         with tf.Session() as sess:
-            
+
             np.random.seed(seed=args.seed)
-            
+
             if args.use_split_dataset:
                 dataset_tmp = facenet.get_dataset(args.data_dir)
-                train_set, test_set = split_dataset(dataset_tmp, args.min_nrof_images_per_class, args.nrof_train_images_per_class)
-                if (args.mode=='TRAIN'):
+                train_set, test_set = split_dataset(
+                    dataset_tmp, args.min_nrof_images_per_class, args.nrof_train_images_per_class)
+                if (args.mode == 'TRAIN'):
                     dataset = train_set
-                elif (args.mode=='CLASSIFY'):
+                elif (args.mode == 'CLASSIFY'):
                     dataset = test_set
             else:
                 dataset = facenet.get_dataset(args.data_dir)
 
             # Check that there are at least one training image per class
             for cls in dataset:
-                assert(len(cls.image_paths)>0, 'There must be at least one image for each class in the dataset')            
+                assert(len(cls.image_paths) > 0,
+                       'There must be at least one image for each class in the dataset')
 
-                 
             paths, labels = facenet.get_image_paths_and_labels(dataset)
-            
+
             print('Number of classes: %d' % len(dataset))
             print('Number of images: %d' % len(paths))
-            
+
             # Load the model
             print('Loading feature extraction model')
             facenet.load_model(args.model)
-            
+
             # Get input and output tensors
             images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
             embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
             phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
             embedding_size = embeddings.get_shape()[1]
-            
+
             # Run forward pass to calculate embeddings
             print('Calculating features for images')
             nrof_images = len(paths)
-            nrof_batches_per_epoch = int(math.ceil(1.0*nrof_images / args.batch_size))
+            nrof_batches_per_epoch = int(
+                math.ceil(1.0 * nrof_images / args.batch_size))
             emb_array = np.zeros((nrof_images, embedding_size))
             for i in range(nrof_batches_per_epoch):
-                start_index = i*args.batch_size
-                end_index = min((i+1)*args.batch_size, nrof_images)
+                start_index = i * args.batch_size
+                end_index = min((i + 1) * args.batch_size, nrof_images)
                 paths_batch = paths[start_index:end_index]
-                images = facenet.load_data(paths_batch, False, False, args.image_size)
-                feed_dict = { images_placeholder:images, phase_train_placeholder:False }
-                emb_array[start_index:end_index,:] = sess.run(embeddings, feed_dict=feed_dict)
-            
-            classifier_filename_exp = os.path.expanduser(args.classifier_filename)
+                images = facenet.load_data(
+                    paths_batch, False, False, args.image_size)
+                feed_dict = {
+                    images_placeholder: images,
+                    phase_train_placeholder: False}
+                emb_array[start_index:end_index, :] = sess.run(
+                    embeddings, feed_dict=feed_dict)
+
+            classifier_filename_exp = os.path.expanduser(
+                args.classifier_filename)
 
-            if (args.mode=='TRAIN'):
+            if (args.mode == 'TRAIN'):
                 # Train classifier
                 print('Training classifier')
                 model = SVC(kernel='linear', probability=True)
                 model.fit(emb_array, labels)
-            
+
                 # Create a list of class names
-                class_names = [ cls.name.replace('_', ' ') for cls in dataset]
+                class_names = [cls.name.replace('_', ' ') for cls in dataset]
 
                 # Saving classifier model
                 with open(classifier_filename_exp, 'wb') as outfile:
                     pickle.dump((model, class_names), outfile)
-                print('Saved classifier model to file "%s"' % classifier_filename_exp)
-                
-            elif (args.mode=='CLASSIFY'):
+                print(
+                    'Saved classifier model to file "%s"' %
+                    classifier_filename_exp)
+
+            elif (args.mode == 'CLASSIFY'):
                 # Classify images
                 print('Testing classifier')
                 with open(classifier_filename_exp, 'rb') as infile:
                     (model, class_names) = pickle.load(infile)
 
-                print('Loaded classifier model from file "%s"' % classifier_filename_exp)
+                print(
+                    'Loaded classifier model from file "%s"' %
+                    classifier_filename_exp)
 
                 predictions = model.predict_proba(emb_array)
                 best_class_indices = np.argmax(predictions, axis=1)
-                best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]
-                
+                best_class_probabilities = predictions[np.arange(
+                    len(best_class_indices)), best_class_indices]
+
                 for i in range(len(best_class_indices)):
-                    print('%4d  %s: %.3f' % (i, class_names[best_class_indices[i]], best_class_probabilities[i]))
-                    
+                    print('%4d  %s: %.3f' % (i,
+                                             class_names[best_class_indices[i]],
+                                             best_class_probabilities[i]))
+
                 accuracy = np.mean(np.equal(best_class_indices, labels))
                 print('Accuracy: %.3f' % accuracy)
-                
-            
-def split_dataset(dataset, min_nrof_images_per_class, nrof_train_images_per_class):
+
+
+def split_dataset(
+        dataset,
+        min_nrof_images_per_class,
+        nrof_train_images_per_class):
     train_set = []
     test_set = []
     for cls in dataset:
         paths = cls.image_paths
         # Remove classes with less than min_nrof_images_per_class
-        if len(paths)>=min_nrof_images_per_class:
+        if len(paths) >= min_nrof_images_per_class:
             np.random.shuffle(paths)
-            train_set.append(facenet.ImageClass(cls.name, paths[:nrof_train_images_per_class]))
-            test_set.append(facenet.ImageClass(cls.name, paths[nrof_train_images_per_class:]))
+            train_set.append(facenet.PersonClass(
+                cls.name, paths[:nrof_train_images_per_class]))
+            test_set.append(facenet.PersonClass(
+                cls.name, paths[nrof_train_images_per_class:]))
     return train_set, test_set
 
-            
+
 def parse_arguments(argv):
     parser = argparse.ArgumentParser()
-    
-    parser.add_argument('mode', type=str, choices=['TRAIN', 'CLASSIFY'],
-        help='Indicates if a new classifier should be trained or a classification ' + 
-        'model should be used for classification', default='CLASSIFY')
-    parser.add_argument('data_dir', type=str,
+
+    parser.add_argument(
+        'mode',
+        type=str,
+        choices=[
+            'TRAIN',
+            'CLASSIFY'],
+        help='Indicates if a new classifier should be trained or a classification ' +
+        'model should be used for classification',
+        default='CLASSIFY')
+    parser.add_argument(
+        'data_dir',
+        type=str,
         help='Path to the data directory containing aligned LFW face patches.')
-    parser.add_argument('model', type=str, 
+    parser.add_argument(
+        'model',
+        type=str,
         help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
-    parser.add_argument('classifier_filename', 
-        help='Classifier model file name as a pickle (.pkl) file. ' + 
+    parser.add_argument(
+        'classifier_filename',
+        help='Classifier model file name as a pickle (.pkl) file. ' +
         'For training this is the output and for classification this is an input.')
-    parser.add_argument('--use_split_dataset', 
-        help='Indicates that the dataset specified by data_dir should be split into a training and test set. ' +  
-        'Otherwise a separate test set can be specified using the test_data_dir option.', action='store_true')
-    parser.add_argument('--test_data_dir', type=str,
+    parser.add_argument(
+        '--use_split_dataset',
+        help='Indicates that the dataset specified by data_dir should be split into a training and test set. ' +
+        'Otherwise a separate test set can be specified using the test_data_dir option.',
+        action='store_true')
+    parser.add_argument(
+        '--test_data_dir',
+        type=str,
         help='Path to the test data directory containing aligned images used for testing.')
-    parser.add_argument('--batch_size', type=int,
-        help='Number of images to process in a batch.', default=90)
-    parser.add_argument('--image_size', type=int,
-        help='Image size (height, width) in pixels.', default=160)
+    parser.add_argument(
+        '--batch_size',
+        type=int,
+        help='Number of images to process in a batch.',
+        default=90)
+    parser.add_argument(
+        '--image_size',
+        type=int,
+        help='Image size (height, width) in pixels.',
+        default=160)
     parser.add_argument('--seed', type=int,
-        help='Random seed.', default=666)
-    parser.add_argument('--min_nrof_images_per_class', type=int,
-        help='Only include classes with at least this number of images in the dataset', default=20)
-    parser.add_argument('--nrof_train_images_per_class', type=int,
-        help='Use this number of images from each class for training and the rest for testing', default=10)
-    
+                        help='Random seed.', default=666)
+    parser.add_argument(
+        '--min_nrof_images_per_class',
+        type=int,
+        help='Only include classes with at least this number of images in the dataset',
+        default=20)
+    parser.add_argument(
+        '--nrof_train_images_per_class',
+        type=int,
+        help='Use this number of images from each class for training and the rest for testing',
+        default=10)
+
     return parser.parse_args(argv)
 
+
 if __name__ == '__main__':
     main(parse_arguments(sys.argv[1:]))
diff --git a/facenet_sandberg/faceboxes/Dockerfile b/facenet_sandberg/faceboxes/Dockerfile
new file mode 100644
index 000000000..32768553e
--- /dev/null
+++ b/facenet_sandberg/faceboxes/Dockerfile
@@ -0,0 +1,11 @@
+FROM tensorflow/tensorflow:1.10.1-py3
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+COPY align.py .
+COPY model.pb .
+
+ENTRYPOINT ["python3", "align.py"]
\ No newline at end of file
diff --git a/facenet_sandberg/faceboxes/align.py b/facenet_sandberg/faceboxes/align.py
new file mode 100644
index 000000000..0bc30eca6
--- /dev/null
+++ b/facenet_sandberg/faceboxes/align.py
@@ -0,0 +1,116 @@
+import argparse
+import json
+import sys
+from typing import Any, List, Tuple, cast
+
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+
+
+class FaceDetector:
+    def __init__(self, model_path: str, gpu_memory_fraction: float=0.25,
+                 visible_device_list: str='0') -> None:
+        """
+        Arguments:
+            model_path: a string, path to a pb file.
+            gpu_memory_fraction: a float number.
+            visible_device_list: a string.
+        """
+        with tf.gfile.GFile(model_path, 'rb') as f:
+            graph_def = tf.GraphDef()
+            graph_def.ParseFromString(f.read())
+
+        graph = tf.Graph()
+        with graph.as_default():
+            tf.import_graph_def(graph_def, name='import')
+
+        self.input_image = graph.get_tensor_by_name('import/image_tensor:0')
+        self.output_ops = [
+            graph.get_tensor_by_name('import/boxes:0'),
+            graph.get_tensor_by_name('import/scores:0'),
+            graph.get_tensor_by_name('import/num_boxes:0'),
+        ]
+
+        gpu_options = tf.GPUOptions(
+            per_process_gpu_memory_fraction=gpu_memory_fraction,
+            visible_device_list=visible_device_list
+        )
+        config_proto = tf.ConfigProto(
+            gpu_options=gpu_options,
+            log_device_placement=False)
+        self.sess = tf.Session(graph=graph, config=config_proto)
+
+    def __call__(self,
+                 image: np.ndarray,
+                 ratio: float=1.0,
+                 score_threshold: float=0.5) -> Tuple[List[List[int]],
+                                                      List[float]]:
+        """Detect faces.
+
+        Arguments:
+            image: a numpy uint8 array with shape [height, width, 3],
+                that represents a RGB image.
+            score_threshold: a float number.
+        Returns:
+            boxes: a float numpy array of shape [num_faces, 4].
+            scores: a float numpy array of shape [num_faces].
+
+        Note that box coordinates are in the order: ymin, xmin, ymax, xmax!
+        """
+        h, w, _ = image.shape
+        image = np.expand_dims(image, 0)
+
+        boxes, scores, num_boxes = self.sess.run(
+            self.output_ops, feed_dict={self.input_image: image}
+        )
+        num_boxes = num_boxes[0]
+        boxes = boxes[0][:num_boxes]
+        scores = scores[0][:num_boxes]
+
+        to_keep = scores > score_threshold
+        boxes = boxes[to_keep]
+        scores = scores[to_keep]
+
+        scaler = np.array([h, w, h, w], dtype='float32')
+        boxes = boxes * scaler
+        box_list = cast(List[List[int]], [])
+        for box in boxes:
+            box /= ratio
+            box = box.astype(int)
+            box_list.append(box.tolist())
+        return box_list, scores.tolist()
+
+
+def main(image: Any, threshold: float=0.5,
+         desired_size: int=768) -> None:
+    face_detector = FaceDetector('model.pb')
+    old_size = image.size
+    ratio = float(desired_size) / max(old_size)
+    new_size = tuple([int(x * ratio) for x in old_size])
+    image = image.resize(new_size, Image.ANTIALIAS)
+    image = np.array(image)
+    boxes, scores = face_detector(
+        image, ratio=ratio, score_threshold=threshold)
+    print(json.dumps({'boxes': boxes, 'scores': scores}))
+
+
+def parse_arguments(argv):
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--image_path',
+        type=str,
+        help='Image path')
+    parser.add_argument(
+        '--threshold',
+        type=float,
+        help='Threshold for finding faces',
+        default=0.5)
+    return parser.parse_args(argv)
+
+
+if __name__ == '__main__':
+    args = parse_arguments(sys.argv[1:])
+    if args:
+        image = Image.open(args.image_path)
+        main(image, args.threshold)
diff --git a/facenet_sandberg/faceboxes/model.pb b/facenet_sandberg/faceboxes/model.pb
new file mode 100644
index 000000000..f577b924c
Binary files /dev/null and b/facenet_sandberg/faceboxes/model.pb differ
diff --git a/facenet_sandberg/faceboxes/requirements.txt b/facenet_sandberg/faceboxes/requirements.txt
new file mode 100644
index 000000000..e06245bc5
--- /dev/null
+++ b/facenet_sandberg/faceboxes/requirements.txt
@@ -0,0 +1,2 @@
+Pillow
+numpy
\ No newline at end of file
diff --git a/src/facenet.py b/facenet_sandberg/facenet.py
similarity index 54%
rename from src/facenet.py
rename to facenet_sandberg/facenet.py
index 0e056765a..9697cb8d1 100644
--- a/src/facenet.py
+++ b/facenet_sandberg/facenet.py
@@ -1,19 +1,19 @@
 """Functions for building the face recognition network.
 """
 # MIT License
-# 
+#
 # Copyright (c) 2016 David Sandberg
-# 
+#
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
 # in the Software without restriction, including without limitation the rights
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
 # furnished to do so, subject to the following conditions:
-# 
+#
 # The above copyright notice and this permission notice shall be included in all
 # copies or substantial portions of the Software.
-# 
+#
 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
@@ -23,51 +23,57 @@
 # SOFTWARE.
 
 # pylint: disable=missing-docstring
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
+import math
 import os
-from subprocess import Popen, PIPE
-import tensorflow as tf
-import numpy as np
-from scipy import misc
-from sklearn.model_selection import KFold
-from scipy import interpolate
-from tensorflow.python.training import training
 import random
 import re
-from tensorflow.python.platform import gfile
-import math
+from subprocess import PIPE, Popen
+
+import numpy as np
+import tensorflow as tf
+from scipy import interpolate, misc
 from six import iteritems
+from sklearn.model_selection import KFold
+from tensorflow.python.platform import gfile
+from tensorflow.python.training import training
+
+from facenet_sandberg.utils import *
+
 
 def triplet_loss(anchor, positive, negative, alpha):
     """Calculate the triplet loss according to the FaceNet paper
-    
+
     Args:
       anchor: the embeddings for the anchor images.
       positive: the embeddings for the positive images.
       negative: the embeddings for the negative images.
-  
+
     Returns:
       the triplet loss according to the FaceNet paper as a float tensor.
     """
     with tf.variable_scope('triplet_loss'):
         pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), 1)
         neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), 1)
-        
-        basic_loss = tf.add(tf.subtract(pos_dist,neg_dist), alpha)
+
+        basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)
         loss = tf.reduce_mean(tf.maximum(basic_loss, 0.0), 0)
-      
+
     return loss
-  
+
+
 def center_loss(features, label, alfa, nrof_classes):
     """Center loss based on the paper "A Discriminative Feature Learning Approach for Deep Face Recognition"
        (http://ydwen.github.io/papers/WenECCV16.pdf)
     """
     nrof_features = features.get_shape()[1]
-    centers = tf.get_variable('centers', [nrof_classes, nrof_features], dtype=tf.float32,
-        initializer=tf.constant_initializer(0), trainable=False)
+    centers = tf.get_variable('centers',
+                              [nrof_classes,
+                               nrof_features],
+                              dtype=tf.float32,
+                              initializer=tf.constant_initializer(0),
+                              trainable=False)
     label = tf.reshape(label, [-1])
     centers_batch = tf.gather(centers, label)
     diff = (1 - alfa) * (centers_batch - features)
@@ -76,6 +82,7 @@ def center_loss(features, label, alfa, nrof_classes):
         loss = tf.reduce_mean(tf.square(features - centers_batch))
     return loss, centers
 
+
 def get_image_paths_and_labels(dataset):
     image_paths_flat = []
     labels_flat = []
@@ -84,67 +91,90 @@ def get_image_paths_and_labels(dataset):
         labels_flat += [i] * len(dataset[i].image_paths)
     return image_paths_flat, labels_flat
 
+
 def shuffle_examples(image_paths, labels):
     shuffle_list = list(zip(image_paths, labels))
     random.shuffle(shuffle_list)
     image_paths_shuff, labels_shuff = zip(*shuffle_list)
     return image_paths_shuff, labels_shuff
 
+
 def random_rotate_image(image):
     angle = np.random.uniform(low=-10.0, high=10.0)
     return misc.imrotate(image, angle, 'bicubic')
-  
-# 1: Random rotate 2: Random crop  4: Random flip  8:  Fixed image standardization  16: Flip
+
+
+# 1: Random rotate 2: Random crop  4: Random flip  8:  Fixed image
+# standardization  16: Flip
 RANDOM_ROTATE = 1
 RANDOM_CROP = 2
 RANDOM_FLIP = 4
 FIXED_STANDARDIZATION = 8
 FLIP = 16
-def create_input_pipeline(input_queue, image_size, nrof_preprocess_threads, batch_size_placeholder):
+
+
+def create_input_pipeline(
+        input_queue,
+        image_size,
+        nrof_preprocess_threads,
+        batch_size_placeholder):
     images_and_labels_list = []
     for _ in range(nrof_preprocess_threads):
         filenames, label, control = input_queue.dequeue()
+        is_rotate = get_control_flag(control[0], RANDOM_ROTATE)
+        is_crop = get_control_flag(control[0], RANDOM_CROP)
+        is_random_flip = get_control_flag(control[0], RANDOM_FLIP)
+        is_flip = get_control_flag(control[0], FLIP)
+        is_standard = get_control_flag(control[0], FIXED_STANDARDIZATION)
         images = []
         for filename in tf.unstack(filenames):
             file_contents = tf.read_file(filename)
             image = tf.image.decode_image(file_contents, 3)
-            image = tf.cond(get_control_flag(control[0], RANDOM_ROTATE),
-                            lambda:tf.py_func(random_rotate_image, [image], tf.uint8), 
-                            lambda:tf.identity(image))
-            image = tf.cond(get_control_flag(control[0], RANDOM_CROP), 
-                            lambda:tf.random_crop(image, image_size + (3,)), 
-                            lambda:tf.image.resize_image_with_crop_or_pad(image, image_size[0], image_size[1]))
-            image = tf.cond(get_control_flag(control[0], RANDOM_FLIP),
-                            lambda:tf.image.random_flip_left_right(image),
-                            lambda:tf.identity(image))
-            image = tf.cond(get_control_flag(control[0], FIXED_STANDARDIZATION),
-                            lambda:(tf.cast(image, tf.float32) - 127.5)/128.0,
-                            lambda:tf.image.per_image_standardization(image))
-            image = tf.cond(get_control_flag(control[0], FLIP),
-                            lambda:tf.image.flip_left_right(image),
-                            lambda:tf.identity(image))
-            #pylint: disable=no-member
+            image = tf.cond(is_rotate,
+                            lambda: tf.py_func(
+                                random_rotate_image,
+                                [image],
+                                tf.uint8),
+                            lambda: tf.identity(image))
+            image = tf.cond(is_crop,
+                            lambda: tf.random_crop(image, image_size + (3,)),
+                            lambda: tf.image.resize_image_with_crop_or_pad(
+                                image, image_size[0], image_size[1]))
+            image = tf.cond(is_random_flip,
+                            lambda: tf.image.random_flip_left_right(image),
+                            lambda: tf.identity(image))
+            image = tf.cond(is_standard,
+                            lambda: (
+                                tf.cast(
+                                    image,
+                                    tf.float32) - 127.5) / 128.0,
+                            lambda: tf.image.per_image_standardization(image))
+            image = tf.cond(is_flip,
+                            lambda: tf.image.flip_left_right(image),
+                            lambda: tf.identity(image))
             image.set_shape(image_size + (3,))
             images.append(image)
         images_and_labels_list.append([images, label])
 
     image_batch, label_batch = tf.train.batch_join(
-        images_and_labels_list, batch_size=batch_size_placeholder, 
+        images_and_labels_list, batch_size=batch_size_placeholder,
         shapes=[image_size + (3,), ()], enqueue_many=True,
         capacity=4 * nrof_preprocess_threads * 100,
         allow_smaller_final_batch=True)
-    
+
     return image_batch, label_batch
 
+
 def get_control_flag(control, field):
     return tf.equal(tf.mod(tf.floor_div(control, field), 2), 1)
-  
+
+
 def _add_loss_summaries(total_loss):
     """Add summaries for losses.
-  
+
     Generates moving average for all losses and associated summaries for
     visualizing the performance of the network.
-  
+
     Args:
       total_loss: Total loss from loss().
     Returns:
@@ -154,93 +184,109 @@ def _add_loss_summaries(total_loss):
     loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
     losses = tf.get_collection('losses')
     loss_averages_op = loss_averages.apply(losses + [total_loss])
-  
+
     # Attach a scalar summmary to all individual losses and the total loss; do the
     # same for the averaged version of the losses.
     for l in losses + [total_loss]:
         # Name each loss as '(raw)' and name the moving average version of the loss
         # as the original loss name.
-        tf.summary.scalar(l.op.name +' (raw)', l)
+        tf.summary.scalar(l.op.name + ' (raw)', l)
         tf.summary.scalar(l.op.name, loss_averages.average(l))
-  
+
     return loss_averages_op
 
-def train(total_loss, global_step, optimizer, learning_rate, moving_average_decay, update_gradient_vars, log_histograms=True):
+
+def train(
+        total_loss,
+        global_step,
+        optimizer,
+        learning_rate,
+        moving_average_decay,
+        update_gradient_vars,
+        log_histograms=True):
     # Generate moving averages of all losses and associated summaries.
     loss_averages_op = _add_loss_summaries(total_loss)
 
     # Compute gradients.
     with tf.control_dependencies([loss_averages_op]):
-        if optimizer=='ADAGRAD':
+        if optimizer == 'ADAGRAD':
             opt = tf.train.AdagradOptimizer(learning_rate)
-        elif optimizer=='ADADELTA':
-            opt = tf.train.AdadeltaOptimizer(learning_rate, rho=0.9, epsilon=1e-6)
-        elif optimizer=='ADAM':
-            opt = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999, epsilon=0.1)
-        elif optimizer=='RMSPROP':
-            opt = tf.train.RMSPropOptimizer(learning_rate, decay=0.9, momentum=0.9, epsilon=1.0)
-        elif optimizer=='MOM':
-            opt = tf.train.MomentumOptimizer(learning_rate, 0.9, use_nesterov=True)
+        elif optimizer == 'ADADELTA':
+            opt = tf.train.AdadeltaOptimizer(
+                learning_rate, rho=0.9, epsilon=1e-6)
+        elif optimizer == 'ADAM':
+            opt = tf.train.AdamOptimizer(
+                learning_rate, beta1=0.9, beta2=0.999, epsilon=0.1)
+        elif optimizer == 'RMSPROP':
+            opt = tf.train.RMSPropOptimizer(
+                learning_rate, decay=0.9, momentum=0.9, epsilon=1.0)
+        elif optimizer == 'MOM':
+            opt = tf.train.MomentumOptimizer(
+                learning_rate, 0.9, use_nesterov=True)
         else:
             raise ValueError('Invalid optimization algorithm')
-    
+
         grads = opt.compute_gradients(total_loss, update_gradient_vars)
-        
+
     # Apply gradients.
     apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
-  
+
     # Add histograms for trainable variables.
     if log_histograms:
         for var in tf.trainable_variables():
             tf.summary.histogram(var.op.name, var)
-   
+
     # Add histograms for gradients.
     if log_histograms:
         for grad, var in grads:
             if grad is not None:
                 tf.summary.histogram(var.op.name + '/gradients', grad)
-  
+
     # Track the moving averages of all trainable variables.
     variable_averages = tf.train.ExponentialMovingAverage(
         moving_average_decay, global_step)
     variables_averages_op = variable_averages.apply(tf.trainable_variables())
-  
+
     with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
         train_op = tf.no_op(name='train')
-  
+
     return train_op
 
-def prewhiten(x):
-    mean = np.mean(x)
-    std = np.std(x)
-    std_adj = np.maximum(std, 1.0/np.sqrt(x.size))
-    y = np.multiply(np.subtract(x, mean), 1/std_adj)
-    return y  
 
 def crop(image, random_crop, image_size):
-    if image.shape[1]>image_size:
-        sz1 = int(image.shape[1]//2)
-        sz2 = int(image_size//2)
+    if image.shape[1] > image_size:
+        sz1 = int(image.shape[1] // 2)
+        sz2 = int(image_size // 2)
         if random_crop:
-            diff = sz1-sz2
-            (h, v) = (np.random.randint(-diff, diff+1), np.random.randint(-diff, diff+1))
+            diff = sz1 - sz2
+            (h, v) = (np.random.randint(-diff, diff + 1),
+                      np.random.randint(-diff, diff + 1))
         else:
-            (h, v) = (0,0)
-        image = image[(sz1-sz2+v):(sz1+sz2+v),(sz1-sz2+h):(sz1+sz2+h),:]
+            (h, v) = (0, 0)
+        image = image[(sz1 - sz2 + v):(sz1 + sz2 + v),
+                      (sz1 - sz2 + h):(sz1 + sz2 + h), :]
     return image
-  
+
+
 def flip(image, random_flip):
     if random_flip and np.random.choice([True, False]):
         image = np.fliplr(image)
     return image
 
+
 def to_rgb(img):
     w, h = img.shape
     ret = np.empty((w, h, 3), dtype=np.uint8)
     ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img
     return ret
-  
-def load_data(image_paths, do_random_crop, do_random_flip, image_size, do_prewhiten=True):
+
+
+def load_data(
+        image_paths,
+        do_random_crop,
+        do_random_flip,
+        image_size,
+        do_prewhiten=True):
     nrof_samples = len(image_paths)
     images = np.zeros((nrof_samples, image_size, image_size, 3))
     for i in range(nrof_samples):
@@ -248,44 +294,48 @@ def load_data(image_paths, do_random_crop, do_random_flip, image_size, do_prewhi
         if img.ndim == 2:
             img = to_rgb(img)
         if do_prewhiten:
-            img = prewhiten(img)
+            img = utils.normalize_image(img)
         img = crop(img, do_random_crop, image_size)
         img = flip(img, do_random_flip)
-        images[i,:,:,:] = img
+        images[i, :, :, :] = img
     return images
 
+
 def get_label_batch(label_data, batch_size, batch_index):
     nrof_examples = np.size(label_data, 0)
-    j = batch_index*batch_size % nrof_examples
-    if j+batch_size<=nrof_examples:
-        batch = label_data[j:j+batch_size]
+    j = batch_index * batch_size % nrof_examples
+    if j + batch_size <= nrof_examples:
+        batch = label_data[j:j + batch_size]
     else:
         x1 = label_data[j:nrof_examples]
-        x2 = label_data[0:nrof_examples-j]
-        batch = np.vstack([x1,x2])
+        x2 = label_data[0:nrof_examples - j]
+        batch = np.vstack([x1, x2])
     batch_int = batch.astype(np.int64)
     return batch_int
 
+
 def get_batch(image_data, batch_size, batch_index):
     nrof_examples = np.size(image_data, 0)
-    j = batch_index*batch_size % nrof_examples
-    if j+batch_size<=nrof_examples:
-        batch = image_data[j:j+batch_size,:,:,:]
+    j = batch_index * batch_size % nrof_examples
+    if j + batch_size <= nrof_examples:
+        batch = image_data[j:j + batch_size, :, :, :]
     else:
-        x1 = image_data[j:nrof_examples,:,:,:]
-        x2 = image_data[0:nrof_examples-j,:,:,:]
-        batch = np.vstack([x1,x2])
+        x1 = image_data[j:nrof_examples, :, :, :]
+        x2 = image_data[0:nrof_examples - j, :, :, :]
+        batch = np.vstack([x1, x2])
     batch_float = batch.astype(np.float32)
     return batch_float
 
+
 def get_triplet_batch(triplets, batch_index, batch_size):
     ax, px, nx = triplets
-    a = get_batch(ax, int(batch_size/3), batch_index)
-    p = get_batch(px, int(batch_size/3), batch_index)
-    n = get_batch(nx, int(batch_size/3), batch_index)
+    a = get_batch(ax, int(batch_size / 3), batch_index)
+    p = get_batch(px, int(batch_size / 3), batch_index)
+    n = get_batch(nx, int(batch_size / 3), batch_index)
     batch = np.vstack([a, p, n])
     return batch
 
+
 def get_learning_rate_from_file(filename, epoch):
     with open(filename, 'r') as f:
         for line in f.readlines():
@@ -293,7 +343,7 @@ def get_learning_rate_from_file(filename, epoch):
             if line:
                 par = line.strip().split(':')
                 e = int(par[0])
-                if par[1]=='-':
+                if par[1] == '-':
                     lr = -1
                 else:
                     lr = float(par[1])
@@ -302,92 +352,39 @@ def get_learning_rate_from_file(filename, epoch):
                 else:
                     return learning_rate
 
-class ImageClass():
-    "Stores the paths to images for a given class"
-    def __init__(self, name, image_paths):
-        self.name = name
-        self.image_paths = image_paths
-  
-    def __str__(self):
-        return self.name + ', ' + str(len(self.image_paths)) + ' images'
-  
-    def __len__(self):
-        return len(self.image_paths)
-  
-def get_dataset(path, has_class_directories=True):
-    dataset = []
-    path_exp = os.path.expanduser(path)
-    classes = [path for path in os.listdir(path_exp) \
-                    if os.path.isdir(os.path.join(path_exp, path))]
-    classes.sort()
-    nrof_classes = len(classes)
-    for i in range(nrof_classes):
-        class_name = classes[i]
-        facedir = os.path.join(path_exp, class_name)
-        image_paths = get_image_paths(facedir)
-        dataset.append(ImageClass(class_name, image_paths))
-  
-    return dataset
-
-def get_image_paths(facedir):
-    image_paths = []
-    if os.path.isdir(facedir):
-        images = os.listdir(facedir)
-        image_paths = [os.path.join(facedir,img) for img in images]
-    return image_paths
-  
-def split_dataset(dataset, split_ratio, min_nrof_images_per_class, mode):
-    if mode=='SPLIT_CLASSES':
-        nrof_classes = len(dataset)
-        class_indices = np.arange(nrof_classes)
-        np.random.shuffle(class_indices)
-        split = int(round(nrof_classes*(1-split_ratio)))
-        train_set = [dataset[i] for i in class_indices[0:split]]
-        test_set = [dataset[i] for i in class_indices[split:-1]]
-    elif mode=='SPLIT_IMAGES':
-        train_set = []
-        test_set = []
-        for cls in dataset:
-            paths = cls.image_paths
-            np.random.shuffle(paths)
-            nrof_images_in_class = len(paths)
-            split = int(math.floor(nrof_images_in_class*(1-split_ratio)))
-            if split==nrof_images_in_class:
-                split = nrof_images_in_class-1
-            if split>=min_nrof_images_per_class and nrof_images_in_class-split>=1:
-                train_set.append(ImageClass(cls.name, paths[:split]))
-                test_set.append(ImageClass(cls.name, paths[split:]))
-    else:
-        raise ValueError('Invalid train/test split mode "%s"' % mode)
-    return train_set, test_set
 
 def load_model(model, input_map=None):
     # Check if the model is a model directory (containing a metagraph and a checkpoint file)
     #  or if it is a protobuf file with a frozen graph
     model_exp = os.path.expanduser(model)
     if (os.path.isfile(model_exp)):
-        print('Model filename: %s' % model_exp)
-        with gfile.FastGFile(model_exp,'rb') as f:
+        with gfile.FastGFile(model_exp, 'rb') as f:
             graph_def = tf.GraphDef()
             graph_def.ParseFromString(f.read())
             tf.import_graph_def(graph_def, input_map=input_map, name='')
     else:
-        print('Model directory: %s' % model_exp)
         meta_file, ckpt_file = get_model_filenames(model_exp)
-        
-        print('Metagraph file: %s' % meta_file)
-        print('Checkpoint file: %s' % ckpt_file)
-      
-        saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file), input_map=input_map)
-        saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file))
-    
+
+        saver = tf.train.import_meta_graph(os.path.join(
+            model_exp, meta_file), input_map=input_map)
+        saver.restore(
+            tf.get_default_session(),
+            os.path.join(
+                model_exp,
+                ckpt_file))
+
+
 def get_model_filenames(model_dir):
     files = os.listdir(model_dir)
     meta_files = [s for s in files if s.endswith('.meta')]
-    if len(meta_files)==0:
-        raise ValueError('No meta file found in the model directory (%s)' % model_dir)
-    elif len(meta_files)>1:
-        raise ValueError('There should not be more than one meta file in the model directory (%s)' % model_dir)
+    if len(meta_files) == 0:
+        raise ValueError(
+            'No meta file found in the model directory (%s)' %
+            model_dir)
+    elif len(meta_files) > 1:
+        raise ValueError(
+            'There should not be more than one meta file in the model directory (%s)' %
+            model_dir)
     meta_file = meta_files[0]
     ckpt = tf.train.get_checkpoint_state(model_dir)
     if ckpt and ckpt.model_checkpoint_path:
@@ -398,107 +395,123 @@ def get_model_filenames(model_dir):
     max_step = -1
     for f in files:
         step_str = re.match(r'(^model-[\w\- ]+.ckpt-(\d+))', f)
-        if step_str is not None and len(step_str.groups())>=2:
+        if step_str is not None and len(step_str.groups()) >= 2:
             step = int(step_str.groups()[1])
             if step > max_step:
                 max_step = step
                 ckpt_file = step_str.groups()[0]
     return meta_file, ckpt_file
-  
-def distance(embeddings1, embeddings2, distance_metric=0):
-    if distance_metric==0:
-        # Euclidian distance
-        diff = np.subtract(embeddings1, embeddings2)
-        dist = np.sum(np.square(diff),1)
-    elif distance_metric==1:
-        # Distance based on cosine similarity
-        dot = np.sum(np.multiply(embeddings1, embeddings2), axis=1)
-        norm = np.linalg.norm(embeddings1, axis=1) * np.linalg.norm(embeddings2, axis=1)
-        similarity = dot / norm
-        dist = np.arccos(similarity) / math.pi
-    else:
-        raise 'Undefined distance metric %d' % distance_metric 
-        
-    return dist
 
-def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, distance_metric=0, subtract_mean=False):
+
+def calculate_roc(
+        thresholds,
+        embeddings1,
+        embeddings2,
+        actual_issame,
+        nrof_folds=10,
+        distance_metric=0,
+        subtract_mean=False):
     assert(embeddings1.shape[0] == embeddings2.shape[0])
     assert(embeddings1.shape[1] == embeddings2.shape[1])
     nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
     nrof_thresholds = len(thresholds)
     k_fold = KFold(n_splits=nrof_folds, shuffle=False)
-    
-    tprs = np.zeros((nrof_folds,nrof_thresholds))
-    fprs = np.zeros((nrof_folds,nrof_thresholds))
+
+    tprs = np.zeros((nrof_folds, nrof_thresholds))
+    fprs = np.zeros((nrof_folds, nrof_thresholds))
     accuracy = np.zeros((nrof_folds))
-    
+
     indices = np.arange(nrof_pairs)
-    
+
     for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
         if subtract_mean:
-            mean = np.mean(np.concatenate([embeddings1[train_set], embeddings2[train_set]]), axis=0)
+            mean = np.mean(np.concatenate(
+                [embeddings1[train_set], embeddings2[train_set]]), axis=0)
         else:
-          mean = 0.0
-        dist = distance(embeddings1-mean, embeddings2-mean, distance_metric)
-        
+            mean = 0.0
+        dist = distance(
+            embeddings1 - mean,
+            embeddings2 - mean,
+            distance_metric)
+
         # Find the best threshold for the fold
         acc_train = np.zeros((nrof_thresholds))
         for threshold_idx, threshold in enumerate(thresholds):
-            _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set])
+            _, _, acc_train[threshold_idx] = calculate_accuracy(
+                threshold, dist[train_set], actual_issame[train_set])
         best_threshold_index = np.argmax(acc_train)
         for threshold_idx, threshold in enumerate(thresholds):
-            tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _ = calculate_accuracy(threshold, dist[test_set], actual_issame[test_set])
-        _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], actual_issame[test_set])
-          
-        tpr = np.mean(tprs,0)
-        fpr = np.mean(fprs,0)
+            tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy(
+                threshold, dist[test_set], actual_issame[test_set])
+        _, _, accuracy[fold_idx] = calculate_accuracy(
+            thresholds[best_threshold_index], dist[test_set], actual_issame[test_set])
+
+        tpr = np.mean(tprs, 0)
+        fpr = np.mean(fprs, 0)
     return tpr, fpr, accuracy
 
+
 def calculate_accuracy(threshold, dist, actual_issame):
     predict_issame = np.less(dist, threshold)
     tp = np.sum(np.logical_and(predict_issame, actual_issame))
     fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
-    tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))
+    tn = np.sum(
+        np.logical_and(
+            np.logical_not(predict_issame),
+            np.logical_not(actual_issame)))
     fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
-  
-    tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn)
-    fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn)
-    acc = float(tp+tn)/dist.size
+
+    tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn)
+    fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn)
+    acc = float(tp + tn) / dist.size
     return tpr, fpr, acc
 
 
-  
-def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10, distance_metric=0, subtract_mean=False):
+def calculate_val(
+        thresholds,
+        embeddings1,
+        embeddings2,
+        actual_issame,
+        far_target,
+        nrof_folds=10,
+        distance_metric=0,
+        subtract_mean=False):
     assert(embeddings1.shape[0] == embeddings2.shape[0])
     assert(embeddings1.shape[1] == embeddings2.shape[1])
     nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
     nrof_thresholds = len(thresholds)
     k_fold = KFold(n_splits=nrof_folds, shuffle=False)
-    
+
     val = np.zeros(nrof_folds)
     far = np.zeros(nrof_folds)
-    
+
     indices = np.arange(nrof_pairs)
-    
+
     for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
         if subtract_mean:
-            mean = np.mean(np.concatenate([embeddings1[train_set], embeddings2[train_set]]), axis=0)
+            mean = np.mean(np.concatenate(
+                [embeddings1[train_set], embeddings2[train_set]]), axis=0)
         else:
-          mean = 0.0
-        dist = distance(embeddings1-mean, embeddings2-mean, distance_metric)
-      
+            mean = 0.0
+        dist = distance(
+            embeddings1 - mean,
+            embeddings2 - mean,
+            distance_metric)
+
         # Find the threshold that gives FAR = far_target
         far_train = np.zeros(nrof_thresholds)
         for threshold_idx, threshold in enumerate(thresholds):
-            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])
-        if np.max(far_train)>=far_target:
+            _, far_train[threshold_idx] = calculate_val_far(
+                threshold, dist[train_set], actual_issame[train_set])
+        if np.max(far_train) >= far_target:
             f = interpolate.interp1d(far_train, thresholds, kind='slinear')
             threshold = f(far_target)
         else:
             threshold = 0.0
-    
-        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])
-  
+
+        val[fold_idx], far[fold_idx] = calculate_val_far(
+            threshold, dist[test_set], actual_issame[test_set])
+
     val_mean = np.mean(val)
     far_mean = np.mean(far)
     val_std = np.std(val)
@@ -508,39 +521,16 @@ def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_targe
 def calculate_val_far(threshold, dist, actual_issame):
     predict_issame = np.less(dist, threshold)
     true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
-    false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
+    false_accept = np.sum(
+        np.logical_and(
+            predict_issame,
+            np.logical_not(actual_issame)))
     n_same = np.sum(actual_issame)
     n_diff = np.sum(np.logical_not(actual_issame))
     val = float(true_accept) / float(n_same)
     far = float(false_accept) / float(n_diff)
     return val, far
 
-def store_revision_info(src_path, output_dir, arg_string):
-    try:
-        # Get git hash
-        cmd = ['git', 'rev-parse', 'HEAD']
-        gitproc = Popen(cmd, stdout = PIPE, cwd=src_path)
-        (stdout, _) = gitproc.communicate()
-        git_hash = stdout.strip()
-    except OSError as e:
-        git_hash = ' '.join(cmd) + ': ' +  e.strerror
-  
-    try:
-        # Get local changes
-        cmd = ['git', 'diff', 'HEAD']
-        gitproc = Popen(cmd, stdout = PIPE, cwd=src_path)
-        (stdout, _) = gitproc.communicate()
-        git_diff = stdout.strip()
-    except OSError as e:
-        git_diff = ' '.join(cmd) + ': ' +  e.strerror
-    
-    # Store a text file in the log directory
-    rev_info_filename = os.path.join(output_dir, 'revision_info.txt')
-    with open(rev_info_filename, "w") as text_file:
-        text_file.write('arguments: %s\n--------------------\n' % arg_string)
-        text_file.write('tensorflow version: %s\n--------------------\n' % tf.__version__)  # @UndefinedVariable
-        text_file.write('git hash: %s\n--------------------\n' % git_hash)
-        text_file.write('%s' % git_diff)
 
 def list_variables(filename):
     reader = training.NewCheckpointReader(filename)
@@ -548,23 +538,27 @@ def list_variables(filename):
     names = sorted(variable_map.keys())
     return names
 
-def put_images_on_grid(images, shape=(16,8)):
+
+def put_images_on_grid(images, shape=(16, 8)):
     nrof_images = images.shape[0]
     img_size = images.shape[1]
     bw = 3
-    img = np.zeros((shape[1]*(img_size+bw)+bw, shape[0]*(img_size+bw)+bw, 3), np.float32)
+    img = np.zeros((shape[1] * (img_size + bw) + bw,
+                    shape[0] * (img_size + bw) + bw, 3), np.float32)
     for i in range(shape[1]):
-        x_start = i*(img_size+bw)+bw
+        x_start = i * (img_size + bw) + bw
         for j in range(shape[0]):
-            img_index = i*shape[0]+j
-            if img_index>=nrof_images:
+            img_index = i * shape[0] + j
+            if img_index >= nrof_images:
                 break
-            y_start = j*(img_size+bw)+bw
-            img[x_start:x_start+img_size, y_start:y_start+img_size, :] = images[img_index, :, :, :]
-        if img_index>=nrof_images:
+            y_start = j * (img_size + bw) + bw
+            img[x_start:x_start + img_size, y_start:y_start +
+                img_size, :] = images[img_index, :, :, :]
+        if img_index >= nrof_images:
             break
     return img
 
+
 def write_arguments_to_file(args, filename):
     with open(filename, 'w') as f:
         for key, value in iteritems(vars(args)):
diff --git a/facenet_sandberg/facenet_config.json b/facenet_sandberg/facenet_config.json
new file mode 100644
index 000000000..09380406d
--- /dev/null
+++ b/facenet_sandberg/facenet_config.json
@@ -0,0 +1,15 @@
+{
+    "IS_RGB": true,
+    "FACE_CROP_HEIGHT": 160,
+    "FACE_CROP_WIDTH": 160,
+    "FACE_CROP_MARGIN": 0.25,
+    "SCALE_FACTOR": 0.85,
+    "STEPS_THRESHOLD": [0.6, 0.7, 0.9],
+    "DETECT_MULTIPLE_FACES": true,
+    "USE_AFFINE": false,
+    "USE_FACEBOXES": false,
+    "NUM_PROCESSES": -1,
+    "FACENET_MODEL_CHECKPOINT": "/Users/armanrahman/models/facenet_model.pb",
+    "INPUT_DIR": "/Users/armanrahman/datasets/eame/eame_test",
+    "OUTPUT_DIR": "/Users/armanrahman/datasets/new_data_tests/facenet_eame"
+}
\ No newline at end of file
diff --git a/facenet_sandberg/generate_pairs.py b/facenet_sandberg/generate_pairs.py
new file mode 100644
index 000000000..de3bea1c3
--- /dev/null
+++ b/facenet_sandberg/generate_pairs.py
@@ -0,0 +1,158 @@
+# Implementation of pairs.txt from lfw dataset
+# Section f: http://vis-www.cs.umass.edu/lfw/lfw.pdf
+# More succint, less explicit: http://vis-www.cs.umass.edu/lfw/README.txt
+
+import glob
+import io
+import os
+import random
+from argparse import ArgumentParser, Namespace
+from typing import List, Optional, Set, Tuple, cast
+
+import numpy as np
+
+from facenet_sandberg.utils import transform_to_lfw_format
+
+Mismatch = Tuple[str, int, str, int]
+Match = Tuple[str, int, int]
+CommandLineArgs = Namespace
+
+
+def write_pairs_to_file(fname: str,
+                        match_folds: List[List[Match]],
+                        mismatch_folds: List[List[Mismatch]],
+                        num_folds: int,
+                        num_matches_mismatches: int) -> None:
+    metadata = '{}\t{}\n'.format(num_folds, num_matches_mismatches)
+    with io.open(fname,
+                 'w',
+                 io.DEFAULT_BUFFER_SIZE,
+                 encoding='utf-8') as fpairs:
+        fpairs.write(metadata)
+        for match_fold, mismatch_fold in zip(match_folds, mismatch_folds):
+            for match in match_fold:
+                line = '{}\t{}\t{}\n'.format(match[0], match[1], match[2])
+                fpairs.write(line)
+            for mismatch in mismatch_fold:
+                line = '{}\t{}\t{}\t{}\n'.format(
+                    mismatch[0], mismatch[1], mismatch[2], mismatch[3])
+                fpairs.write(line)
+        fpairs.flush()
+
+
+def _split_people_into_folds(image_dir: str,
+                             num_folds: int) -> List[List[str]]:
+    names = [d for d in os.listdir(image_dir)
+             if os.path.isdir(os.path.join(image_dir, d))]
+    random.shuffle(names)
+    return [list(arr) for arr in np.array_split(names, num_folds)]
+
+
+def _make_matches(image_dir: str,
+                  people: List[str],
+                  total_matches: int) -> List[Match]:
+    matches = cast(Set[Match], set())
+    curr_matches = 0
+    while curr_matches < total_matches:
+        person = random.choice(people)
+        images = _clean_images(image_dir, person)
+        if len(images) > 1:
+            img1, img2 = sorted(
+                [images.index(random.choice(images)) + 1,
+                 images.index(random.choice(images)) + 1])
+            match = (person, img1, img2)
+            if (img1 != img2) and (match not in matches):
+                matches.add(match)
+                curr_matches += 1
+    return sorted(list(matches), key=lambda x: x[0].lower())
+
+
+def _make_mismatches(image_dir: str,
+                     people: List[str],
+                     total_matches: int) -> List[Mismatch]:
+    mismatches = cast(Set[Mismatch], set())
+    curr_matches = 0
+    while curr_matches < total_matches:
+        person1 = random.choice(people)
+        person2 = random.choice(people)
+        if person1 != person2:
+            person1_images = _clean_images(image_dir, person1)
+            person2_images = _clean_images(image_dir, person2)
+            if person1_images and person2_images:
+                img1 = person1_images.index(random.choice(person1_images)) + 1
+                img2 = person2_images.index(random.choice(person2_images)) + 1
+                if person1.lower() > person2.lower():
+                    person1, img1, person2, img2 = person2, img2, person1, img1
+                mismatch = (person1, img1, person2, img2)
+                if mismatch not in mismatches:
+                    mismatches.add(mismatch)
+                    curr_matches += 1
+    return sorted(list(mismatches), key=lambda x: x[0].lower())
+
+
+def _clean_images(base: str, folder: str):
+    images = os.listdir(os.path.join(base, folder))
+    images = [image for image in images if image.endswith(
+        ".jpg") or image.endswith(".png") or image.endswith(".jpeg")]
+    return images
+
+
+def generate_pairs(
+        image_dir: str,
+        num_folds: int,
+        num_matches_mismatches: int,
+        write_to_file: bool=False,
+        pairs_file_name: str="") -> None:
+    transform_to_lfw_format(image_dir)
+    people_folds = _split_people_into_folds(image_dir, num_folds)
+    matches = []
+    mismatches = []
+    for fold in people_folds:
+        matches.append(_make_matches(image_dir,
+                                     fold,
+                                     num_matches_mismatches))
+        mismatches.append(_make_mismatches(image_dir,
+                                           fold,
+                                           num_matches_mismatches))
+    if write_to_file:
+        write_pairs_to_file(pairs_file_name,
+                            matches,
+                            mismatches,
+                            num_folds,
+                            num_matches_mismatches)
+    return matches, mismatches
+
+
+def _cli() -> None:
+    args = _parse_arguments()
+    generate_pairs(
+        args.image_dir,
+        args.num_folds,
+        args.num_matches_mismatches,
+        True,
+        args.pairs_file_name)
+
+
+def _parse_arguments() -> Namespace:
+    parser = ArgumentParser()
+    parser.add_argument('--image_dir',
+                        type=str,
+                        required=True,
+                        help='Path to the image directory.')
+    parser.add_argument('--pairs_file_name',
+                        type=str,
+                        required=True,
+                        help='Filename of pairs.txt')
+    parser.add_argument('--num_folds',
+                        type=int,
+                        required=True,
+                        help='Number of folds for k-fold cross validation.')
+    parser.add_argument('--num_matches_mismatches',
+                        type=int,
+                        required=True,
+                        help='Number of matches/mismatches per fold.')
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    _cli()
diff --git a/__init__.py b/facenet_sandberg/generative/__init__.py
similarity index 100%
rename from __init__.py
rename to facenet_sandberg/generative/__init__.py
diff --git a/src/generative/calculate_attribute_vectors.py b/facenet_sandberg/generative/calculate_attribute_vectors.py
similarity index 100%
rename from src/generative/calculate_attribute_vectors.py
rename to facenet_sandberg/generative/calculate_attribute_vectors.py
diff --git a/src/align/__init__.py b/facenet_sandberg/generative/models/__init__.py
similarity index 100%
rename from src/align/__init__.py
rename to facenet_sandberg/generative/models/__init__.py
diff --git a/src/generative/models/dfc_vae.py b/facenet_sandberg/generative/models/dfc_vae.py
similarity index 100%
rename from src/generative/models/dfc_vae.py
rename to facenet_sandberg/generative/models/dfc_vae.py
diff --git a/src/generative/models/dfc_vae_large.py b/facenet_sandberg/generative/models/dfc_vae_large.py
similarity index 100%
rename from src/generative/models/dfc_vae_large.py
rename to facenet_sandberg/generative/models/dfc_vae_large.py
diff --git a/src/generative/models/dfc_vae_resnet.py b/facenet_sandberg/generative/models/dfc_vae_resnet.py
similarity index 100%
rename from src/generative/models/dfc_vae_resnet.py
rename to facenet_sandberg/generative/models/dfc_vae_resnet.py
diff --git a/src/generative/models/vae_base.py b/facenet_sandberg/generative/models/vae_base.py
similarity index 100%
rename from src/generative/models/vae_base.py
rename to facenet_sandberg/generative/models/vae_base.py
diff --git a/src/generative/modify_attribute.py b/facenet_sandberg/generative/modify_attribute.py
similarity index 99%
rename from src/generative/modify_attribute.py
rename to facenet_sandberg/generative/modify_attribute.py
index 8187cff47..c96093059 100644
--- a/src/generative/modify_attribute.py
+++ b/facenet_sandberg/generative/modify_attribute.py
@@ -32,7 +32,7 @@
 import sys
 import argparse
 import importlib
-import facenet
+from facenet_sandberg import facenet
 import os
 import numpy as np
 import h5py
diff --git a/src/generative/train_vae.py b/facenet_sandberg/generative/train_vae.py
similarity index 99%
rename from src/generative/train_vae.py
rename to facenet_sandberg/generative/train_vae.py
index c3c882fab..8cc3d9135 100644
--- a/src/generative/train_vae.py
+++ b/facenet_sandberg/generative/train_vae.py
@@ -32,7 +32,7 @@
 import time
 import importlib
 import argparse
-import facenet
+from facenet_sandberg import facenet
 import numpy as np
 import h5py
 import os
diff --git a/facenet_sandberg/inference/__init__.py b/facenet_sandberg/inference/__init__.py
new file mode 100644
index 000000000..37934fff0
--- /dev/null
+++ b/facenet_sandberg/inference/__init__.py
@@ -0,0 +1,3 @@
+from .align import Detector
+from .generate_tsne import tsne_sklearn, tsne_tensorboard
+from .identifier import Identifier
diff --git a/facenet_sandberg/inference/align.py b/facenet_sandberg/inference/align.py
new file mode 100644
index 000000000..df063c49b
--- /dev/null
+++ b/facenet_sandberg/inference/align.py
@@ -0,0 +1,138 @@
+import io
+import json
+import os
+import tempfile
+import warnings
+from typing import List, cast
+
+import cv2
+import docker
+import numpy as np
+import PIL
+from mtcnn.mtcnn import MTCNN
+
+from facenet_sandberg import utils
+from facenet_sandberg.common_types import *
+
+warnings.filterwarnings("ignore", message="numpy.dtype size changed")
+warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+debug = False
+
+
+class Detector:
+    def __init__(
+            self,
+            face_crop_height: int = 160,
+            face_crop_width: int = 160,
+            face_crop_margin: float = .4,
+            detect_multiple_faces: bool = False,
+            min_face_size: int = 20,
+            scale_factor: float = 0.709,
+            steps_threshold: List[float] = [
+                0.6,
+                0.7,
+                0.7],
+            is_rgb: bool = True,
+            use_faceboxes: bool = False,
+            use_affine: bool = False) -> None:
+        import tensorflow as tf
+        self.mtcnn = MTCNN()
+        self.face_crop_height = face_crop_height
+        self.face_crop_width = face_crop_width
+        self.face_crop_margin = face_crop_margin
+        self.detect_multiple_faces = detect_multiple_faces
+        self.min_face_size = min_face_size
+        self.is_rgb = is_rgb
+        self.use_faceboxes = use_faceboxes
+        self.use_affine = use_affine
+
+    def bulk_find_face(self,
+                       images: ImageGenerator,
+                       urls: List[str] = None,
+                       detect_multiple_faces: bool = False,
+                       face_limit: int = 5) -> FacesGenerator:
+        for index, image in enumerate(images):
+            faces = self.find_faces(image, detect_multiple_faces, face_limit)
+            if urls and index < len(urls):
+                for face in faces:
+                    face.url = urls[index]
+                yield faces
+            else:
+                yield faces
+
+    def find_faces(self, image: Image, detect_multiple_faces: bool = False,
+                   face_limit: int = 5) -> List[Face]:
+        faces = []
+        results = cast(List[AlignResult], self._get_align_results(image))
+        if len(results) < face_limit:
+            if not detect_multiple_faces and len(results) > 1:
+                img_size = np.asarray(image.shape)[0:2]
+                results = utils.get_center_box(img_size, results)
+            for result in results:
+                face = Face()
+                bb = result.bounding_box
+                if bb[2] - bb[0] < self.min_face_size or bb[3] - \
+                        bb[1] < self.min_face_size:
+                    pass
+                # preprocess changes RGB -> BGR
+                processed = utils.preprocess(
+                    image,
+                    self.face_crop_height,
+                    self.face_crop_width,
+                    self.face_crop_margin,
+                    bb,
+                    result.landmarks,
+                    self.use_affine)
+                resized = cv2.resize(
+                    processed, (self.face_crop_height, self.face_crop_width))
+                # RGB to BGR
+                if not self.is_rgb:
+                    resized = resized[..., ::-1]
+                face.image = resized
+                faces.append(face)
+        return faces
+
+    def _get_align_results(self, image: Image) -> List[AlignResult]:
+        mtcnn_results = self.mtcnn.detect_faces(image)
+        img_size = np.asarray(image.shape)[0:2]
+        align_results = cast(List[AlignResult], [])
+        faceboxes = cast(List[List[int]], [])
+        if self.use_faceboxes:
+            faceboxes = self._get_faceboxes_results(image)
+        if len(mtcnn_results) >= len(faceboxes):
+            for result in mtcnn_results:
+                bb = result['box']
+                # bb[x, y, dx, dy] -> bb[x1, y1, x2, y2]
+                bb = utils.fix_mtcnn_bb(
+                    img_size[0], img_size[1], bb)
+                align_result = AlignResult(
+                    bounding_box=bb,
+                    landmarks=result['keypoints'])
+                align_results.append(align_result)
+        else:
+            for bb in faceboxes:
+                # bb[y1, x1, y2, x2] -> bb[x1, y1, x2, y2]
+                bb = utils.fix_faceboxes_bb(
+                    img_size[0], img_size[1], bb)
+                align_result = AlignResult(bounding_box=bb)
+                align_results.append(align_result)
+        return align_results
+
+    @staticmethod
+    def _get_faceboxes_results(image_array: np.ndarray,
+                               threshold: float = 0.8) -> List[List[int]]:
+        image = PIL.Image.fromarray(image_array)
+        with tempfile.NamedTemporaryFile(mode="wb", dir=os.getcwd()) as image_file:
+            f = io.BytesIO()
+            image.save(image_file, 'png')
+            image_name = os.path.basename(image_file.name)
+            base_dir = os.path.dirname(image_file.name)
+            command = '--image_path=/images/' + \
+                image_name + ' --threshold ' + str(threshold)
+            volumes = {base_dir: {'bind': '/images', 'mode': 'ro'}}
+            client = docker.from_env()
+            stdout = client.containers.run('arrahm/faceboxes',
+                                           command,
+                                           volumes=volumes)
+            return json.loads(stdout.decode('utf-8').strip())['boxes']
diff --git a/facenet_sandberg/inference/facenet_encoder.py b/facenet_sandberg/inference/facenet_encoder.py
new file mode 100644
index 000000000..8656d2f7c
--- /dev/null
+++ b/facenet_sandberg/inference/facenet_encoder.py
@@ -0,0 +1,128 @@
+import os
+import warnings
+from typing import List, Optional, cast
+
+import numpy as np
+import progressbar as pb
+import tensorflow as tf
+from scipy import misc
+
+from facenet_sandberg import facenet, utils
+from facenet_sandberg.common_types import (DistanceMetric, Embedding, Face,
+                                           FacesGenerator, Image,
+                                           ImageGenerator)
+
+warnings.filterwarnings("ignore", message="numpy.dtype size changed")
+warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+tf.logging.set_verbosity(tf.logging.ERROR)
+
+
+class Facenet:
+    def __init__(
+            self,
+            model_path: str,
+            image_height: int = 160,
+            image_width: int = 160,
+            batch_size: int = 64) -> None:
+        import tensorflow as tf
+        self.sess = tf.Session()
+        with self.sess.as_default():
+            facenet.load_model(model_path)
+        # Get input and output tensors
+        self.images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
+        self.embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
+        self.phase_train_placeholder = tf.get_default_graph(
+        ).get_tensor_by_name("phase_train:0")
+        self.image_height = image_height
+        self.image_width = image_width
+        self.batch_size = batch_size
+
+    def extract_batch(self, batch: np.ndarray) -> List[Embedding]:
+        feed_dict = {
+            self.images_placeholder: batch,
+            self.phase_train_placeholder: False}
+        embeddings = self.sess.run(self.embeddings, feed_dict=feed_dict)
+        return [embedding for embedding in embeddings]
+
+    def generate_embedding(self, image: Image) -> Embedding:
+        h, w, c = image.shape
+        assert h == self.image_height and w == self.image_width
+        prewhiten_face = utils.fixed_standardize(image)
+
+        # Run forward pass to calculate embeddings
+        feed_dict = {self.images_placeholder: [
+            prewhiten_face], self.phase_train_placeholder: False}
+        return self.sess.run(self.embeddings, feed_dict=feed_dict)[0]
+
+    def generate_embeddings(self,
+                            all_images: ImageGenerator) -> List[Embedding]:
+        featurized_batches = cast(List[Embedding], [])
+        clean_images = np.array(list(map(utils.fixed_standardize, all_images)))
+
+        widgets = ['Encoding:', pb.Percentage(), ' ',
+                   pb.Bar(), ' ', pb.ETA(), ' ', pb.Timer()]
+        timer = pb.ProgressBar(
+            widgets=widgets,
+            max_value=clean_images.shape[0])
+        for index in range(0, clean_images.shape[0], self.batch_size):
+            end_index = min(index + self.batch_size, clean_images.shape[0])
+            timer.update(end_index)
+            batch = clean_images[index:end_index, :]
+            featurized_batches += self.extract_batch(batch)
+        timer.finish()
+        return featurized_batches
+
+    def get_face_embeddings(self,
+                            all_faces: FacesGenerator,
+                            save_memory: bool = False) -> FacesGenerator:
+        """Generates embeddings from generator of Faces
+        Keyword Arguments:
+            save_memory -- save memory by deleting image from Face object  (default: {False})
+        """
+
+        face_list = list(all_faces)
+        total_num_faces = sum([1 for faces in face_list for face in faces])
+        images = (face.image for faces in face_list for face in faces)
+        embed_array = self.generate_embeddings(images)
+        total_num_embeddings = len(embed_array)
+        assert total_num_embeddings == total_num_faces
+
+        index = 0
+        for faces in face_list:
+            for face in faces:
+                if save_memory:
+                    face.image = None
+                    face.container_image = None
+                face.embedding = embed_array[index]
+                index += 1
+            yield faces
+
+    def get_best_match(
+            self,
+            anchor: Face,
+            faces: List[Face]) -> Optional[Face]:
+        anchor_image = misc.imresize(
+            anchor.image,
+            (self.image_height,
+             self.image_width),
+            interp='bilinear')
+        anchor.embedding = self.generate_embedding(anchor_image)
+        min_dist = float('inf')
+        min_face = faces[0] if faces else None
+        for face in faces:
+            face_image = misc.imresize(
+                face.image, (self.image_height, self.image_width), interp='bilinear')
+            face.embedding = self.generate_embedding(face_image)
+            dist = utils.embedding_distance(
+                anchor.embedding,
+                face.embedding,
+                DistanceMetric.EUCLIDEAN_SQUARED)
+            if dist < min_dist and dist < 1.0:
+                min_dist = dist
+                min_face = face
+        return min_face
+
+    def tear_down(self):
+        self.sess.close()
+        self.sess = None
diff --git a/facenet_sandberg/inference/generate_tsne.py b/facenet_sandberg/inference/generate_tsne.py
new file mode 100644
index 000000000..0c5b83670
--- /dev/null
+++ b/facenet_sandberg/inference/generate_tsne.py
@@ -0,0 +1,207 @@
+from argparse import ArgumentParser, Namespace
+from typing import List, Tuple
+
+import matplotlib.pyplot as plt
+import numpy as np
+import PIL
+import torch
+from sklearn import preprocessing
+from sklearn.manifold import TSNE
+
+from facenet_sandberg.common_types import Face, Image
+from facenet_sandberg.utils import (get_dataset, get_image_from_path_bgr,
+                                    get_image_from_path_rgb)
+from tensorboardX import SummaryWriter
+
+from .identifier import Identifier
+
+
+def _normalize(image: Image):
+    return (image.astype(float) - 128) / 128
+
+
+def _resize(img_array: List[Image]):
+    resized = []
+    for image in img_array:
+        img = PIL.Image.fromarray(image)
+        img.thumbnail((64, 64), PIL.Image.ANTIALIAS)
+        resized.append(_normalize(np.array(img)))
+    resized = np.array(resized)
+    return resized
+
+
+def _get_data(img_dir: str, is_insightface: bool,
+              is_flat: bool) -> Tuple[np.ndarray, List[str]]:
+    people = get_dataset(img_dir, is_flat=is_flat)
+
+    # Get Images
+    people_paths = [person.image_paths for person in people]
+    all_image_paths = [path for paths in people_paths for path in paths]
+    if is_insightface:
+        images = map(get_image_from_path_bgr, all_image_paths)
+    else:
+        images = map(get_image_from_path_rgb, all_image_paths)
+
+    # Get Labels
+    names = [person.name for person in people]
+    labels = []
+    for index, person in enumerate(names):
+        labels += [person] * len(people_paths[index])
+    return np.array(list(images)), labels
+
+
+def _vectorize_images(
+        model_path: str,
+        is_insightface: bool,
+        prealigned: bool,
+        images: List[Image],
+        labels: List[str]) -> List[Face]:
+
+    identifier = Identifier(
+        model_path=model_path,
+        is_insightface=is_insightface)
+    all_faces = identifier.detect_encode_all(
+        images, detect_multiple_faces=prealigned, urls=labels)
+    faces_flat = [faces[0] for faces in all_faces if faces]
+    return faces_flat
+
+
+def write_to_tf_logdir(
+        features: np.ndarray,
+        labels: np.ndarray,
+        torch_array: torch.Tensor,
+        log_dir: str) -> None:
+
+    writer = SummaryWriter(log_dir=log_dir)
+    writer.add_embedding(features, metadata=labels, label_img=torch_array)
+    writer.close()
+
+
+def generate_tsne_data(
+        img_dir: str,
+        model_path: str,
+        is_insightface: bool,
+        prealigned: bool,
+        is_flat: bool):
+    images, labels = _get_data(img_dir, is_insightface, is_flat)
+    faces = _vectorize_images(
+        model_path,
+        is_insightface,
+        prealigned,
+        images,
+        labels)
+    face_images = np.array([face.image for face in faces])
+    face_vectors = np.array([face.embedding for face in faces])
+    face_labels = np.array([face.url for face in faces])
+    resized = _resize(face_images)
+    torch_array = torch.from_numpy(resized).permute(0, 3, 1, 2).float()
+    return face_vectors, face_labels, torch_array
+
+
+def tsne_tensorboard(
+        img_dir: str,
+        model_path: str,
+        is_insightface: bool,
+        prealigned: bool,
+        is_flat: bool,
+        log_dir: str):
+
+    face_vectors, face_labels, torch_array = generate_tsne_data(
+        img_dir, model_path, is_insightface, prealigned, is_flat)
+    write_to_tf_logdir(face_vectors, face_labels, torch_array, log_dir)
+
+
+def tsne_sklearn(
+        img_dir: str,
+        model_path: str,
+        is_insightface: bool,
+        prealigned: bool,
+        is_flat: bool,
+        save_plt: bool=True):
+
+    face_vectors, face_labels, _ = generate_tsne_data(
+        img_dir, model_path, is_insightface, prealigned, is_flat)
+    tsne = TSNE(n_components=2, random_state=0)
+    reduced = tsne.fit_transform(face_vectors)
+    plt = tsne_plt(reduced, face_labels, save_plt)
+    return plt
+
+
+def get_cmap(n, name='hsv'):
+    '''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
+    RGB color; the keyword argument name must be a standard mpl colormap name.'''
+    return plt.cm.get_cmap(name, n)
+
+
+def tsne_plt(
+        reduced: np.ndarray,
+        labels_str: List[str],
+        save_plt: bool):
+    le = preprocessing.LabelEncoder()
+    labels_num = le.fit_transform(labels_str)
+    plt.figure(figsize=(30, 30))
+    # cmap = get_cmap(len(le.classes_))
+    cmap = plt.cm.get_cmap("hsv", len(le.classes_) + 1)
+    for i, label in zip(range(len(le.classes_)), le.classes_):
+        plt.scatter(reduced[labels_num == i, 0],
+                    reduced[labels_num == i, 1], c=cmap(i), cmap=cmap, label=label)
+    plt.legend()
+    if save_plt:
+        plt.savefig("tsne.jpg")
+    return plt
+
+
+def _cli() -> None:
+    args = _parse_arguments()
+    if args.save_plt:
+        tsne_sklearn(
+            args.img_dir,
+            args.model_path,
+            args.is_insightface,
+            args.prealigned,
+            args.is_flat,
+            True)
+    else:
+        tsne_tensorboard(
+            args.img_dir,
+            args.model_path,
+            args.is_insightface,
+            args.prealigned,
+            args.is_flat,
+            args.log_dir)
+
+
+def _parse_arguments() -> Namespace:
+    parser = ArgumentParser()
+    parser.add_argument('--img_dir',
+                        type=str,
+                        required=True,
+                        help='Path to the image directory.')
+    parser.add_argument('--model_path',
+                        type=str,
+                        required=True,
+                        help='path to facial recognition model')
+    parser.add_argument('--is_insightface',
+                        action='store_true',
+                        help='Set this flag if the model is insightface')
+    parser.add_argument(
+        '--prealigned_flag',
+        action='store_true',
+        help='Specify if the images have already been aligned.')
+    parser.add_argument(
+        '--is_flat',
+        action='store_true',
+        help='Set this flag if the image directory is flat with one photo per person')
+    parser.add_argument(
+        '--save_plt',
+        action='store_true',
+        help='Set this flag if you want to save a matplot lib plot instead of tensorboard')
+    parser.add_argument('--log_dir',
+                        type=str,
+                        required=True,
+                        help='path to output the tensorflow logs')
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    _cli()
diff --git a/facenet_sandberg/inference/identifier.py b/facenet_sandberg/inference/identifier.py
new file mode 100644
index 000000000..848cbd174
--- /dev/null
+++ b/facenet_sandberg/inference/identifier.py
@@ -0,0 +1,207 @@
+"""Face Detection and Recognition"""
+
+import itertools
+import os
+import warnings
+from typing import List, Tuple
+
+import tensorflow as tf
+
+from facenet_sandberg import utils
+from facenet_sandberg.common_types import (DistanceMetric, Embedding, Face,
+                                           FacesGenerator, Image,
+                                           ImageGenerator, Match)
+from facenet_sandberg.inference import (align, facenet_encoder,
+                                        insightface_encoder)
+
+warnings.filterwarnings("ignore", message="numpy.dtype size changed")
+warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+tf.logging.set_verbosity(tf.logging.ERROR)
+
+
+class Identifier:
+    """Class to detect, encode, and match faces
+
+    Arguments:
+        threshold {Float} -- Distance threshold to determine matches
+    """
+
+    def __init__(
+            self,
+            model_path: str,
+            threshold: float = 1.10,
+            is_insightface: bool = False,
+            is_centerface: bool = False,
+            batch_size: int = 64):
+        if is_insightface:
+            self.detector = align.Detector(
+                face_crop_height=112,
+                face_crop_width=112,
+                steps_threshold=[0.6, 0.7, 0.9],
+                scale_factor=0.85,
+                is_rgb=False)
+            self.encoder = insightface_encoder.Insightface(
+                model_path=model_path,
+                batch_size=batch_size)
+        elif is_centerface:
+            self.detector = align.Detector(
+                face_crop_height=112,
+                face_crop_width=96,
+                steps_threshold=[0.6, 0.7, 0.9],
+                scale_factor=0.85,
+                is_rgb=False)
+            self.encoder = insightface_encoder.Insightface(
+                model_path=model_path,
+                batch_size=batch_size,
+                image_height=112,
+                image_width=96)
+        else:
+            self.detector = align.Detector()
+            self.encoder = facenet_encoder.Facenet(
+                model_path=model_path,
+                batch_size=batch_size)
+        self.threshold = threshold
+
+    def vectorize(self, image: Image,
+                  prealigned: bool = False,
+                  detect_multiple_faces: bool = True,
+                  face_limit: int = 5) -> List[Embedding]:
+        """Gets face embeddings in a single image
+        Keyword Arguments:
+            prealigned {bool} -- is the image already aligned
+            face_limit {int} -- max number of faces allowed
+                                before image is discarded. (default: {5})
+        """
+        if not prealigned:
+            faces = self.detect_encode(
+                image, detect_multiple_faces, face_limit)
+            vectors = [face.embedding for face in faces]
+        else:
+            vectors = [self.encoder.generate_embedding(image)]
+        return vectors
+
+    def vectorize_all(self,
+                      images: ImageGenerator,
+                      prealigned: bool = False,
+                      detect_multiple_faces: bool = True,
+                      face_limit: int = 5) -> List[List[Embedding]]:
+        """Gets face embeddings from a generator of images
+        Keyword Arguments:
+            prealigned {bool} -- is the image already aligned
+            face_limit {int} -- max number of faces allowed
+                                before image is discarded. (default: {5})
+        """
+        vectors = []
+        if not prealigned:
+            all_faces = self.detect_encode_all(
+                images=images,
+                save_memory=True,
+                detect_multiple_faces=detect_multiple_faces,
+                face_limit=face_limit)
+            for faces in all_faces:
+                vectors.append([face.embedding for face in faces])
+        else:
+            embeddings = self.encoder.generate_embeddings(
+                images)
+            for embedding in embeddings:
+                vectors.append([embedding])
+        return vectors
+
+    def detect_encode(self, image: Image,
+                      detect_multiple_faces: bool = True,
+                      face_limit: int = 5) -> List[Face]:
+        """Detects faces in an image and encodes them
+        """
+
+        faces = self.detector.find_faces(
+            image, detect_multiple_faces, face_limit)
+        for face in faces:
+            face.embedding = self.encoder.generate_embedding(face.image)
+        return faces
+
+    def detect_encode_all(self,
+                          images: ImageGenerator,
+                          urls: List[str] = None,
+                          save_memory: bool = False,
+                          detect_multiple_faces: bool = True,
+                          face_limit: int = 5) -> FacesGenerator:
+        """For a list of images finds and encodes all faces
+
+        Keyword Arguments:
+            save_memory {bool} -- Saves memory by deleting image from Face objects.
+                                Should only be used if with you have some other kind
+                                of refference to the original image like a url. (default: {False})
+        """
+
+        all_faces = self.detector.bulk_find_face(
+            images, urls, detect_multiple_faces, face_limit)
+        return self.encoder.get_face_embeddings(all_faces, save_memory)
+
+    def compare_embedding(self,
+                          embedding_1: Embedding,
+                          embedding_2: Embedding,
+                          distance_metric: DistanceMetric) -> Tuple[bool,
+                                                                    float]:
+        """Compares the distance between two embeddings
+        """
+
+        distance = utils.embedding_distance(
+            embedding_1, embedding_2, distance_metric)
+        is_match = False
+        if distance < self.threshold:
+            is_match = True
+        return is_match, distance
+
+    def compare_images(
+            self,
+            image_1: Image,
+            image_2: Image,
+            detect_multiple_faces: bool = True,
+            face_limit: int = 5) -> Match:
+        match = Match()
+        image_1_faces = self.detect_encode(
+            image_1, detect_multiple_faces, face_limit)
+        image_2_faces = self.detect_encode(
+            image_2, detect_multiple_faces, face_limit)
+        if image_1_faces and image_2_faces:
+            for face_1 in image_1_faces:
+                for face_2 in image_2_faces:
+                    is_match, score = self.compare_embedding(
+                        face_1.embedding, face_2.embedding, DistanceMetric.EUCLIDEAN_SQUARED)
+                    if score < match.score:
+                        match.score = score
+                        match.face_1 = face_1
+                        match.face_2 = face_2
+                        match.is_match = is_match
+        return match
+
+    def find_all_matches(
+            self,
+            image_directory: str,
+            recursive: bool,
+            distance_metric: DistanceMetric = DistanceMetric.EUCLIDEAN_SQUARED) -> List[Match]:
+        """Finds all matches in a directory of images
+        """
+
+        all_images = utils.get_images_from_dir(image_directory, recursive)
+        all_matches = []
+        all_faces_lists = self.detect_encode_all(all_images)
+        all_faces = (face for faces in all_faces_lists for face in faces)
+        # Really inefficient way to check all combinations
+        for face_1, face_2 in itertools.combinations(all_faces, 2):
+            is_match, score = self.compare_embedding(
+                face_1.embedding, face_2.embedding, distance_metric)
+            if is_match:
+                match = Match()
+                match.face_1 = face_1
+                match.face_2 = face_2
+                match.is_match = True
+                match.score = score
+                all_matches.append(match)
+                face_1.matches.append(match)
+                face_2.matches.append(match)
+        return all_matches
+
+    def tear_down(self):
+        self.encoder.tear_down()
diff --git a/facenet_sandberg/inference/insightface_encoder.py b/facenet_sandberg/inference/insightface_encoder.py
new file mode 100644
index 000000000..0219cabd2
--- /dev/null
+++ b/facenet_sandberg/inference/insightface_encoder.py
@@ -0,0 +1,142 @@
+import os
+import warnings
+from typing import List, cast
+
+import numpy as np
+import progressbar as pb
+import tensorflow as tf
+import tensorlayer as tl
+
+from facenet_sandberg import utils
+from facenet_sandberg.common_types import *
+from facenet_sandberg.models.L_Resnet_E_IR_fix_issue9 import get_resnet
+
+warnings.filterwarnings("ignore", message="numpy.dtype size changed")
+warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+tf.logging.set_verbosity(tf.logging.ERROR)
+
+
+class Insightface:
+    def __init__(self, model_path: str,
+                 image_height: int = 112,
+                 image_width: int = 112,
+                 batch_size: int = 64) -> None:
+        import tensorflow as tf
+        import tensorflow.contrib.slim as slim
+        from tensorflow.core.protobuf import config_pb2
+        # save context
+        sess, embedding_tensor, feed_dict, input_placeholder = self._get_extractor(
+            model_path, image_height, image_width)
+        self.embedding_tensor = embedding_tensor
+        self.batch_size = batch_size
+        self.sess = sess
+        self.feed_dict = feed_dict
+        self.input_placeholder = input_placeholder
+        self.model_path = model_path
+        self.image_height = image_height
+        self.image_width = image_width
+
+    def _get_extractor(self, model_path: str,
+                       image_height: int, image_width: int):
+        images = tf.placeholder(
+            name='img_inputs',
+            shape=[
+                None,
+                image_height,
+                image_width,
+                3],
+            dtype=tf.float32)
+        dropout_rate = tf.placeholder(name='dropout_rate', dtype=tf.float32)
+
+        w_init_method = tf.contrib.layers.xavier_initializer(uniform=False)
+        with tl.ops.suppress_stdout():
+            tl.layers.set_name_reuse(True)
+            test_net = get_resnet(
+                images,
+                50,
+                type='ir',
+                w_init=w_init_method,
+                trainable=False,
+                reuse=tf.AUTO_REUSE,
+                keep_rate=dropout_rate)
+        embedding_tensor = test_net.outputs
+        # define sess
+        gpu_config = tf.ConfigProto(allow_soft_placement=True)
+        gpu_config.gpu_options.allow_growth = True
+
+        sess = tf.Session(config=gpu_config)
+        # init all variables
+        sess.run(tf.global_variables_initializer())
+
+        # restore weights
+        saver = tf.train.Saver()
+        saver.restore(sess, model_path)
+
+        feed_dict = {images: None, dropout_rate: 1.0}
+        return sess, embedding_tensor, feed_dict, images
+
+    def extract_batch(self, batch: np.ndarray) -> List[Embedding]:
+        self.feed_dict.setdefault(self.input_placeholder, None)
+        self.feed_dict[self.input_placeholder] = batch
+        feat = self.sess.run([self.embedding_tensor], feed_dict=self.feed_dict)
+        feat = np.array(feat)
+        if feat.ndim > 2:
+            feat = np.squeeze(feat, axis=0)
+        return [embedding for embedding in feat]
+
+    def generate_embedding(self, image: Image) -> Embedding:
+        image = utils.fixed_standardize(image)
+        self.feed_dict.setdefault(self.input_placeholder, None)
+        self.feed_dict[self.input_placeholder] = [image]
+        feat = self.sess.run([self.embedding_tensor], feed_dict=self.feed_dict)
+        feat = np.array(feat)
+        feat = np.squeeze(feat)
+        return feat
+
+    def generate_embeddings(
+            self,
+            all_images: ImageGenerator) -> List[Embedding]:
+        featurized_batches = cast(List[Embedding], [])
+        clean_images = np.array(list(map(utils.fixed_standardize, all_images)))
+
+        widgets = ['Encoding:', pb.Percentage(), ' ',
+                   pb.Bar(), ' ', pb.ETA(), ' ', pb.Timer()]
+        timer = pb.ProgressBar(
+            widgets=widgets,
+            max_value=clean_images.shape[0])
+        for index in range(0, clean_images.shape[0], self.batch_size):
+            end_index = min(index + self.batch_size, clean_images.shape[0])
+            timer.update(end_index)
+            batch = clean_images[index:end_index, :]
+            featurized_batches += self.extract_batch(batch)
+        timer.finish()
+        return featurized_batches
+
+    def get_face_embeddings(self,
+                            all_faces: FacesGenerator,
+                            save_memory: bool = False) -> FacesGenerator:
+        """Generates embeddings from generator of Faces
+        Keyword Arguments:
+            save_memory -- save memory by deleting image from Face object  (default: {False})
+        """
+
+        face_list = list(all_faces)
+        total_num_faces = sum([1 for faces in face_list for face in faces])
+        images = (face.image for faces in face_list for face in faces)
+        embed_array = self.generate_embeddings(images)
+        total_num_embeddings = len(embed_array)
+        assert total_num_embeddings == total_num_faces
+
+        index = 0
+        for faces in face_list:
+            for face in faces:
+                if save_memory:
+                    face.image = None
+                    face.container_image = None
+                face.embedding = embed_array[index]
+                index += 1
+            yield faces
+
+    def tear_down(self):
+        self.sess.close()
diff --git a/facenet_sandberg/inference/validate.py b/facenet_sandberg/inference/validate.py
new file mode 100644
index 000000000..2a4c83ef6
--- /dev/null
+++ b/facenet_sandberg/inference/validate.py
@@ -0,0 +1,249 @@
+import sys
+from argparse import ArgumentParser, Namespace
+from typing import List, Tuple, Union
+
+import numpy as np
+from sklearn.metrics import accuracy_score, precision_score, recall_score
+from sklearn.model_selection import KFold
+
+from facenet_sandberg import utils
+from facenet_sandberg.common_types import DistanceMetric, ThresholdMetric
+from facenet_sandberg.config import ValidateConfig
+from facenet_sandberg.inference import Identifier
+
+FaceVector = List[float]
+Match = Tuple[str, int, int]
+Mismatch = Tuple[str, int, str, int]
+Pair = Union[Match, Mismatch]
+Path = Tuple[str, str]
+Label = bool
+
+
+def evaluate(embeddings: np.ndarray,
+             labels: np.ndarray,
+             num_folds: int,
+             distance_metric: DistanceMetric,
+             threshold_metric: ThresholdMetric,
+             subtract_mean: bool,
+             divide_stddev: bool,
+             threshold_start: float,
+             threshold_end: float,
+             threshold_step: float) -> Tuple[np.float, np.float, np.float]:
+    import pdb; pdb.set_trace()
+    thresholds = np.arange(threshold_start, threshold_end, threshold_step)
+    embeddings1 = embeddings[0::2]
+    embeddings2 = embeddings[1::2]
+    accuracy, recall, precision = _score_k_fold(thresholds,
+                                                embeddings1,
+                                                embeddings2,
+                                                labels,
+                                                num_folds,
+                                                distance_metric,
+                                                threshold_metric,
+                                                subtract_mean,
+                                                divide_stddev)
+    return np.mean(accuracy), np.mean(recall), np.mean(precision)
+
+
+def _calculate_best_threshold(thresholds: np.ndarray,
+                              dist: np.ndarray,
+                              labels: np.ndarray,
+                              threshold_metric: ThresholdMetric) -> np.float:
+    if threshold_metric == ThresholdMetric.ACCURACY:
+        threshold_score = accuracy_score
+    elif threshold_metric == ThresholdMetric.PRECISION:
+        threshold_score = precision_score
+    elif threshold_metric == ThresholdMetric.RECALL:
+        threshold_score = recall_score
+    threshold_scores = np.zeros((len(thresholds)))
+    for threshold_idx, threshold in enumerate(thresholds):
+        import pdb; pdb.set_trace()
+        predictions = np.less(dist, threshold)
+        threshold_scores[threshold_idx] = threshold_score(labels, predictions)
+    best_threshold_index = np.argmax(threshold_scores)
+    return thresholds[best_threshold_index]
+
+
+def _score_k_fold(thresholds: np.ndarray,
+                  embeddings1: np.ndarray,
+                  embeddings2: np.ndarray,
+                  labels: np.ndarray,
+                  num_folds: int,
+                  distance_metric: DistanceMetric,
+                  threshold_metric: str,
+                  subtract_mean: bool,
+                  divide_stddev: bool) -> Tuple[np.ndarray,
+                                                np.ndarray,
+                                                np.ndarray]:
+    import pdb; pdb.set_trace()
+    k_fold = KFold(n_splits=num_folds, shuffle=True)
+    accuracy = np.zeros((num_folds))
+    recall = np.zeros((num_folds))
+    precision = np.zeros((num_folds))
+    splits = k_fold.split(np.arange(len(labels)))
+    for fold_idx, (train_set, test_set) in enumerate(splits):
+        train_embeddings = np.concatenate([embeddings1[train_set],
+                                           embeddings2[train_set]])
+        mean = np.mean(train_embeddings, axis=0) if subtract_mean else 0.0
+        stddev = np.std(train_embeddings, axis=0) if divide_stddev else 1.0
+        e1 = (embeddings1 - mean) / stddev
+        e2 = (embeddings2 - mean) / stddev
+        dist = utils.embedding_distance_bulk(
+            (embeddings1 - mean) / stddev,
+            (embeddings2 - mean) / stddev,
+            distance_metric)
+        best_threshold = _calculate_best_threshold(thresholds,
+                                                   dist[train_set],
+                                                   labels[train_set],
+                                                   threshold_metric)
+        predictions = np.less(dist[test_set], best_threshold)
+        accuracy[fold_idx] = accuracy_score(labels[test_set], predictions)
+        recall[fold_idx] = recall_score(labels[test_set], predictions)
+        precision[fold_idx] = precision_score(labels[test_set], predictions)
+    return accuracy, recall, precision
+
+
+def _get_target_faces(embeddings1: List[FaceVector],
+                      embeddings2: List[FaceVector],
+                      distance_metric: DistanceMetric,
+                      is_match: bool) -> Tuple[FaceVector, FaceVector]:
+    import pdb; pdb.set_trace()
+    X, Y = zip(*[(emb1, emb2) for emb1 in embeddings1 for emb2 in embeddings2])
+    distances = utils.embedding_distance_bulk(X, Y, distance_metric)
+    distance_criteria = min if is_match else max
+    index, _ = distance_criteria(enumerate(distances), key=lambda x: x[1])
+    return X[index], Y[index]
+
+
+def _get_container_metrics(face_vectors: List[List[FaceVector]]) -> Tuple[
+        int,
+        int,
+        float]:
+    num_expected = len(face_vectors)
+    num_missing = sum([1 for i in face_vectors if not i])
+    percentage_missing = 100 * (num_missing / num_expected)
+    return num_expected, num_missing, percentage_missing
+
+
+def _remove_empty_embeddings(config: ValidateConfig,
+                             embeddings: np.ndarray,
+                             labels: np.ndarray) -> Tuple[np.ndarray,
+                                                          np.ndarray]:
+    if config.remove_empty_embeddings:
+        embs_filter = embeddings == np.asarray([[0] * config.embedding_size])
+        empty_indices = np.where(np.all(embs_filter, axis=1))[0]
+        pair_empty_indices = np.asarray([i + 1
+                                         if i % 2 == 0
+                                         else i - 1
+                                         for i in empty_indices])
+        embedding_indices = np.unique(np.concatenate((empty_indices,
+                                                      pair_empty_indices)))
+        embeddings = np.delete(embeddings, embedding_indices, axis=0)
+        label_indices = np.unique(embedding_indices // 2)
+        labels = np.delete(labels, label_indices, axis=0)
+    return embeddings, labels
+
+
+def _prealigned(config: ValidateConfig, num_matches_mismatches: int,
+                face_vectors: List[List[FaceVector]]) -> np.ndarray:
+    if config.prealigned:
+        embeddings = np.asarray([[0] * config.embedding_size
+                                 if not embedding else embedding[0]
+                                 for embedding in face_vectors])
+    else:
+        embeddings = []
+        is_match = False
+        for i, (embs1, embs2) in enumerate(zip(face_vectors[0::2],
+                                               face_vectors[1::2])):
+            if i % num_matches_mismatches == 0:
+                is_match = not is_match
+
+            embs1, embs2 = _get_target_faces(
+                embs1 or [[0] * config.embedding_size],
+                embs2 or [[0] * config.embedding_size],
+                config.distance_metric,
+                is_match)
+            embeddings += [embs1, embs2]
+        embeddings = np.asarray(embeddings)
+    return embeddings
+
+
+def _handle_flags(config: ValidateConfig,
+                  num_matches_mismatches: int,
+                  face_vectors: List[List[FaceVector]],
+                  labels: np.ndarray) -> Tuple[np.ndarray,
+                                               np.ndarray]:
+    embeddings = _prealigned(config, num_matches_mismatches, face_vectors)
+    embeddings, labels = _remove_empty_embeddings(config, embeddings, labels)
+    return embeddings, labels
+
+
+def _parse_arguments(argv):
+    parser = ArgumentParser()
+    parser.add_argument(
+        '--config_file',
+        type=str,
+        help='Path to validate config file',
+        default='validate_config.json')
+    parser.add_argument(
+        '--model_path',
+        type=str,
+        help='Path to facial recognition model (facenet or insightface)')
+    parser.add_argument(
+        '--is_insightface',
+        help='Set this flag if using insightface',
+        action='store_true')
+    return parser.parse_args(argv)
+
+
+def validate(config_file: str,
+             identifier: Identifier) -> Tuple[np.float, np.float, np.float]:
+    config = ValidateConfig(config_file)
+    pairs, _, num_matches_mismatches = utils.read_pairs_file(
+        config.pairs_file_name)
+    pair_paths, labels = utils.get_paths_and_labels(config.image_dir, pairs)
+    flat_paths = [path for pair in pair_paths for path in pair]
+
+    images = map(utils.get_image_from_path_rgb, flat_paths)
+
+    all_vectors = identifier.vectorize_all(
+        images, prealigned=config.prealigned)
+    face_vectors = []
+    for vectors in all_vectors:
+        face_vectors.append([vector.tolist() for vector in vectors])
+    num_expected, num_missing, percentage_missing = _get_container_metrics(
+        face_vectors)
+    print('Number of expected face vectors: {}'.format(num_expected))
+    print('Number of missing face vectors: {}'.format(num_missing))
+    print('Percentage missing: {}'.format(percentage_missing))
+
+    embeddings, labels = _handle_flags(config,
+                                       num_matches_mismatches,
+                                       face_vectors,
+                                       np.asarray(labels))
+    accuracy, recall, precision = evaluate(embeddings,
+                                           labels,
+                                           config.num_folds,
+                                           config.distance_metric,
+                                           config.threshold_metric,
+                                           config.subtract_mean,
+                                           config.divide_stddev,
+                                           config.threshold_start,
+                                           config.threshold_end,
+                                           config.threshold_step)
+    print('Accuracy: {}'.format(accuracy))
+    print('Recall: {}'.format(recall))
+    print('Precision: {}'.format(precision))
+    return accuracy, recall, precision
+
+
+def _cli() -> None:
+    args = _parse_arguments(sys.argv[1:])
+    identifier = Identifier(
+        model_path=args.model_path,
+        is_insightface=args.is_insightface)
+    validate(args.config_file, identifier)
+
+
+if __name__ == '__main__':
+    _cli()
diff --git a/facenet_sandberg/inference/validate_config.json b/facenet_sandberg/inference/validate_config.json
new file mode 100644
index 000000000..22827ecb1
--- /dev/null
+++ b/facenet_sandberg/inference/validate_config.json
@@ -0,0 +1,15 @@
+{
+    "IMAGE_DIR": "/Users/armanrahman/datasets/new_data_tests/facenet_eame",
+    "PAIRS_FILE_NAME": "/Users/armanrahman/datasets/new_data_tests/pairs.txt",
+    "NUM_FOLDS": 4,
+    "DISTANCE_METRIC": "EUCLIDEAN_SQUARED",
+    "THRESHOLD_START": 0,
+    "THRESHOLD_END": 1,
+    "THRESHOLD_STEP": 0.1,
+    "THRESHOLD_METRIC": "ACCURACY",
+    "EMBEDDING_SIZE": 512,
+    "REMOVE_EMPTY_EMBEDDINGS": false,
+    "SUBTRACT_MEAN": false,
+    "DIVIDE_STDDEV": false,
+    "PREALIGNED": true
+}
\ No newline at end of file
diff --git a/facenet_sandberg/insightface_config.json b/facenet_sandberg/insightface_config.json
new file mode 100644
index 000000000..ae43f5da0
--- /dev/null
+++ b/facenet_sandberg/insightface_config.json
@@ -0,0 +1,15 @@
+{
+    "IS_RGB": false,
+    "FACE_CROP_HEIGHT": 112,
+    "FACE_CROP_WIDTH": 112,
+    "FACE_CROP_MARGIN": 0.25,
+    "SCALE_FACTOR": 0.85,
+    "STEPS_THRESHOLD": [0.6, 0.7, 0.9],
+    "DETECT_MULTIPLE_FACES": true,
+    "USE_AFFINE": false,
+    "USE_FACEBOXES": false,
+    "NUM_PROCESSES": -1,
+    "FACENET_MODEL_CHECKPOINT": "/Users/armanrahman/models/facenet_model.pb",
+    "INPUT_DIR": "/Users/armanrahman/datasets/eame/eame_test",
+    "OUTPUT_DIR": "/Users/armanrahman/datasets/new_data_tests/insightface_eame"
+}
\ No newline at end of file
diff --git a/facenet_sandberg/lfw.py b/facenet_sandberg/lfw.py
new file mode 100644
index 000000000..893718f83
--- /dev/null
+++ b/facenet_sandberg/lfw.py
@@ -0,0 +1,192 @@
+"""Helper for evaluation on the Labeled Faces in the Wild dataset
+"""
+
+# MIT License
+#
+# Copyright (c) 2016 David Sandberg
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from __future__ import absolute_import, division, print_function
+
+import argparse
+import glob
+import os
+import sys
+from multiprocessing import Lock, Manager, Pool, Queue, Value
+from multiprocessing.dummy import Pool as ThreadPool
+from pathlib import Path
+
+import numpy as np
+
+from facenet_sandberg import facenet
+
+
+def evaluate(embeddings, labels, nrof_folds=10,
+             distance_metric=0, subtract_mean=False):
+    # Calculate evaluation metrics
+    thresholds = np.arange(0, 4, 0.01)
+    embeddings1 = embeddings[0::2]
+    embeddings2 = embeddings[1::2]
+    tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2, np.asarray(
+        labels), nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)
+    thresholds = np.arange(0, 4, 0.001)
+    val, val_std, far = facenet.calculate_val(thresholds, embeddings1, embeddings2, np.asarray(
+        labels), 1e-3, nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)
+    return tpr, fpr, accuracy, val, val_std, far
+
+
+def get_paths(lfw_dir, pairs):
+    """Gets full paths for image pairs and labels (same person or not)
+
+    Arguments:
+        lfw_dir {str} -- Base directory of testing data
+        pairs {[[str]]} -- List of pairs of form:
+                            - For same person: [name, image 1 index, image 2 index]
+                            - For different: [name 1, image index 1, name 2, image index 2]
+
+    Returns:
+        [(str, str)], [bool] -- list of image pair paths and labels
+    """
+
+    nrof_skipped_pairs = 0
+    path_list = []
+    labels = []
+    for pair in pairs:
+        if len(pair) == 3:
+            path0 = add_extension(os.path.join(
+                lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])))
+            path1 = add_extension(os.path.join(
+                lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[2])))
+            is_same_person = True
+        elif len(pair) == 4:
+            path0 = add_extension(os.path.join(
+                lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])))
+            path1 = add_extension(os.path.join(
+                lfw_dir, pair[2], pair[2] + '_' + '%04d' % int(pair[3])))
+            is_same_person = False
+        if os.path.exists(path0) and os.path.exists(
+                path1):    # Only add the pair if both paths exist
+            path_list += (path0, path1)
+            labels.append(is_same_person)
+        else:
+            nrof_skipped_pairs += 1
+    if nrof_skipped_pairs > 0:
+        print('Skipped %d image pairs' % nrof_skipped_pairs)
+
+    return path_list, labels
+
+
+def transform_to_lfw_format(image_directory, num_processes=1):
+    """Transforms an image dataset to lfw format image names.
+       Base directory should have a folder per person with the person's name.
+
+    Arguments:
+        image_directory {str} -- base directory of people folders
+    """
+    all_folders = os.path.join(image_directory, "*", "")
+    people_folders = glob.iglob(all_folders)
+    if num_processes != 1:
+        process_pool = Pool(num_processes)
+        process_pool.imap(rename, people_folders)
+        process_pool.close()
+        process_pool.join()
+    else:
+        for person_folder in people_folders:
+            rename(person_folder)
+
+
+def rename(person_folder):
+    """Renames all the images in a folder in lfw format
+
+    Arguments:
+        person_folder {str} -- path to folder named after person
+    """
+    all_image_paths = glob.glob(os.path.join(person_folder, "*.*"))
+    all_image_paths = sorted([image for image in all_image_paths if image.endswith(
+        ".jpg") or image.endswith(".png")])
+    person_name = os.path.basename(os.path.normpath(person_folder))
+    concat_name = '_'.join(person_name.split())
+    for index, image_path in enumerate(all_image_paths):
+        image_name = concat_name + '_' + '%04d' % (index + 1)
+        file_ext = Path(image_path).suffix
+        new_image_path = os.path.join(person_folder, image_name + file_ext)
+        os.rename(image_path, new_image_path)
+    os.rename(person_folder, person_folder.replace(person_name, concat_name))
+
+
+def add_extension(path):
+    """Adds a image file extension to the path if it exists
+
+    Arguments:
+        path {str} -- base path to image file
+
+    Raises:
+        RuntimeError -- [description]
+
+    Returns:
+        str -- base path plus image file extension
+    """
+
+    if os.path.exists(path + '.jpg'):
+        return path + '.jpg'
+    elif os.path.exists(path + '.png'):
+        return path + '.png'
+    else:
+        raise RuntimeError('No file "%s" with extension png or jpg.' % path)
+
+
+def read_pairs(pairs_filename):
+    """Reads a pairs.txt file to array. Each file line is of format:
+        - If same person: "{person} {image 1 index} {image 2 index}"
+        - If different: "{person 1} {image 1 index} {person 2} {image 2 index}"
+
+    Arguments:
+        pairs_filename {str} -- path to pairs.txt file
+
+    Returns:
+        np.ndarray -- numpy array of pairs
+    """
+
+    pairs = []
+    with open(pairs_filename, 'r') as f:
+        for line in f.readlines()[1:]:
+            pair = line.strip().split()
+            pairs.append(pair)
+    return np.array(pairs)
+
+
+def parse_arguments(argv):
+    """Argument parser
+    """
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        'image_directory',
+        type=str,
+        help='Path to the data directory containing images to fix names')
+
+    return parser.parse_args(argv)
+
+
+if __name__ == '__main__':
+    args = parse_arguments(sys.argv[1:])
+    if args:
+        transform_to_lfw_format(args.image_directory)
diff --git a/facenet_sandberg/models/L_Resnet_E_IR_fix_issue9.py b/facenet_sandberg/models/L_Resnet_E_IR_fix_issue9.py
new file mode 100644
index 000000000..d7c6075d2
--- /dev/null
+++ b/facenet_sandberg/models/L_Resnet_E_IR_fix_issue9.py
@@ -0,0 +1,797 @@
+import collections
+
+import tensorflow as tf
+import tensorlayer as tl
+from tensorflow.contrib.layers.python.layers import utils
+from tensorlayer.layers import Layer, list_remove_repeat
+
+
+class ElementwiseLayer(Layer):
+    """
+    The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation.
+
+    Parameters
+    ----------
+    layer : a list of :class:`Layer` instances
+        The `Layer` class feeding into this layer.
+    combine_fn : a TensorFlow elemwise-merge function
+        e.g. AND is ``tf.minimum`` ;  OR is ``tf.maximum`` ; ADD is ``tf.add`` ; MUL is ``tf.multiply`` and so on.
+        See `TensorFlow Math API <https://www.tensorflow.org/versions/master/api_docs/python/math_ops.html#math>`_ .
+    name : a string or None
+        An optional name to attach to this layer.
+    """
+
+    def __init__(
+        self,
+        layer=[],
+        combine_fn=tf.minimum,
+        name='elementwise_layer',
+        act=None,
+    ):
+        Layer.__init__(self, name=name)
+        '''
+        if act:
+            #print("  [TL] ElementwiseLayer %s: size:%s fn:%s, act:%s" % (
+            #self.name, layer[0].outputs.get_shape(), combine_fn.__name__, act.__name__))
+        else:
+            #print("  [TL] ElementwiseLayer %s: size:%s fn:%s" % (
+            #self.name, layer[0].outputs.get_shape(), combine_fn.__name__))
+        '''
+        self.outputs = layer[0].outputs
+        # #print(self.outputs._shape, type(self.outputs._shape))
+        for l in layer[1:]:
+            # assert str(self.outputs.get_shape()) == str(l.outputs.get_shape()), "Hint: the input shapes should be the same. %s != %s" %  (self.outputs.get_shape() , str(l.outputs.get_shape()))
+            self.outputs = combine_fn(self.outputs, l.outputs, name=name)
+        if act:
+            self.outputs = act(self.outputs)
+        self.all_layers = list(layer[0].all_layers)
+        self.all_params = list(layer[0].all_params)
+        self.all_drop = dict(layer[0].all_drop)
+
+        for i in range(1, len(layer)):
+            self.all_layers.extend(list(layer[i].all_layers))
+            self.all_params.extend(list(layer[i].all_params))
+            self.all_drop.update(dict(layer[i].all_drop))
+
+        self.all_layers = list_remove_repeat(self.all_layers)
+        self.all_params = list_remove_repeat(self.all_params)
+
+
+class BatchNormLayer(Layer):
+    """
+    The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``.
+
+    Batch normalization on fully-connected or convolutional maps.
+
+    ```
+        https://www.tensorflow.org/api_docs/python/tf/cond
+        If x < y, the tf.add operation will be executed and tf.square operation will not be executed.
+        Since z is needed for at least one branch of the cond, the tf.multiply operation is always executed, unconditionally.
+    ```
+
+    Parameters
+    -----------
+    layer : a :class:`Layer` instance
+        The `Layer` class feeding into this layer.
+    decay : float, default is 0.9.
+        A decay factor for ExponentialMovingAverage, use larger value for large dataset.
+    epsilon : float
+        A small float number to avoid dividing by 0.
+    act : activation function.
+    is_train : boolean
+        Whether train or inference.
+    beta_init : beta initializer
+        The initializer for initializing beta
+    gamma_init : gamma initializer
+        The initializer for initializing gamma
+    dtype : tf.float32 (default) or tf.float16
+    name : a string or None
+        An optional name to attach to this layer.
+
+    References
+    ----------
+    - `Source <https://github.com/ry/tensorflow-resnet/blob/master/resnet.py>`_
+    - `stackoverflow <http://stackoverflow.com/questions/38312668/how-does-one-do-inference-with-batch-normalization-with-tensor-flow>`_
+
+    """
+
+    def __init__(
+            self,
+            layer=None,
+            decay=0.9,
+            epsilon=2e-5,
+            act=tf.identity,
+            is_train=False,
+            fix_gamma=True,
+            beta_init=tf.zeros_initializer,
+            gamma_init=tf.random_normal_initializer(
+                mean=1.0, stddev=0.002),  # tf.ones_initializer,
+            # dtype = tf.float32,
+            trainable=None,
+            name='batchnorm_layer',
+    ):
+        Layer.__init__(self, name=name)
+        self.inputs = layer.outputs
+        # print("  [TL] BatchNormLayer %s: decay:%f epsilon:%f act:%s is_train:%s" % (self.name, decay, epsilon, act.__name__, is_train))
+        x_shape = self.inputs.get_shape()
+        params_shape = x_shape[-1:]
+
+        from tensorflow.python.training import moving_averages
+        from tensorflow.python.ops import control_flow_ops
+
+        with tf.variable_scope(name) as vs:
+            axis = list(range(len(x_shape) - 1))
+
+            # 1. beta, gamma
+            if tf.__version__ > '0.12.1' and beta_init == tf.zeros_initializer:
+                beta_init = beta_init()
+            beta = tf.get_variable(
+                'beta',
+                shape=params_shape,
+                initializer=beta_init,
+                dtype=tf.float32,
+                trainable=is_train)  # , restore=restore)
+
+            gamma = tf.get_variable(
+                'gamma',
+                shape=params_shape,
+                initializer=gamma_init,
+                dtype=tf.float32,
+                trainable=fix_gamma,
+            )  # restore=restore)
+
+            # 2.
+            if tf.__version__ > '0.12.1':
+                moving_mean_init = tf.zeros_initializer()
+            else:
+                moving_mean_init = tf.zeros_initializer
+            moving_mean = tf.get_variable(
+                'moving_mean',
+                params_shape,
+                initializer=moving_mean_init,
+                dtype=tf.float32,
+                trainable=False)  # restore=restore)
+            moving_variance = tf.get_variable(
+                'moving_variance',
+                params_shape,
+                initializer=tf.constant_initializer(1.),
+                dtype=tf.float32,
+                trainable=False,
+            )  # restore=restore)
+
+            # 3.
+            # These ops will only be preformed when training.
+            mean, variance = tf.nn.moments(self.inputs, axis)
+            try:  # TF12
+                update_moving_mean = moving_averages.assign_moving_average(
+                    moving_mean, mean, decay, zero_debias=False)  # if zero_debias=True, has bias
+                update_moving_variance = moving_averages.assign_moving_average(
+                    moving_variance, variance, decay, zero_debias=False)  # if zero_debias=True, has bias
+                # #print("TF12 moving")
+            except Exception as e:  # TF11
+                update_moving_mean = moving_averages.assign_moving_average(
+                    moving_mean, mean, decay)
+                update_moving_variance = moving_averages.assign_moving_average(
+                    moving_variance, variance, decay)
+                # #print("TF11 moving")
+
+            def mean_var_with_update():
+                with tf.control_dependencies([update_moving_mean, update_moving_variance]):
+                    return tf.identity(mean), tf.identity(variance)
+            if trainable:
+                mean, var = mean_var_with_update()
+                # print(mean)
+                # print(var)
+                self.outputs = act(
+                    tf.nn.batch_normalization(
+                        self.inputs, mean, var, beta, gamma, epsilon))
+            else:
+                self.outputs = act(
+                    tf.nn.batch_normalization(
+                        self.inputs,
+                        moving_mean,
+                        moving_variance,
+                        beta,
+                        gamma,
+                        epsilon))
+            variables = [beta, gamma, moving_mean, moving_variance]
+        self.all_layers = list(layer.all_layers)
+        self.all_params = list(layer.all_params)
+        self.all_drop = dict(layer.all_drop)
+        self.all_layers.extend([self.outputs])
+        self.all_params.extend(variables)
+
+
+def subsample(inputs, factor, scope=None):
+    if factor == 1:
+        return inputs
+    else:
+        return tl.layers.MaxPool2d(
+            inputs, [
+                1, 1], strides=(
+                factor, factor), name=scope)
+
+
+def conv2d_same(
+        inputs,
+        num_outputs,
+        kernel_size,
+        strides,
+        rate=1,
+        w_init=None,
+        scope=None,
+        trainable=None):
+    '''
+    Reference slim resnet
+    :param inputs:
+    :param num_outputs:
+    :param kernel_size:
+    :param strides:
+    :param rate:
+    :param scope:
+    :return:
+    '''
+    if strides == 1:
+        if rate == 1:
+            nets = tl.layers.Conv2d(
+                inputs,
+                n_filter=num_outputs,
+                filter_size=(
+                    kernel_size,
+                    kernel_size),
+                b_init=None,
+                strides=(
+                    strides,
+                    strides),
+                W_init=w_init,
+                act=None,
+                padding='SAME',
+                name=scope,
+                use_cudnn_on_gpu=True)
+            nets = BatchNormLayer(
+                nets,
+                act=tf.identity,
+                is_train=True,
+                trainable=trainable,
+                name=scope +
+                '_bn/BatchNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(
+                inputs,
+                n_filter=num_outputs,
+                filter_size=(
+                    kernel_size,
+                    kernel_size),
+                rate=rate,
+                act=None,
+                W_init=w_init,
+                padding='SAME',
+                name=scope)
+            nets = BatchNormLayer(
+                nets,
+                act=tf.identity,
+                is_train=True,
+                trainable=trainable,
+                name=scope +
+                '_bn/BatchNorm')
+        return nets
+    else:
+        kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
+        pad_total = kernel_size_effective - 1
+        pad_beg = pad_total // 2
+        pad_end = pad_total - pad_beg
+        inputs = tl.layers.PadLayer(
+            inputs, [
+                [
+                    0, 0], [
+                    pad_beg, pad_end], [
+                    pad_beg, pad_end], [
+                        0, 0]], name='padding_%s' %
+            scope)
+        if rate == 1:
+            nets = tl.layers.Conv2d(
+                inputs,
+                n_filter=num_outputs,
+                filter_size=(
+                    kernel_size,
+                    kernel_size),
+                b_init=None,
+                strides=(
+                    strides,
+                    strides),
+                W_init=w_init,
+                act=None,
+                padding='VALID',
+                name=scope,
+                use_cudnn_on_gpu=True)
+            nets = BatchNormLayer(
+                nets,
+                act=tf.identity,
+                is_train=True,
+                trainable=trainable,
+                name=scope +
+                '_bn/BatchNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(
+                inputs,
+                n_filter=num_outputs,
+                filter_size=(
+                    kernel_size,
+                    kernel_size),
+                b_init=None,
+                rate=rate,
+                act=None,
+                W_init=w_init,
+                padding='SAME',
+                name=scope)
+            nets = BatchNormLayer(
+                nets,
+                act=tf.identity,
+                is_train=True,
+                trainable=trainable,
+                name=scope +
+                '_bn/BatchNorm')
+        return nets
+
+
+def bottleneck_IR(
+        inputs,
+        depth,
+        depth_bottleneck,
+        stride,
+        rate=1,
+        w_init=None,
+        scope=None,
+        trainable=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = tl.layers.Conv2d(
+                inputs,
+                depth,
+                filter_size=(
+                    1,
+                    1),
+                strides=(
+                    stride,
+                    stride),
+                act=None,
+                W_init=w_init,
+                b_init=None,
+                name='shortcut_conv',
+                use_cudnn_on_gpu=True)
+            shortcut = BatchNormLayer(
+                shortcut,
+                act=tf.identity,
+                is_train=True,
+                trainable=trainable,
+                name='shortcut_bn/BatchNorm')
+        # bottleneck layer 1
+        residual = BatchNormLayer(
+            inputs,
+            act=tf.identity,
+            is_train=True,
+            trainable=trainable,
+            name='conv1_bn1')
+        residual = tl.layers.Conv2d(
+            residual,
+            depth_bottleneck,
+            filter_size=(
+                3,
+                3),
+            strides=(
+                1,
+                1),
+            act=None,
+            b_init=None,
+            W_init=w_init,
+            name='conv1',
+            use_cudnn_on_gpu=True)
+        residual = BatchNormLayer(
+            residual,
+            act=tf.identity,
+            is_train=True,
+            trainable=trainable,
+            name='conv1_bn2')
+        # bottleneck prelu
+        residual = tl.layers.PReluLayer(residual)
+        # bottleneck layer 2
+        residual = conv2d_same(
+            residual,
+            depth,
+            kernel_size=3,
+            strides=stride,
+            rate=rate,
+            w_init=w_init,
+            scope='conv2',
+            trainable=trainable)
+        output = ElementwiseLayer(layer=[shortcut, residual],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=None)
+        return output
+
+
+def bottleneck_IR_SE(
+        inputs,
+        depth,
+        depth_bottleneck,
+        stride,
+        rate=1,
+        w_init=None,
+        scope=None,
+        trainable=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = tl.layers.Conv2d(
+                inputs,
+                depth,
+                filter_size=(
+                    1,
+                    1),
+                strides=(
+                    stride,
+                    stride),
+                act=None,
+                W_init=w_init,
+                b_init=None,
+                name='shortcut_conv',
+                use_cudnn_on_gpu=True)
+            shortcut = BatchNormLayer(
+                shortcut,
+                act=tf.identity,
+                is_train=True,
+                trainable=trainable,
+                name='shortcut_bn/BatchNorm')
+        # bottleneck layer 1
+        residual = BatchNormLayer(
+            inputs,
+            act=tf.identity,
+            is_train=True,
+            trainable=trainable,
+            name='conv1_bn1')
+        residual = tl.layers.Conv2d(
+            residual,
+            depth_bottleneck,
+            filter_size=(
+                3,
+                3),
+            strides=(
+                1,
+                1),
+            act=None,
+            b_init=None,
+            W_init=w_init,
+            name='conv1',
+            use_cudnn_on_gpu=True)
+        residual = BatchNormLayer(
+            residual,
+            act=tf.identity,
+            is_train=True,
+            trainable=trainable,
+            name='conv1_bn2')
+        # bottleneck prelu
+        residual = tl.layers.PReluLayer(residual)
+        # bottleneck layer 2
+        residual = conv2d_same(
+            residual,
+            depth,
+            kernel_size=3,
+            strides=stride,
+            rate=rate,
+            w_init=w_init,
+            scope='conv2',
+            trainable=trainable)
+        # squeeze
+        squeeze = tl.layers.InputLayer(
+            tf.reduce_mean(
+                residual.outputs, axis=[
+                    1, 2]), name='squeeze_layer')
+        # excitation
+        excitation1 = tl.layers.DenseLayer(
+            squeeze,
+            n_units=int(
+                depth / 16.0),
+            act=tf.nn.relu,
+            W_init=w_init,
+            name='excitation_1')
+        # excitation1 = tl.layers.PReluLayer(excitation1, name='excitation_prelu')
+        excitation2 = tl.layers.DenseLayer(
+            excitation1,
+            n_units=depth,
+            act=tf.nn.sigmoid,
+            W_init=w_init,
+            name='excitation_2')
+        # scale
+        scale = tl.layers.ReshapeLayer(
+            excitation2,
+            shape=[
+                tf.shape(
+                    excitation2.outputs)[0],
+                1,
+                1,
+                depth],
+            name='excitation_reshape')
+
+        residual_se = ElementwiseLayer(layer=[residual, scale],
+                                       combine_fn=tf.multiply,
+                                       name='scale_layer',
+                                       act=None)
+
+        output = ElementwiseLayer(layer=[shortcut, residual_se],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=tf.nn.relu)
+        return output
+
+
+def resnet(
+        inputs,
+        bottle_neck,
+        blocks,
+        w_init=None,
+        trainable=None,
+        reuse=False,
+        keep_rate=None,
+        scope=None):
+    with tf.variable_scope(scope, reuse=reuse):
+        # inputs = tf.subtract(inputs, 127.5)
+        # inputs = tf.multiply(inputs, 0.0078125)
+        net_inputs = tl.layers.InputLayer(inputs, name='input_layer')
+        if bottle_neck:
+            net = tl.layers.Conv2d(
+                net_inputs,
+                n_filter=64,
+                filter_size=(
+                    3,
+                    3),
+                strides=(
+                    1,
+                    1),
+                act=None,
+                W_init=w_init,
+                b_init=None,
+                name='conv1',
+                use_cudnn_on_gpu=True)
+            net = BatchNormLayer(
+                net,
+                act=tf.identity,
+                name='bn0',
+                is_train=True,
+                trainable=trainable)
+            net = tl.layers.PReluLayer(net, name='prelu0')
+        else:
+            raise ValueError(
+                'The standard resnet must support the bottleneck layer')
+        for block in blocks:
+            with tf.variable_scope(block.scope):
+                for i, var in enumerate(block.args):
+                    with tf.variable_scope('unit_%d' % (i + 1)):
+                        net = block.unit_fn(
+                            net,
+                            depth=var['depth'],
+                            depth_bottleneck=var['depth_bottleneck'],
+                            w_init=w_init,
+                            stride=var['stride'],
+                            rate=var['rate'],
+                            scope=None,
+                            trainable=trainable)
+        net = BatchNormLayer(
+            net,
+            act=tf.identity,
+            is_train=True,
+            name='E_BN1',
+            trainable=trainable)
+        # net = tl.layers.DropoutLayer(net, keep=0.4, name='E_Dropout')
+        net.outputs = tf.nn.dropout(
+            net.outputs,
+            keep_prob=keep_rate,
+            name='E_Dropout')
+        net_shape = net.outputs.get_shape()
+        net = tl.layers.ReshapeLayer(
+            net, shape=[-1, net_shape[1] * net_shape[2] * net_shape[3]], name='E_Reshapelayer')
+        net = tl.layers.DenseLayer(
+            net,
+            n_units=512,
+            W_init=w_init,
+            name='E_DenseLayer')
+        net = BatchNormLayer(
+            net,
+            act=tf.identity,
+            is_train=True,
+            fix_gamma=False,
+            trainable=trainable,
+            name='E_BN2')
+        return net
+
+
+class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
+    """A named tuple describing a ResNet block.
+
+    Its parts are:
+      scope: The scope of the `Block`.
+      unit_fn: The ResNet unit function which takes as input a `Tensor` and
+        returns another `Tensor` with the output of the ResNet unit.
+      args: A list of length equal to the number of units in the `Block`. The list
+        contains one (depth, depth_bottleneck, stride) tuple for each unit in the
+        block to serve as argument to unit_fn.
+    """
+
+
+def resnetse_v1_block(
+        scope,
+        base_depth,
+        num_units,
+        stride,
+        rate=1,
+        unit_fn=None):
+    """Helper function for creating a resnet_v1 bottleneck block.
+
+    Args:
+      scope: The scope of the block.
+      base_depth: The depth of the bottleneck layer for each unit.
+      num_units: The number of units in the block.
+      stride: The stride of the block, implemented as a stride in the last unit.
+        All other units have stride=1.
+
+    Returns:
+      A resnet_v1 bottleneck block.
+    """
+    return Block(scope, unit_fn, [{
+        'depth': base_depth,
+        'depth_bottleneck': base_depth,
+        'stride': stride,
+        'rate': rate
+    }] + [{
+        'depth': base_depth,
+        'depth_bottleneck': base_depth,
+        'stride': 1,
+        'rate': rate
+    }] * (num_units - 1))
+
+
+def get_resnet(
+        inputs,
+        num_layers,
+        type=None,
+        w_init=None,
+        trainable=None,
+        sess=None,
+        reuse=False,
+        keep_rate=None):
+    if type == 'ir':
+        unit_fn = bottleneck_IR
+    elif type == 'se_ir':
+        unit_fn = bottleneck_IR_SE
+    else:
+        raise ValueError('the input fn is unknown')
+
+    if num_layers == 50:
+        blocks = [
+            resnetse_v1_block(
+                'block1',
+                base_depth=64,
+                num_units=3,
+                stride=2,
+                rate=1,
+                unit_fn=unit_fn),
+            resnetse_v1_block(
+                'block2',
+                base_depth=128,
+                num_units=4,
+                stride=2,
+                rate=1,
+                unit_fn=unit_fn),
+            resnetse_v1_block(
+                'block3',
+                base_depth=256,
+                num_units=14,
+                stride=2,
+                rate=1,
+                unit_fn=unit_fn),
+            resnetse_v1_block(
+                'block4',
+                base_depth=512,
+                num_units=3,
+                stride=2,
+                rate=1,
+                unit_fn=unit_fn)]
+    elif num_layers == 100:
+        blocks = [
+            resnetse_v1_block(
+                'block1',
+                base_depth=64,
+                num_units=3,
+                stride=2,
+                rate=1,
+                unit_fn=unit_fn),
+            resnetse_v1_block(
+                'block2',
+                base_depth=128,
+                num_units=13,
+                stride=2,
+                rate=1,
+                unit_fn=unit_fn),
+            resnetse_v1_block(
+                'block3',
+                base_depth=256,
+                num_units=30,
+                stride=2,
+                rate=1,
+                unit_fn=unit_fn),
+            resnetse_v1_block(
+                'block4',
+                base_depth=512,
+                num_units=3,
+                stride=2,
+                rate=1,
+                unit_fn=unit_fn)]
+    elif num_layers == 152:
+        blocks = [
+            resnetse_v1_block(
+                'block1',
+                base_depth=64,
+                num_units=3,
+                stride=2,
+                rate=1,
+                unit_fn=unit_fn),
+            resnetse_v1_block(
+                'block2',
+                base_depth=128,
+                num_units=8,
+                stride=2,
+                rate=1,
+                unit_fn=unit_fn),
+            resnetse_v1_block(
+                'block3',
+                base_depth=256,
+                num_units=36,
+                stride=2,
+                rate=1,
+                unit_fn=unit_fn),
+            resnetse_v1_block(
+                'block4',
+                base_depth=512,
+                num_units=3,
+                stride=2,
+                rate=1,
+                unit_fn=unit_fn)]
+    else:
+        raise ValueError('Resnet layer %d is not supported now.' % num_layers)
+    net = resnet(inputs=inputs,
+                 bottle_neck=True,
+                 blocks=blocks,
+                 w_init=w_init,
+                 trainable=trainable,
+                 reuse=reuse,
+                 keep_rate=keep_rate,
+                 scope='resnet_v1_%d' % num_layers)
+    return net
+
+
+if __name__ == '__main__':
+    x = tf.placeholder(
+        dtype=tf.float32,
+        shape=[
+            None,
+            112,
+            112,
+            3],
+        name='input_place')
+    sess = tf.Session()
+    # w_init = tf.truncated_normal_initializer(mean=10, stddev=5e-2)
+    w_init = tf.contrib.layers.xavier_initializer(uniform=False)
+    # test resnetse
+    nets = get_resnet(x, 50, type='ir', w_init=w_init, sess=sess)
+    tl.layers.initialize_global_variables(sess)
+
+    for p in tl.layers.get_variables_with_name('W_conv2d', True, True):
+        print(p.op.name)
+    # print('##############'*30)
+    with sess:
+        nets.print_params()
diff --git a/src/__init__.py b/facenet_sandberg/models/__init__.py
similarity index 100%
rename from src/__init__.py
rename to facenet_sandberg/models/__init__.py
diff --git a/facenet_sandberg/models/convert_to_keras.py b/facenet_sandberg/models/convert_to_keras.py
new file mode 100644
index 000000000..950cb4b35
--- /dev/null
+++ b/facenet_sandberg/models/convert_to_keras.py
@@ -0,0 +1,120 @@
+import argparse
+import os
+import re
+import sys
+
+import numpy as np
+import tensorflow as tf
+from facenet_sandberg.models.keras_inception_resnet_v1 import *
+
+re_repeat = re.compile(r'Repeat_[0-9_]*b')
+re_block8 = re.compile(r'Block8_[A-Za-z]')
+
+
+def main(tf_ckpt_path, output_base_path, output_model_name):
+    weights_filename = output_model_name + '_weights.h5'
+    model_filename = output_model_name + '.h5'
+
+    npy_weights_dir, weights_dir, model_dir = create_output_directories(
+        output_base_path)
+
+    extract_tensors_from_checkpoint_file(tf_ckpt_path, npy_weights_dir)
+    model = InceptionResNetV1()
+
+    print('Loading numpy weights from', npy_weights_dir)
+    for layer in model.layers:
+        if layer.weights:
+            weights = []
+            for w in layer.weights:
+                weight_name = os.path.basename(w.name).replace(':0', '')
+                weight_file = layer.name + '_' + weight_name + '.npy'
+                weight_arr = np.load(
+                    os.path.join(
+                        npy_weights_dir,
+                        weight_file))
+                weights.append(weight_arr)
+            layer.set_weights(weights)
+
+    print('Saving weights...')
+    model.save_weights(os.path.join(weights_dir, weights_filename))
+    print('Saving model...')
+    model.save(os.path.join(model_dir, model_filename))
+
+
+def create_output_directories(output_base_path):
+    npy_weights_dir = os.path.join(output_base_path, 'npy_weights')
+    weights_dir = os.path.join(output_base_path, 'weights')
+    model_dir = os.path.join(output_base_path, 'model')
+    os.makedirs(npy_weights_dir, exist_ok=True)
+    os.makedirs(weights_dir, exist_ok=True)
+    os.makedirs(model_dir, exist_ok=True)
+    return npy_weights_dir, weights_dir, model_dir
+
+
+def get_filename(key):
+    filename = str(key)
+    filename = filename.replace('/', '_')
+    filename = filename.replace('InceptionResnetV1_', '')
+
+    # remove "Repeat" scope from filename
+    filename = re_repeat.sub('B', filename)
+
+    if re_block8.match(filename):
+        # the last block8 has different name with the previous 5 occurrences
+        filename = filename.replace('Block8', 'Block8_6')
+
+    # from TF to Keras naming
+    filename = filename.replace('_weights', '_kernel')
+    filename = filename.replace('_biases', '_bias')
+
+    return filename + '.npy'
+
+
+def extract_tensors_from_checkpoint_file(filename, output_folder):
+    reader = tf.train.NewCheckpointReader(filename)
+
+    for key in reader.get_variable_to_shape_map():
+        # not saving the following tensors
+        if key == 'global_step':
+            continue
+        if 'AuxLogit' in key:
+            continue
+
+        # convert tensor name into the corresponding Keras layer weight name
+        # and save
+        path = os.path.join(output_folder, get_filename(key))
+        arr = reader.get_tensor(key)
+        np.save(path, arr)
+
+
+def parse_arguments(argv):
+    """Argument parser
+    """
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        'tf_ckpt_path',
+        type=str,
+        help='Path to the directory containing pretrained tensorflow checkpoints.')
+
+    parser.add_argument(
+        'output_base_path',
+        type=str,
+        help='Base path for the desired output directory.')
+
+    parser.add_argument(
+        'output_model_name',
+        type=str,
+        help='Name for the new model (do not include .h5)')
+
+    return parser.parse_args(argv)
+
+
+if __name__ == '__main__':
+    args = parse_arguments(sys.argv[1:])
+    if args:
+        main(
+            args.tf_ckpt_path,
+            args.output_base_path,
+            args.output_model_name)
diff --git a/src/download_and_extract.py b/facenet_sandberg/models/download_and_extract.py
similarity index 67%
rename from src/download_and_extract.py
rename to facenet_sandberg/models/download_and_extract.py
index a835ac284..cd2998b22 100644
--- a/src/download_and_extract.py
+++ b/facenet_sandberg/models/download_and_extract.py
@@ -1,14 +1,16 @@
-import requests
-import zipfile
 import os
+import zipfile
+
+import requests
 
 model_dict = {
-    'lfw-subset':      '1B5BQUZuJO-paxdN8UclxeHAR1WnR_Tzi', 
+    'lfw-subset': '1B5BQUZuJO-paxdN8UclxeHAR1WnR_Tzi',
     '20170131-234652': '0B5MzpY9kBtDVSGM0RmVET2EwVEk',
     '20170216-091149': '0B5MzpY9kBtDVTGZjcWkzT3pldDA',
     '20170512-110547': '0B5MzpY9kBtDVZ2RpVDYwWmxoSUk',
     '20180402-114759': '1EXPBSXwTaqrSC0OhUdXNmKSh9qJUQ55-'
-    }
+}
+
 
 def download_and_extract_file(model_name, data_dir):
     file_id = model_dict[model_name]
@@ -20,20 +22,22 @@ def download_and_extract_file(model_name, data_dir):
             print('Extracting file to %s' % data_dir)
             zip_ref.extractall(data_dir)
 
+
 def download_file_from_google_drive(file_id, destination):
-    
-        URL = "https://drive.google.com/uc?export=download"
-    
-        session = requests.Session()
-    
-        response = session.get(URL, params = { 'id' : file_id }, stream = True)
-        token = get_confirm_token(response)
-    
-        if token:
-            params = { 'id' : file_id, 'confirm' : token }
-            response = session.get(URL, params = params, stream = True)
-    
-        save_response_content(response, destination)    
+
+    URL = "https://drive.google.com/uc?export=download"
+
+    session = requests.Session()
+
+    response = session.get(URL, params={'id': file_id}, stream=True)
+    token = get_confirm_token(response)
+
+    if token:
+        params = {'id': file_id, 'confirm': token}
+        response = session.get(URL, params=params, stream=True)
+
+    save_response_content(response, destination)
+
 
 def get_confirm_token(response):
     for key, value in response.cookies.items():
@@ -42,10 +46,11 @@ def get_confirm_token(response):
 
     return None
 
+
 def save_response_content(response, destination):
     CHUNK_SIZE = 32768
 
     with open(destination, "wb") as f:
         for chunk in response.iter_content(CHUNK_SIZE):
-            if chunk: # filter out keep-alive new chunks
+            if chunk:  # filter out keep-alive new chunks
                 f.write(chunk)
diff --git a/src/models/dummy.py b/facenet_sandberg/models/dummy.py
similarity index 100%
rename from src/models/dummy.py
rename to facenet_sandberg/models/dummy.py
diff --git a/src/freeze_graph.py b/facenet_sandberg/models/freeze_graph.py
similarity index 77%
rename from src/freeze_graph.py
rename to facenet_sandberg/models/freeze_graph.py
index 3584c186e..eb8ceb4ea 100644
--- a/src/freeze_graph.py
+++ b/facenet_sandberg/models/freeze_graph.py
@@ -2,19 +2,19 @@
 and exports the model as a graphdef protobuf
 """
 # MIT License
-# 
+#
 # Copyright (c) 2016 David Sandberg
-# 
+#
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
 # in the Software without restriction, including without limitation the rights
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
 # furnished to do so, subject to the following conditions:
-# 
+#
 # The above copyright notice and this permission notice shall be included in all
 # copies or substantial portions of the Software.
-# 
+#
 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
@@ -23,45 +23,55 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
-from tensorflow.python.framework import graph_util
-import tensorflow as tf
 import argparse
 import os
 import sys
-import facenet
+
+import tensorflow as tf
+from facenet_sandberg import facenet
 from six.moves import xrange  # @UnresolvedImport
+from tensorflow.python.framework import graph_util
+
 
 def main(args):
     with tf.Graph().as_default():
         with tf.Session() as sess:
             # Load the model metagraph and checkpoint
             print('Model directory: %s' % args.model_dir)
-            meta_file, ckpt_file = facenet.get_model_filenames(os.path.expanduser(args.model_dir))
-            
+            meta_file, ckpt_file = facenet.get_model_filenames(
+                os.path.expanduser(args.model_dir))
+
             print('Metagraph file: %s' % meta_file)
             print('Checkpoint file: %s' % ckpt_file)
 
             model_dir_exp = os.path.expanduser(args.model_dir)
-            saver = tf.train.import_meta_graph(os.path.join(model_dir_exp, meta_file), clear_devices=True)
+            saver = tf.train.import_meta_graph(os.path.join(
+                model_dir_exp, meta_file), clear_devices=True)
             tf.get_default_session().run(tf.global_variables_initializer())
             tf.get_default_session().run(tf.local_variables_initializer())
-            saver.restore(tf.get_default_session(), os.path.join(model_dir_exp, ckpt_file))
-            
-            # Retrieve the protobuf graph definition and fix the batch norm nodes
+            saver.restore(
+                tf.get_default_session(),
+                os.path.join(
+                    model_dir_exp,
+                    ckpt_file))
+
+            # Retrieve the protobuf graph definition and fix the batch norm
+            # nodes
             input_graph_def = sess.graph.as_graph_def()
-            
+
             # Freeze the graph def
-            output_graph_def = freeze_graph_def(sess, input_graph_def, 'embeddings,label_batch')
+            output_graph_def = freeze_graph_def(
+                sess, input_graph_def, 'embeddings,label_batch')
 
         # Serialize and dump the output graph to the filesystem
         with tf.gfile.GFile(args.output_file, 'wb') as f:
             f.write(output_graph_def.SerializeToString())
-        print("%d ops in the final graph: %s" % (len(output_graph_def.node), args.output_file))
-        
+        print("%d ops in the final graph: %s" %
+              (len(output_graph_def.node), args.output_file))
+
+
 def freeze_graph_def(sess, input_graph_def, output_node_names):
     for node in input_graph_def.node:
         if node.op == 'RefSwitch':
@@ -71,15 +81,17 @@ def freeze_graph_def(sess, input_graph_def, output_node_names):
                     node.input[index] = node.input[index] + '/read'
         elif node.op == 'AssignSub':
             node.op = 'Sub'
-            if 'use_locking' in node.attr: del node.attr['use_locking']
+            if 'use_locking' in node.attr:
+                del node.attr['use_locking']
         elif node.op == 'AssignAdd':
             node.op = 'Add'
-            if 'use_locking' in node.attr: del node.attr['use_locking']
-    
+            if 'use_locking' in node.attr:
+                del node.attr['use_locking']
+
     # Get the list of important nodes
     whitelist_names = []
     for node in input_graph_def.node:
-        if (node.name.startswith('InceptionResnet') or node.name.startswith('embeddings') or 
+        if (node.name.startswith('InceptionResnet') or node.name.startswith('embeddings') or
                 node.name.startswith('image_batch') or node.name.startswith('label_batch') or
                 node.name.startswith('phase_train') or node.name.startswith('Logits')):
             whitelist_names.append(node.name)
@@ -89,15 +101,21 @@ def freeze_graph_def(sess, input_graph_def, output_node_names):
         sess, input_graph_def, output_node_names.split(","),
         variable_names_whitelist=whitelist_names)
     return output_graph_def
-  
+
+
 def parse_arguments(argv):
     parser = argparse.ArgumentParser()
-    
-    parser.add_argument('model_dir', type=str, 
+
+    parser.add_argument(
+        'model_dir',
+        type=str,
         help='Directory containing the metagraph (.meta) file and the checkpoint (ckpt) file containing model parameters')
-    parser.add_argument('output_file', type=str, 
+    parser.add_argument(
+        'output_file',
+        type=str,
         help='Filename for the exported graphdef protobuf (.pb)')
     return parser.parse_args(argv)
 
+
 if __name__ == '__main__':
     main(parse_arguments(sys.argv[1:]))
diff --git a/src/models/inception_resnet_v1.py b/facenet_sandberg/models/inception_resnet_v1.py
similarity index 100%
rename from src/models/inception_resnet_v1.py
rename to facenet_sandberg/models/inception_resnet_v1.py
diff --git a/src/models/inception_resnet_v2.py b/facenet_sandberg/models/inception_resnet_v2.py
similarity index 100%
rename from src/models/inception_resnet_v2.py
rename to facenet_sandberg/models/inception_resnet_v2.py
diff --git a/facenet_sandberg/models/keras_inception_resnet_v1.py b/facenet_sandberg/models/keras_inception_resnet_v1.py
new file mode 100644
index 000000000..9235ef0a8
--- /dev/null
+++ b/facenet_sandberg/models/keras_inception_resnet_v1.py
@@ -0,0 +1,227 @@
+"""Inception-ResNet V1 model for Keras.
+# Reference
+http://arxiv.org/abs/1602.07261
+https://github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py
+https://github.com/myutwo150/keras-inception-resnet-v2/blob/master/inception_resnet_v2.py
+"""
+from functools import partial
+
+from keras.models import Model
+from keras.layers import Activation
+from keras.layers import BatchNormalization
+from keras.layers import Concatenate
+from keras.layers import Conv2D
+from keras.layers import Dense
+from keras.layers import Dropout
+from keras.layers import GlobalAveragePooling2D
+from keras.layers import Input
+from keras.layers import Lambda
+from keras.layers import MaxPooling2D
+from keras.layers import add
+from keras import backend as K
+
+
+def scaling(x, scale):
+    return x * scale
+
+
+def conv2d_bn(x,
+              filters,
+              kernel_size,
+              strides=1,
+              padding='same',
+              activation='relu',
+              use_bias=False,
+              name=None):
+    x = Conv2D(filters,
+               kernel_size,
+               strides=strides,
+               padding=padding,
+               use_bias=use_bias,
+               name=name)(x)
+    if not use_bias:
+        bn_axis = 1 if K.image_data_format() == 'channels_first' else 3
+        bn_name = _generate_layer_name('BatchNorm', prefix=name)
+        x = BatchNormalization(axis=bn_axis, momentum=0.995, epsilon=0.001,
+                               scale=False, name=bn_name)(x)
+    if activation is not None:
+        ac_name = _generate_layer_name('Activation', prefix=name)
+        x = Activation(activation, name=ac_name)(x)
+    return x
+
+
+def _generate_layer_name(name, branch_idx=None, prefix=None):
+    if prefix is None:
+        return None
+    if branch_idx is None:
+        return '_'.join((prefix, name))
+    return '_'.join((prefix, 'Branch', str(branch_idx), name))
+
+
+def _inception_resnet_block(x, scale, block_type, block_idx, activation='relu'):
+    channel_axis = 1 if K.image_data_format() == 'channels_first' else 3
+    if block_idx is None:
+        prefix = None
+    else:
+        prefix = '_'.join((block_type, str(block_idx)))
+    name_fmt = partial(_generate_layer_name, prefix=prefix)
+
+    if block_type == 'Block35':
+        branch_0 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_1x1', 0))
+        branch_1 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_0a_1x1', 1))
+        branch_1 = conv2d_bn(
+            branch_1, 32, 3, name=name_fmt('Conv2d_0b_3x3', 1))
+        branch_2 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_0a_1x1', 2))
+        branch_2 = conv2d_bn(
+            branch_2, 32, 3, name=name_fmt('Conv2d_0b_3x3', 2))
+        branch_2 = conv2d_bn(
+            branch_2, 32, 3, name=name_fmt('Conv2d_0c_3x3', 2))
+        branches = [branch_0, branch_1, branch_2]
+    elif block_type == 'Block17':
+        branch_0 = conv2d_bn(x, 128, 1, name=name_fmt('Conv2d_1x1', 0))
+        branch_1 = conv2d_bn(x, 128, 1, name=name_fmt('Conv2d_0a_1x1', 1))
+        branch_1 = conv2d_bn(
+            branch_1, 128, [1, 7], name=name_fmt('Conv2d_0b_1x7', 1))
+        branch_1 = conv2d_bn(
+            branch_1, 128, [7, 1], name=name_fmt('Conv2d_0c_7x1', 1))
+        branches = [branch_0, branch_1]
+    elif block_type == 'Block8':
+        branch_0 = conv2d_bn(x, 192, 1, name=name_fmt('Conv2d_1x1', 0))
+        branch_1 = conv2d_bn(x, 192, 1, name=name_fmt('Conv2d_0a_1x1', 1))
+        branch_1 = conv2d_bn(
+            branch_1, 192, [1, 3], name=name_fmt('Conv2d_0b_1x3', 1))
+        branch_1 = conv2d_bn(
+            branch_1, 192, [3, 1], name=name_fmt('Conv2d_0c_3x1', 1))
+        branches = [branch_0, branch_1]
+    else:
+        raise ValueError('Unknown Inception-ResNet block type. '
+                         'Expects "Block35", "Block17" or "Block8", '
+                         'but got: ' + str(block_type))
+
+    mixed = Concatenate(axis=channel_axis,
+                        name=name_fmt('Concatenate'))(branches)
+    up = conv2d_bn(mixed,
+                   K.int_shape(x)[channel_axis],
+                   1,
+                   activation=None,
+                   use_bias=True,
+                   name=name_fmt('Conv2d_1x1'))
+    up = Lambda(scaling,
+                output_shape=K.int_shape(up)[1:],
+                arguments={'scale': scale})(up)
+    x = add([x, up])
+    if activation is not None:
+        x = Activation(activation, name=name_fmt('Activation'))(x)
+    return x
+
+
+def InceptionResNetV1(input_shape=(160, 160, 3),
+                      classes=128,
+                      dropout_keep_prob=0.8,
+                      weights_path=None):
+    inputs = Input(shape=input_shape)
+    x = conv2d_bn(inputs, 32, 3, strides=2,
+                  padding='valid', name='Conv2d_1a_3x3')
+    x = conv2d_bn(x, 32, 3, padding='valid', name='Conv2d_2a_3x3')
+    x = conv2d_bn(x, 64, 3, name='Conv2d_2b_3x3')
+    x = MaxPooling2D(3, strides=2, name='MaxPool_3a_3x3')(x)
+    x = conv2d_bn(x, 80, 1, padding='valid', name='Conv2d_3b_1x1')
+    x = conv2d_bn(x, 192, 3, padding='valid', name='Conv2d_4a_3x3')
+    x = conv2d_bn(x, 256, 3, strides=2, padding='valid', name='Conv2d_4b_3x3')
+
+    # 5x Block35 (Inception-ResNet-A block):
+    for block_idx in range(1, 6):
+        x = _inception_resnet_block(x,
+                                    scale=0.17,
+                                    block_type='Block35',
+                                    block_idx=block_idx)
+
+    # Mixed 6a (Reduction-A block):
+    channel_axis = 1 if K.image_data_format() == 'channels_first' else 3
+    name_fmt = partial(_generate_layer_name, prefix='Mixed_6a')
+    branch_0 = conv2d_bn(x,
+                         384,
+                         3,
+                         strides=2,
+                         padding='valid',
+                         name=name_fmt('Conv2d_1a_3x3', 0))
+    branch_1 = conv2d_bn(x, 192, 1, name=name_fmt('Conv2d_0a_1x1', 1))
+    branch_1 = conv2d_bn(branch_1, 192, 3, name=name_fmt('Conv2d_0b_3x3', 1))
+    branch_1 = conv2d_bn(branch_1,
+                         256,
+                         3,
+                         strides=2,
+                         padding='valid',
+                         name=name_fmt('Conv2d_1a_3x3', 1))
+    branch_pool = MaxPooling2D(3,
+                               strides=2,
+                               padding='valid',
+                               name=name_fmt('MaxPool_1a_3x3', 2))(x)
+    branches = [branch_0, branch_1, branch_pool]
+    x = Concatenate(axis=channel_axis, name='Mixed_6a')(branches)
+
+    # 10x Block17 (Inception-ResNet-B block):
+    for block_idx in range(1, 11):
+        x = _inception_resnet_block(x,
+                                    scale=0.1,
+                                    block_type='Block17',
+                                    block_idx=block_idx)
+
+    # Mixed 7a (Reduction-B block): 8 x 8 x 2080
+    name_fmt = partial(_generate_layer_name, prefix='Mixed_7a')
+    branch_0 = conv2d_bn(x, 256, 1, name=name_fmt('Conv2d_0a_1x1', 0))
+    branch_0 = conv2d_bn(branch_0,
+                         384,
+                         3,
+                         strides=2,
+                         padding='valid',
+                         name=name_fmt('Conv2d_1a_3x3', 0))
+    branch_1 = conv2d_bn(x, 256, 1, name=name_fmt('Conv2d_0a_1x1', 1))
+    branch_1 = conv2d_bn(branch_1,
+                         256,
+                         3,
+                         strides=2,
+                         padding='valid',
+                         name=name_fmt('Conv2d_1a_3x3', 1))
+    branch_2 = conv2d_bn(x, 256, 1, name=name_fmt('Conv2d_0a_1x1', 2))
+    branch_2 = conv2d_bn(branch_2, 256, 3, name=name_fmt('Conv2d_0b_3x3', 2))
+    branch_2 = conv2d_bn(branch_2,
+                         256,
+                         3,
+                         strides=2,
+                         padding='valid',
+                         name=name_fmt('Conv2d_1a_3x3', 2))
+    branch_pool = MaxPooling2D(3,
+                               strides=2,
+                               padding='valid',
+                               name=name_fmt('MaxPool_1a_3x3', 3))(x)
+    branches = [branch_0, branch_1, branch_2, branch_pool]
+    x = Concatenate(axis=channel_axis, name='Mixed_7a')(branches)
+
+    # 5x Block8 (Inception-ResNet-C block):
+    for block_idx in range(1, 6):
+        x = _inception_resnet_block(x,
+                                    scale=0.2,
+                                    block_type='Block8',
+                                    block_idx=block_idx)
+    x = _inception_resnet_block(x,
+                                scale=1.,
+                                activation=None,
+                                block_type='Block8',
+                                block_idx=6)
+
+    # Classification block
+    x = GlobalAveragePooling2D(name='AvgPool')(x)
+    x = Dropout(1.0 - dropout_keep_prob, name='Dropout')(x)
+    # Bottleneck
+    x = Dense(classes, use_bias=False, name='Bottleneck')(x)
+    bn_name = _generate_layer_name('BatchNorm', prefix='Bottleneck')
+    x = BatchNormalization(momentum=0.995, epsilon=0.001, scale=False,
+                           name=bn_name)(x)
+
+    # Create model
+    model = Model(inputs, x, name='inception_resnet_v1')
+    if weights_path is not None:
+        model.load_weights(weights_path)
+
+    return model
diff --git a/src/models/squeezenet.py b/facenet_sandberg/models/squeezenet.py
similarity index 100%
rename from src/models/squeezenet.py
rename to facenet_sandberg/models/squeezenet.py
diff --git a/facenet_sandberg/readme.md b/facenet_sandberg/readme.md
new file mode 100644
index 000000000..b78b96709
--- /dev/null
+++ b/facenet_sandberg/readme.md
@@ -0,0 +1,46 @@
+# Usage
+
+## Image directory structure
+
+This repo assumes images are in [LFW format](http://vis-www.cs.umass.edu/lfw/README.txt):
+
+```bash
+-/base_images_folder
+  -/person_1
+    -person_1_0001.jpg
+    -person_1_0002.jpg
+    -person_1_0003.jpg
+  -/person_2
+    -person_2_0001.jpg
+    -person_2_0002.jpg
+  ...
+```
+
+If your dataset is not like this you can use [lfw.py](https://github.com/armanrahman22/facenet/blob/master/facenet_sandberg/lfw.py) to put your images into the right format like so (from facenet_sandberg/facenet_sandberg):
+
+```bash
+python lfw.py --image_directory PATH_TO_YOUR_BASE_IMAGE_DIRECTORY
+```
+
+## Alignment
+
+Alignment is done with a combination of Faceboxes and MTCNN. While Faceboxes is more accurate and works with more images than MTCNN, it does not return [facial landmarks](https://raw.githubusercontent.com/ipazc/mtcnn/master/result.jpg). Whichever algorithm returns more results is used.
+
+Use the [align_dataset.py](https://github.com/armanrahman22/facenet/blob/master/facenet_sandberg/align_dataset.py) script to align an entire image directory:
+
+```bash
+python align_dataset.py --config_file PATH_TO_YOUR_CONFIG_FILE
+```
+
+* You can either alter the default config files provided in facenet_config.json and insightface_config.json or create your own following the same format
+
+## Generate Pairs.txt
+
+A pairs.txt file is used in training and testing. It follows this [format](http://vis-www.cs.umass.edu/lfw/README.txt). In order to generate your own pairs.txt run:
+
+```bash
+python align_dataset.py --image_dir PATH_TO_YOUR_BASE_IMAGE_DIRECTORY \
+                        --pairs_file_name OUTPUT_NAME_OF_PAIRS_FILE \
+                        --num_folds NUMBER_OF_FOLDS_FOR_CROSS_VALIDATION \
+                        --num_matches_mismatches NUMBER_OF_MATCHES_AND_MISMATCHES
+```
\ No newline at end of file
diff --git a/facenet_sandberg/train_facenet/train_softmax.py b/facenet_sandberg/train_facenet/train_softmax.py
new file mode 100644
index 000000000..e982a431b
--- /dev/null
+++ b/facenet_sandberg/train_facenet/train_softmax.py
@@ -0,0 +1,818 @@
+"""Training a face recognizer with TensorFlow using softmax cross entropy loss
+"""
+# MIT License
+#
+# Copyright (c) 2016 David Sandberg
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from __future__ import absolute_import, division, print_function
+
+import argparse
+import importlib
+import math
+import os.path
+import random
+import sys
+import time
+from datetime import datetime
+
+import h5py
+import numpy as np
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+from facenet_sandberg import facenet, lfw
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops, data_flow_ops
+
+# parser.add_argument(
+#     '--optimizer',
+#     type=str,
+#     choices=[
+#         'ADAGRAD',
+#         'ADADELTA',
+#         'ADAM',
+#         'RMSPROP',
+#         'MOM'],
+#     help='The optimization algorithm to use',
+#     default='ADAGRAD')
+
+
+def main(
+        pretrained_model: str,
+        logs_base_dir: str='~/logs/facenet',
+        models_base_dir: str ='~/models/facenet',
+        gpu_memory_fraction: float=1.0,
+        data_dir: str='~/datasets/casia/casia_maxpy_mtcnnalign_182_160',
+        model_def: str='models.inception_resnet_v1',
+        max_nrof_epochs: int=500,
+        batch_size: int=100,
+        image_size: int=160,
+        epoch_size: int=1000,
+        embedding_size: int=128,
+        random_crop: bool=False,
+        random_flip: bool=False,
+        random_rotate: bool=False,
+        use_fixed_image_standardization: bool=False,
+        keep_probability: float=1.0,
+        weight_decay: float=0.0,
+        center_loss_factor: float=0.0,
+        center_loss_alfa: float=0.95,
+        prelogits_norm_loss_factor: float=0.0,
+        prelogits_norm_p: float=1.0,
+        prelogits_hist_max: float=10.0,
+        optimizer: str='ADAGRAD',
+        learning_rate: float=0.1,
+        learning_rate_decay_epochs: int=100,
+        learning_rate_decay_factor: float=1.0,
+        moving_average_decay: float=0.9999,
+        seed: int=666,
+        nrof_preprocess_threads: int=4,
+        log_histograms: bool=False,
+        learning_rate_schedule_file: str='data/learning_rate_schedule.txt',
+        filter_filename: str='',
+        filter_percentile: float=100.0,
+        filter_min_nrof_images_per_class: int=0,
+        validate_every_n_epochs: int=5,
+        validation_set_split_ratio: float=0.0,
+        min_nrof_val_images_per_class: int=0,
+        lfw_pairs: str='data/pairs.txt',
+        lfw_dir: str='',
+        lfw_batch_size: int=100,
+        lfw_nrof_folds: int=10,
+        lfw_distance_metric: int=0,
+        lfw_use_flipped_images: bool=False,
+        lfw_subtract_mean: bool=False):
+    """Train with softmax
+
+    Arguments:
+        pretrained_model {str} -- Load a pretrained model before training starts.
+
+    Keyword Arguments:
+        logs_base_dir {str} -- Directory where to write event logs. (default: {'~/logs/facenet'})
+        models_base_dir {str} -- Directory where to write trained models and checkpoints. (default: {'~/models/facenet'})
+        gpu_memory_fraction {float} -- Upper bound on the amount of GPU memory that will be used by the process. (default: {1.0})
+        data_dir {str} -- Path to the data directory containing aligned face patches. (default: {'~/datasets/casia/casia_maxpy_mtcnnalign_182_160'})
+        model_def {str} -- Model definition. Points to a module containing the definition of the inference graph. (default: {'models.inception_resnet_v1'})
+        max_nrof_epochs {int} -- Number of epochs to run. (default: {500})
+        batch_size {int} -- Number of images to process in a batch. (default: {100})
+        image_size {int} -- Image size (height, width) in pixels. (default: {160})
+        epoch_size {int} -- Number of batches per epoch. (default: {1000})
+        embedding_size {int} -- Dimensionality of the embedding. (default: {128})
+        random_crop {bool} -- Performs random cropping of training images. If false, the center image_size pixels from the training images are used. If the size of the images in the data directory is equal to image_size no cropping is performed (default: {False})
+        random_flip {bool} -- Performs random horizontal flipping of training images. (default: {False})
+        random_rotate {bool} -- Performs random rotations of training images. (default: {False})
+        use_fixed_image_standardization {bool} -- Performs fixed standardization of images. (default: {False})
+        keep_probability {float} -- Keep probability of dropout for the fully connected layer(s). (default: {1.0})
+        weight_decay {float} -- L2 weight regularization. (default: {0.0})
+        center_loss_factor {float} -- Center loss factor. (default: {0.0})
+        center_loss_alfa {float} -- Center update rate for center loss. (default: {0.95})
+        prelogits_norm_loss_factor {float} -- Loss based on the norm of the activations in the prelogits layer. (default: {0.0})
+        prelogits_norm_p {float} -- Norm to use for prelogits norm loss. (default: {1.0})
+        prelogits_hist_max {float} -- The max value for the prelogits histogram. (default: {10.0})
+        optimizer {str} -- The optimization algorithm to use (default: {'ADAGRAD'})
+        learning_rate {float} -- Initial learning rate. If set to a negative value a learning rate schedule can be specified in the file "learning_rate_schedule.txt" (default: {0.1})
+        learning_rate_decay_epochs {int} -- Number of epochs between learning rate decay. (default: {100})
+        learning_rate_decay_factor {float} -- Learning rate decay factor. (default: {1.0})
+        moving_average_decay {float} -- Exponential decay for tracking of training parameters. (default: {0.9999})
+        seed {int} -- Random seed. (default: {666})
+        nrof_preprocess_threads {int} -- Number of preprocessing (data loading and augmentation) threads. (default: {4})
+        log_histograms {bool} -- Enables logging of weight/bias histograms in tensorboard. (default: {False})
+        learning_rate_schedule_file {str} -- File containing the learning rate schedule that is used when learning_rate is set to to -1. (default: {'data/learning_rate_schedule.txt'})
+        filter_filename {str} -- File containing image data used for dataset filtering (default: {''})
+        filter_percentile {float} -- Keep only the percentile images closed to its class center (default: {100.0})
+        filter_min_nrof_images_per_class {int} -- Keep only the classes with this number of examples or more (default: {0})
+        validate_every_n_epochs {int} -- Number of epoch between validation (default: {5})
+        validation_set_split_ratio {float} -- The ratio of the total dataset to use for validation (default: {0.0})
+        min_nrof_val_images_per_class {int} -- Classes with fewer images will be removed from the validation set (default: {0})
+        lfw_pairs {str} -- The file containing the pairs to use for validation. (default: {'data/pairs.txt'})
+        lfw_dir {str} -- Path to the data directory containing aligned face patches. (default: {''})
+        lfw_batch_size {int} -- Number of images to process in a batch in the LFW test set. (default: {100})
+        lfw_nrof_folds {int} -- Number of folds to use for cross validation. Mainly used for testing. (default: {10})
+        lfw_distance_metric {int} -- Type of distance metric to use. 0: Euclidian, 1:Cosine similarity distance. (default: {0})
+        lfw_use_flipped_images {bool} -- Concatenates embeddings for the image and its horizontally flipped counterpart. (default: {False})
+        lfw_subtract_mean {bool} -- Subtract feature mean before calculating distance. (default: {False})
+
+    Returns:
+        [type] -- [description]
+    """
+
+    network = importlib.import_module(model_def)
+    image_size = (image_size, image_size)
+
+    subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
+    log_dir = os.path.join(os.path.expanduser(logs_base_dir), subdir)
+    os.makedirs(log_dir, exist_ok=True)
+    model_dir = os.path.join(os.path.expanduser(models_base_dir), subdir)
+    os.makedirs(model_dir, exist_ok=True)
+
+    stat_file_name = os.path.join(log_dir, 'stat.h5')
+
+    # Write arguments to a text file
+    # facenet.write_arguments_to_file(
+    #     args, os.path.join(log_dir, 'arguments.txt'))
+
+    # Store some git revision info in a text file in the log directory
+    # src_path, _ = os.path.split(os.path.realpath(__file__))
+    # facenet.store_revision_info(src_path, log_dir, ' '.join(sys.argv))
+
+    np.random.seed(seed=seed)
+    random.seed(seed)
+    dataset = facenet.get_dataset(data_dir)
+    if filter_filename:
+        dataset = filter_dataset(
+            dataset,
+            os.path.expanduser(
+                filter_filename),
+            filter_percentile,
+            filter_min_nrof_images_per_class)
+
+    if validation_set_split_ratio > 0.0:
+        train_set, val_set = facenet.split_dataset(
+            dataset, validation_set_split_ratio, min_nrof_val_images_per_class, 'SPLIT_IMAGES')
+    else:
+        train_set, val_set = dataset, []
+    image_list, label_list = facenet.get_image_paths_and_labels(train_set)
+    val_image_list, val_label_list = facenet.get_image_paths_and_labels(
+        val_set)
+    assert len(image_list) > 0, 'The training set should not be empty'
+
+    nrof_classes = len(train_set)
+
+    print('Model directory: %s' % model_dir)
+    print('Log directory: %s' % log_dir)
+    pretrained_model = None
+    if pretrained_model:
+        pretrained_model = os.path.expanduser(pretrained_model)
+        print('Pre-trained model: %s' % pretrained_model)
+
+    if lfw_dir:
+        print('LFW directory: %s' % lfw_dir)
+        # Read the file containing the pairs used for testing
+        pairs = lfw.read_pairs(os.path.expanduser(lfw_pairs))
+        # Get the paths for the corresponding images
+        lfw_paths, lfw_labels = lfw.get_paths(
+            os.path.expanduser(lfw_dir), pairs)
+
+    with tf.Graph().as_default():
+        tf.set_random_seed(seed)
+        global_step = tf.Variable(0, trainable=False)
+
+        # Create a queue that produces indices into the image_list and
+        # label_list
+        labels = ops.convert_to_tensor(label_list, dtype=tf.int32)
+        range_size = array_ops.shape(labels)[0]
+        index_queue = tf.train.range_input_producer(
+            range_size, num_epochs=None, shuffle=True, seed=None, capacity=32)
+
+        index_dequeue_op = index_queue.dequeue_many(
+            batch_size * epoch_size, 'index_dequeue')
+
+        learning_rate_placeholder = tf.placeholder(
+            tf.float32, name='learning_rate')
+        batch_size_placeholder = tf.placeholder(tf.int32, name='batch_size')
+        phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
+        image_paths_placeholder = tf.placeholder(
+            tf.string, shape=(None, 1), name='image_paths')
+        labels_placeholder = tf.placeholder(
+            tf.int32, shape=(None, 1), name='labels')
+        control_placeholder = tf.placeholder(
+            tf.int32, shape=(None, 1), name='control')
+
+        nrof_preprocess_threads = 4
+        input_queue = data_flow_ops.FIFOQueue(capacity=2000000,
+                                              dtypes=[tf.string,
+                                                      tf.int32, tf.int32],
+                                              shapes=[(1,), (1,), (1,)],
+                                              shared_name=None, name=None)
+        enqueue_op = input_queue.enqueue_many(
+            [image_paths_placeholder, labels_placeholder, control_placeholder], name='enqueue_op')
+        image_batch, label_batch = facenet.create_input_pipeline(
+            input_queue, image_size, nrof_preprocess_threads, batch_size_placeholder)
+
+        image_batch = tf.identity(image_batch, 'image_batch')
+        image_batch = tf.identity(image_batch, 'input')
+        label_batch = tf.identity(label_batch, 'label_batch')
+
+        print('Number of classes in training set: %d' % nrof_classes)
+        print('Number of examples in training set: %d' % len(image_list))
+
+        print('Number of classes in validation set: %d' % len(val_set))
+        print('Number of examples in validation set: %d' % len(val_image_list))
+
+        print('Building training graph')
+
+        # Build the inference graph
+        prelogits, _ = network.inference(image_batch, keep_probability,
+                                         phase_train=phase_train_placeholder, bottleneck_layer_size=embedding_size,
+                                         weight_decay=weight_decay)
+        logits = slim.fully_connected(
+            prelogits,
+            len(train_set),
+            activation_fn=None,
+            weights_initializer=slim.initializers.xavier_initializer(),
+            weights_regularizer=slim.l2_regularizer(
+                weight_decay),
+            scope='Logits',
+            reuse=False)
+
+        embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')
+
+        # Norm for the prelogits
+        eps = 1e-4
+        prelogits_norm = tf.reduce_mean(
+            tf.norm(
+                tf.abs(prelogits) + eps,
+                ord=prelogits_norm_p,
+                axis=1))
+        tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
+                             prelogits_norm * prelogits_norm_loss_factor)
+
+        # Add center loss
+        prelogits_center_loss, _ = facenet.center_loss(
+            prelogits, label_batch, center_loss_alfa, nrof_classes)
+        tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
+                             prelogits_center_loss * center_loss_factor)
+
+        learning_rate = tf.train.exponential_decay(
+            learning_rate_placeholder,
+            global_step,
+            learning_rate_decay_epochs *
+            epoch_size,
+            learning_rate_decay_factor,
+            staircase=True)
+        tf.summary.scalar('learning_rate', learning_rate)
+
+        # Calculate the average cross entropy loss across the batch
+        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
+            labels=label_batch, logits=logits, name='cross_entropy_per_example')
+        cross_entropy_mean = tf.reduce_mean(
+            cross_entropy, name='cross_entropy')
+        tf.add_to_collection('losses', cross_entropy_mean)
+
+        correct_prediction = tf.cast(
+            tf.equal(
+                tf.argmax(
+                    logits, 1), tf.cast(
+                    label_batch, tf.int64)), tf.float32)
+        accuracy = tf.reduce_mean(correct_prediction)
+
+        # Calculate the total losses
+        regularization_losses = tf.get_collection(
+            tf.GraphKeys.REGULARIZATION_LOSSES)
+        total_loss = tf.add_n([cross_entropy_mean] +
+                              regularization_losses, name='total_loss')
+
+        # Build a Graph that trains the model with one batch of examples and
+        # updates the model parameters
+        train_op = facenet.train(
+            total_loss,
+            global_step,
+            optimizer,
+            learning_rate,
+            moving_average_decay,
+            tf.global_variables(),
+            log_histograms)
+
+        # Create a saver
+        saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=3)
+
+        # Build the summary operation based on the TF collection of Summaries.
+        summary_op = tf.summary.merge_all()
+
+        # Start running operations on the Graph.
+        gpu_options = tf.GPUOptions(
+            per_process_gpu_memory_fraction=gpu_memory_fraction)
+        sess = tf.Session(config=tf.ConfigProto(
+            gpu_options=gpu_options, log_device_placement=False))
+        sess.run(tf.global_variables_initializer())
+        sess.run(tf.local_variables_initializer())
+        summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
+        coord = tf.train.Coordinator()
+        tf.train.start_queue_runners(coord=coord, sess=sess)
+
+        with sess.as_default():
+
+            if pretrained_model:
+                print('Restoring pretrained model: %s' % pretrained_model)
+                saver.restore(sess, pretrained_model)
+
+            # Training and validation loop
+            print('Running training')
+            nrof_steps = max_nrof_epochs * epoch_size
+            # Validate every validate_every_n_epochs as well as in the last
+            # epoch
+            nrof_val_samples = int(
+                math.ceil(max_nrof_epochs / validate_every_n_epochs))
+            stat = {
+                'loss': np.zeros((nrof_steps,), np.float32),
+                'center_loss': np.zeros((nrof_steps,), np.float32),
+                'reg_loss': np.zeros((nrof_steps,), np.float32),
+                'xent_loss': np.zeros((nrof_steps,), np.float32),
+                'prelogits_norm': np.zeros((nrof_steps,), np.float32),
+                'accuracy': np.zeros((nrof_steps,), np.float32),
+                'val_loss': np.zeros((nrof_val_samples,), np.float32),
+                'val_xent_loss': np.zeros((nrof_val_samples,), np.float32),
+                'val_accuracy': np.zeros((nrof_val_samples,), np.float32),
+                'lfw_accuracy': np.zeros((max_nrof_epochs,), np.float32),
+                'lfw_valrate': np.zeros((max_nrof_epochs,), np.float32),
+                'learning_rate': np.zeros((max_nrof_epochs,), np.float32),
+                'time_train': np.zeros((max_nrof_epochs,), np.float32),
+                'time_validate': np.zeros((max_nrof_epochs,), np.float32),
+                'time_evaluate': np.zeros((max_nrof_epochs,), np.float32),
+                'prelogits_hist': np.zeros((max_nrof_epochs, 1000), np.float32),
+            }
+            for epoch in range(1, max_nrof_epochs + 1):
+                step = sess.run(global_step, feed_dict=None)
+                # Train for one epoch
+                t = time.time()
+                cont = train(
+                    args,
+                    sess,
+                    epoch,
+                    image_list,
+                    label_list,
+                    index_dequeue_op,
+                    enqueue_op,
+                    image_paths_placeholder,
+                    labels_placeholder,
+                    learning_rate_placeholder,
+                    phase_train_placeholder,
+                    batch_size_placeholder,
+                    control_placeholder,
+                    global_step,
+                    total_loss,
+                    train_op,
+                    summary_op,
+                    summary_writer,
+                    regularization_losses,
+                    learning_rate_schedule_file,
+                    stat,
+                    cross_entropy_mean,
+                    accuracy,
+                    learning_rate,
+                    prelogits,
+                    prelogits_center_loss,
+                    random_rotate,
+                    random_crop,
+                    random_flip,
+                    prelogits_norm,
+                    prelogits_hist_max,
+                    use_fixed_image_standardization)
+                stat['time_train'][epoch - 1] = time.time() - t
+
+                if not cont:
+                    break
+
+                t = time.time()
+                if len(val_image_list) > 0 and (
+                    (epoch - 1) %
+                    validate_every_n_epochs == validate_every_n_epochs -
+                        1 or epoch == max_nrof_epochs):
+                    validate(
+                        args,
+                        sess,
+                        epoch,
+                        val_image_list,
+                        val_label_list,
+                        enqueue_op,
+                        image_paths_placeholder,
+                        labels_placeholder,
+                        control_placeholder,
+                        phase_train_placeholder,
+                        batch_size_placeholder,
+                        stat,
+                        total_loss,
+                        regularization_losses,
+                        cross_entropy_mean,
+                        accuracy,
+                        validate_every_n_epochs,
+                        use_fixed_image_standardization)
+                stat['time_validate'][epoch - 1] = time.time() - t
+
+                # Save variables and the metagraph if it doesn't exist already
+                save_variables_and_metagraph(
+                    sess, saver, summary_writer, model_dir, subdir, epoch)
+
+                # Evaluate on LFW
+                t = time.time()
+                if lfw_dir:
+                    evaluate(
+                        sess,
+                        enqueue_op,
+                        image_paths_placeholder,
+                        labels_placeholder,
+                        phase_train_placeholder,
+                        batch_size_placeholder,
+                        control_placeholder,
+                        embeddings,
+                        label_batch,
+                        lfw_paths,
+                        lfw_labels,
+                        lfw_batch_size,
+                        lfw_nrof_folds,
+                        log_dir,
+                        step,
+                        summary_writer,
+                        stat,
+                        epoch,
+                        lfw_distance_metric,
+                        lfw_subtract_mean,
+                        lfw_use_flipped_images,
+                        use_fixed_image_standardization)
+                stat['time_evaluate'][epoch - 1] = time.time() - t
+
+                print('Saving statistics')
+                with h5py.File(stat_file_name, 'w') as f:
+                    for key, value in stat.iteritems():
+                        f.create_dataset(key, data=value)
+
+    return model_dir
+
+
+def train(
+        args,
+        sess,
+        epoch,
+        image_list,
+        label_list,
+        index_dequeue_op,
+        enqueue_op,
+        image_paths_placeholder,
+        labels_placeholder,
+        learning_rate_placeholder,
+        phase_train_placeholder,
+        batch_size_placeholder,
+        control_placeholder,
+        step,
+        loss,
+        train_op,
+        summary_op,
+        summary_writer,
+        reg_losses,
+        learning_rate_schedule_file,
+        stat,
+        cross_entropy_mean,
+        accuracy,
+        learning_rate,
+        prelogits,
+        prelogits_center_loss,
+        random_rotate,
+        random_crop,
+        random_flip,
+        prelogits_norm,
+        prelogits_hist_max,
+        use_fixed_image_standardization):
+    batch_number = 0
+
+    if learning_rate > 0.0:
+        lr = learning_rate
+    else:
+        lr = facenet.get_learning_rate_from_file(
+            learning_rate_schedule_file, epoch)
+
+    if lr <= 0:
+        return False
+
+    index_epoch = sess.run(index_dequeue_op)
+    label_epoch = np.array(label_list)[index_epoch]
+    image_epoch = np.array(image_list)[index_epoch]
+
+    # Enqueue one epoch of image paths and labels
+    labels_array = np.expand_dims(np.array(label_epoch), 1)
+    image_paths_array = np.expand_dims(np.array(image_epoch), 1)
+    control_value = facenet.RANDOM_ROTATE * random_rotate + facenet.RANDOM_CROP * random_crop + \
+        facenet.RANDOM_FLIP * random_flip + \
+        facenet.FIXED_STANDARDIZATION * use_fixed_image_standardization
+    control_array = np.ones_like(labels_array) * control_value
+    sess.run(enqueue_op,
+             {image_paths_placeholder: image_paths_array,
+              labels_placeholder: labels_array,
+              control_placeholder: control_array})
+
+    # Training loop
+    train_time = 0
+    while batch_number < epoch_size:
+        start_time = time.time()
+        feed_dict = {
+            learning_rate_placeholder: lr,
+            phase_train_placeholder: True,
+            batch_size_placeholder: batch_size}
+        tensor_list = [
+            loss,
+            train_op,
+            step,
+            reg_losses,
+            prelogits,
+            cross_entropy_mean,
+            learning_rate,
+            prelogits_norm,
+            accuracy,
+            prelogits_center_loss]
+        if batch_number % 100 == 0:
+            loss_, _, step_, reg_losses_, prelogits_, cross_entropy_mean_, lr_, prelogits_norm_, accuracy_, center_loss_, summary_str = sess.run(
+                tensor_list + [summary_op], feed_dict=feed_dict)
+            summary_writer.add_summary(summary_str, global_step=step_)
+        else:
+            loss_, _, step_, reg_losses_, prelogits_, cross_entropy_mean_, lr_, prelogits_norm_, accuracy_, center_loss_ = sess.run(
+                tensor_list, feed_dict=feed_dict)
+
+        duration = time.time() - start_time
+        stat['loss'][step_ - 1] = loss_
+        stat['center_loss'][step_ - 1] = center_loss_
+        stat['reg_loss'][step_ - 1] = np.sum(reg_losses_)
+        stat['xent_loss'][step_ - 1] = cross_entropy_mean_
+        stat['prelogits_norm'][step_ - 1] = prelogits_norm_
+        stat['learning_rate'][epoch - 1] = lr_
+        stat['accuracy'][step_ - 1] = accuracy_
+        stat['prelogits_hist'][epoch - 1,
+                               :] += np.histogram(np.minimum(np.abs(prelogits_),
+                                                             prelogits_hist_max),
+                                                  bins=1000,
+                                                  range=(0.0,
+                                                         prelogits_hist_max))[0]
+
+        duration = time.time() - start_time
+        print(
+            'Epoch: [%d][%d/%d]\tTime %.3f\tLoss %2.3f\tXent %2.3f\tRegLoss %2.3f\tAccuracy %2.3f\tLr %2.5f\tCl %2.3f' %
+            (epoch,
+             batch_number +
+             1,
+             epoch_size,
+             duration,
+             loss_,
+             cross_entropy_mean_,
+             np.sum(reg_losses_),
+                accuracy_,
+                lr_,
+                center_loss_))
+        batch_number += 1
+        train_time += duration
+    # Add validation loss and accuracy to summary
+    summary = tf.Summary()
+    # pylint: disable=maybe-no-member
+    summary.value.add(tag='time/total', simple_value=train_time)
+    summary_writer.add_summary(summary, global_step=step_)
+    return True
+
+
+def parse_arguments(argv):
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        '--logs_base_dir',
+        type=str,
+        help='Directory where to write event logs.',
+        default='~/logs/facenet')
+    parser.add_argument(
+        '--models_base_dir',
+        type=str,
+        help='Directory where to write trained models and checkpoints.',
+        default='~/models/facenet')
+    parser.add_argument(
+        '--gpu_memory_fraction',
+        type=float,
+        help='Upper bound on the amount of GPU memory that will be used by the process.',
+        default=1.0)
+    parser.add_argument('--pretrained_model', type=str,
+                        help='Load a pretrained model before training starts.')
+    parser.add_argument(
+        '--data_dir',
+        type=str,
+        help='Path to the data directory containing aligned face patches.',
+        default='~/datasets/casia/casia_maxpy_mtcnnalign_182_160')
+    parser.add_argument(
+        '--model_def',
+        type=str,
+        help='Model definition. Points to a module containing the definition of the inference graph.',
+        default='models.inception_resnet_v1')
+    parser.add_argument('--max_nrof_epochs', type=int,
+                        help='Number of epochs to run.', default=500)
+    parser.add_argument(
+        '--batch_size',
+        type=int,
+        help='Number of images to process in a batch.',
+        default=90)
+    parser.add_argument(
+        '--image_size',
+        type=int,
+        help='Image size (height, width) in pixels.',
+        default=160)
+    parser.add_argument('--epoch_size', type=int,
+                        help='Number of batches per epoch.', default=1000)
+    parser.add_argument('--embedding_size', type=int,
+                        help='Dimensionality of the embedding.', default=128)
+    parser.add_argument(
+        '--random_crop',
+        help='Performs random cropping of training images. If false, the center image_size pixels from the training images are used. ' +
+        'If the size of the images in the data directory is equal to image_size no cropping is performed',
+        action='store_true')
+    parser.add_argument(
+        '--random_flip',
+        help='Performs random horizontal flipping of training images.',
+        action='store_true')
+    parser.add_argument(
+        '--random_rotate',
+        help='Performs random rotations of training images.',
+        action='store_true')
+    parser.add_argument(
+        '--use_fixed_image_standardization',
+        help='Performs fixed standardization of images.',
+        action='store_true')
+    parser.add_argument(
+        '--keep_probability',
+        type=float,
+        help='Keep probability of dropout for the fully connected layer(s).',
+        default=1.0)
+    parser.add_argument('--weight_decay', type=float,
+                        help='L2 weight regularization.', default=0.0)
+    parser.add_argument('--center_loss_factor', type=float,
+                        help='Center loss factor.', default=0.0)
+    parser.add_argument(
+        '--center_loss_alfa',
+        type=float,
+        help='Center update rate for center loss.',
+        default=0.95)
+    parser.add_argument(
+        '--prelogits_norm_loss_factor',
+        type=float,
+        help='Loss based on the norm of the activations in the prelogits layer.',
+        default=0.0)
+    parser.add_argument(
+        '--prelogits_norm_p',
+        type=float,
+        help='Norm to use for prelogits norm loss.',
+        default=1.0)
+    parser.add_argument(
+        '--prelogits_hist_max',
+        type=float,
+        help='The max value for the prelogits histogram.',
+        default=10.0)
+    parser.add_argument(
+        '--optimizer',
+        type=str,
+        choices=[
+            'ADAGRAD',
+            'ADADELTA',
+            'ADAM',
+            'RMSPROP',
+            'MOM'],
+        help='The optimization algorithm to use',
+        default='ADAGRAD')
+    parser.add_argument(
+        '--learning_rate',
+        type=float,
+        help='Initial learning rate. If set to a negative value a learning rate ' +
+        'schedule can be specified in the file "learning_rate_schedule.txt"',
+        default=0.1)
+    parser.add_argument(
+        '--learning_rate_decay_epochs',
+        type=int,
+        help='Number of epochs between learning rate decay.',
+        default=100)
+    parser.add_argument('--learning_rate_decay_factor', type=float,
+                        help='Learning rate decay factor.', default=1.0)
+    parser.add_argument(
+        '--moving_average_decay',
+        type=float,
+        help='Exponential decay for tracking of training parameters.',
+        default=0.9999)
+    parser.add_argument('--seed', type=int,
+                        help='Random seed.', default=666)
+    parser.add_argument(
+        '--nrof_preprocess_threads',
+        type=int,
+        help='Number of preprocessing (data loading and augmentation) threads.',
+        default=4)
+    parser.add_argument(
+        '--log_histograms',
+        help='Enables logging of weight/bias histograms in tensorboard.',
+        action='store_true')
+    parser.add_argument(
+        '--learning_rate_schedule_file',
+        type=str,
+        help='File containing the learning rate schedule that is used when learning_rate is set to to -1.',
+        default='data/learning_rate_schedule.txt')
+    parser.add_argument(
+        '--filter_filename',
+        type=str,
+        help='File containing image data used for dataset filtering',
+        default='')
+    parser.add_argument(
+        '--filter_percentile',
+        type=float,
+        help='Keep only the percentile images closed to its class center',
+        default=100.0)
+    parser.add_argument(
+        '--filter_min_nrof_images_per_class',
+        type=int,
+        help='Keep only the classes with this number of examples or more',
+        default=0)
+    parser.add_argument('--validate_every_n_epochs', type=int,
+                        help='Number of epoch between validation', default=5)
+    parser.add_argument(
+        '--validation_set_split_ratio',
+        type=float,
+        help='The ratio of the total dataset to use for validation',
+        default=0.0)
+    parser.add_argument(
+        '--min_nrof_val_images_per_class',
+        type=float,
+        help='Classes with fewer images will be removed from the validation set',
+        default=0)
+
+    # Parameters for validation on LFW
+    parser.add_argument(
+        '--lfw_pairs',
+        type=str,
+        help='The file containing the pairs to use for validation.',
+        default='data/pairs.txt')
+    parser.add_argument(
+        '--lfw_dir',
+        type=str,
+        help='Path to the data directory containing aligned face patches.',
+        default='')
+    parser.add_argument(
+        '--lfw_batch_size',
+        type=int,
+        help='Number of images to process in a batch in the LFW test set.',
+        default=100)
+    parser.add_argument(
+        '--lfw_nrof_folds',
+        type=int,
+        help='Number of folds to use for cross validation. Mainly used for testing.',
+        default=10)
+    parser.add_argument(
+        '--lfw_distance_metric',
+        type=int,
+        help='Type of distance metric to use. 0: Euclidian, 1:Cosine similarity distance.',
+        default=0)
+    parser.add_argument(
+        '--lfw_use_flipped_images',
+        help='Concatenates embeddings for the image and its horizontally flipped counterpart.',
+        action='store_true')
+    parser.add_argument(
+        '--lfw_subtract_mean',
+        help='Subtract feature mean before calculating distance.',
+        action='store_true')
+    return parser.parse_args(argv)
+
+
+if __name__ == '__main__':
+    main(parse_arguments(sys.argv[1:]))
diff --git a/facenet_sandberg/train_facenet/train_tripletloss.py b/facenet_sandberg/train_facenet/train_tripletloss.py
new file mode 100644
index 000000000..2ba136332
--- /dev/null
+++ b/facenet_sandberg/train_facenet/train_tripletloss.py
@@ -0,0 +1,711 @@
+"""Training a face recognizer with TensorFlow based on the FaceNet paper
+FaceNet: A Unified Embedding for Face Recognition and Clustering: http://arxiv.org/abs/1503.03832
+"""
+# MIT License
+#
+# Copyright (c) 2016 David Sandberg
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from __future__ import absolute_import, division, print_function
+
+import argparse
+import importlib
+import itertools
+import os.path
+import sys
+import time
+from datetime import datetime
+
+import numpy as np
+import tensorflow as tf
+from facenet_sandberg import facenet, lfw
+from six.moves import xrange  # @UnresolvedImport
+from tensorflow.python.ops import data_flow_ops
+
+
+def main(args):
+
+    network = importlib.import_module(args.model_def)
+
+    subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
+    log_dir = os.path.join(os.path.expanduser(args.logs_base_dir), subdir)
+    if not os.path.isdir(
+            log_dir):  # Create the log directory if it doesn't exist
+        os.makedirs(log_dir)
+    model_dir = os.path.join(os.path.expanduser(args.models_base_dir), subdir)
+    if not os.path.isdir(
+            model_dir):  # Create the model directory if it doesn't exist
+        os.makedirs(model_dir)
+
+    # Write arguments to a text file
+    facenet.write_arguments_to_file(
+        args, os.path.join(
+            log_dir, 'arguments.txt'))
+
+    # Store some git revision info in a text file in the log directory
+    src_path, _ = os.path.split(os.path.realpath(__file__))
+    facenet.store_revision_info(src_path, log_dir, ' '.join(sys.argv))
+
+    np.random.seed(seed=args.seed)
+    train_set = facenet.get_dataset(args.data_dir)
+
+    print('Model directory: %s' % model_dir)
+    print('Log directory: %s' % log_dir)
+    if args.pretrained_model:
+        print(
+            'Pre-trained model: %s' %
+            os.path.expanduser(
+                args.pretrained_model))
+
+    if args.lfw_dir:
+        print('LFW directory: %s' % args.lfw_dir)
+        # Read the file containing the pairs used for testing
+        pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
+        # Get the paths for the corresponding images
+        lfw_paths, actual_issame = lfw.get_paths(
+            os.path.expanduser(args.lfw_dir), pairs)
+
+    with tf.Graph().as_default():
+        tf.set_random_seed(args.seed)
+        global_step = tf.Variable(0, trainable=False)
+
+        # Placeholder for the learning rate
+        learning_rate_placeholder = tf.placeholder(
+            tf.float32, name='learning_rate')
+
+        batch_size_placeholder = tf.placeholder(tf.int32, name='batch_size')
+
+        phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
+
+        image_paths_placeholder = tf.placeholder(
+            tf.string, shape=(None, 3), name='image_paths')
+        labels_placeholder = tf.placeholder(
+            tf.int64, shape=(None, 3), name='labels')
+
+        input_queue = data_flow_ops.FIFOQueue(capacity=100000,
+                                              dtypes=[tf.string, tf.int64],
+                                              shapes=[(3,), (3,)],
+                                              shared_name=None, name=None)
+        enqueue_op = input_queue.enqueue_many(
+            [image_paths_placeholder, labels_placeholder])
+
+        nrof_preprocess_threads = 4
+        images_and_labels = []
+        for _ in range(nrof_preprocess_threads):
+            filenames, label = input_queue.dequeue()
+            images = []
+            for filename in tf.unstack(filenames):
+                file_contents = tf.read_file(filename)
+                image = tf.image.decode_image(file_contents, channels=3)
+
+                if args.random_crop:
+                    image = tf.random_crop(
+                        image, [args.image_size, args.image_size, 3])
+                else:
+                    image = tf.image.resize_image_with_crop_or_pad(
+                        image, args.image_size, args.image_size)
+                if args.random_flip:
+                    image = tf.image.random_flip_left_right(image)
+
+                # pylint: disable=no-member
+                image.set_shape((args.image_size, args.image_size, 3))
+                images.append(tf.image.per_image_standardization(image))
+            images_and_labels.append([images, label])
+
+        image_batch, labels_batch = tf.train.batch_join(
+            images_and_labels, batch_size=batch_size_placeholder,
+            shapes=[(args.image_size, args.image_size, 3), ()], enqueue_many=True,
+            capacity=4 * nrof_preprocess_threads * args.batch_size,
+            allow_smaller_final_batch=True)
+        image_batch = tf.identity(image_batch, 'image_batch')
+        image_batch = tf.identity(image_batch, 'input')
+        labels_batch = tf.identity(labels_batch, 'label_batch')
+
+        # Build the inference graph
+        prelogits, _ = network.inference(image_batch, args.keep_probability,
+                                         phase_train=phase_train_placeholder, bottleneck_layer_size=args.embedding_size,
+                                         weight_decay=args.weight_decay)
+
+        embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')
+        # Split embeddings into anchor, positive and negative and calculate
+        # triplet loss
+        anchor, positive, negative = tf.unstack(tf.reshape(
+            embeddings, [-1, 3, args.embedding_size]), 3, 1)
+        triplet_loss = facenet.triplet_loss(
+            anchor, positive, negative, args.alpha)
+
+        learning_rate = tf.train.exponential_decay(
+            learning_rate_placeholder,
+            global_step,
+            args.learning_rate_decay_epochs *
+            args.epoch_size,
+            args.learning_rate_decay_factor,
+            staircase=True)
+        tf.summary.scalar('learning_rate', learning_rate)
+
+        # Calculate the total losses
+        regularization_losses = tf.get_collection(
+            tf.GraphKeys.REGULARIZATION_LOSSES)
+        total_loss = tf.add_n(
+            [triplet_loss] +
+            regularization_losses,
+            name='total_loss')
+
+        # Build a Graph that trains the model with one batch of examples and
+        # updates the model parameters
+        train_op = facenet.train(
+            total_loss,
+            global_step,
+            args.optimizer,
+            learning_rate,
+            args.moving_average_decay,
+            tf.global_variables())
+
+        # Create a saver
+        saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=3)
+
+        # Build the summary operation based on the TF collection of Summaries.
+        summary_op = tf.summary.merge_all()
+
+        # Start running operations on the Graph.
+        gpu_options = tf.GPUOptions(
+            per_process_gpu_memory_fraction=args.gpu_memory_fraction)
+        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
+
+        # Initialize variables
+        sess.run(tf.global_variables_initializer(),
+                 feed_dict={phase_train_placeholder: True})
+        sess.run(tf.local_variables_initializer(),
+                 feed_dict={phase_train_placeholder: True})
+
+        summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
+        coord = tf.train.Coordinator()
+        tf.train.start_queue_runners(coord=coord, sess=sess)
+
+        with sess.as_default():
+
+            if args.pretrained_model:
+                print('Restoring pretrained model: %s' % args.pretrained_model)
+                saver.restore(sess, os.path.expanduser(args.pretrained_model))
+
+            # Training and validation loop
+            epoch = 0
+            while epoch < args.max_nrof_epochs:
+                step = sess.run(global_step, feed_dict=None)
+                epoch = step // args.epoch_size
+                # Train for one epoch
+                train(
+                    args,
+                    sess,
+                    train_set,
+                    epoch,
+                    image_paths_placeholder,
+                    labels_placeholder,
+                    labels_batch,
+                    batch_size_placeholder,
+                    learning_rate_placeholder,
+                    phase_train_placeholder,
+                    enqueue_op,
+                    input_queue,
+                    global_step,
+                    embeddings,
+                    total_loss,
+                    train_op,
+                    summary_op,
+                    summary_writer,
+                    args.learning_rate_schedule_file,
+                    args.embedding_size,
+                    anchor,
+                    positive,
+                    negative,
+                    triplet_loss)
+
+                # Save variables and the metagraph if it doesn't exist already
+                save_variables_and_metagraph(
+                    sess, saver, summary_writer, model_dir, subdir, step)
+
+                # Evaluate on LFW
+                if args.lfw_dir:
+                    evaluate(
+                        sess,
+                        lfw_paths,
+                        embeddings,
+                        labels_batch,
+                        image_paths_placeholder,
+                        labels_placeholder,
+                        batch_size_placeholder,
+                        learning_rate_placeholder,
+                        phase_train_placeholder,
+                        enqueue_op,
+                        actual_issame,
+                        args.batch_size,
+                        args.lfw_nrof_folds,
+                        log_dir,
+                        step,
+                        summary_writer,
+                        args.embedding_size)
+
+    return model_dir
+
+
+def train(
+        args,
+        sess,
+        dataset,
+        epoch,
+        image_paths_placeholder,
+        labels_placeholder,
+        labels_batch,
+        batch_size_placeholder,
+        learning_rate_placeholder,
+        phase_train_placeholder,
+        enqueue_op,
+        input_queue,
+        global_step,
+        embeddings,
+        loss,
+        train_op,
+        summary_op,
+        summary_writer,
+        learning_rate_schedule_file,
+        embedding_size,
+        anchor,
+        positive,
+        negative,
+        triplet_loss):
+    batch_number = 0
+
+    if args.learning_rate > 0.0:
+        lr = args.learning_rate
+    else:
+        lr = facenet.get_learning_rate_from_file(
+            learning_rate_schedule_file, epoch)
+    while batch_number < args.epoch_size:
+        # Sample people randomly from the dataset
+        image_paths, num_per_class = sample_people(
+            dataset, args.people_per_batch, args.images_per_person)
+
+        print('Running forward pass on sampled images: ', end='')
+        start_time = time.time()
+        nrof_examples = args.people_per_batch * args.images_per_person
+        labels_array = np.reshape(np.arange(nrof_examples), (-1, 3))
+        image_paths_array = np.reshape(
+            np.expand_dims(
+                np.array(image_paths), 1), (-1, 3))
+        sess.run(enqueue_op,
+                 {image_paths_placeholder: image_paths_array,
+                  labels_placeholder: labels_array})
+        emb_array = np.zeros((nrof_examples, embedding_size))
+        nrof_batches = int(np.ceil(nrof_examples / args.batch_size))
+        for i in range(nrof_batches):
+            batch_size = min(
+                nrof_examples - i * args.batch_size,
+                args.batch_size)
+            emb, lab = sess.run([embeddings, labels_batch], feed_dict={
+                                batch_size_placeholder: batch_size, learning_rate_placeholder: lr, phase_train_placeholder: True})
+            emb_array[lab, :] = emb
+        print('%.3f' % (time.time() - start_time))
+
+        # Select triplets based on the embeddings
+        print('Selecting suitable triplets for training')
+        triplets, nrof_random_negs, nrof_triplets = select_triplets(
+            emb_array, num_per_class, image_paths, args.people_per_batch, args.alpha)
+        selection_time = time.time() - start_time
+        print(
+            '(nrof_random_negs, nrof_triplets) = (%d, %d): time=%.3f seconds' %
+            (nrof_random_negs, nrof_triplets, selection_time))
+
+        # Perform training on the selected triplets
+        nrof_batches = int(np.ceil(nrof_triplets * 3 / args.batch_size))
+        triplet_paths = list(itertools.chain(*triplets))
+        labels_array = np.reshape(np.arange(len(triplet_paths)), (-1, 3))
+        triplet_paths_array = np.reshape(
+            np.expand_dims(np.array(triplet_paths), 1), (-1, 3))
+        sess.run(enqueue_op,
+                 {image_paths_placeholder: triplet_paths_array,
+                  labels_placeholder: labels_array})
+        nrof_examples = len(triplet_paths)
+        train_time = 0
+        i = 0
+        emb_array = np.zeros((nrof_examples, embedding_size))
+        loss_array = np.zeros((nrof_triplets,))
+        summary = tf.Summary()
+        step = 0
+        while i < nrof_batches:
+            start_time = time.time()
+            batch_size = min(
+                nrof_examples - i * args.batch_size,
+                args.batch_size)
+            feed_dict = {
+                batch_size_placeholder: batch_size,
+                learning_rate_placeholder: lr,
+                phase_train_placeholder: True}
+            err, _, step, emb, lab = sess.run(
+                [loss, train_op, global_step, embeddings, labels_batch], feed_dict=feed_dict)
+            emb_array[lab, :] = emb
+            loss_array[i] = err
+            duration = time.time() - start_time
+            print('Epoch: [%d][%d/%d]\tTime %.3f\tLoss %2.3f' %
+                  (epoch, batch_number + 1, args.epoch_size, duration, err))
+            batch_number += 1
+            i += 1
+            train_time += duration
+            summary.value.add(tag='loss', simple_value=err)
+
+        # Add validation loss and accuracy to summary
+        # pylint: disable=maybe-no-member
+        summary.value.add(tag='time/selection', simple_value=selection_time)
+        summary_writer.add_summary(summary, step)
+    return step
+
+
+def select_triplets(
+        embeddings,
+        nrof_images_per_class,
+        image_paths,
+        people_per_batch,
+        alpha):
+    """ Select the triplets for training
+    """
+    trip_idx = 0
+    emb_start_idx = 0
+    num_trips = 0
+    triplets = []
+
+    # VGG Face: Choosing good triplets is crucial and should strike a balance between
+    #  selecting informative (i.e. challenging) examples and swamping training with examples that
+    #  are too hard. This is achieve by extending each pair (a, p) to a triplet (a, p, n) by sampling
+    #  the image n at random, but only between the ones that violate the triplet loss margin. The
+    #  latter is a form of hard-negative mining, but it is not as aggressive (and much cheaper) than
+    # choosing the maximally violating example, as often done in structured
+    # output learning.
+
+    for i in xrange(people_per_batch):
+        nrof_images = int(nrof_images_per_class[i])
+        for j in xrange(1, nrof_images):
+            a_idx = emb_start_idx + j - 1
+            neg_dists_sqr = np.sum(
+                np.square(
+                    embeddings[a_idx] -
+                    embeddings),
+                1)
+            # For every possible positive pair.
+            for pair in xrange(j, nrof_images):
+                p_idx = emb_start_idx + pair
+                pos_dist_sqr = np.sum(
+                    np.square(
+                        embeddings[a_idx] -
+                        embeddings[p_idx]))
+                neg_dists_sqr[emb_start_idx:emb_start_idx +
+                              nrof_images] = np.NaN
+                # all_neg =
+                # np.where(np.logical_and(neg_dists_sqr-pos_dist_sqr<alpha,
+                # pos_dist_sqr<neg_dists_sqr))[0]  # FaceNet selection
+                all_neg = np.where(
+                    neg_dists_sqr -
+                    pos_dist_sqr < alpha)[0]  # VGG Face selecction
+                nrof_random_negs = all_neg.shape[0]
+                if nrof_random_negs > 0:
+                    rnd_idx = np.random.randint(nrof_random_negs)
+                    n_idx = all_neg[rnd_idx]
+                    triplets.append(
+                        (image_paths[a_idx], image_paths[p_idx], image_paths[n_idx]))
+                    # print('Triplet %d: (%d, %d, %d), pos_dist=%2.6f, neg_dist=%2.6f (%d, %d, %d, %d, %d)' %
+                    #    (trip_idx, a_idx, p_idx, n_idx, pos_dist_sqr, neg_dists_sqr[n_idx], nrof_random_negs, rnd_idx, i, j, emb_start_idx))
+                    trip_idx += 1
+
+                num_trips += 1
+
+        emb_start_idx += nrof_images
+
+    np.random.shuffle(triplets)
+    return triplets, num_trips, len(triplets)
+
+
+def sample_people(dataset, people_per_batch, images_per_person):
+    nrof_images = people_per_batch * images_per_person
+
+    # Sample classes from the dataset
+    nrof_classes = len(dataset)
+    class_indices = np.arange(nrof_classes)
+    np.random.shuffle(class_indices)
+
+    i = 0
+    image_paths = []
+    num_per_class = []
+    sampled_class_indices = []
+    # Sample images from these classes until we have enough
+    while len(image_paths) < nrof_images:
+        class_index = class_indices[i]
+        nrof_images_in_class = len(dataset[class_index])
+        image_indices = np.arange(nrof_images_in_class)
+        np.random.shuffle(image_indices)
+        nrof_images_from_class = min(
+            nrof_images_in_class,
+            images_per_person,
+            nrof_images - len(image_paths))
+        idx = image_indices[0:nrof_images_from_class]
+        image_paths_for_class = [
+            dataset[class_index].image_paths[j] for j in idx]
+        sampled_class_indices += [class_index] * nrof_images_from_class
+        image_paths += image_paths_for_class
+        num_per_class.append(nrof_images_from_class)
+        i += 1
+
+    return image_paths, num_per_class
+
+
+def evaluate(
+        sess,
+        image_paths,
+        embeddings,
+        labels_batch,
+        image_paths_placeholder,
+        labels_placeholder,
+        batch_size_placeholder,
+        learning_rate_placeholder,
+        phase_train_placeholder,
+        enqueue_op,
+        actual_issame,
+        batch_size,
+        nrof_folds,
+        log_dir,
+        step,
+        summary_writer,
+        embedding_size):
+    start_time = time.time()
+    # Run forward pass to calculate embeddings
+    print('Running forward pass on LFW images: ', end='')
+
+    nrof_images = len(actual_issame) * 2
+    assert(len(image_paths) == nrof_images)
+    labels_array = np.reshape(np.arange(nrof_images), (-1, 3))
+    image_paths_array = np.reshape(
+        np.expand_dims(
+            np.array(image_paths), 1), (-1, 3))
+    sess.run(enqueue_op,
+             {image_paths_placeholder: image_paths_array,
+              labels_placeholder: labels_array})
+    emb_array = np.zeros((nrof_images, embedding_size))
+    nrof_batches = int(np.ceil(nrof_images / batch_size))
+    label_check_array = np.zeros((nrof_images,))
+    for i in xrange(nrof_batches):
+        batch_size = min(nrof_images - i * batch_size, batch_size)
+        emb, lab = sess.run([embeddings, labels_batch], feed_dict={
+                            batch_size_placeholder: batch_size, learning_rate_placeholder: 0.0, phase_train_placeholder: False})
+        emb_array[lab, :] = emb
+        label_check_array[lab] = 1
+    print('%.3f' % (time.time() - start_time))
+
+    assert(np.all(label_check_array == 1))
+
+    _, _, accuracy, val, val_std, far = lfw.evaluate(
+        emb_array, actual_issame, nrof_folds=nrof_folds)
+
+    print('Accuracy: %1.3f+-%1.3f' % (np.mean(accuracy), np.std(accuracy)))
+    print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
+    lfw_time = time.time() - start_time
+    # Add validation loss and accuracy to summary
+    summary = tf.Summary()
+    # pylint: disable=maybe-no-member
+    summary.value.add(tag='lfw/accuracy', simple_value=np.mean(accuracy))
+    summary.value.add(tag='lfw/val_rate', simple_value=val)
+    summary.value.add(tag='time/lfw', simple_value=lfw_time)
+    summary_writer.add_summary(summary, step)
+    with open(os.path.join(log_dir, 'lfw_result.txt'), 'at') as f:
+        f.write('%d\t%.5f\t%.5f\n' % (step, np.mean(accuracy), val))
+
+
+def save_variables_and_metagraph(
+        sess,
+        saver,
+        summary_writer,
+        model_dir,
+        model_name,
+        step):
+    # Save the model checkpoint
+    print('Saving variables')
+    start_time = time.time()
+    checkpoint_path = os.path.join(model_dir, 'model-%s.ckpt' % model_name)
+    saver.save(sess, checkpoint_path, global_step=step, write_meta_graph=False)
+    save_time_variables = time.time() - start_time
+    print('Variables saved in %.2f seconds' % save_time_variables)
+    metagraph_filename = os.path.join(model_dir, 'model-%s.meta' % model_name)
+    save_time_metagraph = 0
+    if not os.path.exists(metagraph_filename):
+        print('Saving metagraph')
+        start_time = time.time()
+        saver.export_meta_graph(metagraph_filename)
+        save_time_metagraph = time.time() - start_time
+        print('Metagraph saved in %.2f seconds' % save_time_metagraph)
+    summary = tf.Summary()
+    # pylint: disable=maybe-no-member
+    summary.value.add(
+        tag='time/save_variables',
+        simple_value=save_time_variables)
+    summary.value.add(
+        tag='time/save_metagraph',
+        simple_value=save_time_metagraph)
+    summary_writer.add_summary(summary, step)
+
+
+def get_learning_rate_from_file(filename, epoch):
+    with open(filename, 'r') as f:
+        for line in f.readlines():
+            line = line.split('#', 1)[0]
+            if line:
+                par = line.strip().split(':')
+                e = int(par[0])
+                lr = float(par[1])
+                if e <= epoch:
+                    learning_rate = lr
+                else:
+                    return learning_rate
+
+
+def parse_arguments(argv):
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        '--logs_base_dir',
+        type=str,
+        help='Directory where to write event logs.',
+        default='~/logs/facenet')
+    parser.add_argument(
+        '--models_base_dir',
+        type=str,
+        help='Directory where to write trained models and checkpoints.',
+        default='~/models/facenet')
+    parser.add_argument(
+        '--gpu_memory_fraction',
+        type=float,
+        help='Upper bound on the amount of GPU memory that will be used by the process.',
+        default=1.0)
+    parser.add_argument('--pretrained_model', type=str,
+                        help='Load a pretrained model before training starts.')
+    parser.add_argument(
+        '--data_dir',
+        type=str,
+        help='Path to the data directory containing aligned face patches.',
+        default='~/datasets/casia/casia_maxpy_mtcnnalign_182_160')
+    parser.add_argument(
+        '--model_def',
+        type=str,
+        help='Model definition. Points to a module containing the definition of the inference graph.',
+        default='models.inception_resnet_v1')
+    parser.add_argument('--max_nrof_epochs', type=int,
+                        help='Number of epochs to run.', default=500)
+    parser.add_argument(
+        '--batch_size',
+        type=int,
+        help='Number of images to process in a batch.',
+        default=90)
+    parser.add_argument(
+        '--image_size',
+        type=int,
+        help='Image size (height, width) in pixels.',
+        default=160)
+    parser.add_argument('--people_per_batch', type=int,
+                        help='Number of people per batch.', default=45)
+    parser.add_argument('--images_per_person', type=int,
+                        help='Number of images per person.', default=40)
+    parser.add_argument('--epoch_size', type=int,
+                        help='Number of batches per epoch.', default=1000)
+    parser.add_argument(
+        '--alpha',
+        type=float,
+        help='Positive to negative triplet distance margin.',
+        default=0.2)
+    parser.add_argument('--embedding_size', type=int,
+                        help='Dimensionality of the embedding.', default=128)
+    parser.add_argument(
+        '--random_crop',
+        help='Performs random cropping of training images. If false, the center image_size pixels from the training images are used. ' +
+        'If the size of the images in the data directory is equal to image_size no cropping is performed',
+        action='store_true')
+    parser.add_argument(
+        '--random_flip',
+        help='Performs random horizontal flipping of training images.',
+        action='store_true')
+    parser.add_argument(
+        '--keep_probability',
+        type=float,
+        help='Keep probability of dropout for the fully connected layer(s).',
+        default=1.0)
+    parser.add_argument('--weight_decay', type=float,
+                        help='L2 weight regularization.', default=0.0)
+    parser.add_argument(
+        '--optimizer',
+        type=str,
+        choices=[
+            'ADAGRAD',
+            'ADADELTA',
+            'ADAM',
+            'RMSPROP',
+            'MOM'],
+        help='The optimization algorithm to use',
+        default='ADAGRAD')
+    parser.add_argument(
+        '--learning_rate',
+        type=float,
+        help='Initial learning rate. If set to a negative value a learning rate ' +
+        'schedule can be specified in the file "learning_rate_schedule.txt"',
+        default=0.1)
+    parser.add_argument(
+        '--learning_rate_decay_epochs',
+        type=int,
+        help='Number of epochs between learning rate decay.',
+        default=100)
+    parser.add_argument('--learning_rate_decay_factor', type=float,
+                        help='Learning rate decay factor.', default=1.0)
+    parser.add_argument(
+        '--moving_average_decay',
+        type=float,
+        help='Exponential decay for tracking of training parameters.',
+        default=0.9999)
+    parser.add_argument('--seed', type=int,
+                        help='Random seed.', default=666)
+    parser.add_argument(
+        '--learning_rate_schedule_file',
+        type=str,
+        help='File containing the learning rate schedule that is used when learning_rate is set to to -1.',
+        default='data/learning_rate_schedule.txt')
+
+    # Parameters for validation on LFW
+    parser.add_argument(
+        '--lfw_pairs',
+        type=str,
+        help='The file containing the pairs to use for validation.',
+        default='data/pairs.txt')
+    parser.add_argument(
+        '--lfw_dir',
+        type=str,
+        help='Path to the data directory containing aligned face patches.',
+        default='')
+    parser.add_argument(
+        '--lfw_nrof_folds',
+        type=int,
+        help='Number of folds to use for cross validation. Mainly used for testing.',
+        default=10)
+    return parser.parse_args(argv)
+
+
+if __name__ == '__main__':
+    main(parse_arguments(sys.argv[1:]))
diff --git a/facenet_sandberg/train_facenet/train_utils.py b/facenet_sandberg/train_facenet/train_utils.py
new file mode 100644
index 000000000..388e42f96
--- /dev/null
+++ b/facenet_sandberg/train_facenet/train_utils.py
@@ -0,0 +1,241 @@
+import argparse
+import importlib
+import math
+import os.path
+import random
+import sys
+import time
+from datetime import datetime
+
+import h5py
+import numpy as np
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+from facenet_sandberg import facenet, lfw
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops, data_flow_ops
+
+
+def filter_dataset(dataset, data_filename, percentile,
+                   min_nrof_images_per_class):
+    with h5py.File(data_filename, 'r') as f:
+        distance_to_center = np.array(f.get('distance_to_center'))
+        label_list = np.array(f.get('label_list'))
+        image_list = np.array(f.get('image_list'))
+        distance_to_center_threshold = find_threshold(
+            distance_to_center, percentile)
+        indices = np.where(distance_to_center >=
+                           distance_to_center_threshold)[0]
+        filtered_dataset = dataset
+        removelist = []
+        for i in indices:
+            label = label_list[i]
+            image = image_list[i]
+            if image in filtered_dataset[label].image_paths:
+                filtered_dataset[label].image_paths.remove(image)
+            if len(
+                    filtered_dataset[label].image_paths) < min_nrof_images_per_class:
+                removelist.append(label)
+
+        ix = sorted(list(set(removelist)), reverse=True)
+        for i in ix:
+            del(filtered_dataset[i])
+
+    return filtered_dataset
+
+
+def find_threshold(var, percentile):
+    hist, bin_edges = np.histogram(var, 100)
+    cdf = np.float32(np.cumsum(hist)) / np.sum(hist)
+    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
+    # plt.plot(bin_centers, cdf)
+    threshold = np.interp(percentile * 0.01, cdf, bin_centers)
+    return threshold
+
+
+def save_variables_and_metagraph(
+        sess,
+        saver,
+        summary_writer,
+        model_dir: str,
+        model_name: str,
+        step: int):
+    # Save the model checkpoint
+    print('Saving variables')
+    start_time = time.time()
+    checkpoint_path = os.path.join(model_dir, 'model-%s.ckpt' % model_name)
+    saver.save(sess, checkpoint_path, global_step=step, write_meta_graph=False)
+    save_time_variables = time.time() - start_time
+    print('Variables saved in %.2f seconds' % save_time_variables)
+    metagraph_filename = os.path.join(model_dir, 'model-%s.meta' % model_name)
+    save_time_metagraph = 0.0
+    if not os.path.exists(metagraph_filename):
+        print('Saving metagraph')
+        start_time = time.time()
+        saver.export_meta_graph(metagraph_filename)
+        save_time_metagraph = time.time() - start_time
+        print('Metagraph saved in %.2f seconds' % save_time_metagraph)
+    summary = tf.Summary()
+    # pylint: disable=maybe-no-member
+    summary.value.add(tag='time/save_variables',
+                      simple_value=save_time_variables)
+    summary.value.add(tag='time/save_metagraph',
+                      simple_value=save_time_metagraph)
+    summary_writer.add_summary(summary, step)
+
+
+def validate(
+        args,
+        sess,
+        epoch,
+        image_list,
+        label_list,
+        enqueue_op,
+        image_paths_placeholder,
+        labels_placeholder,
+        control_placeholder,
+        phase_train_placeholder,
+        batch_size_placeholder,
+        stat,
+        loss,
+        regularization_losses,
+        cross_entropy_mean,
+        accuracy,
+        validate_every_n_epochs,
+        use_fixed_image_standardization):
+
+    print('Running forward pass on validation set')
+
+    nrof_batches = len(label_list) // lfw_batch_size
+    nrof_images = nrof_batches * lfw_batch_size
+
+    # Enqueue one epoch of image paths and labels
+    labels_array = np.expand_dims(np.array(label_list[:nrof_images]), 1)
+    image_paths_array = np.expand_dims(np.array(image_list[:nrof_images]), 1)
+    control_array = np.ones_like(
+        labels_array,
+        np.int32) * facenet.FIXED_STANDARDIZATION * use_fixed_image_standardization
+    sess.run(enqueue_op,
+             {image_paths_placeholder: image_paths_array,
+              labels_placeholder: labels_array,
+              control_placeholder: control_array})
+
+    loss_array = np.zeros((nrof_batches,), np.float32)
+    xent_array = np.zeros((nrof_batches,), np.float32)
+    accuracy_array = np.zeros((nrof_batches,), np.float32)
+
+    # Training loop
+    start_time = time.time()
+    for i in range(nrof_batches):
+        feed_dict = {phase_train_placeholder: False,
+                     batch_size_placeholder: lfw_batch_size}
+        loss_, cross_entropy_mean_, accuracy_ = sess.run(
+            [loss, cross_entropy_mean, accuracy], feed_dict=feed_dict)
+        loss_array[i], xent_array[i], accuracy_array[i] = (
+            loss_, cross_entropy_mean_, accuracy_)
+        if i % 10 == 9:
+            print('.', end='')
+            sys.stdout.flush()
+    print('')
+
+    duration = time.time() - start_time
+
+    val_index = (epoch - 1) // validate_every_n_epochs
+    stat['val_loss'][val_index] = np.mean(loss_array)
+    stat['val_xent_loss'][val_index] = np.mean(xent_array)
+    stat['val_accuracy'][val_index] = np.mean(accuracy_array)
+
+    print('Validation Epoch: %d\tTime %.3f\tLoss %2.3f\tXent %2.3f\tAccuracy %2.3f' % (
+        epoch, duration, np.mean(loss_array), np.mean(xent_array), np.mean(accuracy_array)))
+
+
+def evaluate(
+        sess,
+        enqueue_op,
+        image_paths_placeholder,
+        labels_placeholder,
+        phase_train_placeholder,
+        batch_size_placeholder,
+        control_placeholder,
+        embeddings,
+        labels,
+        image_paths,
+        actual_issame,
+        batch_size,
+        nrof_folds,
+        log_dir,
+        step,
+        summary_writer,
+        stat,
+        epoch,
+        distance_metric,
+        subtract_mean,
+        use_flipped_images,
+        use_fixed_image_standardization):
+    start_time = time.time()
+    # Run forward pass to calculate embeddings
+    print('Runnning forward pass on LFW images')
+
+    # Enqueue one epoch of image paths and labels
+    # nrof_pairs * nrof_images_per_pair
+    nrof_embeddings = len(actual_issame) * 2
+    nrof_flips = 2 if use_flipped_images else 1
+    nrof_images = nrof_embeddings * nrof_flips
+    labels_array = np.expand_dims(np.arange(0, nrof_images), 1)
+    image_paths_array = np.expand_dims(
+        np.repeat(np.array(image_paths), nrof_flips), 1)
+    control_array = np.zeros_like(labels_array, np.int32)
+    if use_fixed_image_standardization:
+        control_array += np.ones_like(labels_array) * \
+            facenet.FIXED_STANDARDIZATION
+    if use_flipped_images:
+        # Flip every second image
+        control_array += (labels_array % 2) * facenet.FLIP
+    sess.run(enqueue_op,
+             {image_paths_placeholder: image_paths_array,
+              labels_placeholder: labels_array,
+              control_placeholder: control_array})
+
+    embedding_size = int(embeddings.get_shape()[1])
+    assert nrof_images % batch_size == 0, 'The number of LFW images must be an integer multiple of the LFW batch size'
+    nrof_batches = nrof_images // batch_size
+    emb_array = np.zeros((nrof_images, embedding_size))
+    lab_array = np.zeros((nrof_images,))
+    for i in range(nrof_batches):
+        feed_dict = {phase_train_placeholder: False,
+                     batch_size_placeholder: batch_size}
+        emb, lab = sess.run([embeddings, labels], feed_dict=feed_dict)
+        lab_array[lab] = lab
+        emb_array[lab, :] = emb
+        if i % 10 == 9:
+            print('.', end='')
+            sys.stdout.flush()
+    print('')
+    embeddings = np.zeros((nrof_embeddings, embedding_size * nrof_flips))
+    if use_flipped_images:
+        # Concatenate embeddings for flipped and non flipped version of the
+        # images
+        embeddings[:, :embedding_size] = emb_array[0::2, :]
+        embeddings[:, embedding_size:] = emb_array[1::2, :]
+    else:
+        embeddings = emb_array
+
+    assert np.array_equal(lab_array, np.arange(
+        nrof_images)), 'Wrong labels used for evaluation, possibly caused by training examples left in the input pipeline'
+    _, _, accuracy, val, val_std, far = lfw.evaluate(
+        embeddings, actual_issame, nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)
+
+    print('Accuracy: %2.5f+-%2.5f' % (np.mean(accuracy), np.std(accuracy)))
+    print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
+    lfw_time = time.time() - start_time
+    # Add validation loss and accuracy to summary
+    summary = tf.Summary()
+    # pylint: disable=maybe-no-member
+    summary.value.add(tag='lfw/accuracy', simple_value=np.mean(accuracy))
+    summary.value.add(tag='lfw/val_rate', simple_value=val)
+    summary.value.add(tag='time/lfw', simple_value=lfw_time)
+    summary_writer.add_summary(summary, step)
+    with open(os.path.join(log_dir, 'lfw_result.txt'), 'at') as f:
+        f.write('%d\t%.5f\t%.5f\n' % (step, np.mean(accuracy), val))
+    stat['lfw_accuracy'][epoch - 1] = np.mean(accuracy)
+    stat['lfw_valrate'][epoch - 1] = val
diff --git a/facenet_sandberg/train_insightface/EMA.py b/facenet_sandberg/train_insightface/EMA.py
new file mode 100644
index 000000000..ad54e4140
--- /dev/null
+++ b/facenet_sandberg/train_insightface/EMA.py
@@ -0,0 +1,14 @@
+
+class EMA:
+    def __init__(self, s=0.01):
+        self.s = s
+        self.v = 0
+        self.n = 0
+
+    def __call__(self, v):
+        if self.n == 0:
+            self.v = v
+        else:
+            self.v = self.v * (1 - self.s) + v * self.s
+        self.n = self.n + 1
+        return self.v
diff --git a/facenet_sandberg/train_insightface/config.ini b/facenet_sandberg/train_insightface/config.ini
new file mode 100644
index 000000000..e624c5228
--- /dev/null
+++ b/facenet_sandberg/train_insightface/config.ini
@@ -0,0 +1,29 @@
+; Testing dataset
+[lfw]
+enable=True
+image_dir=./dataset/faces_ms1m_112x112/lfw.np
+pairs=.
+
+[ytf]
+enable=False
+pairs =./dataset/YouTubeFaces/ytf_img_pair.txt
+prefix=./dataset/YouTubeFaces/112x112
+splits=./dataset/YouTubeFaces/splits_corrected.txt
+ytf_error =./dataset/ytf-error.txt
+
+; Training dataset
+[WebFace]
+train_list=.
+prefix=.
+
+[ms1m]
+mxrec=./dataset/faces_ms1m_112x112
+
+[vgg]
+mxrec=./dataset/faces_vgg_112x112
+
+
+
+
+
+
diff --git a/facenet_sandberg/train_insightface/config.py b/facenet_sandberg/train_insightface/config.py
new file mode 100644
index 000000000..5e24c797b
--- /dev/null
+++ b/facenet_sandberg/train_insightface/config.py
@@ -0,0 +1,60 @@
+# -*- coding:utf-8 -*-
+import os
+
+from six.moves import configparser
+
+
+class OperationalError(Exception):
+    """operation error."""
+
+
+class Dictionary(dict):
+    """ custom dict."""
+
+    def __getattr__(self, key):
+        return self.get(key, None)
+
+    __setattr__ = dict.__setitem__
+    __delattr__ = dict.__delitem__
+
+
+class Config:
+    def __init__(self, cfg="config.conf"):
+        """
+        @param file_name: file name without extension.
+        @param cfg: configuration file path.
+        """
+        config = configparser.ConfigParser()
+        config.read(cfg)
+
+        for section in config.sections():
+            setattr(self, section, Dictionary())
+            for name, raw_value in config.items(section):
+                try:
+                    # Ugly fix to avoid '0' and '1' to be parsed as a
+                    # boolean value.
+                    # We raise an exception to goto fail^w parse it
+                    # as integer.
+                    if config.get(section, name) in ["0", "1"]:
+                        raise ValueError
+
+                    value = config.getboolean(section, name)
+                except ValueError:
+                    try:
+                        value = config.getint(section, name)
+                    except ValueError:
+                        value = config.get(section, name)
+
+                setattr(getattr(self, section), name, value)
+
+    def get(self, section):
+        """Get option.
+        @param section: section to fetch.
+        @return: option value.
+        """
+        try:
+            return getattr(self, section)
+        except AttributeError as e:
+            raise OperationalError("Option %s is not found in "
+                                   "configuration, error: %s" %
+                                   (section, e))
diff --git a/facenet_sandberg/train_insightface/dataset_all.py b/facenet_sandberg/train_insightface/dataset_all.py
new file mode 100644
index 000000000..6dad21a97
--- /dev/null
+++ b/facenet_sandberg/train_insightface/dataset_all.py
@@ -0,0 +1,111 @@
+
+import sys
+import time
+
+import numpy as np
+
+from config import Config
+from dataset_list import FilelistReader
+from dataset_multi import MultiDataset
+from dataset_mxnet import MxReader
+from mt_loader import MultiThreadLoader
+
+
+def get_WebFace(config, count=-1):
+    ds = FilelistReader(config.get('WebFace').prefix, name='WebFace')
+    with open(config.get('WebFace').train_list, 'r') as f:
+        idx = 0
+        for line in f.readlines():
+            ds.add(line)
+            idx += 1
+            if idx == count:
+                break
+
+    ds.digest()
+    return ds
+
+
+def get_ms1m(config):
+    ds = MxReader(config.get('ms1m').mxrec, name='ms1m')
+    ds.digest()
+    return ds
+
+
+def get_vgg(config):
+    ds = MxReader(config.get('vgg').mxrec, name='vgg')
+    ds.digest()
+    return ds
+
+
+def dataset_factory(name, config):
+    '''
+    surport ms1m vgg WebFace YTF
+    '''
+    dict = {
+        'WebFace': get_WebFace,
+        'ms1m': get_ms1m,
+        'vgg': get_vgg,
+    }
+    return dict[name](config)
+
+
+def build_dataset(config, list, balance=False, num_per_class=1):
+    ds = MultiDataset(balance, num_per_class=num_per_class)
+    for i in list:
+        name, weight = i[0], i[1]
+        ds_ = dataset_factory(name, config)
+        ds_.setBalance(balance)
+        ds.add(ds_, weight)
+    ds.verbose()
+    return ds
+
+
+def test_ds(dg):
+    for i in range(10000):
+        func, param = dg.nextTask()
+        img, label = func(param)
+        print("%d label:%d [%d, %d]" % (i, label, img.shape[0], img.shape[1]))
+
+
+def test_db():
+    dataset_list = []
+    dataset_list.append(('vgg', -1))
+    # dataset_list.append(('ms1m',    -1))
+    dataset_list.append(('WebFace', -1))
+    dataset = build_dataset(dataset_list, balance=True, num_per_class=4)
+    for i in range(1000000):
+        start = time.time()
+        func, param = dataset.nextTask()
+        img, label = func(param)
+        # x, y = dg.getBatch()
+        end = time.time()
+        # plot_fbank_cm(fbank_feat)
+        # print("x.shape:{0}, y.shape:{1}".format(x.shape, y.shape))
+        print(label)
+        if i % 1000 == 0:
+            print('batch:%d t:%f ' % (i, end - start))
+
+        # print('t:%f' % (end - start) )
+
+
+if __name__ == '__main__':
+    test_db()
+    exit()
+    list_ = []
+    list_.append(('vgg', 1))
+    list_.append(('ms1m', -1))
+    dataset = build_dataset(list_)
+    # test_ds(dataset)
+    dg = MultiThreadLoader(dataset, 5, 1)
+    print(dg.numOfClass())
+    for i in range(100):
+        print('batch:%d' % (i))
+        start = time.time()
+        x, y = dg.getBatch()
+        end = time.time()
+        # plot_fbank_cm(fbank_feat)
+        # print("x.shape:{0}, y.shape:{1}".format(x.shape, y.shape))
+        print(y)
+        # print('t:%f' % (end - start) )
+    dg.close()
+    # sys.exit()
diff --git a/facenet_sandberg/train_insightface/dataset_list.py b/facenet_sandberg/train_insightface/dataset_list.py
new file mode 100644
index 000000000..6a62c85f0
--- /dev/null
+++ b/facenet_sandberg/train_insightface/dataset_list.py
@@ -0,0 +1,131 @@
+# -*- coding:utf-8 -*-
+from __future__ import print_function
+import scipy
+import numpy as np
+import os
+import sys
+import time
+import random
+import math
+import cv2
+
+
+def read_img(p):
+    path, y = p[0], p[1]
+    x = cv2.imread(path)
+    x = cv2.cvtColor(x, cv2.COLOR_RGB2BGR)
+    return x, y
+
+
+class FilelistReader:
+    def __init__(self, datadir, balance=False, name='--'):
+        self.setname = name
+        self.datadir = datadir
+        self.celeb = []
+        self.dict = {}
+        self.list = []
+        self.balance = balance
+        self.cursor = 0
+        self.cid = 0
+        self.cbuf = []
+        self.id_offset = 0
+
+    def setBalance(self, balance):
+        self.balance = balance
+
+    def name(self):
+        return self.setname
+
+    def add(self, line):
+        line = line.strip()
+        if line.find(',') > 0:
+            segs = line.split(',')
+        else:
+            segs = line.split()
+        # print(segs)
+        if len(segs) != 2:
+            return False
+        rel_path = segs[0]
+        label = int(segs[1])
+        item = (rel_path, label)
+        # add to list
+        self.list.append(item)
+
+    def digest(self):
+        random.shuffle(self.list)
+        self.dict = {}
+        self.cursor = 0
+        for index in range(len(self.list)):
+            item = self.list[index]
+            label = item[1]
+            # add to dict[label]
+            if label not in self.dict.keys():
+                self.dict[label] = []
+            self.dict[label].append(index)
+        # update celeb
+        # print(self.dict)
+        keys = sorted(self.dict.keys())
+        self.celeb = keys
+        self.cid = 0
+        self.cbuf = [i for i in range(len(self.celeb))]
+        # random.shuffle(self.cbuf)
+
+    def verbose(self):
+        print('Dataset:%8s size:%6d nclass:%d' %
+              (self.setname, self.size(), self.numOfClass()))
+
+    def size(self):
+        return len(self.list)
+
+    def numOfClass(self):
+        return len(self.celeb)
+
+    def maxClass(self):
+        return max(self.celeb)
+
+    def minClass(self):
+        return min(self.celeb)
+
+    def getSample(self, index):
+        if self.balance:
+            # balanced
+            if self.cid >= len(self.celeb):
+                random.shuffle(self.cbuf)
+                self.cid = 0
+            label = self.celeb[self.cbuf[self.cid]]
+            index = np.random.choice(self.dict[label])
+            self.cid += 1
+        item = self.list[index]
+        rel_path = item[0]
+        path = os.path.join(self.datadir, rel_path)
+        return path, item[1]
+
+    def next(self):
+        '''
+        Return audio path, and label
+        '''
+        index = self.cursor
+        self.cursor += 1
+        self.cursor %= self.size()
+        return self.getSample(index)
+
+    def nextTask(self):
+        path, y = self.next()
+        return (read_img, (path, y))
+
+    def moreTask(self, y):
+        index = np.random.choice(self.dict[y])
+        item = self.list[index]
+        rel_path = item[0]
+        path = os.path.join(self.datadir, rel_path)
+        return (read_img, (path, y))
+
+    def close(self):
+        self.celeb = []
+        self.dict = {}
+        self.list = []
+        print("%s closed" % (self.name()))
+
+
+if __name__ == '__main__':
+    pass
diff --git a/facenet_sandberg/train_insightface/dataset_multi.py b/facenet_sandberg/train_insightface/dataset_multi.py
new file mode 100644
index 000000000..b219e75ce
--- /dev/null
+++ b/facenet_sandberg/train_insightface/dataset_multi.py
@@ -0,0 +1,113 @@
+import random
+import time
+
+import numpy as np
+
+
+class MultiDataset:
+    def __init__(self, balance=False, num_per_class=1):
+        self.balance = balance
+        self.num_per_class = num_per_class
+        self.last_ds = None
+        self.last_y = -1
+        self.last_n = 0
+        self.datasets = []
+        self.weights_ = []
+        self.weights = []
+        self.totalClass = 0
+        self.totalSize = 0
+
+    def add(self, dataset, weight=-1):
+        self.datasets.append(dataset)
+        dataset.id_offset = self.totalClass
+        if weight < 0:
+            if self.balance:
+                weight = dataset.numOfClass()
+            else:
+                weight = dataset.size()
+
+        self.weights_.append(weight)
+        self._normWeights()
+        self.totalClass += dataset.numOfClass()
+        self.totalSize += dataset.size()
+
+    def _normWeights(self):
+        sw = float(sum(self.weights_))
+        self.weights = [i / sw for i in self.weights_]
+
+    def digest(self):
+        _normWeights()
+
+    def size(self):
+        return self.totalSize
+
+    def numOfClass(self):
+        return self.totalClass
+
+    def maxClass(self):
+        return self.totalClass - 1
+
+    def minClass(self):
+        return 0
+
+    def verbose(self):
+        for i in range(len(self.datasets)):
+            dataset = self.datasets[i]
+            print(
+                'Dataset:%8s size:%6d nclass:%6d  %.3f' %
+                (dataset.name(),
+                 dataset.size(),
+                 dataset.numOfClass(),
+                 self.weights[i]))
+        print('------------------------------------------------------------')
+        print('Dataset:%8s size:%6d nclass:%6d' %
+              ('total', self.size(), self.numOfClass()))
+
+    def nextTask(self):
+        if self.last_y < 0 or self.last_n >= self.num_per_class:
+            self.last_y = -1
+            # next class
+            ds = np.random.choice(self.datasets, p=self.weights)
+            func, param = ds.nextTask()
+            lp = list(param)
+            # y
+            self.last_y = lp[1]
+            self.last_n = 1
+            self.last_ds = ds
+            lp[1] += ds.id_offset
+            param = tuple(lp)
+            return (func, param)
+        else:
+            func, param = self.last_ds.moreTask(self.last_y)
+            lp = list(param)
+            # y
+            self.last_n += 1
+            lp[1] += self.last_ds.id_offset
+            param = tuple(lp)
+            return (func, param)
+
+    def close(self):
+        for ds in self.datasets:
+            ds.close()
+        self.datasets = []
+
+
+if __name__ == '__main__':
+    '''
+    y = [1,2,3,0]
+    y_train = np_utils.to_categorical(y, 4)
+    print(y_train)
+    exit()
+    '''
+    dg = DataGeneratorMT(32, 1)
+    print(dg.numOfClass())
+    for i in range(100000):
+        print('batch:%d' % (i))
+        start = time.time()
+        x, y = dg.getBatch()
+        end = time.time()
+        # plot_fbank_cm(fbank_feat)
+        # print("x.shape:{0}, y.shape:{1}".format(x.shape, y.shape))
+        print(y)
+        # print('t:%f' % (end - start) )
+    dg.close()
diff --git a/facenet_sandberg/train_insightface/dataset_mxnet.py b/facenet_sandberg/train_insightface/dataset_mxnet.py
new file mode 100644
index 000000000..53fa64010
--- /dev/null
+++ b/facenet_sandberg/train_insightface/dataset_mxnet.py
@@ -0,0 +1,177 @@
+# -*- coding:utf-8 -*-
+from __future__ import print_function
+
+import io
+import math
+import os
+import random
+import sys
+import time
+
+import cv2
+import mxnet as mx
+import numpy as np
+import PIL.Image
+from PIL import Image
+
+
+def read_idx(p):
+    s = p[0]
+    label = p[1]
+    header, img = mx.recordio.unpack(s)
+    try:
+        label_ = int(header.label)
+    except Exception as e:
+        print(header.label)
+    # assert label == label_
+    # img, label = p[0], p[1]
+    encoded_jpg_io = io.BytesIO(img)
+    image = PIL.Image.open(encoded_jpg_io)
+    np_img = np.array(image)
+    img = cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR)
+    return img, label
+
+
+class MxReader:
+    def __init__(self, datadir, balance=False, name='ms1m'):
+        self.setname = name
+        self.datadir = datadir
+        self.balance = balance
+
+        idx_path = os.path.join(datadir, 'train.idx')
+        bin_path = os.path.join(datadir, 'train.rec')
+        imgrec = mx.recordio.MXIndexedRecordIO(idx_path, bin_path, 'r')
+        s = imgrec.read_idx(0)
+        header, _ = mx.recordio.unpack(s)
+        assert header.flag > 0
+        max_index = int(header.label[0])
+        min_seq_id = max_index
+        max_seq_id = int(header.label[1])
+        identities = max_seq_id - min_seq_id
+        id2range = []
+        for id in range(identities):
+            identity = id + min_seq_id
+            s = imgrec.read_idx(identity)
+            header, _ = mx.recordio.unpack(s)
+            a, b = int(header.label[0]), int(header.label[1])
+            size = b - a
+            id2range.append((a, b, size))
+        '''
+        print(max_index)
+        print(max_seq_id)
+        print(id2range[0])
+        print(id2range[1])
+        print(id2range[-1])
+        '''
+        self.imgrec = imgrec
+        self.images = max_index - 1
+        self.identities = identities
+        self.id2range = id2range
+        self.cbuf = [i for i in range(identities)]
+        self.cid = 0
+        self.list = [i for i in range(1, max_index)]
+        self.cursor = 0
+        self.id_offset = 0
+        # shuffle
+        self.digest()
+
+    def setBalance(self, balance):
+        self.balance = balance
+
+    def name(self):
+        return self.setname
+
+    def add(self, line):
+        pass
+
+    def digest(self):
+        random.shuffle(self.cbuf)
+        self.cid = 0
+        random.shuffle(self.list)
+        self.cursor = 0
+
+    def verbose(self):
+        print('Dataset:%8s size:%6d nclass:%d' %
+              (self.setname, self.size(), self.numOfClass()))
+
+    def size(self):
+        return self.images
+
+    def numOfClass(self):
+        return self.identities
+
+    def maxClass(self):
+        return self.identities - 1
+
+    def minClass(self):
+        return 0
+
+    def findLabel(self, index):
+        low = 0
+        high = len(self.id2range)
+        while low <= high:
+            mid = (low + high) / 2
+            y = self.id2range[mid]
+            if index >= y[0] and index < y[1]:
+                return mid
+            elif index >= y[1]:
+                low = mid + 1
+            else:
+                high = mid - 1
+
+    def getSample(self, index):
+        if self.balance:
+            # balanced
+            if self.cid >= self.identities:
+                random.shuffle(self.cbuf)
+                self.cid = 0
+            label = self.cbuf[self.cid]
+            a, b, _ = self.id2range[label]
+            index = random.randint(a, b - 1)
+            self.cid += 1
+        else:
+            index = self.list[index]
+            # index to label
+            label = self.findLabel(index)
+
+        return index, label
+
+    def next(self):
+        '''
+        Return audio path, and label
+        '''
+        index = self.cursor
+        self.cursor += 1
+        self.cursor %= self.size()
+        return self.getSample(index)
+
+    def nextTask(self):
+        index, label = self.next()
+        s = self.imgrec.read_idx(index)
+        return (read_idx, (s, label))
+
+    def moreTask(self, y):
+        a, b, _ = self.id2range[y]
+        index = random.randint(a, b - 1)
+        s = self.imgrec.read_idx(index)
+        return (read_idx, (s, y))
+
+    def close(self):
+        self.imgrec.close()
+        print("%s closed" % (self.name()))
+
+
+if __name__ == '__main__':
+    ms1m = MxReader('/home/ysten/data/insightface/faces_ms1m_112x112')
+    ms1m.verbose()
+    s = ms1m.imgrec.read_idx(1)
+    read_idx((s, 0))
+    s = ms1m.imgrec.read_idx(ms1m.list[-1])
+    read_idx((s, 0))
+    s = ms1m.imgrec.read_idx(ms1m.size())
+    read_idx((s, 0))
+    exit()
+    for i in range(10000):
+        func, param = ms1m.nextTask()
+        img, label = func(param)
+        print("%d label:%d [%d, %d]" % (i, label, img.shape[0], img.shape[1]))
diff --git a/facenet_sandberg/train_insightface/distance.py b/facenet_sandberg/train_insightface/distance.py
new file mode 100644
index 000000000..c757bdf20
--- /dev/null
+++ b/facenet_sandberg/train_insightface/distance.py
@@ -0,0 +1,35 @@
+# -*- coding:utf-8 -*-
+import math
+import numpy as np
+
+
+def cosine_similarity(v1, v2):
+    # compute cosine similarity of v1 to v2: (v1 dot v2)/{||v1||*||v2||)
+    sumxx, sumxy, sumyy = 0, 0, 0
+    for i in range(len(v1)):
+        x = v1[i]
+        y = v2[i]
+        sumxx += x * x
+        sumyy += y * y
+        sumxy += x * y
+    return sumxy / math.sqrt(sumxx * sumyy)
+
+
+def cosine_distance(v1, v2):
+    return 1 - cosine_similarity(v1, v2)
+
+
+def L2_distance(v1, v2):
+    return np.sqrt(np.sum(np.square(v1 - v2)))
+
+
+def SSD_distance(v1, v2):
+    return np.sum(np.square(v1 - v2))
+
+
+def get_distance(dist_type):
+    loss_map = {
+        'cosine': cosine_distance,
+        'L2': L2_distance,
+        'SSD': SSD_distance}
+    return loss_map[dist_type]
diff --git a/facenet_sandberg/train_insightface/face_losses.py b/facenet_sandberg/train_insightface/face_losses.py
new file mode 100644
index 000000000..840f147d5
--- /dev/null
+++ b/facenet_sandberg/train_insightface/face_losses.py
@@ -0,0 +1,218 @@
+import math
+
+import tensorflow as tf
+
+
+def softmax_loss(embedding, labels, out_num, w_init=None, w_norm=False):
+    '''
+    :param embedding: the input embedding vectors
+    :param labels:  the input labels, the shape should be eg: (batch_size, 1)
+    :param out_num: output class num
+    :return: the final cacualted output, this output is send into the tf.nn.softmax directly
+    '''
+    with tf.variable_scope('softmax_loss'):
+        # inputs and weights norm
+        # embedding_norm = tf.norm(embedding, axis=1, keep_dims=True)
+        # embedding = tf.div(embedding, embedding_norm, name='norm_embedding')
+        weights = tf.get_variable(name='embedding_weights', shape=(embedding.get_shape().as_list()[-1], out_num),
+                                  initializer=w_init, dtype=tf.float32)
+        if w_norm:
+            weights_norm = tf.norm(weights, axis=0, keep_dims=True)
+            weights = tf.div(weights, weights_norm, name='norm_weights')
+        # cos_theta - m
+        output = tf.matmul(embedding, weights, name='logit')
+
+    return output
+
+
+def arcface_loss(embedding, labels, out_num, w_init=None, s=64., m=0.5):
+    '''
+    :param embedding: the input embedding vectors
+    :param labels:  the input labels, the shape should be eg: (batch_size, 1)
+    :param s: scalar value default is 64
+    :param out_num: output class num
+    :param m: the margin value, default is 0.5
+    :return: the final cacualted output, this output is send into the tf.nn.softmax directly
+    '''
+    cos_m = math.cos(m)
+    sin_m = math.sin(m)
+    mm = sin_m * m  # issue 1
+    threshold = math.cos(math.pi - m)
+    with tf.variable_scope('arc_loss'):
+        # inputs and weights norm
+        embedding_norm = tf.norm(embedding, axis=1, keep_dims=True)
+        embedding = tf.div(embedding, embedding_norm, name='norm_embedding')
+        weights = tf.get_variable(name='embedding_weights', shape=(embedding.get_shape().as_list()[-1], out_num),
+                                  initializer=w_init, dtype=tf.float32)
+        weights_norm = tf.norm(weights, axis=0, keep_dims=True)
+        weights = tf.div(weights, weights_norm, name='norm_weights')
+        # cos(theta+m)
+        cos_t = tf.matmul(embedding, weights, name='cos_t')
+        cos_t2 = tf.square(cos_t, name='cos_2')
+        sin_t2 = tf.subtract(1., cos_t2, name='sin_2')
+        sin_t = tf.sqrt(sin_t2, name='sin_t')
+        cos_mt = s * tf.subtract(tf.multiply(cos_t, cos_m),
+                                 tf.multiply(sin_t, sin_m), name='cos_mt')
+
+        # this condition controls the theta+m should in range [0, pi]
+        #      0<=theta+m<=pi
+        #     -m<=theta<=pi-m
+        cond_v = cos_t - threshold
+        cond = tf.cast(tf.nn.relu(cond_v, name='if_else'), dtype=tf.bool)
+
+        keep_val = s * (cos_t - mm)
+        cos_mt_temp = tf.where(cond, cos_mt, keep_val)
+
+        mask = tf.one_hot(labels, depth=out_num, name='one_hot_mask')
+        # mask = tf.squeeze(mask, 1)
+        inv_mask = tf.subtract(1., mask, name='inverse_mask')
+
+        s_cos_t = tf.multiply(s, cos_t, name='scalar_cos_t')
+        loss = tf.reduce_sum(tf.multiply(cos_t, mask))
+
+        output = tf.add(
+            tf.multiply(
+                s_cos_t, inv_mask), tf.multiply(
+                cos_mt_temp, mask), name='arcface_loss_output')
+    return output, loss
+
+
+def cosineface_losses(embedding, labels, out_num, w_init=None, s=30., m=0.4):
+    '''
+    :param embedding: the input embedding vectors
+    :param labels:  the input labels, the shape should be eg: (batch_size, 1)
+    :param s: scalar value, default is 30
+    :param out_num: output class num
+    :param m: the margin value, default is 0.4
+    :return: the final cacualted output, this output is send into the tf.nn.softmax directly
+    '''
+    with tf.variable_scope('cosineface_loss'):
+        # inputs and weights norm
+        embedding_norm = tf.norm(embedding, axis=1, keep_dims=True)
+        embedding = tf.div(embedding, embedding_norm, name='norm_embedding')
+        weights = tf.get_variable(name='embedding_weights', shape=(embedding.get_shape().as_list()[-1], out_num),
+                                  initializer=w_init, dtype=tf.float32)
+        weights_norm = tf.norm(weights, axis=0, keep_dims=True)
+        weights = tf.div(weights, weights_norm, name='norm_weights')
+        # cos_theta - m
+        cos_t = tf.matmul(embedding, weights, name='cos_t')
+        cos_t_m = tf.subtract(cos_t, m, name='cos_t_m')
+
+        mask = tf.one_hot(labels, depth=out_num, name='one_hot_mask')
+        inv_mask = tf.subtract(1., mask, name='inverse_mask')
+
+        output = tf.add(
+            s *
+            tf.multiply(
+                cos_t,
+                inv_mask),
+            s *
+            tf.multiply(
+                cos_t_m,
+                mask),
+            name='cosineface_loss_output')
+    return output
+
+
+def margin_loss(embedding, labels, out_num, w_init=None):
+    '''
+    :param embedding: the input embedding vectors
+    :param labels:  the input labels, the shape should be eg: (batch_size, 1)
+    :param s: scalar value, default is 30
+    :param out_num: output class num
+    :param m: the margin value, default is 0.4
+    :return: the final cacualted output, this output is send into the tf.nn.softmax directly
+    '''
+    with tf.variable_scope('margin_loss'):
+        # inputs and weights norm
+        embedding_norm = tf.norm(embedding, axis=1, keep_dims=True)
+        embedding = tf.div(embedding, embedding_norm, name='norm_embedding')
+        weights = tf.get_variable(name='embedding_weights', shape=(embedding.get_shape().as_list()[-1], out_num),
+                                  initializer=w_init, dtype=tf.float32)
+        weights_norm = tf.norm(weights, axis=0, keep_dims=True)
+        weights = tf.div(weights, weights_norm, name='norm_weights')
+        # cos_theta
+        cos_t = tf.matmul(embedding, weights, name='cos_t')
+        mask = tf.one_hot(labels, depth=out_num, name='one_hot_mask')
+        inv_mask = tf.subtract(1., mask, name='inverse_mask')
+        # loss
+        batch_size = embedding.shape.as_list()[0]
+
+        loss = tf.reduce_sum(
+            tf.multiply(
+                cos_t,
+                mask) -
+            tf.multiply(
+                cos_t,
+                inv_mask))
+        loss = loss / batch_size / out_num
+        loss = 1 - loss
+    return cos_t, loss
+
+
+def combine_loss_val(embedding, labels, w_init, out_num,
+                     margin_a, margin_m, margin_b, s):
+    '''
+    This code is contributed by RogerLo. Thanks for you contribution.
+
+    :param embedding: the input embedding vectors
+    :param labels:  the input labels, the shape should be eg: (batch_size, 1)
+    :param s: scalar value default is 64
+    :param out_num: output class num
+    :param m: the margin value, default is 0.5
+    :return: the final cacualted output, this output is send into the tf.nn.softmax directly
+    '''
+    weights = tf.get_variable(name='embedding_weights', shape=(embedding.get_shape().as_list()[-1], out_num),
+                              initializer=w_init, dtype=tf.float32)
+    weights_unit = tf.nn.l2_normalize(weights, axis=0)
+    embedding_unit = tf.nn.l2_normalize(embedding, axis=1)
+    cos_t = tf.matmul(embedding_unit, weights_unit)
+    ordinal = tf.constant(
+        list(
+            range(
+                0,
+                embedding.get_shape().as_list()[0])),
+        tf.int64)
+    ordinal_y = tf.stack([ordinal, labels], axis=1)
+    zy = cos_t * s
+    sel_cos_t = tf.gather_nd(zy, ordinal_y)
+    if margin_a != 1.0 or margin_m != 0.0 or margin_b != 0.0:
+        if margin_a == 1.0 and margin_m == 0.0:
+            s_m = s * margin_b
+            new_zy = sel_cos_t - s_m
+        else:
+            cos_value = sel_cos_t / s
+            t = tf.acos(cos_value)
+            if margin_a != 1.0:
+                t = t * margin_a
+            if margin_m > 0.0:
+                t = t + margin_m
+            body = tf.cos(t)
+            if margin_b > 0.0:
+                body = body - margin_b
+            new_zy = body * s
+    updated_logits = tf.add(
+        zy,
+        tf.scatter_nd(
+            ordinal_y,
+            tf.subtract(
+                new_zy,
+                sel_cos_t),
+            zy.get_shape()))
+    loss = tf.reduce_mean(
+        tf.nn.sparse_softmax_cross_entropy_with_logits(
+            labels=labels, logits=updated_logits))
+    predict_cls = tf.argmax(updated_logits, 1)
+    accuracy = tf.reduce_mean(
+        tf.cast(
+            tf.equal(
+                tf.cast(
+                    predict_cls, tf.int64), tf.cast(
+                    labels, tf.int64)), 'float'))
+    predict_cls_s = tf.argmax(zy, 1)
+    accuracy_s = tf.reduce_mean(
+        tf.cast(
+            tf.equal(
+                tf.cast(
+                    predict_cls_s, tf.int64), tf.cast(
+                    labels, tf.int64)), 'float'))
diff --git a/facenet_sandberg/train_insightface/gen_train_list.py b/facenet_sandberg/train_insightface/gen_train_list.py
new file mode 100644
index 000000000..a8e562f0c
--- /dev/null
+++ b/facenet_sandberg/train_insightface/gen_train_list.py
@@ -0,0 +1,74 @@
+# -*- coding:utf-8 -*-
+from __future__ import print_function
+
+import argparse
+import os
+import random
+import sys
+
+import numpy as np
+from tinytag import TinyTag
+
+
+def gen_balanced(voxceleb2_dir, output_dir, max_per_class=-1e5):
+    output_list = output_dir + 'train_balance.txt'
+    id_name_list = output_dir + 'id_name_balance.csv'
+    voxdirlen = len(voxceleb2_dir) + 1
+    id_list = os.listdir(voxceleb2_dir)
+
+    # files
+    idx = 0
+    id_count = []
+    list_file = open(output_list, 'w')
+    for id in id_list:
+        subdir = voxceleb2_dir + '/' + id
+        seqs = os.listdir(subdir)
+        seq_list = []
+        for seq in seqs:
+            seq_dir = subdir + '/' + seq
+            files = os.listdir(seq_dir)
+            seq_pass = []
+            for fpath in files:
+                ftitle, fext = os.path.splitext(fpath)
+                # images
+                if fext.lower() in ['.jpg', '.png', '.bmp', '.jpeg']:
+                    rel_path = id + '/' + seq + '/' + fpath
+                    seq_pass.append(rel_path)
+            seq_list.append(seq_pass)
+        # each seq
+        valid = 0
+        seq_prob = float(max_per_class) / len(seq_list)
+        for list in seq_list:
+            item_prob = seq_prob / len(list)
+            for item in list:
+                p = random.random()
+                if item_prob < 0 or p <= item_prob:
+                    list_file.write('%d,%s\n' % (idx, item))
+                    valid += 1
+        stats = (idx, id, valid)
+        id_count.append(stats)
+        print(stats)
+        idx += 1
+    list_file.close()
+
+    # write id_name_list
+    with open(id_name_list, 'w') as f:
+        for idx, id, valid, in id_count:
+            f.write('%d,%s,%d\n' % (idx, id, valid))
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(description='parameters test')
+    parser.add_argument('--src', default='.', help='source dir')
+    parser.add_argument('--dst', default='', help='dst dir')
+    parser.add_argument('--mpc', default='-1000', help='max per class')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = get_parser()
+    voxceleb2_dir = args.src
+    output_dir = args.dst
+    max_per_class = float(args.mpc)
+    gen_balanced(voxceleb2_dir, output_dir, max_per_class)
diff --git a/facenet_sandberg/train_insightface/mt_loader.py b/facenet_sandberg/train_insightface/mt_loader.py
new file mode 100644
index 000000000..62bc2835b
--- /dev/null
+++ b/facenet_sandberg/train_insightface/mt_loader.py
@@ -0,0 +1,148 @@
+
+import logging
+import random
+import sys
+import time
+from multiprocessing import Process, Queue, Value
+
+import numpy as np
+
+
+def transform_mirror(x):
+    if random.random() < 0.5:
+        x = np.fliplr(x)
+    return x
+
+
+def crop_image(img1, imsize):
+    h, w, c = img1.shape
+    x1 = (w - imsize[0]) / 2
+    y1 = (h - imsize[1]) / 2
+    img1_ = img1[y1:(y1 + imsize[1]), x1:(x1 + imsize[0]), :]
+    return img1_
+
+
+def transform_crop_96x112(x):
+    return crop_image(x, (96, 112))
+
+
+transforms = [transform_crop_96x112]
+
+
+def addTransform(self, func):
+    global transforms
+    if func not in transforms:
+        transforms.append(func)
+
+
+def threadProc(todo, done, quit_signal):
+    global transforms
+    while quit_signal != 1:
+        try:
+            task = todo.get()
+            func = task[0]
+            param = task[1]
+            start = time.time()
+            x, y = func(param)
+            # do transform
+            for t in transforms:
+                x = t(x)
+            if quit_signal == 1:
+                break
+            done.put((x, y))
+            # print("done id:%d" % y)
+        except Exception as e:
+            # time.sleep(0.5)
+            # print(task)
+            print(e)
+            sys.exit(0)
+
+
+class MultiThreadLoader:
+    def __init__(self, dataset, batch_size, nworkers=1):
+        self.B = dataset
+        self.batch_size = batch_size
+        # todo list
+        self.maxsize = batch_size * 2
+        self.todo = Queue(self.maxsize)
+        # done list
+        self.done = Queue(self.maxsize)
+        # create threads
+        self.feed()
+        self.quit_signal = Value('i', 0)
+        self.createThread(nworkers)
+
+    def numOfClass(self):
+        return self.B.numOfClass()
+
+    def size(self):
+        return self.B.size()
+
+    def shuffle(self):
+        # shuffle
+        self.B.digest()
+        # prefeed
+        self.feed()
+
+    def createThread(self, nworkers):
+        self.threads = []
+        # self.db_lock = threading.Lock()
+        for i in range(nworkers):
+            t = Process(
+                target=threadProc,
+                args=(
+                    self.todo,
+                    self.done,
+                    self.quit_signal),
+                name='worker/' +
+                str(i))
+            t.start()
+            self.threads.append(t)
+
+    def feed(self):
+        if self.todo.full():
+            return
+        n = self.maxsize - self.todo.qsize()
+        for i in range(n):
+            task = self.B.nextTask()
+            self.todo.put(task)
+            # print("todo id:%d" % y)
+
+    def fetch(self):
+        x_list = []
+        y_list = []
+        for i in range(self.batch_size):
+            x, y = self.done.get()
+            # print("fetch id:%d" % y)
+            x_list.append(x)
+            y_list.append(y)
+        x_batch = np.stack(x_list, axis=0)
+        y_batch = np.array(y_list)
+        # x_batch = np.transpose(x_batch,[0,2,1,3])
+        return x_batch, y_batch
+
+    def getBatch(self):
+        start = time.time()
+        ret = self.fetch()
+        end = time.time()
+        self.feed()
+        t2 = time.time()
+        # print('fetch:%f feed:%f' % (end - start, t2 - end) )
+        return ret
+
+    def close(self):
+        self.quit_signal = 1
+        print("mtloader close")
+
+        for t in self.threads:
+            try:
+                t.terminate()
+                t.process.signal(signal.SIGINT)
+            except BaseException:
+                pass
+        for t in self.threads:
+            print(t.is_alive())
+
+        self.threads = []
+        # close datasets
+        self.B.close()
diff --git a/facenet_sandberg/train_insightface/tensorflow_extractor.py b/facenet_sandberg/train_insightface/tensorflow_extractor.py
new file mode 100644
index 000000000..b1ae76dc9
--- /dev/null
+++ b/facenet_sandberg/train_insightface/tensorflow_extractor.py
@@ -0,0 +1,30 @@
+# -*- coding:utf-8 -*-
+import math
+import os
+import time
+
+import numpy as np
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+
+
+class TensorflowExtractor:
+    def __init__(self, sess, embedding_tensor, batch_size,
+                 feed_dict, input_placeholder):
+        # save context
+        self.embedding_tensor = embedding_tensor
+        self.batch_size = batch_size
+        self.sess = sess
+        self.feed_dict = feed_dict
+        self.input_placeholder = input_placeholder
+
+    def extract(self, x_batch: np.ndarray):
+        self.feed_dict.setdefault(self.input_placeholder, None)
+        self.feed_dict[self.input_placeholder] = x_batch
+        feat = self.sess.run(self.embedding_tensor, feed_dict=self.feed_dict)
+        feat = np.array(feat)
+        feat = np.squeeze(feat)
+        return feat
+
+    def close(self):
+        self.sess.close()
diff --git a/facenet_sandberg/train_insightface/train_center.py b/facenet_sandberg/train_insightface/train_center.py
new file mode 100644
index 000000000..e218048b8
--- /dev/null
+++ b/facenet_sandberg/train_insightface/train_center.py
@@ -0,0 +1,404 @@
+import argparse
+import os
+import pickle
+import time
+from enum import Enum, auto
+from os.path import exists, join
+from typing import List, Tuple, Union, cast
+
+import cv2
+import numpy as np
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+import tensorlayer as tl
+from facenet_sandberg.models.L_Resnet_E_IR_fix_issue9 import get_resnet
+from sklearn.metrics import accuracy_score, precision_score, recall_score
+from sklearn.metrics.pairwise import paired_distances
+from sklearn.model_selection import KFold
+from tensorflow.core.protobuf import config_pb2
+
+from config import Config
+from dataset_all import build_dataset
+from EMA import EMA
+from face_losses import arcface_loss, softmax_loss
+from mt_loader import MultiThreadLoader
+from tensorflow_extractor import TensorflowExtractor
+from utils import *
+from verification import extract_list_feature, verification
+
+FaceVector = List[float]
+Match = Tuple[str, int, int]
+Mismatch = Tuple[str, int, str, int]
+Pair = Union[Match, Mismatch]
+Path = Tuple[str, str]
+Label = bool
+Image = np.ndarray
+ImagePairs = List[Tuple[Image, Image]]
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(description='parameters to train net')
+    parser.add_argument('--config', default='config.ini', help='config file')
+    parser.add_argument(
+        '--net_depth',
+        default=50,
+        help='resnet depth, default is 50')
+    parser.add_argument(
+        '--epoch',
+        default=100000,
+        help='epoch to train the network')
+    parser.add_argument(
+        '--batch_size',
+        default=64,
+        help='batch size to train network')
+    parser.add_argument(
+        '--lr_steps',
+        default=[
+            40000,
+            60000,
+            80000],
+        help='learning rate to train network')
+    parser.add_argument(
+        '--momentum',
+        default=0.9,
+        help='learning alg momentum')
+    parser.add_argument(
+        '--weight_deacy',
+        default=1e-4,
+        help='learning alg momentum')
+
+    parser.add_argument(
+        '--image_size',
+        default=[
+            112,
+            112],
+        help='image size height, width')
+    parser.add_argument('--num_output', default=85164, help='the image size')
+
+    parser.add_argument(
+        '--summary_path',
+        default='./output/summary',
+        help='the summary file save path')
+    parser.add_argument(
+        '--ckpt_path',
+        default='./output/ckpt',
+        help='the ckpt file save path')
+    parser.add_argument(
+        '--log_file_path',
+        default='./output/logs',
+        help='the ckpt file save path')
+    parser.add_argument(
+        '--saver_maxkeep',
+        default=100,
+        help='tf.train.Saver max keep ckpt files')
+    parser.add_argument(
+        '--log_device_mapping',
+        default=True,
+        help='show device placement log')
+    parser.add_argument(
+        '--summary_interval',
+        default=300,
+        help='interval to save summary')
+    parser.add_argument(
+        '--ckpt_interval',
+        default=20000,
+        help='intervals to save ckpt file')
+    parser.add_argument(
+        '--validate_interval',
+        default=2000,
+        help='intervals to save ckpt file')
+    parser.add_argument(
+        '--show_info_interval',
+        default=20,
+        help='intervals to save ckpt file')
+    # triplet
+    parser.add_argument(
+        '--model_path',
+        default=None,
+        help='baseline model ckpt')
+    parser.add_argument(
+        '--centre_weight',
+        default=0.3,
+        help='centre loss weight')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    os.environ["CUDA_VISIBLE_DEVICES"] = "3"
+    args = get_parser()
+    model_path = args.model_path
+    best_lfw = 0
+    count = 0
+    if model_path:
+        _segs = model_path.split('_')
+        count = int(_segs[1])
+        best_lfw = float(_segs[3])
+    print('best lfw accuracy is %.5f' % best_lfw)
+    print('iteration:%d' % count)
+
+    # 1. define global parameters
+    image_size = (args.image_size[1], args.image_size[0])
+    global_step = tf.Variable(
+        name='global_step',
+        initial_value=0,
+        trainable=False)
+    inc_op = tf.assign_add(global_step, 1, name='increment_global_step')
+    images = tf.placeholder(
+        name='img_inputs',
+        shape=[
+            None,
+            args.image_size[0],
+            args.image_size[1],
+            3],
+        dtype=tf.float32)
+    labels = tf.placeholder(name='img_labels', shape=[None, ], dtype=tf.int64)
+    # trainable = tf.placeholder(name='trainable_bn', dtype=tf.bool)
+    dropout_rate = tf.placeholder(name='dropout_rate', dtype=tf.float32)
+    # load config
+    config = Config(args.config)
+
+    # 2 prepare train datasets and test datasets by using tensorflow dataset api
+    # 2.1 train datasets
+    # the image is substracted 127.5 and multiplied 1/128.
+    # random flip left right
+    args.ckpt_path = './output/vgg-ms1m'
+    dataset_list = []
+    dataset_list.append(('vgg', -1))
+    dataset_list.append(('ms1m', -1))
+    # dataset_list.append(('WebFace', -1))
+    dataset = build_dataset(config, dataset_list, balance=False)
+    db = MultiThreadLoader(dataset, args.batch_size, 1)
+    args.num_output = db.numOfClass()
+    batch_per_epoch = db.size() / args.batch_size
+
+    # 2.2 prepare validate datasets
+    # lfw
+    if config.get('lfw').enable:
+        pos_img, neg_img = load_lfw(config)
+
+    # 3. define network, loss, optimize method, learning rate schedule, summary writer, saver
+    # 3.1 inference phase
+    print('Buiding net structure')
+    w_init_method = tf.contrib.layers.xavier_initializer(uniform=False)
+    net = get_resnet(
+        images,
+        args.net_depth,
+        type='ir',
+        w_init=w_init_method,
+        trainable=True,
+        keep_rate=dropout_rate)
+
+    # 3.2 get arcface loss
+    logit, inner_sim = arcface_loss(
+        embedding=net.outputs, labels=labels, w_init=w_init_method, out_num=args.num_output)
+    # test net  because of batch normal layer
+    tl.layers.set_name_reuse(True)
+    test_net = get_resnet(
+        images,
+        args.net_depth,
+        type='ir',
+        w_init=w_init_method,
+        trainable=False,
+        reuse=True,
+        keep_rate=dropout_rate)
+    embedding_tensor = test_net.outputs
+
+    # 3.3 define the cross entropy
+    inference_loss = tf.reduce_mean(
+        tf.nn.sparse_softmax_cross_entropy_with_logits(
+            logits=logit, labels=labels))
+    wd_loss = 0
+    for weights in tl.layers.get_variables_with_name('W_conv2d', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(weights)
+    for W in tl.layers.get_variables_with_name(
+            'resnet_v1_50/E_DenseLayer/W', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(W)
+    for weights in tl.layers.get_variables_with_name(
+            'embedding_weights', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(weights)
+    for gamma in tl.layers.get_variables_with_name('gamma', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(gamma)
+    # for beta in tl.layers.get_variables_with_name('beta', True, True):
+    #     wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(beta)
+    for alphas in tl.layers.get_variables_with_name('alphas', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(alphas)
+    # for bias in tl.layers.get_variables_with_name('resnet_v1_50/E_DenseLayer/b', True, True):
+    #     wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(bias)
+
+    # 3.5 total losses
+    inner_loss = (1 - inner_sim / args.batch_size) * 64 * args.centre_weight
+    total_loss = inference_loss + wd_loss + inner_loss
+
+    # 3.6 define the learning rate schedule
+    p = int(512.0 / args.batch_size)
+    lr_steps = [p * val for val in args.lr_steps]
+    # print(lr_steps)
+    lr = tf.train.piecewise_constant(
+        global_step, boundaries=lr_steps, values=[
+            0.001, 0.0005, 0.0003, 0.0001], name='lr_schedule')
+
+    # 3.7 define the optimize method
+    opt = tf.train.MomentumOptimizer(learning_rate=lr, momentum=args.momentum)
+
+    # 3.8 get train op
+    grads = opt.compute_gradients(total_loss)
+    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+    with tf.control_dependencies(update_ops):
+        train_op = opt.apply_gradients(grads, global_step=global_step)
+    # train_op = opt.minimize(total_loss, global_step=global_step)
+
+    # 3.9 define the inference accuracy used during validate or test
+    pred = tf.nn.softmax(logit)
+    train_acc = tf.reduce_mean(
+        tf.cast(
+            tf.equal(
+                tf.argmax(
+                    pred,
+                    axis=1),
+                labels),
+            dtype=tf.float32))
+
+    # 3.10 define sess
+    # sess = tf.Session()
+    sess_config = tf.ConfigProto(
+        allow_soft_placement=True,
+        log_device_placement=args.log_device_mapping)
+    sess_config.gpu_options.allow_growth = True
+    sess = tf.Session(config=sess_config)
+
+    # 3.11 summary writer
+    summary = tf.summary.FileWriter(args.summary_path, sess.graph)
+    summaries = []
+
+    # # 3.11.1 add grad histogram op
+    for grad, var in grads:
+        if grad is not None:
+            summaries.append(
+                tf.summary.histogram(
+                    var.op.name +
+                    '/gradients',
+                    grad))
+
+    # 3.11.2 add trainabel variable gradients
+    for var in tf.trainable_variables():
+        summaries.append(tf.summary.histogram(var.op.name, var))
+
+    # 3.11.3 add loss summary
+    summaries.append(tf.summary.scalar('inference_loss', inference_loss))
+    summaries.append(tf.summary.scalar('wd_loss', wd_loss))
+    summaries.append(tf.summary.scalar('total_loss', total_loss))
+
+    # 3.11.4 add learning rate
+    summaries.append(tf.summary.scalar('leraning_rate', lr))
+    summary_op = tf.summary.merge(summaries)
+
+    # 3.12 saver
+    saver = tf.train.Saver(max_to_keep=args.saver_maxkeep)
+
+    # 3.13 init all variables
+    sess.run(tf.global_variables_initializer())
+
+    # restore weights
+    learn_vars = tf.trainable_variables()
+    model_vars = []
+    for var in learn_vars:
+        if var.name.find('_loss') < 0:
+            model_vars.append(var)
+    model_vars = learn_vars
+    # latest_checkpoint = get_latest_checkpoint(model_path)
+    latest_checkpoint = model_path
+    # latest_checkpoint = None
+    if latest_checkpoint is not None:
+        restore = slim.assign_from_checkpoint_fn(
+            latest_checkpoint, var_list=model_vars, ignore_missing_vars=True)
+        # restore(sess)
+        saver.restore(sess, latest_checkpoint)
+
+    # 4 begin iteration
+    ema_iloss = EMA()
+    ema_tloss = EMA()
+    ema_acc = EMA()
+    print('\n\nTraining started ...')
+    for i in range(args.epoch):
+        for batch in range(batch_per_epoch):
+            try:
+                # get batch
+                images_train, labels_train = db.getBatch()
+                # print(images_train)
+                images_train = (np.float32(images_train) - 127.5) / 128
+                # images_train = np.float32(images_train)
+                # print(images_train)
+                feed_dict = {
+                    images: images_train,
+                    labels: labels_train,
+                    dropout_rate: 0.4}
+                feed_dict.update(net.all_drop)
+                start = time.time()
+
+                _, total_loss_val, inference_loss_val, wd_loss_val, inner_loss_val, _, acc_val = \
+                    sess.run([train_op, total_loss, inference_loss, wd_loss, inner_loss, inc_op, train_acc],
+                             feed_dict=feed_dict)
+
+                end = time.time()
+                pre_sec = args.batch_size / (end - start)
+                inference_loss_val = ema_iloss(inference_loss_val)
+                total_loss_val = ema_tloss(total_loss_val)
+                acc_val = ema_acc(acc_val)
+                # print training information
+                if count > 0 and count % args.show_info_interval == 0:
+                    print(
+                        '%2d, %d/%d, loss:%.2f , iloss:%.2f, wloss:%.2f, inner:%.2f, acc:%.4f, speed:%.1f' %
+                        (i,
+                         batch,
+                         batch_per_epoch,
+                         total_loss_val,
+                         inference_loss_val,
+                         wd_loss_val,
+                         inner_loss_val,
+                         acc_val,
+                         pre_sec))
+
+                # save summary
+                '''
+                if count > 0 and count % args.summary_interval == 0:
+                    feed_dict = {images: images_train, labels: labels_train, dropout_rate: 0.4}
+                    feed_dict.update(net.all_drop)
+                    summary_op_val = sess.run(summary_op, feed_dict=feed_dict)
+                    summary.add_summary(summary_op_val, count)
+                '''
+
+                # lfw validate
+                is_model_good = False
+                model_lfw = 0
+                if config.get(
+                        'lfw').enable and count > 0 and count % args.validate_interval == 0:
+                    feed_dict_test = {dropout_rate: 1.0}
+                    feed_dict_test.update(tl.utils.dict_to_one(net.all_drop))
+                    extractor = TensorflowExtractor(
+                        sess, embedding_tensor, args.batch_size, feed_dict, images)
+                    results, precision, _std = ver_test(
+                        pos_img, neg_img, extractor)
+                    print(
+                        '------------------------------------------------------------')
+                    print(
+                        'Precision on %s : %1.5f+-%1.5f' %
+                        ('lfw', precision, _std))
+                    model_lfw = precision
+                    if precision > best_lfw:
+                        best_lfw = precision
+                        if precision > 0.99:
+                            is_model_good = True
+                    print('best lfw accuracy is %.5f' % best_lfw)
+                    print('\n')
+
+                # save ckpt files
+                if is_model_good or count % args.ckpt_interval == 0:
+                    filename = 'iter_%d_lfw_%.5f' % (
+                        count, model_lfw) + '.ckpt'
+                    filename = os.path.join(args.ckpt_path, filename)
+                    saver.save(sess, filename)
+
+                count += 1
+            except Exception as e:
+                print(e)
diff --git a/facenet_sandberg/train_insightface/train_triplet.py b/facenet_sandberg/train_insightface/train_triplet.py
new file mode 100644
index 000000000..cb1dd4824
--- /dev/null
+++ b/facenet_sandberg/train_insightface/train_triplet.py
@@ -0,0 +1,375 @@
+import argparse
+import os
+import pickle
+import time
+
+import cv2
+import numpy as np
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+import tensorlayer as tl
+from facenet_sandberg.models.L_Resnet_E_IR_fix_issue9 import get_resnet
+from tensorflow.core.protobuf import config_pb2
+
+from config import Config
+from dataset_all import build_dataset
+from EMA import EMA
+from mt_loader import MultiThreadLoader
+from tensorflow_extractor import TensorflowExtractor
+from triplet_loss import batch_all_triplet_loss, batch_hard_triplet_loss
+from utils import *
+from verification import extract_list_feature, verification
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(description='parameters to train net')
+    parser.add_argument('--config', default='config.ini', help='config file')
+    parser.add_argument(
+        '--net_depth',
+        default=50,
+        help='resnet depth, default is 50')
+    parser.add_argument(
+        '--epoch',
+        default=100000,
+        help='epoch to train the network')
+    parser.add_argument(
+        '--batch_size',
+        default=64,
+        help='batch size to train network')
+    parser.add_argument(
+        '--lr_steps',
+        default=[
+            40000,
+            60000,
+            80000],
+        help='learning rate to train network')
+    parser.add_argument(
+        '--momentum',
+        default=0.9,
+        help='learning alg momentum')
+    parser.add_argument(
+        '--weight_deacy',
+        default=1e-4,
+        help='learning alg momentum')
+
+    parser.add_argument(
+        '--image_size',
+        default=[
+            112,
+            112],
+        help='image size height, width')
+    parser.add_argument('--num_output', default=85164, help='the image size')
+
+    parser.add_argument(
+        '--summary_path',
+        default='./output/summary',
+        help='the summary file save path')
+    parser.add_argument(
+        '--ckpt_path',
+        default='./output/ckpt',
+        help='the ckpt file save path')
+    parser.add_argument(
+        '--log_file_path',
+        default='./output/logs',
+        help='the ckpt file save path')
+    parser.add_argument(
+        '--saver_maxkeep',
+        default=100,
+        help='tf.train.Saver max keep ckpt files')
+    parser.add_argument(
+        '--log_device_mapping',
+        default=False,
+        help='show device placement log')
+    parser.add_argument(
+        '--summary_interval',
+        default=300,
+        help='interval to save summary')
+    parser.add_argument(
+        '--ckpt_interval',
+        default=20000,
+        help='intervals to save ckpt file')
+    parser.add_argument(
+        '--validate_interval',
+        default=2000,
+        help='intervals to save ckpt file')
+    parser.add_argument(
+        '--show_info_interval',
+        default=20,
+        help='intervals to save ckpt file')
+    # triplet
+    parser.add_argument(
+        '--model_path',
+        default=None,
+        help='baseline model ckpt')
+    parser.add_argument('--triplet_margin', default=0.3, help='triplet margin')
+    parser.add_argument(
+        '--triplet_weight',
+        default=10,
+        help='triplet loss weight')
+    parser.add_argument(
+        '--sample_per_class',
+        default=4,
+        help='num of samples each class in a minibatch')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    os.environ["CUDA_VISIBLE_DEVICES"] = "3"
+    args = get_parser()
+    model_path = args.model_path
+    best_lfw = 0
+    count = 0
+    if model_path:
+        _segs = model_path.split('_')
+        count = int(_segs[1])
+        best_lfw = float(_segs[3])
+    print('best lfw accuracy is %.5f' % best_lfw)
+    print('iteration:%d' % count)
+
+    # 1. define global parameters
+    image_size = (args.image_size[1], args.image_size[0])
+    global_step = tf.Variable(
+        name='global_step',
+        initial_value=0,
+        trainable=False)
+    inc_op = tf.assign_add(global_step, 1, name='increment_global_step')
+    images = tf.placeholder(
+        name='img_inputs',
+        shape=[
+            None,
+            args.image_size[0],
+            args.image_size[1],
+            3],
+        dtype=tf.float32)
+    labels = tf.placeholder(name='img_labels', shape=[None, ], dtype=tf.int64)
+    # trainable = tf.placeholder(name='trainable_bn', dtype=tf.bool)
+    dropout_rate = tf.placeholder(name='dropout_rate', dtype=tf.float32)
+    # load config
+    config = Config(args.config)
+    # 2 prepare train datasets and test datasets by using tensorflow dataset api
+    # 2.1 train datasets
+    # the image is substracted 127.5 and multiplied 1/128.
+    # random flip left right
+    args.ckpt_path = './output/vgg-triplet'
+    dataset_list = []
+    dataset_list.append(('vgg', -1))
+    # dataset_list.append(('ms1m',    -1))
+    # dataset_list.append(('WebFace', -1))
+    dataset = build_dataset(
+        config,
+        dataset_list,
+        balance=False,
+        num_per_class=args.sample_per_class)
+    db = MultiThreadLoader(dataset, args.batch_size, 1)
+    args.num_output = db.numOfClass()
+    batch_per_epoch = db.size() / args.batch_size
+    # 2.2 prepare validate datasets
+    # lfw
+    if config.get('lfw').enable:
+        pos_img, neg_img = load_lfw(config)
+
+    # 3. define network, loss, optimize method, learning rate schedule, summary writer, saver
+    # 3.1 inference phase
+    print('Buiding net structure')
+    w_init_method = tf.contrib.layers.xavier_initializer(uniform=False)
+    net = get_resnet(
+        images,
+        args.net_depth,
+        type='ir',
+        w_init=w_init_method,
+        trainable=True,
+        keep_rate=dropout_rate)
+    # 3.2 get arcface loss
+    logit = net.outputs
+    logit_norm = tf.norm(logit, axis=1, keep_dims=True)
+    logit = tf.div(logit, logit_norm, name='norm_logit')
+
+    # test net  because of batch normal layer
+    tl.layers.set_name_reuse(True)
+    test_net = get_resnet(
+        images,
+        args.net_depth,
+        type='ir',
+        w_init=w_init_method,
+        trainable=False,
+        reuse=True,
+        keep_rate=dropout_rate)
+    embedding_tensor = test_net.outputs
+    # 3.3 define the cross entropy
+    t_loss = batch_hard_triplet_loss(
+        labels, logit, margin=args.triplet_margin) * args.triplet_weight
+
+    wd_loss = 0
+    for weights in tl.layers.get_variables_with_name('W_conv2d', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(weights)
+    for W in tl.layers.get_variables_with_name(
+            'resnet_v1_50/E_DenseLayer/W', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(W)
+    for weights in tl.layers.get_variables_with_name(
+            'embedding_weights', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(weights)
+    for gamma in tl.layers.get_variables_with_name('gamma', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(gamma)
+    # for beta in tl.layers.get_variables_with_name('beta', True, True):
+    #     wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(beta)
+    for alphas in tl.layers.get_variables_with_name('alphas', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(alphas)
+    # for bias in tl.layers.get_variables_with_name('resnet_v1_50/E_DenseLayer/b', True, True):
+    #     wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(bias)
+
+    # 3.5 total losses
+    total_loss = t_loss + wd_loss
+    # 3.6 define the learning rate schedule
+    p = int(512.0 / args.batch_size)
+    lr_steps = [p * val for val in args.lr_steps]
+    print(lr_steps)
+    lr = tf.train.piecewise_constant(
+        global_step, boundaries=lr_steps, values=[
+            0.001, 0.0005, 0.0003, 0.0001], name='lr_schedule')
+    # 3.7 define the optimize method
+    opt = tf.train.MomentumOptimizer(learning_rate=lr, momentum=args.momentum)
+    # 3.8 get train op
+    grads = opt.compute_gradients(total_loss)
+    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+    with tf.control_dependencies(update_ops):
+        train_op = opt.apply_gradients(grads, global_step=global_step)
+    # train_op = opt.minimize(total_loss, global_step=global_step)
+    # 3.10 define sess
+    # sess = tf.Session()
+    gpu_config = tf.ConfigProto(
+        allow_soft_placement=True,
+        log_device_placement=args.log_device_mapping)
+    gpu_config.gpu_options.allow_growth = True
+    sess = tf.Session(config=gpu_config)
+
+    # 3.11 summary writer
+    summary = tf.summary.FileWriter(args.summary_path, sess.graph)
+    summaries = []
+
+    # # 3.11.1 add grad histogram op
+    for grad, var in grads:
+        if grad is not None:
+            summaries.append(
+                tf.summary.histogram(
+                    var.op.name +
+                    '/gradients',
+                    grad))
+
+    # 3.11.2 add trainabel variable gradients
+    for var in tf.trainable_variables():
+        summaries.append(tf.summary.histogram(var.op.name, var))
+
+    # 3.11.3 add loss summary
+    summaries.append(tf.summary.scalar('t_loss', t_loss))
+    summaries.append(tf.summary.scalar('wd_loss', wd_loss))
+    summaries.append(tf.summary.scalar('total_loss', total_loss))
+
+    # 3.11.4 add learning rate
+    summaries.append(tf.summary.scalar('leraning_rate', lr))
+    summary_op = tf.summary.merge(summaries)
+
+    # 3.12 saver
+    saver = tf.train.Saver(max_to_keep=args.saver_maxkeep)
+
+    # 3.13 init all variables
+    sess.run(tf.global_variables_initializer())
+
+    # restore weights
+    learn_vars = tf.trainable_variables()
+    model_vars = []
+    for var in learn_vars:
+        if var.name.find('_loss') < 0:
+            model_vars.append(var)
+    model_vars = learn_vars
+    # latest_checkpoint = get_latest_checkpoint(model_path)
+    latest_checkpoint = model_path
+    # latest_checkpoint = None
+    if latest_checkpoint is not None:
+        restore = slim.assign_from_checkpoint_fn(
+            latest_checkpoint, var_list=model_vars, ignore_missing_vars=True)
+        restore(sess)
+
+    # 4 begin iteration
+    ema_iloss = EMA()
+    ema_tloss = EMA()
+    ema_acc = EMA()
+    print('\n\nTraining started ...')
+    for i in range(args.epoch):
+        for batch in range(batch_per_epoch):
+            try:
+                # get batch
+                images_train, labels_train = db.getBatch()
+                # print(images_train)
+                images_train = (np.float32(images_train) - 127.5) / 128
+                # images_train = np.float32(images_train)
+                # print(images_train)
+                feed_dict = {
+                    images: images_train,
+                    labels: labels_train,
+                    dropout_rate: 0.4}
+                feed_dict.update(net.all_drop)
+                start = time.time()
+
+                _, total_loss_val, inference_loss_val, wd_loss_val, _ = \
+                    sess.run([train_op, total_loss, t_loss, wd_loss, inc_op],
+                             feed_dict=feed_dict)
+
+                end = time.time()
+                pre_sec = args.batch_size / (end - start)
+                inference_loss_val = ema_iloss(inference_loss_val)
+                total_loss_val = ema_tloss(total_loss_val)
+
+                # print training information
+                if count > 0 and count % args.show_info_interval == 0:
+                    print(
+                        'epoch:%d, %d/%d, loss:%.2f , iloss:%.2f, wloss:%.2f, speed:%.1f' %
+                        (i,
+                         batch,
+                         batch_per_epoch,
+                         total_loss_val,
+                         inference_loss_val,
+                         wd_loss_val,
+                         pre_sec))
+                count += 1
+
+                # save summary
+                '''
+                if count > 0 and count % args.summary_interval == 0:
+                    feed_dict = {images: images_train, labels: labels_train, dropout_rate: 0.4}
+                    feed_dict.update(net.all_drop)
+                    summary_op_val = sess.run(summary_op, feed_dict=feed_dict)
+                    summary.add_summary(summary_op_val, count)
+                '''
+
+                # lfw validate
+                is_model_good = False
+                model_lfw = 0
+                if count > 0 and count % args.validate_interval == 0:
+                    feed_dict_test = {dropout_rate: 1.0}
+                    feed_dict_test.update(tl.utils.dict_to_one(net.all_drop))
+                    extractor = TensorflowExtractor(
+                        sess, embedding_tensor, args.batch_size, feed_dict, images)
+                    results, precision, _std = ver_test(
+                        pos_img, neg_img, extractor)
+                    print(
+                        '------------------------------------------------------------')
+                    print(
+                        'Precision on %s : %1.5f+-%1.5f' %
+                        ('lfw', precision, _std))
+                    model_lfw = precision
+                    if precision > best_lfw:
+                        best_lfw = precision
+                        if precision > 0.99:
+                            is_model_good = True
+                    print('best lfw accuracy is %.5f' % best_lfw)
+                    print('\n')
+
+                # save ckpt files
+                if is_model_good:
+                    filename = 'iter_%d_lfw_%.5f' % (
+                        count, model_lfw) + '.ckpt'
+                    filename = os.path.join(args.ckpt_path, filename)
+                    saver.save(sess, filename)
+            except Exception as e:
+                print(e)
diff --git a/facenet_sandberg/train_insightface/triplet_loss.py b/facenet_sandberg/train_insightface/triplet_loss.py
new file mode 100644
index 000000000..5c43b152e
--- /dev/null
+++ b/facenet_sandberg/train_insightface/triplet_loss.py
@@ -0,0 +1,261 @@
+"""Define functions to create the triplet loss with online triplet mining."""
+
+import tensorflow as tf
+
+
+def _pairwise_distances(embeddings, squared=False):
+    """Compute the 2D matrix of distances between all the embeddings.
+
+    Args:
+        embeddings: tensor of shape (batch_size, embed_dim)
+        squared: Boolean. If true, output is the pairwise squared euclidean distance matrix.
+                 If false, output is the pairwise euclidean distance matrix.
+
+    Returns:
+        pairwise_distances: tensor of shape (batch_size, batch_size)
+    """
+    # Get the dot product between all embeddings
+    # shape (batch_size, batch_size)
+    dot_product = tf.matmul(embeddings, tf.transpose(embeddings))
+
+    # Get squared L2 norm for each embedding. We can just take the diagonal of `dot_product`.
+    # This also provides more numerical stability (the diagonal of the result will be exactly 0).
+    # shape (batch_size,)
+    square_norm = tf.diag_part(dot_product)
+
+    # Compute the pairwise distance matrix as we have:
+    # ||a - b||^2 = ||a||^2  - 2 <a, b> + ||b||^2
+    # shape (batch_size, batch_size)
+    distances = tf.expand_dims(square_norm, 1) - 2.0 * \
+        dot_product + tf.expand_dims(square_norm, 0)
+
+    # Because of computation errors, some distances might be negative so we
+    # put everything >= 0.0
+    distances = tf.maximum(distances, 0.0)
+
+    if not squared:
+        # Because the gradient of sqrt is infinite when distances == 0.0 (ex: on the diagonal)
+        # we need to add a small epsilon where distances == 0.0
+        mask = tf.to_float(tf.equal(distances, 0.0))
+        distances = distances + mask * 1e-16
+
+        distances = tf.sqrt(distances)
+
+        # Correct the epsilon added: set the distances on the mask to be
+        # exactly 0.0
+        distances = distances * (1.0 - mask)
+
+    return distances
+
+
+def _get_anchor_positive_triplet_mask(labels):
+    """Return a 2D mask where mask[a, p] is True iff a and p are distinct and have same label.
+
+    Args:
+        labels: tf.int32 `Tensor` with shape [batch_size]
+
+    Returns:
+        mask: tf.bool `Tensor` with shape [batch_size, batch_size]
+    """
+    # Check that i and j are distinct
+    indices_equal = tf.cast(tf.eye(tf.shape(labels)[0]), tf.bool)
+    indices_not_equal = tf.logical_not(indices_equal)
+
+    # Check if labels[i] == labels[j]
+    # Uses broadcasting where the 1st argument has shape (1, batch_size) and
+    # the 2nd (batch_size, 1)
+    labels_equal = tf.equal(
+        tf.expand_dims(
+            labels, 0), tf.expand_dims(
+            labels, 1))
+
+    # Combine the two masks
+    mask = tf.logical_and(indices_not_equal, labels_equal)
+
+    return mask
+
+
+def _get_anchor_negative_triplet_mask(labels):
+    """Return a 2D mask where mask[a, n] is True iff a and n have distinct labels.
+
+    Args:
+        labels: tf.int32 `Tensor` with shape [batch_size]
+
+    Returns:
+        mask: tf.bool `Tensor` with shape [batch_size, batch_size]
+    """
+    # Check if labels[i] != labels[k]
+    # Uses broadcasting where the 1st argument has shape (1, batch_size) and
+    # the 2nd (batch_size, 1)
+    labels_equal = tf.equal(
+        tf.expand_dims(
+            labels, 0), tf.expand_dims(
+            labels, 1))
+
+    mask = tf.logical_not(labels_equal)
+
+    return mask
+
+
+def _get_triplet_mask(labels):
+    """Return a 3D mask where mask[a, p, n] is True iff the triplet (a, p, n) is valid.
+
+    A triplet (i, j, k) is valid if:
+        - i, j, k are distinct
+        - labels[i] == labels[j] and labels[i] != labels[k]
+
+    Args:
+        labels: tf.int32 `Tensor` with shape [batch_size]
+    """
+    # Check that i, j and k are distinct
+    indices_equal = tf.cast(tf.eye(tf.shape(labels)[0]), tf.bool)
+    indices_not_equal = tf.logical_not(indices_equal)
+    i_not_equal_j = tf.expand_dims(indices_not_equal, 2)
+    i_not_equal_k = tf.expand_dims(indices_not_equal, 1)
+    j_not_equal_k = tf.expand_dims(indices_not_equal, 0)
+
+    distinct_indices = tf.logical_and(
+        tf.logical_and(
+            i_not_equal_j,
+            i_not_equal_k),
+        j_not_equal_k)
+
+    # Check if labels[i] == labels[j] and labels[i] != labels[k]
+    label_equal = tf.equal(
+        tf.expand_dims(
+            labels, 0), tf.expand_dims(
+            labels, 1))
+    i_equal_j = tf.expand_dims(label_equal, 2)
+    i_equal_k = tf.expand_dims(label_equal, 1)
+
+    valid_labels = tf.logical_and(i_equal_j, tf.logical_not(i_equal_k))
+
+    # Combine the two masks
+    mask = tf.logical_and(distinct_indices, valid_labels)
+
+    return mask
+
+
+def batch_all_triplet_loss(labels, embeddings, margin, squared=False):
+    """Build the triplet loss over a batch of embeddings.
+
+    We generate all the valid triplets and average the loss over the positive ones.
+
+    Args:
+        labels: labels of the batch, of size (batch_size,)
+        embeddings: tensor of shape (batch_size, embed_dim)
+        margin: margin for triplet loss
+        squared: Boolean. If true, output is the pairwise squared euclidean distance matrix.
+                 If false, output is the pairwise euclidean distance matrix.
+
+    Returns:
+        triplet_loss: scalar tensor containing the triplet loss
+    """
+    # Get the pairwise distance matrix
+    pairwise_dist = _pairwise_distances(embeddings, squared=squared)
+
+    # shape (batch_size, batch_size, 1)
+    anchor_positive_dist = tf.expand_dims(pairwise_dist, 2)
+    assert anchor_positive_dist.shape[2] == 1, "{}".format(
+        anchor_positive_dist.shape)
+    # shape (batch_size, 1, batch_size)
+    anchor_negative_dist = tf.expand_dims(pairwise_dist, 1)
+    assert anchor_negative_dist.shape[1] == 1, "{}".format(
+        anchor_negative_dist.shape)
+
+    # Compute a 3D tensor of size (batch_size, batch_size, batch_size)
+    # triplet_loss[i, j, k] will contain the triplet loss of anchor=i, positive=j, negative=k
+    # Uses broadcasting where the 1st argument has shape (batch_size, batch_size, 1)
+    # and the 2nd (batch_size, 1, batch_size)
+    triplet_loss = anchor_positive_dist - anchor_negative_dist
+    triplet_loss = triplet_loss + margin
+
+    # Put to zero the invalid triplets
+    # (where label(a) != label(p) or label(n) == label(a) or a == p)
+    mask = _get_triplet_mask(labels)
+    mask = tf.to_float(mask)
+    triplet_loss = tf.multiply(mask, triplet_loss)
+
+    # Remove negative losses (i.e. the easy triplets)
+    triplet_loss = tf.maximum(triplet_loss, 0.0)
+
+    # Count number of positive triplets (where triplet_loss > 0)
+    valid_triplets = tf.to_float(tf.greater(triplet_loss, 1e-16))
+    num_positive_triplets = tf.reduce_sum(valid_triplets)
+    num_valid_triplets = tf.reduce_sum(mask)
+    fraction_positive_triplets = num_positive_triplets / \
+        (num_valid_triplets + 1e-16)
+
+    # Get final mean triplet loss over the positive valid triplets
+    triplet_loss = tf.reduce_sum(triplet_loss) / \
+        (num_positive_triplets + 1e-16)
+
+    return triplet_loss, fraction_positive_triplets
+
+
+def batch_hard_triplet_loss(labels, embeddings, margin, squared=False):
+    """Build the triplet loss over a batch of embeddings.
+
+    For each anchor, we get the hardest positive and hardest negative to form a triplet.
+
+    Args:
+        labels: labels of the batch, of size (batch_size,)
+        embeddings: tensor of shape (batch_size, embed_dim)
+        margin: margin for triplet loss
+        squared: Boolean. If true, output is the pairwise squared euclidean distance matrix.
+                 If false, output is the pairwise euclidean distance matrix.
+
+    Returns:
+        triplet_loss: scalar tensor containing the triplet loss
+    """
+    # Get the pairwise distance matrix
+    pairwise_dist = _pairwise_distances(embeddings, squared=squared)
+
+    # For each anchor, get the hardest positive
+    # First, we need to get a mask for every valid positive (they should have
+    # same label)
+    mask_anchor_positive = _get_anchor_positive_triplet_mask(labels)
+    mask_anchor_positive = tf.to_float(mask_anchor_positive)
+
+    # We put to 0 any element where (a, p) is not valid (valid if a != p and
+    # label(a) == label(p))
+    anchor_positive_dist = tf.multiply(mask_anchor_positive, pairwise_dist)
+
+    # shape (batch_size, 1)
+    hardest_positive_dist = tf.reduce_max(
+        anchor_positive_dist, axis=1, keep_dims=True)
+    tf.summary.scalar(
+        "hardest_positive_dist",
+        tf.reduce_mean(hardest_positive_dist))
+
+    # For each anchor, get the hardest negative
+    # First, we need to get a mask for every valid negative (they should have
+    # different labels)
+    mask_anchor_negative = _get_anchor_negative_triplet_mask(labels)
+    mask_anchor_negative = tf.to_float(mask_anchor_negative)
+
+    # We add the maximum value in each row to the invalid negatives (label(a)
+    # == label(n))
+    max_anchor_negative_dist = tf.reduce_max(
+        pairwise_dist, axis=1, keep_dims=True)
+    anchor_negative_dist = pairwise_dist + \
+        max_anchor_negative_dist * (1.0 - mask_anchor_negative)
+
+    # shape (batch_size,)
+    hardest_negative_dist = tf.reduce_min(
+        anchor_negative_dist, axis=1, keep_dims=True)
+    tf.summary.scalar(
+        "hardest_negative_dist",
+        tf.reduce_mean(hardest_negative_dist))
+
+    # Combine biggest d(a, p) and smallest d(a, n) into final triplet loss
+    triplet_loss = tf.maximum(
+        hardest_positive_dist -
+        hardest_negative_dist +
+        margin,
+        0.0)
+
+    # Get final mean triplet loss
+    triplet_loss = tf.reduce_mean(triplet_loss)
+
+    return triplet_loss
diff --git a/facenet_sandberg/train_insightface/utils.py b/facenet_sandberg/train_insightface/utils.py
new file mode 100644
index 000000000..5110c96f6
--- /dev/null
+++ b/facenet_sandberg/train_insightface/utils.py
@@ -0,0 +1,175 @@
+import argparse
+import os
+import pickle
+import time
+from enum import Enum, auto
+from os.path import exists, join
+from typing import List, Tuple, Union, cast
+
+import cv2
+import numpy as np
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+import tensorlayer as tl
+
+from config import Config
+from tensorflow_extractor import TensorflowExtractor
+from verification import extract_list_feature, verification
+
+FaceVector = List[float]
+Match = Tuple[str, int, int]
+Mismatch = Tuple[str, int, str, int]
+Pair = Union[Match, Mismatch]
+Path = Tuple[str, str]
+Label = bool
+Image = np.ndarray
+ImagePairs = List[Tuple[Image, Image]]
+
+
+def load_lfw(config: Config) -> Tuple[ImagePairs, ImagePairs]:
+    print('Loading lfw data:')
+    pairs, _, num_matches_mismatches = _read_pairs(config.get('lfw').pairs)
+    pair_paths, labels = _get_paths_and_labels(
+        config.get('lfw').image_dir, pairs)
+    pos_img, neg_img = split(
+        config.get('lfw').image_dir, pair_paths, labels, image_size)
+
+    # crop image
+    pos_img = crop_image_list(pos_img, image_size)
+    neg_img = crop_image_list(neg_img, image_size)
+    return pos_img, neg_img
+
+
+def ver_test(pos_list: ImagePairs, neg_list: ImagePairs,
+             extractor: TensorflowExtractor):
+    pos_feat = extract_list_feature(
+        extractor,
+        pos_list,
+        len(pos_list),
+        extractor.batch_size)
+    neg_feat = extract_list_feature(
+        extractor,
+        neg_list,
+        len(neg_list),
+        extractor.batch_size)
+    _acc, _std, _threshold, _pos, _neg, _accu_list = verification(
+        pos_feat, neg_feat, 'cosine')
+    return _accu_list, _acc, _std
+
+
+def crop_image_list(
+        img_list: List[Tuple[Image, Image]], imsize: Tuple[int, int]):
+    out_list = []
+    h, w, c = img_list[0][0].shape
+    x1 = (w - imsize[0]) // 2
+    y1 = (h - imsize[1]) // 2
+    for pair in img_list:
+        img1 = pair[0]
+        img2 = pair[1]
+        img1 = img1[y1:(y1 + imsize[1]), x1:(x1 + imsize[0]), :]
+        img1 = (np.float32(img1) - 127.5) / 128
+        img2 = img2[y1:(y1 + imsize[1]), x1:(x1 + imsize[0]), :]
+        img2 = (np.float32(img2) - 127.5) / 128
+        out_list.append([img1, img2])
+    return out_list
+
+
+def _read_pairs(pairs_filename: str) -> Tuple[List[Pair], int, int]:
+    pairs = []
+    with open(pairs_filename, 'r') as pair_file:
+        num_sets, num_matches_mismatches = [int(i)
+                                            for i in next(pair_file).split()]
+        for line_num, line in enumerate(pair_file):
+            pair = cast(Pair, tuple([int(i) if i.isdigit() else i
+                                     for i in line.strip().split()]))
+            pairs.append(pair)
+    return pairs, num_sets, num_matches_mismatches
+
+
+def _get_paths_and_labels(image_dir: str,
+                          pairs: List[Pair]) -> Tuple[List[Path], List[Label]]:
+    paths = []
+    labels = []
+    for pair in pairs:
+        _add_extension = (lambda rel_image_path, image_dir:
+                          f'{rel_image_path}.jpg'
+                          if exists(join(image_dir, f'{rel_image_path}.jpg'))
+                          else f'{rel_image_path}.png')
+        if len(pair) == 3:
+            person, image_num_0, image_num_1 = cast(Match, pair)
+            rel_image_path_no_ext = join(person,
+                                         f'{person}_{image_num_0:04d}')
+            rel_image_path_0 = _add_extension(rel_image_path_no_ext, image_dir)
+            rel_image_path_no_ext = join(person,
+                                         f'{person}_{image_num_1:04d}')
+            rel_image_path_1 = _add_extension(rel_image_path_no_ext, image_dir)
+            is_same_person = True
+        elif len(pair) == 4:
+            person_0, image_num_0, person_1, image_num_1 = cast(Mismatch, pair)
+            rel_image_path_no_ext = join(person_0,
+                                         f'{person_0}_{image_num_0:04d}')
+            rel_image_path_0 = _add_extension(rel_image_path_no_ext, image_dir)
+            rel_image_path_no_ext = join(person_1,
+                                         f'{person_1}_{image_num_1:04d}')
+            rel_image_path_1 = _add_extension(rel_image_path_no_ext, image_dir)
+            is_same_person = False
+        if (exists(join(image_dir, rel_image_path_0))
+                and
+                exists(join(image_dir, rel_image_path_1))):
+            paths.append((rel_image_path_0, rel_image_path_1))
+            labels.append(is_same_person)
+        else:
+            err = f'{rel_image_path_no_ext} with .jpg or .png extensions'
+            raise FileNotFoundError(err)
+    return paths, labels
+
+
+def split(base_path: str,
+          paths: List[Path],
+          labels: List[Label],
+          image_size: Tuple[int,
+                            int]) -> Tuple[ImagePairs, ImagePairs]:
+    pos = []
+    neg = []
+    for index in range(len(paths)):
+        img1 = cv2.imread(os.path.join(base_path, paths[index][0]))
+        img1 = cv2.resize(img1, (image_size[0], image_size[1]))
+        img2 = cv2.imread(os.path.join(base_path, paths[index][1]))
+        img2 = cv2.resize(img2, (image_size[0], image_size[1]))
+        if labels[index]:
+            pos.append((img1, img2))
+        else:
+            neg.append((img1, img2))
+    return pos, neg
+
+
+def load_image_list(pair_list):
+    img_list = []
+    for pair in pair_list:
+        # skip invalid pairs
+        if not os.path.exists(pair[0]) or not os.path.exists(pair[1]):
+            continue
+        img1 = cv2.imread(pair[0])
+        img2 = cv2.imread(pair[1])
+        img_list.append([img1, img2, pair[0], pair[1]])
+    return img_list
+
+
+def load_ytf_pairs(path, prefix):
+    pos_list_ = []
+    neg_list_ = []
+    with open(path, 'r') as f:
+        for line in f.readlines():
+            line = line.strip()
+            flag, a, b = line.split(',')
+            flag = int(flag)
+            a = os.path.join(prefix, a)
+            b = os.path.join(prefix, b)
+            if flag == 1:
+                pos_list_.append([a, b])
+            else:
+                neg_list_.append([a, b])
+
+    pos_img = load_image_list(pos_list_)
+    neg_img = load_image_list(neg_list_)
+    return pos_img, neg_img
diff --git a/facenet_sandberg/train_insightface/verification.py b/facenet_sandberg/train_insightface/verification.py
new file mode 100644
index 000000000..4d6e67729
--- /dev/null
+++ b/facenet_sandberg/train_insightface/verification.py
@@ -0,0 +1,174 @@
+# -*- coding:utf-8 -*-
+from __future__ import absolute_import, division, print_function
+
+import argparse
+import math
+import os
+import pickle
+import sys
+
+import numpy as np
+import progressbar as pb
+
+from util.distance import get_distance
+
+
+def find_threshold_sort(pos, neg):
+    pos_list = sorted(pos, key=lambda x: x[0])
+    neg_list = sorted(neg, key=lambda x: x[0], reverse=True)
+    pos_count = len(pos_list)
+    neg_count = len(neg_list)
+    correct = 0
+    threshold = 0
+    # print('sort pos')
+    # print(pos_list)
+    # print('sort neg')
+    # print(neg_list)
+    for i in range(min(pos_count, neg_count)):
+        if pos_list[i][0] > neg_list[i][0]:
+            correct = i
+            threshold = (pos_list[i][0] + neg_list[i][0]) / 2
+            break
+    # print("%d/%d" % (correct, pos_count))
+    precision = (correct * 2.0) / (pos_count + neg_count)
+    return precision, threshold
+
+
+def get_accuracy(pos_list, neg_list, threshold):
+    pos_count = len(pos_list)
+    neg_count = len(neg_list)
+    correct = 0
+    for i in range(pos_count):
+        if pos_list[i][0] < threshold:
+            correct += 1
+
+    for i in range(neg_count):
+        if neg_list[i][0] > threshold:
+            correct += 1
+    precision = float(correct) / (pos_count + neg_count)
+    return precision
+
+
+def best_threshold(pos_list, neg_list, thrNum=10000):
+    ts = np.linspace(-1, 1, thrNum * 2 + 1)
+    best_acc = 0
+    best_t = 0
+    for t in ts:
+        acc = get_accuracy(pos_list, neg_list, t)
+        if acc > best_acc:
+            best_acc = acc
+            best_t = t
+    return best_acc, best_t
+
+
+def test_kfold(pos_list, neg_list, k=10):
+    fold_size = len(pos_list) // k
+    sum_acc = 0
+    sum_thresh = 0
+    sum_n = 0
+    accu_list = []
+    for i in range(k):
+        val_pos = []
+        val_neg = []
+        test_pos = []
+        test_neg = []
+        for j in range(len(pos_list)):
+            fi = j // fold_size
+            if fi != i:
+                val_pos.append(pos_list[j])
+                val_neg.append(neg_list[j])
+            else:
+                test_pos.append(pos_list[j])
+                test_neg.append(neg_list[j])
+        precision, threshold = find_threshold_sort(val_pos, val_neg)
+        accuracy = get_accuracy(test_pos, test_neg, threshold)
+        accu_list.append(accuracy)
+        sum_acc += accuracy
+        sum_thresh += threshold
+        sum_n += 1
+        # verbose
+        print('precision:%.4f threshold:%f' % (accuracy, threshold))
+    return sum_acc / sum_n, sum_thresh / sum_n, accu_list
+
+
+def compute_distance(pos_list, neg_list, dist_type='L2'):
+    '''
+    [
+      [feat1, feat2, ..],
+        ...
+      [feat1, feat2, ..]
+    ]
+    '''
+    # distance measure
+    if isinstance(dist_type, str):
+        dist_func = get_distance(dist_type)
+    else:
+        dist_func = dist_type
+    # get dist
+    pos_dist = []
+    for i in pos_list:
+        dist = dist_func(i[0], i[1])
+        pos_dist.append([dist])
+
+    neg_dist = []
+    for i in neg_list:
+        dist = dist_func(i[0], i[1])
+        neg_dist.append([dist])
+    return pos_dist, neg_dist
+
+
+def verification(pos_list, neg_list, dist_type='L2'):
+    '''
+    [
+      [feat1, feat2, ..],
+        ...
+      [feat1, feat2, ..]
+    ]
+    '''
+    pos_dist, neg_dist = compute_distance(pos_list, neg_list, dist_type)
+    precision, threshold, accu_list = test_kfold(pos_dist, neg_dist)
+    pos = sorted(pos_dist, key=lambda x: x[0])
+    neg = sorted(neg_dist, key=lambda x: x[0], reverse=True)
+    pos = [x[0] for x in pos]
+    neg = [x[0] for x in neg]
+    acc, std = np.mean(accu_list), np.std(accu_list)
+
+    return acc, std, threshold, pos, neg, accu_list
+
+
+def extract_list_feature(extractor, pair_list, batch_size, size=0):
+    feat_list = []
+    npairs = len(pair_list)
+    if size == 0:
+        size = npairs * 2
+    size = min(size, npairs * 2)
+    num_batches = (size + batch_size - 1) // batch_size
+
+    widgets = ['Batch Processing', pb.Percentage(), ' ',
+               pb.Bar(marker=pb.Bar()), ' ', pb.ETA()]
+    timer = pb.ProgressBar(
+        widgets=widgets, max_value=int(num_batches + 1)).start()
+
+    for batch_num in range(num_batches):
+        # make a batch
+        x_list = []
+        for i in range(0, batch_size, 2):
+            pairid = (batch_num * batch_size + i) // 2
+            if pairid >= npairs:
+                pairid = npairs - 1
+            x_list.append(pair_list[pairid][0])
+            x_list.append(pair_list[pairid][1])
+        x_batch = np.stack(x_list, axis=0)
+        feat = extractor.extract(x_batch)
+
+        for i in range(0, batch_size, 2):
+            a = feat[i, :]
+            p = feat[i + 1, :]
+            if len(feat_list) < size:
+                feat_list.append([a, p])
+        try:
+            timer.update(batch_num)
+        except BaseException:
+            pass
+    timer.finish()
+    return feat_list
diff --git a/facenet_sandberg/utils.py b/facenet_sandberg/utils.py
new file mode 100644
index 000000000..7d2053b12
--- /dev/null
+++ b/facenet_sandberg/utils.py
@@ -0,0 +1,455 @@
+import math
+import os
+import pathlib
+from glob import iglob
+from multiprocessing import Pool
+from os.path import exists, join
+from typing import List, Optional, Tuple, cast
+from urllib.request import urlopen
+
+import cv2
+import numpy as np
+import PIL
+from sklearn.metrics.pairwise import paired_distances
+
+from facenet_sandberg.common_types import (AlignResult, DistanceMetric,
+                                           Embedding, Image, ImageExtensions,
+                                           ImageGenerator, Label, Landmarks,
+                                           Match, Mismatch, Pair, PersonClass)
+
+
+def normalize_image(image: Image) -> Image:
+    mean = np.mean(image)
+    std = np.std(image)
+    std_adj = np.maximum(std, 1.0 / np.sqrt(image.size))
+    y = np.multiply(np.subtract(image, mean), 1 / std_adj)
+    return y
+
+
+def fixed_standardize(image: Image) -> Image:
+    image = image - 127.5
+    image = image / 128.0
+    return image
+
+
+def fix_image(image: Image) -> Image:
+    if image.ndim < 2:
+        image = image[:, :, np.newaxis]
+    if image.ndim == 2:
+        image = add_color(image)
+    image = image[:, :, 0:3]
+    return image
+
+
+def resize(image: Image, height: int, width: int) -> Image:
+    img = PIL.Image.fromarray(image)
+    img.thumbnail((height, width), PIL.Image.ANTIALIAS)
+    resized = np.array(img)
+    return resized
+
+
+def add_color(image: Image) -> Image:
+    w, h = image.shape
+    ret = np.empty((w, h, 3), dtype=np.uint8)
+    ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = image
+    return ret
+
+
+def fix_mtcnn_bb(max_y: int, max_x: int, bounding_box: List[int]) -> List[int]:
+    """ mtcnn results can be out of image so fix results
+    """
+    x1, y1, dx, dy = bounding_box[:4]
+    x2 = x1 + dx
+    y2 = y1 + dy
+    x1 = max(min(x1, max_x), 0)
+    x2 = max(min(x2, max_x), 0)
+    y1 = max(min(y1, max_y), 0)
+    y2 = max(min(y2, max_y), 0)
+    return [x1, y1, x2, y2]
+
+
+def fix_faceboxes_bb(
+        max_y: int,
+        max_x: int,
+        bounding_box: List[int]) -> List[int]:
+    """ faceboxes order is different
+    """
+    y1, x1, y2, x2 = bounding_box[:4]
+    x1 = max(min(x1, max_x), 0)
+    x2 = max(min(x2, max_x), 0)
+    y1 = max(min(y1, max_y), 0)
+    y2 = max(min(y2, max_y), 0)
+    return [x1, y1, x2, y2]
+
+
+def embedding_distance(embedding_1: Embedding,
+                       embedding_2: Embedding,
+                       distance_metric: DistanceMetric) -> float:
+    """Compares the distance between two embeddings
+    """
+    distance = embedding_distance_bulk(embedding_1.reshape(
+        1, -1), embedding_2.reshape(1, -1), distance_metric=distance_metric)[0]
+    return distance
+
+
+def embedding_distance_bulk(
+        embeddings1: Embedding,
+        embeddings2: Embedding,
+        distance_metric: DistanceMetric) -> np.ndarray:
+    """Compares the distance between two arrays of embeddings
+    """
+    if distance_metric == DistanceMetric.EUCLIDEAN_SQUARED:
+        return np.square(
+            paired_distances(
+                embeddings1,
+                embeddings2,
+                metric='euclidean'))
+    elif distance_metric == DistanceMetric.ANGULAR_DISTANCE:
+        # Angular Distance: https://en.wikipedia.org/wiki/Cosine_similarity
+        similarity = 1 - paired_distances(
+            embeddings1,
+            embeddings2,
+            metric='cosine')
+        return np.arccos(similarity) / math.pi
+
+
+def download_image(url: str, is_rgb: bool = True) -> Optional[Image]:
+    try:
+        req = urlopen(url)
+        arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
+        # BGR color space
+        image = cv2.imdecode(arr, -1)
+        if is_rgb:
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        return image
+    except BaseException:
+        print('Couldn\'t read: {}'.format(url))
+        return None
+
+
+def get_image_from_path_rgb(image_path: str) -> Optional[Image]:
+    # BGR color space
+    try:
+        image = cv2.imread(image_path, cv2.IMREAD_COLOR)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        return fix_image(image)
+    except BaseException:
+        print('Couldn\'t read: {}'.format(image_path))
+        return None
+
+
+def get_image_from_path_bgr(image_path: str) -> Optional[Image]:
+    # BGR color space
+    try:
+        image = cv2.imread(image_path, cv2.IMREAD_COLOR)
+    except BaseException:
+        print('Couldn\'t read: {}'.format(image_path))
+        return None
+    return fix_image(image)
+
+
+def get_images_from_dir(
+        directory: str,
+        recursive: bool,
+        is_rgb: bool = True) -> ImageGenerator:
+    if recursive:
+        image_paths = iglob(os.path.join(
+            directory, '**', '*.*'), recursive=recursive)
+    else:
+        image_paths = iglob(os.path.join(directory, '*.*'))
+    for image_path in image_paths:
+        # BGR color space
+        image = cv2.imread(image_path)
+        if is_rgb:
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        yield image
+
+
+def get_dataset(path: str, is_flat: bool = False) -> List[PersonClass]:
+    """Gets a dataset from a directory. If is_flat then it assumes that
+       there is only one image per person in a flat directory.
+    """
+
+    dataset = cast(List[PersonClass], [])
+    path_exp = os.path.expanduser(path)
+    if is_flat:
+        people = [os.path.basename(path) for path in iglob(path_exp)]
+        image_paths = get_image_paths(path_exp)
+        dataset = [PersonClass(name, [image_path])
+                   for name, image_path in zip(people, image_paths)]
+        return dataset
+
+    people = sorted([path for path in os.listdir(path_exp)
+                     if os.path.isdir(os.path.join(path_exp, path))])
+    num_people = len(people)
+    for i in range(num_people):
+        person_name = people[i]
+        facedir = os.path.join(path_exp, person_name)
+        image_paths = get_image_paths(facedir)
+        dataset.append(PersonClass(person_name, image_paths))
+
+    return dataset
+
+
+def get_image_paths(facedir: str) -> List[str]:
+    image_paths = cast(List[str], [])
+    if os.path.isdir(facedir):
+        images = os.listdir(facedir)
+        image_paths = [os.path.join(facedir, img)
+                       for img in images if is_image(img)]
+    return image_paths
+
+
+def is_image(image_path: str) -> bool:
+    suffix = pathlib.Path(image_path).suffix
+    return suffix == '.jpg' or suffix == '.png' or suffix == '.jpeg'
+
+
+def find_image_with_type(image_base_path: str, image_dir: str):
+    for image_ext in ImageExtensions:
+        image_path = '{}.{}'.format(image_base_path, image_ext.value)
+        possible_path = join(image_dir, image_path)
+        if exists(possible_path):
+            return possible_path
+    err = 'No Image found with name {} in directory {}'.format(
+        image_base_path,
+        image_dir)
+    raise FileNotFoundError(err)
+
+
+def get_pair_image_path(person_name: str, image_number: int, image_dir: str):
+    """This is a utility function for parsing a pairs.txt file in LFW format
+    """
+    # e.g. person_name: Noam_Chomsky, image_number: 2 -> Noam_Chomsky_0002
+    image_number_name = '{}_{}'.format(person_name, '%04d' % int(image_number))
+    # e.g. Noam_Chomsky_0002 -> Noam_Chomsky/Noam_Chomsky_0002
+    # This is the relative path to the image assuming LFW directory format
+    relative_path = join(person_name, image_number_name)
+    # e.g. Noam_Chomsky/Noam_Chomsky_0002 ->
+    # {path_to_image_dir}/Noam_Chomsky/Noam_Chomsky_0002.jpg
+    path_with_type = find_image_with_type(relative_path, image_dir)
+    return path_with_type
+
+
+def read_pairs_file(pairs_filename: str) -> Tuple[List[Pair], int, int]:
+    pairs = []
+    with open(pairs_filename, 'r') as pair_file:
+        num_sets, num_matches_mismatches = [int(i)
+                                            for i in next(pair_file).split()]
+        for line in pair_file:
+            pair = cast(Pair, tuple([int(i) if i.isdigit() else i
+                                     for i in line.strip().split()]))
+            pairs.append(pair)
+    return pairs, num_sets, num_matches_mismatches
+
+
+def get_paths_and_labels(
+        image_dir: str, pairs: List[Pair]) -> Tuple[List[Tuple[str, str]], List[Label]]:
+    paths = []
+    labels = []
+    for pair in pairs:
+        if len(pair) == 3:
+            person, num_0, num_1 = cast(Match, pair)
+            rel_image_path_0 = get_pair_image_path(person, num_0, image_dir)
+            rel_image_path_1 = get_pair_image_path(person, num_1, image_dir)
+            is_same_person = True
+        elif len(pair) == 4:
+            person_0, num_0, person_1, num_1 = cast(Mismatch, pair)
+            rel_image_path_0 = get_pair_image_path(person_0, num_0, image_dir)
+            rel_image_path_1 = get_pair_image_path(person_1, num_1, image_dir)
+            is_same_person = False
+        else:
+            raise SyntaxError(
+                "Bad LFW format in pairs.txt: pair {} doesn't have length 3 or 4".format(pair))
+        paths.append((rel_image_path_0, rel_image_path_1))
+        labels.append(is_same_person)
+    return paths, labels
+
+
+def transform_to_lfw_format(image_directory: str,
+                            num_processes: Optional[int]=os.cpu_count()):
+    """Transforms an image dataset to lfw format image names.
+       Base directory should have a folder per person with the person's name:
+       -/base_folder
+        -/person_1
+          -image_1.jpg
+          -image_2.jpg
+          -image_3.jpg
+        -/person_2
+          -image_1.jpg
+          -image_2.jpg
+        ...
+    """
+    all_folders = os.path.join(image_directory, "*", "")
+    people_folders = iglob(all_folders)
+    process_pool = Pool(num_processes)
+    process_pool.imap(_rename, people_folders)
+    process_pool.close()
+    process_pool.join()
+
+
+def _rename(person_folder: str):
+    """Renames all the images in a folder in lfw format
+    """
+    all_image_paths = iglob(os.path.join(person_folder, "*.*"))
+    all_image_paths = sorted([image for image in all_image_paths if image.endswith(
+        ".jpg") or image.endswith(".png") or image.endswith(".jpeg")])
+    person_name = os.path.basename(os.path.normpath(person_folder))
+    concat_name = '_'.join(person_name.split())
+    for index, image_path in enumerate(all_image_paths):
+        image_name = concat_name + '_' + '%04d' % (index + 1)
+        file_ext = pathlib.Path(image_path).suffix
+        new_image_path = os.path.join(person_folder, image_name + file_ext)
+        os.rename(image_path, new_image_path)
+    os.rename(person_folder, person_folder.replace(person_name, concat_name))
+
+
+def split_dataset(dataset, split_ratio, min_nrof_images_per_class, mode):
+    if mode == 'SPLIT_CLASSES':
+        nrof_classes = len(dataset)
+        class_indices = np.arange(nrof_classes)
+        np.random.shuffle(class_indices)
+        split = int(round(nrof_classes * (1 - split_ratio)))
+        train_set = [dataset[i] for i in class_indices[0:split]]
+        test_set = [dataset[i] for i in class_indices[split:-1]]
+    elif mode == 'SPLIT_IMAGES':
+        train_set = []
+        test_set = []
+        for cls in dataset:
+            paths = cls.image_paths
+            np.random.shuffle(paths)
+            nrof_images_in_class = len(paths)
+            split = int(math.floor(nrof_images_in_class * (1 - split_ratio)))
+            if split == nrof_images_in_class:
+                split = nrof_images_in_class - 1
+            if split >= min_nrof_images_per_class and nrof_images_in_class - split >= 1:
+                train_set.append(PersonClass(cls.name, paths[:split]))
+                test_set.append(PersonClass(cls.name, paths[split:]))
+    else:
+        raise ValueError('Invalid train/test split mode "%s"' % mode)
+    return train_set, test_set
+
+
+def crop(image: Image, bounding_box: List[int], margin: float) -> Image:
+    """
+    img = image from misc.imread, which should be in (H, W, C) format
+    bounding_box = pixel coordinates of bounding box: (x0, y0, x1, y1)
+    margin = float from 0 to 1 for the amount of margin to add, relative to the
+        bounding box dimensions (half margin added to each side)
+    """
+
+    if margin < 0:
+        raise ValueError("the margin must be a value between 0 and 1")
+    if margin > 1:
+        raise ValueError(
+            "the margin must be a value between 0 and 1 - this is a change from the existing API")
+
+    img_height = image.shape[0]
+    img_width = image.shape[1]
+    x_0, y_0, x_1, y_1 = bounding_box[:4]
+    margin_height = (y_1 - y_0) * margin / 2
+    margin_width = (x_1 - x_0) * margin / 2
+    x_0 = int(np.maximum(x_0 - margin_width, 0))
+    y_0 = int(np.maximum(y_0 - margin_height, 0))
+    x_1 = int(np.minimum(x_1 + margin_width, img_width))
+    y_1 = int(np.minimum(y_1 + margin_height, img_height))
+    return image[y_0:y_1, x_0:x_1, :], (x_0, y_0, x_1, y_1)
+
+
+def get_transform_matrix(left_eye: Tuple[int,
+                                         int],
+                         right_eye: Tuple[int,
+                                          int],
+                         desired_left_eye: Tuple[float,
+                                                 float]=(0.35,
+                                                         0.35),
+                         desired_face_height: int=112,
+                         desired_face_width: int=112,
+                         margin: float=0.0):
+    # compute the angle between the eye centers
+    dY = right_eye[1] - left_eye[1]
+    dX = right_eye[0] - left_eye[0]
+    angle = np.degrees(np.arctan2(dY, dX))
+
+    # compute the desired right eye x-coordinate
+    desiredRightEyeX = 1.0 - desired_left_eye[0]
+
+    # determine the scale of the new resulting image by taking
+    dist = np.sqrt((dX ** 2) + (dY ** 2))
+    desiredDist = (desiredRightEyeX - desired_left_eye[0])
+    desiredDist *= desired_face_width
+    scale = (desiredDist / dist)
+
+    # median point between the two eyes in the input image
+    x_center = (left_eye[0] + right_eye[0]) // 2
+    y_center = (left_eye[1] + right_eye[1]) // 2
+    eye_center = (x_center, y_center)
+
+    # grab the rotation matrix for rotating and scaling the face
+    M = cv2.getRotationMatrix2D(eye_center, angle, scale)
+
+    # update the translation component of the matrix
+    tX = (desired_face_width * (margin + 1)) * 0.5
+    tY = (desired_face_height * (margin + 1)) * desired_left_eye[1]
+    x_shift = (tX - eye_center[0])
+    y_shift = (tY - eye_center[1])
+    M[0, 2] += x_shift
+    M[1, 2] += y_shift
+    return M
+
+
+def preprocess(
+        image: Image,
+        desired_height: int,
+        desired_width: int,
+        margin: float,
+        bbox: List[int]=None,
+        landmark: Landmarks=None,
+        use_affine: bool=False):
+    image_height, image_width = image.shape[:2]
+    margin_height = int(desired_height + desired_height * margin)
+    margin_width = int(desired_width + desired_width * margin)
+    M = None
+    if landmark is not None and use_affine:
+        M = get_transform_matrix(landmark['left_eye'],
+                                 landmark['right_eye'],
+                                 (0.35, 0.35),
+                                 desired_height,
+                                 desired_width,
+                                 margin)
+
+    if bbox is None:
+        # use center crop
+        bbox = [0, 0, 0, 0]
+        bbox[0] = int(image_height * 0.0625)
+        bbox[1] = int(image_width * 0.0625)
+        bbox[2] = image.shape[1] - bbox[0]
+        bbox[3] = image.shape[0] - bbox[1]
+    if M is None:
+        cropped = crop(image, bbox, margin)[0]
+        return cropped
+    else:
+        # do align using landmark
+        warped = cv2.warpAffine(
+            image, M, (margin_height, margin_width), flags=cv2.INTER_CUBIC)
+        return warped
+
+
+def get_center_box(img_size: np.ndarray, results: List[AlignResult]):
+    # x1, y1, x2, y2
+    all_bbs = np.asarray([result.bounding_box for result in results])
+    all_landmarks = [result.landmarks for result in results]
+    bounding_box_size = (all_bbs[:, 2] - all_bbs[:, 0]) * \
+        (all_bbs[:, 3] - all_bbs[:, 1])
+    img_center = img_size / 2
+    offsets = np.vstack([(all_bbs[:, 0] + all_bbs[:, 2]) / 2 - img_center[1],
+                         (all_bbs[:, 1] + all_bbs[:, 3]) / 2 - img_center[0]])
+    offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
+    index = np.argmax(
+        bounding_box_size -
+        offset_dist_squared *
+        2.0)  # some extra weight on the centering
+    out_bb = all_bbs[index, :]
+    out_landmark = all_landmarks[index] if index < len(all_landmarks) else None
+    align_result = AlignResult(bounding_box=out_bb, landmarks=out_landmark)
+    return [align_result]
diff --git a/facenet_sandberg/validate_on_lfw.py b/facenet_sandberg/validate_on_lfw.py
new file mode 100644
index 000000000..4a81d0e25
--- /dev/null
+++ b/facenet_sandberg/validate_on_lfw.py
@@ -0,0 +1,409 @@
+"""Validate a face recognizer on the "Labeled Faces in the Wild" dataset (http://vis-www.cs.umass.edu/lfw/).
+Embeddings are calculated using the pairs from http://vis-www.cs.umass.edu/lfw/pairs.txt and the ROC curve
+is calculated and plotted. Both the model metagraph and the model parameters need to exist
+in the same directory, and the metagraph should have the extension '.meta'.
+"""
+# MIT License
+#
+# Copyright (c) 2016 David Sandberg
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from __future__ import absolute_import, division, print_function
+
+import argparse
+import math
+import os
+import sys
+import warnings
+from enum import Enum
+from typing import List, Tuple, Union, cast
+
+import numpy as np
+import progressbar as pb
+import tensorflow as tf
+from facenet_sandberg import facenet, lfw, utils
+from facenet_sandberg.common_types import *
+from scipy import interpolate
+from scipy.optimize import brentq
+from sklearn import metrics
+from sklearn.metrics import accuracy_score, precision_score, recall_score
+from sklearn.metrics.pairwise import paired_distances
+from sklearn.model_selection import KFold
+from tensorflow.python.ops import data_flow_ops
+
+warnings.filterwarnings("ignore", message="numpy.dtype size changed")
+warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+tf.logging.set_verbosity(tf.logging.ERROR)
+
+
+def main(lfw_dir, model, lfw_pairs, use_flipped_images, subtract_mean,
+         use_fixed_image_standardization, image_size=160, lfw_nrof_folds=10,
+         distance_metric=0, lfw_batch_size=128):
+    """Runs testing on dataset
+
+    Arguments:
+        lfw_dir {str} -- Path to the data directory containing aligned LFW face patches.
+        model {str} -- Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file.
+        lfw_pairs {str} -- The file containing the pairs to use for validation.
+        use_flipped_images {bool} -- Concatenates embeddings for the image and its horizontally flipped counterpart.
+        subtract_mean {bool} -- Subtract feature mean before calculating distance.
+        use_fixed_image_standardization {bool} -- Performs fixed standardization of images.
+
+    Keyword Arguments:
+        image_size {int} -- [description] (default: {160})
+        lfw_nrof_folds {int} -- Number of folds to use for cross validation. Mainly used for testing. (default: {10})
+        distance_metric {int} -- Distance metric  0:euclidian, 1:cosine similarity. (default: {0})
+        lfw_batch_size {int} -- Number of images to process in a batch in the LFW test set. (default: {128})
+    """
+
+    with tf.Graph().as_default():
+        with tf.Session() as sess:
+            # Read the file containing the pairs used for testing
+            pairs = lfw.read_pairs(os.path.expanduser(lfw_pairs))
+
+            # Get the paths for the corresponding images
+            paths, labels = lfw.get_paths(os.path.expanduser(lfw_dir), pairs)
+
+            image_paths_placeholder = tf.placeholder(
+                tf.string, shape=(None, 1), name='image_paths')
+            labels_placeholder = tf.placeholder(
+                tf.int32, shape=(None, 1), name='labels')
+            batch_size_placeholder = tf.placeholder(
+                tf.int32, name='batch_size')
+            control_placeholder = tf.placeholder(
+                tf.int32, shape=(None, 1), name='control')
+            phase_train_placeholder = tf.placeholder(
+                tf.bool, name='phase_train')
+
+            nrof_preprocess_threads = 4
+            image_size = (image_size, image_size)
+            eval_input_queue = data_flow_ops.FIFOQueue(
+                capacity=2000000, dtypes=[
+                    tf.string, tf.int32, tf.int32], shapes=[
+                    (1,), (1,), (1,)], shared_name=None, name=None)
+            eval_enqueue_op = eval_input_queue.enqueue_many([image_paths_placeholder, labels_placeholder,
+                                                             control_placeholder], name='eval_enqueue_op')
+            image_batch, label_batch = facenet.create_input_pipeline(
+                eval_input_queue, image_size, nrof_preprocess_threads, batch_size_placeholder)
+
+            # Load the model
+            input_map = {
+                'image_batch': image_batch,
+                'label_batch': label_batch,
+                'phase_train': phase_train_placeholder}
+            facenet.load_model(model, input_map=input_map)
+
+            # Get output tensor
+            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
+
+            coord = tf.train.Coordinator()
+            tf.train.start_queue_runners(coord=coord, sess=sess)
+
+            evaluate(
+                sess,
+                eval_enqueue_op,
+                image_paths_placeholder,
+                labels_placeholder,
+                phase_train_placeholder,
+                batch_size_placeholder,
+                control_placeholder,
+                embeddings,
+                label_batch,
+                paths,
+                labels,
+                lfw_batch_size,
+                lfw_nrof_folds,
+                distance_metric,
+                subtract_mean,
+                use_flipped_images,
+                use_fixed_image_standardization)
+
+
+def evaluate(
+        sess,
+        enqueue_op,
+        image_paths_placeholder,
+        labels_placeholder,
+        phase_train_placeholder,
+        batch_size_placeholder,
+        control_placeholder,
+        embeddings,
+        labels,
+        image_paths,
+        actual_issame,
+        batch_size,
+        nrof_folds,
+        distance_metric,
+        subtract_mean,
+        use_flipped_images,
+        use_fixed_image_standardization):
+    # Run forward pass to calculate embeddings
+    widgets = ['Scoring', pb.Percentage(), ' ',
+               pb.Bar(marker=pb.Bar()), ' ', pb.ETA()]
+
+    # Enqueue one epoch of image paths and labels
+    # nrof_pairs * nrof_images_per_pair
+    nrof_embeddings = len(actual_issame) * 2
+    nrof_flips = 2 if use_flipped_images else 1
+    nrof_images = nrof_embeddings * nrof_flips
+
+    labels_array = np.expand_dims(np.arange(0, nrof_images), 1)
+    image_paths_array = np.expand_dims(
+        np.repeat(np.array(image_paths), nrof_flips), 1)
+    control_array = np.zeros_like(labels_array, np.int32)
+
+    if use_fixed_image_standardization:
+        control_array += np.ones_like(labels_array) * \
+            facenet.FIXED_STANDARDIZATION
+    if use_flipped_images:
+        # Flip every second image
+        control_array += (labels_array % 2) * facenet.FLIP
+
+    sess.run(enqueue_op,
+             {image_paths_placeholder: image_paths_array,
+              labels_placeholder: labels_array,
+              control_placeholder: control_array})
+
+    embedding_size = int(embeddings.get_shape()[1])
+    assert nrof_images % batch_size == 0, 'The number of LFW images must be an integer multiple of the LFW batch size'
+    nrof_batches = nrof_images // batch_size
+    emb_array = np.zeros((nrof_images, embedding_size))
+    lab_array = np.zeros((nrof_images,))
+
+    timer = pb.ProgressBar(
+        widgets=widgets, maxval=int(
+            nrof_batches + 1)).start()
+    for i in range(nrof_batches):
+        feed_dict = {phase_train_placeholder: False,
+                     batch_size_placeholder: batch_size}
+        emb, lab = sess.run([embeddings, labels], feed_dict=feed_dict)
+        lab_array[lab] = lab
+        emb_array[lab, :] = emb
+        timer.update(i + 1)
+    timer.finish()
+    embeddings = np.zeros((nrof_embeddings, embedding_size * nrof_flips))
+    if use_flipped_images:
+        # Concatenate embeddings for flipped and non flipped version of the
+        # images
+        embeddings[:, :embedding_size] = emb_array[0::2, :]
+        embeddings[:, embedding_size:] = emb_array[1::2, :]
+    else:
+        embeddings = emb_array
+
+    accuracy, recall, precision = score(embeddings,
+                                        np.asarray(actual_issame),
+                                        nrof_folds,
+                                        'ANGULAR_DISTANCE',
+                                        'ACCURACY',
+                                        subtract_mean,
+                                        False,
+                                        0,
+                                        4,
+                                        0.01)
+    print('Accuracy: {}'.format(accuracy))
+    print('Recall: {}'.format(recall))
+    print('Precision: {}'.format(precision))
+
+    # assert np.array_equal(lab_array, np.arange(
+    #     nrof_images)), 'Wrong labels used for evaluation, possibly caused by training examples left in the input pipeline'
+    # tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(
+    # embeddings, actual_issame, nrof_folds=nrof_folds,
+    # distance_metric=distance_metric, subtract_mean=subtract_mean)
+
+    # print('Accuracy: %2.5f+-%2.5f' % (np.mean(accuracy), np.std(accuracy)))
+    # print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
+
+    # auc = metrics.auc(fpr, tpr)
+    # print('Area Under Curve (AUC): %1.3f' % auc)
+    # eer = brentq(lambda x: 1. - x - interpolate.interp1d(fpr, tpr)(x), 0., 1.)
+    # print('Equal Error Rate (EER): %1.3f' % eer)
+
+
+def score(embeddings: np.ndarray,
+          labels: np.ndarray,
+          num_folds: int,
+          distance_metric: DistanceMetric,
+          threshold_metric: str,
+          subtract_mean: bool,
+          divide_stddev: bool,
+          threshold_start: float,
+          threshold_end: float,
+          threshold_step: float) -> Tuple[np.float, np.float, np.float]:
+    thresholds = np.arange(threshold_start, threshold_end, threshold_step)
+    embeddings1 = embeddings[0::2]
+    embeddings2 = embeddings[1::2]
+    accuracy, recall, precision = _score_k_fold(thresholds,
+                                                embeddings1,
+                                                embeddings2,
+                                                labels,
+                                                num_folds,
+                                                threshold_metric,
+                                                subtract_mean,
+                                                divide_stddev)
+    return np.mean(accuracy), np.mean(recall), np.mean(precision)
+
+
+def _score_k_fold(thresholds: np.ndarray,
+                  embeddings1: np.ndarray,
+                  embeddings2: np.ndarray,
+                  labels: np.ndarray,
+                  num_folds: int,
+                  threshold_metric: str,
+                  subtract_mean: bool,
+                  divide_stddev: bool) -> Tuple[np.ndarray,
+                                                np.ndarray,
+                                                np.ndarray]:
+    k_fold = KFold(n_splits=num_folds, shuffle=True)
+    accuracy = np.zeros((num_folds))
+    recall = np.zeros((num_folds))
+    precision = np.zeros((num_folds))
+    splits = k_fold.split(np.arange(len(labels)))
+    for fold_idx, (train_set, test_set) in enumerate(splits):
+        train_embeddings = np.concatenate([embeddings1[train_set],
+                                           embeddings2[train_set]])
+        mean = np.mean(train_embeddings, axis=0) if subtract_mean else 0.0
+        stddev = np.std(train_embeddings, axis=0) if divide_stddev else 1.0
+        dist = _distance_between_embeddings((embeddings1 - mean) / stddev,
+                                            (embeddings2 - mean) / stddev)
+        best_threshold = _calculate_best_threshold(thresholds,
+                                                   dist[train_set],
+                                                   labels[train_set],
+                                                   threshold_metric)
+        predictions = np.less(dist[test_set], best_threshold)
+        accuracy[fold_idx] = accuracy_score(labels[test_set], predictions)
+        recall[fold_idx] = recall_score(labels[test_set], predictions)
+        precision[fold_idx] = precision_score(labels[test_set], predictions)
+    return accuracy, recall, precision
+
+
+def _distance_between_embeddings(
+        embeddings1: np.ndarray,
+        embeddings2: np.ndarray) -> np.ndarray:
+    # if distance_metric == DistanceMetric.EUCLIDEAN_SQUARED:
+    #     return np.square(
+    #         paired_distances(
+    #             embeddings1,
+    #             embeddings2,
+    #             metric='euclidean'))
+    # elif distance_metric == DistanceMetric.ANGULAR_DISTANCE:
+        # Angular Distance: https://en.wikipedia.org/wiki/Cosine_similarity
+    similarity = 1 - paired_distances(
+        embeddings1,
+        embeddings2,
+        metric='cosine')
+    return np.arccos(similarity) / math.pi
+
+
+def _calculate_best_threshold(thresholds: np.ndarray,
+                              dist: np.ndarray,
+                              labels: np.ndarray,
+                              threshold_metric: str) -> np.float:
+
+    if threshold_metric == 'ACCURACY':
+        threshold_score = accuracy_score
+    elif threshold_metric == 'PRECISION':
+        threshold_score = precision_score
+    elif threshold_metric == 'RECALL':
+        threshold_score = recall_score
+    threshold_scores = np.zeros((len(thresholds)))
+    for threshold_idx, threshold in enumerate(thresholds):
+        predictions = np.less(dist, threshold)
+        threshold_scores[threshold_idx] = threshold_score(labels, predictions)
+    best_threshold_index = np.argmax(threshold_scores)
+    return thresholds[best_threshold_index]
+
+
+def parse_arguments(argv):
+    """Argument parser
+
+    Arguments:
+        argv {} -- arguments
+
+    Returns:
+        {} -- parsed arguments
+    """
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        'lfw_dir',
+        type=str,
+        help='Path to the data directory containing aligned LFW face patches.',
+        default='/Users/armanrahman/datasets/eame_test_facenet_old')
+    parser.add_argument(
+        '--lfw_batch_size',
+        type=int,
+        help='Number of images to process in a batch in the LFW test set.',
+        default=100)
+    parser.add_argument(
+        'model',
+        type=str,
+        help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file',
+        default='/Users/armanrahman/models/facenet_model.pb')
+    parser.add_argument(
+        '--image_size',
+        type=int,
+        help='Image size (height, width) in pixels.',
+        default=160)
+    parser.add_argument(
+        '--lfw_pairs',
+        type=str,
+        help='The file containing the pairs to use for validation.',
+        default='/Users/armanrahman/datasets/eame_test_pairs_facenet.txt')
+    parser.add_argument(
+        '--lfw_nrof_folds',
+        type=int,
+        help='Number of folds to use for cross validation. Mainly used for testing.',
+        default=10)
+    parser.add_argument(
+        '--distance_metric',
+        type=int,
+        help='Distance metric  0:euclidian, 1:cosine similarity.',
+        default=1)
+    parser.add_argument(
+        '--use_flipped_images',
+        help='Concatenates embeddings for the image and its horizontally flipped counterpart.',
+        action='store_true')
+    parser.add_argument(
+        '--subtract_mean',
+        help='Subtract feature mean before calculating distance.',
+        action='store_true')
+    parser.add_argument(
+        '--use_fixed_image_standardization',
+        help='Performs fixed standardization of images.',
+        action='store_true')
+    return parser.parse_args(argv)
+
+
+if __name__ == '__main__':
+    args = parse_arguments(sys.argv[1:])
+    if args:
+        main(
+            args.lfw_dir,
+            args.model,
+            args.lfw_pairs,
+            args.use_flipped_images,
+            args.subtract_mean,
+            args.use_fixed_image_standardization,
+            args.image_size,
+            args.lfw_nrof_folds,
+            args.distance_metric,
+            args.lfw_batch_size)
diff --git a/facenet_sandberg/validation/Dockerfile b/facenet_sandberg/validation/Dockerfile
new file mode 100644
index 000000000..abd48ab19
--- /dev/null
+++ b/facenet_sandberg/validation/Dockerfile
@@ -0,0 +1,20 @@
+# https://getintodevops.com/blog/the-simple-way-to-run-docker-in-docker-for-ci
+FROM python:3.6-slim
+RUN apt-get update && \
+apt-get -y install apt-transport-https \
+     ca-certificates \
+     curl \
+     gnupg2 \
+     software-properties-common && \
+curl -fsSL https://download.docker.com/linux/$(. /etc/os-release; echo "$ID")\
+/gpg > /tmp/dkey; apt-key add /tmp/dkey && \
+add-apt-repository \
+   "deb [arch=amd64] https://download.docker.com/linux/\
+$(. /etc/os-release; echo "$ID") $(lsb_release -cs) stable" && \
+apt-get update && \
+apt-get -y install docker-ce
+COPY requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir -r /app/requirements.txt
+COPY src /app/validation/src
+WORKDIR /app/validation/src
+ENTRYPOINT ["python", "validate.py"]
diff --git a/facenet_sandberg/validation/README.md b/facenet_sandberg/validation/README.md
new file mode 100644
index 000000000..0a1e8bacc
--- /dev/null
+++ b/facenet_sandberg/validation/README.md
@@ -0,0 +1,19 @@
+# Instructions
+
+## With docker-compose
+
+* Replace values in .env
+* Run ```docker-compose up```
+
+## Without docker
+
+* The validation script requires python 3.6
+    * If using [anaconda](https://www.anaconda.com/download/), create a virtual environment with ```conda create -n py36 python=3.6```
+    * Activate the virtual environment with ```conda activate py36```
+    * Install requirements with ```pip install -r requirements.txt```
+* Example to run validation script:
+
+```bash
+python validate.py --image_dir /images --model_path /facenet.pb --distance_metric ANGULAR_DISTANCE --pairs_fname /pairs/pairs.txt --threshold_start 0 --threshold_end 4 --threshold_step 0.01 --embedding_size 128 --threshold_metric ACCURACY --prealigned_flag --remove_empty_embeddings_flag
+--is_insightface
+```
diff --git a/facenet_sandberg/validation/__init__.py b/facenet_sandberg/validation/__init__.py
new file mode 100644
index 000000000..a75b11e43
--- /dev/null
+++ b/facenet_sandberg/validation/__init__.py
@@ -0,0 +1,3 @@
+from .align import Detector
+from .generate_tsne import generate_tsne
+from .identifier import Identifier
diff --git a/facenet_sandberg/validation/docker-compose.yml b/facenet_sandberg/validation/docker-compose.yml
new file mode 100644
index 000000000..38327f105
--- /dev/null
+++ b/facenet_sandberg/validation/docker-compose.yml
@@ -0,0 +1,26 @@
+version: '3'
+services:
+  validation:
+    build: .
+    command: "--image_dir ${IMAGE_DIR} \
+--container_name algorithm \
+--distance_metric ${DISTANCE_METRIC} \
+--pairs_fname ${PAIRS_FNAME} \
+--threshold_start ${THRESHOLD_START} \
+--threshold_end ${THRESHOLD_END} \
+--threshold_step ${THRESHOLD_STEP} \
+--embedding_size ${EMBEDDING_SIZE} \
+--threshold_metric ${THRESHOLD_METRIC} \
+${PREALIGNED_FLAG} \
+${REMOVE_EMPTY_EMBEDDINGS_FLAG}"
+    volumes:
+      - "/var/run/docker.sock:/var/run/docker.sock"
+      - "${IMAGE_DIR}:${IMAGE_DIR}"
+      - "${PAIRS_FNAME}:${PAIRS_FNAME}"
+    depends_on:
+      - algorithm
+  algorithm:
+    build: ${ALGORITHM_CONTAINER_DIR}
+    image: algorithm
+    entrypoint: ["/bin/sh", "-c"]
+    command: "\"echo built algorithm container\""
diff --git a/facenet_sandberg/validation/requirements.txt b/facenet_sandberg/validation/requirements.txt
new file mode 100644
index 000000000..41e8c9911
--- /dev/null
+++ b/facenet_sandberg/validation/requirements.txt
@@ -0,0 +1,6 @@
+numpy==1.15.1
+scikit-learn==0.19.2
+scipy==1.1.0
+mypy==0.620
+pylint==2.1.1
+flake8==3.5.0
diff --git a/src/generative/__init__.py b/facenet_sandberg/validation/src/__init__.py
similarity index 100%
rename from src/generative/__init__.py
rename to facenet_sandberg/validation/src/__init__.py
diff --git a/src/generative/models/__init__.py b/facenet_sandberg/validation/src/calculator/__init__.py
similarity index 100%
rename from src/generative/models/__init__.py
rename to facenet_sandberg/validation/src/calculator/__init__.py
diff --git a/facenet_sandberg/validation/src/calculator/calculator.py b/facenet_sandberg/validation/src/calculator/calculator.py
new file mode 100644
index 000000000..e552d6a2c
--- /dev/null
+++ b/facenet_sandberg/validation/src/calculator/calculator.py
@@ -0,0 +1,15 @@
+from abc import ABC
+from abc import abstractmethod
+from parser.pair import Pair
+from typing import Generic
+from typing import Iterable
+from typing import TypeVar
+
+T = TypeVar('T')
+
+
+class Calculator(ABC, Generic[T]):  # pylint: disable=too-few-public-methods
+
+    @abstractmethod
+    def calculate(self, pairs: Iterable[Pair]) -> T:
+        pass
diff --git a/facenet_sandberg/validation/src/calculator/distance_calculator.py b/facenet_sandberg/validation/src/calculator/distance_calculator.py
new file mode 100644
index 000000000..94b5c86bd
--- /dev/null
+++ b/facenet_sandberg/validation/src/calculator/distance_calculator.py
@@ -0,0 +1,47 @@
+import math
+from parser.pair import Pair
+from typing import Iterable
+from typing import Union
+from typing import cast
+
+import numpy as np
+from sklearn.metrics.pairwise import paired_distances
+
+from calculator.calculator import Calculator
+from metrics.metrics import DistanceMetric
+from metrics.metrics import DistanceMetricException
+
+
+# pylint: disable=too-few-public-methods
+class DistanceCalculator(Calculator):
+
+    def __init__(self, distance_metric: Union[str, DistanceMetric]) -> None:
+        if isinstance(distance_metric, str):
+            self._distance_metric = getattr(DistanceMetric,
+                                            cast(str, distance_metric))
+        else:
+            self._distance_metric = distance_metric
+
+    def calculate(self, pairs: Iterable[Pair]) -> np.ndarray:
+        embeddings1 = []
+        embeddings2 = []
+        for pair in pairs:
+            embeddings1.append(pair.image1)
+            embeddings2.append(pair.image2)
+        if self._distance_metric == DistanceMetric.EUCLIDEAN_SQUARED:
+            return np.square(
+                paired_distances(
+                    embeddings1,
+                    embeddings2,
+                    metric='euclidean'))
+        if self._distance_metric == DistanceMetric.ANGULAR_DISTANCE:
+            # Angular Distance: https://en.wikipedia.org/wiki/Cosine_similarity
+            similarity = 1 - paired_distances(
+                embeddings1,
+                embeddings2,
+                metric='cosine')
+            return np.arccos(similarity) / math.pi
+        metrics = [str(metric) for metric in DistanceMetric]
+        err = f"Undefined {DistanceMetric.__qualname__}. \
+Choose from {metrics}"
+        raise DistanceMetricException(err)
diff --git a/facenet_sandberg/validation/src/calculator/threshold_calculator.py b/facenet_sandberg/validation/src/calculator/threshold_calculator.py
new file mode 100644
index 000000000..d0781ab22
--- /dev/null
+++ b/facenet_sandberg/validation/src/calculator/threshold_calculator.py
@@ -0,0 +1,70 @@
+from parser.pair import Pair
+from typing import Callable
+from typing import Iterable
+from typing import Union
+from typing import cast
+
+import numpy as np
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import f1_score
+from sklearn.metrics import precision_score
+from sklearn.metrics import recall_score
+
+from calculator.calculator import Calculator
+from calculator.distance_calculator import DistanceCalculator
+from metrics.metrics import DistanceMetric
+from metrics.metrics import ThresholdMetric
+from metrics.metrics import ThresholdMetricException
+
+
+# pylint: disable=too-few-public-methods
+class ThresholdCalculator(Calculator):
+
+    # pylint: disable=too-many-arguments
+    def __init__(self,
+                 distance_metric: Union[str, DistanceMetric],
+                 threshold_metric: Union[str, ThresholdMetric],
+                 threshold_start: float,
+                 threshold_end: float,
+                 threshold_step: float) -> None:
+        if isinstance(threshold_metric, str):
+            self._threshold_metric = getattr(ThresholdMetric,
+                                             cast(str, threshold_metric))
+        else:
+            self._threshold_metric = threshold_metric
+        self._distance_metric = distance_metric
+        self._threshold_start = threshold_start
+        self._threshold_end = threshold_end
+        self._threshold_step = threshold_step
+
+    def calculate(self, pairs: Iterable[Pair]) -> float:
+        threshold_scorer = self._get_threshold_scorer()
+        dist = DistanceCalculator(self._distance_metric).calculate(pairs)
+        labels = [pair.is_match for pair in pairs]
+        best_score = float('-inf')
+        best_threshold_index = 0
+        thresholds = np.arange(self._threshold_start,
+                               self._threshold_end,
+                               self._threshold_step)
+        for i, threshold in enumerate(thresholds):
+            predictions = np.less(dist, threshold)
+            score = threshold_scorer(labels, predictions)
+            if score > best_score:
+                best_score = score
+                best_threshold_index = i
+        return thresholds[best_threshold_index]
+
+    def _get_threshold_scorer(
+            self) -> Callable[[np.ndarray, np.ndarray], float]:
+        if self._threshold_metric == ThresholdMetric.ACCURACY:
+            return accuracy_score
+        if self._threshold_metric == ThresholdMetric.PRECISION:
+            return precision_score
+        if self._threshold_metric == ThresholdMetric.RECALL:
+            return recall_score
+        if self._threshold_metric == ThresholdMetric.F1:
+            return f1_score
+        metrics = [str(metric) for metric in ThresholdMetric]
+        err = f"Undefined {ThresholdMetric.__qualname__}. \
+Choose from {metrics}"
+        raise ThresholdMetricException(err)
diff --git a/facenet_sandberg/validation/src/evaluator/__init__.py b/facenet_sandberg/validation/src/evaluator/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/facenet_sandberg/validation/src/evaluator/evaluator.py b/facenet_sandberg/validation/src/evaluator/evaluator.py
new file mode 100644
index 000000000..7543e532b
--- /dev/null
+++ b/facenet_sandberg/validation/src/evaluator/evaluator.py
@@ -0,0 +1,62 @@
+from argparse import Namespace
+from parser.container_parser import ContainerParser
+from parser.face_vector_fill_parser import FaceVectorFillParser
+from parser.face_vector_parser import FaceVectorParser
+from parser.face_vector_remove_parser import FaceVectorRemoveParser
+from parser.pair_parser import PairParser
+
+import numpy as np
+from sklearn.metrics import accuracy_score, precision_score, recall_score
+
+from calculator.distance_calculator import DistanceCalculator
+from calculator.threshold_calculator import ThresholdCalculator
+from metrics.metrics import EvaluationMetric, FaceVectorMetric
+
+
+class Evaluator:
+
+    def __init__(self,
+                 face_vector_parser: FaceVectorParser,
+                 threshold_calculator: ThresholdCalculator,
+                 distance_calculator: DistanceCalculator) -> None:
+        self._face_vector_parser = face_vector_parser
+        self._threshold_calculator = threshold_calculator
+        self._distance_calculator = distance_calculator
+
+    @classmethod
+    def create_evaluator(cls, args: Namespace) -> 'Evaluator':
+        pair_parser = PairParser(args.pairs_fname, args.image_dir)
+        container_parser = ContainerParser(pair_parser,
+                                           args.model_path,
+                                           args.is_insightface,
+                                           args.prealigned_flag)
+        face_vector_parser: FaceVectorParser
+        if args.remove_empty_embeddings_flag:
+            face_vector_parser = FaceVectorRemoveParser(container_parser,
+                                                        args.distance_metric)
+        else:
+            face_vector_parser = FaceVectorFillParser(container_parser,
+                                                      args.embedding_size,
+                                                      args.distance_metric)
+        threshold_calculator = ThresholdCalculator(args.distance_metric,
+                                                   args.threshold_metric,
+                                                   args.threshold_start,
+                                                   args.threshold_end,
+                                                   args.threshold_step)
+        distance_calculator = DistanceCalculator(args.distance_metric)
+        return cls(face_vector_parser,
+                   threshold_calculator,
+                   distance_calculator)
+
+    def compute_metrics(self) -> FaceVectorMetric:
+        return self._face_vector_parser.compute_metrics()
+
+    def evaluate(self) -> EvaluationMetric:
+        pairs = list(self._face_vector_parser.compute_pairs())
+        threshold = self._threshold_calculator.calculate(pairs)
+        dist = self._distance_calculator.calculate(pairs)
+        predictions = np.less(dist, threshold)
+        labels = [pair.is_match for pair in pairs]
+        return EvaluationMetric(accuracy_score(labels, predictions),
+                                recall_score(labels, predictions),
+                                precision_score(labels, predictions))
diff --git a/facenet_sandberg/validation/src/metrics/__init__.py b/facenet_sandberg/validation/src/metrics/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/facenet_sandberg/validation/src/metrics/metrics.py b/facenet_sandberg/validation/src/metrics/metrics.py
new file mode 100644
index 000000000..dadf3f9d3
--- /dev/null
+++ b/facenet_sandberg/validation/src/metrics/metrics.py
@@ -0,0 +1,78 @@
+from enum import Enum
+from enum import auto
+
+
+class EvaluationMetric:
+    def __init__(self,
+                 accuracy: float,
+                 recall: float,
+                 precision: float) -> None:
+        self._accuracy = accuracy
+        self._recall = recall
+        self._precision = precision
+
+    @property
+    def accuracy(self):
+        return self._accuracy
+
+    @property
+    def recall(self):
+        return self._recall
+
+    @property
+    def precision(self):
+        return self._precision
+
+    def __str__(self):
+        return 'Evaluation Metrics - accuracy: {:.2f}, \
+recall: {:.2f}, precision: {:.2f}'.format(self._accuracy,
+                                          self._recall,
+                                          self._precision)
+
+
+class FaceVectorMetric:
+    def __init__(self,
+                 num_expected: int,
+                 num_missing: int,
+                 percentage_missing: float) -> None:
+        self._num_expected = num_expected
+        self._num_missing = num_missing
+        self._percentage_missing = percentage_missing
+
+    @property
+    def num_expected(self):
+        return self._num_expected
+
+    @property
+    def num_missing(self):
+        return self._num_missing
+
+    @property
+    def percentage_missing(self):
+        return self._percentage_missing
+
+    def __str__(self):
+        return 'Face Vector Metrics - num_expected: {}, \
+num_missing: {}, percentage_missing: {:.2f}'.format(self._num_expected,
+                                                    self.num_missing,
+                                                    self.percentage_missing)
+
+
+class DistanceMetric(Enum):
+    ANGULAR_DISTANCE = auto()
+    EUCLIDEAN_SQUARED = auto()
+
+
+class ThresholdMetric(Enum):
+    ACCURACY = auto()
+    PRECISION = auto()
+    RECALL = auto()
+    F1 = auto()
+
+
+class DistanceMetricException(Exception):
+    pass
+
+
+class ThresholdMetricException(Exception):
+    pass
diff --git a/facenet_sandberg/validation/src/parser/__init__.py b/facenet_sandberg/validation/src/parser/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/facenet_sandberg/validation/src/parser/container_parser.py b/facenet_sandberg/validation/src/parser/container_parser.py
new file mode 100644
index 000000000..f3915cce4
--- /dev/null
+++ b/facenet_sandberg/validation/src/parser/container_parser.py
@@ -0,0 +1,65 @@
+import json
+from os.path import basename, dirname, join
+from parser.pair import Pair
+from parser.pair_parser import PairParser
+from parser.parser_base import ParserBase
+from typing import Dict, Iterable, List
+
+from facenet_sandberg import (Identifier, get_image_from_path_bgr,
+                              get_image_from_path_rgb)
+
+
+class ContainerParser(ParserBase):
+
+    def __init__(self,
+                 pair_parser: PairParser,
+                 model_path: str,
+                 is_insightface: bool,
+                 is_prealigned: bool) -> None:
+        self._pair_parser = pair_parser
+        self._model_path = model_path
+        self._is_insightface = is_insightface
+        self._is_prealigned = is_prealigned
+        self.__face_vectors = None
+
+    @property
+    def _face_vectors(self):
+        if not self.__face_vectors:
+            self.__face_vectors = self._compute_face_vectors()
+        return self.__face_vectors
+
+    def compute_pairs(self) -> Iterable[Pair]:
+        pairs = self._pair_parser.compute_pairs()
+        return (Pair(image1, image2, pair.is_match)
+                for image1, image2, pair in
+                zip(self._face_vectors[0::2], self._face_vectors[1::2], pairs))
+
+    def compute_metrics(self) -> Dict[str, float]:
+        raise NotImplementedError()
+
+    def _compute_face_vectors(self) -> List[List[List[float]]]:
+        pairs = list(self._pair_parser.compute_pairs())
+
+        identifier = Identifier(
+            model_path=self._model_path,
+            is_insightface=self._is_insightface)
+
+        img_paths = [image_path
+                     for pair in pairs
+                     for image_path in [pair.image1, pair.image2]]
+        if self._is_insightface:
+            images = map(get_image_from_path_bgr, img_paths)
+        else:
+            images = map(get_image_from_path_rgb, img_paths)
+
+        all_vectors = identifier.vectorize_all(
+            images, prealigned=self._is_prealigned)
+        np_to_list = []
+        for vectors in all_vectors:
+            np_to_list.append([vector.tolist() for vector in vectors])
+
+        return np_to_list
+
+    @staticmethod
+    def _get_base_dir_for_volume_mapping(full_image_path: str) -> str:
+        return dirname(dirname(full_image_path))
diff --git a/facenet_sandberg/validation/src/parser/face_vector_fill_parser.py b/facenet_sandberg/validation/src/parser/face_vector_fill_parser.py
new file mode 100644
index 000000000..34c66155e
--- /dev/null
+++ b/facenet_sandberg/validation/src/parser/face_vector_fill_parser.py
@@ -0,0 +1,26 @@
+from functools import partial
+from parser.container_parser import ContainerParser
+from parser.face_vector_parser import FaceVectorParser
+from parser.pipeline.parser_pipeline import ParserPipeline
+from parser.pipeline.parser_pipeline_funcs import fill_empty
+from parser.pipeline.parser_pipeline_funcs import filter_target
+
+
+class FaceVectorFillParser(FaceVectorParser):
+    def __init__(self,
+                 container_parser: ContainerParser,
+                 embedding_size: int,
+                 distance_metric: str) -> None:
+        self._parser_pipeline = ParserPipeline(container_parser)
+        self._build_pipeline(embedding_size, distance_metric)
+        super().__init__(container_parser,
+                         self._parser_pipeline,
+                         distance_metric)
+
+    def _build_pipeline(self,
+                        embedding_size: int,
+                        distance_metric: str) -> None:
+        partial_fill = partial(fill_empty, embedding_size=embedding_size)
+        partial_filter = partial(filter_target,
+                                 distance_metric=distance_metric)
+        self._parser_pipeline.build([partial_fill, partial_filter])
diff --git a/facenet_sandberg/validation/src/parser/face_vector_parser.py b/facenet_sandberg/validation/src/parser/face_vector_parser.py
new file mode 100644
index 000000000..a18a1e31a
--- /dev/null
+++ b/facenet_sandberg/validation/src/parser/face_vector_parser.py
@@ -0,0 +1,29 @@
+from parser.container_parser import ContainerParser
+from parser.pair import Pair
+from parser.parser_base import ParserBase
+from parser.pipeline.parser_pipeline import ParserPipeline
+from typing import Iterable
+
+from metrics.metrics import FaceVectorMetric
+
+
+class FaceVectorParser(ParserBase):
+
+    def __init__(self,
+                 container_parser: ContainerParser,
+                 parser_pipeline: ParserPipeline,
+                 distance_metric: str) -> None:
+        self._container_parser = container_parser
+        self._distance_metric = distance_metric
+        self._parser_pipeline = parser_pipeline
+
+    def compute_pairs(self) -> Iterable[Pair]:
+        return self._parser_pipeline.execute_pipeline()
+
+    def compute_metrics(self) -> FaceVectorMetric:
+        pairs = list(self._container_parser.compute_pairs())
+        num_expected = len(pairs)
+        num_existing = sum(1 for pair in pairs if pair.image1 and pair.image2)
+        num_missing = num_expected - num_existing
+        percentage_missing = 100 * (num_missing / num_expected)
+        return FaceVectorMetric(num_expected, num_missing, percentage_missing)
diff --git a/facenet_sandberg/validation/src/parser/face_vector_remove_parser.py b/facenet_sandberg/validation/src/parser/face_vector_remove_parser.py
new file mode 100644
index 000000000..5fad0ab5a
--- /dev/null
+++ b/facenet_sandberg/validation/src/parser/face_vector_remove_parser.py
@@ -0,0 +1,22 @@
+from functools import partial
+from parser.container_parser import ContainerParser
+from parser.face_vector_parser import FaceVectorParser
+from parser.pipeline.parser_pipeline import ParserPipeline
+from parser.pipeline.parser_pipeline_funcs import filter_target
+from parser.pipeline.parser_pipeline_funcs import remove_empty
+
+
+class FaceVectorRemoveParser(FaceVectorParser):
+    def __init__(self,
+                 container_parser: ContainerParser,
+                 distance_metric: str) -> None:
+        self._parser_pipeline = ParserPipeline(container_parser)
+        self._build_pipeline(distance_metric)
+        super().__init__(container_parser,
+                         self._parser_pipeline,
+                         distance_metric)
+
+    def _build_pipeline(self, distance_metric: str) -> None:
+        partial_filter = partial(filter_target,
+                                 distance_metric=distance_metric)
+        self._parser_pipeline.build([remove_empty, partial_filter])
diff --git a/facenet_sandberg/validation/src/parser/pair.py b/facenet_sandberg/validation/src/parser/pair.py
new file mode 100644
index 000000000..b62ebc3f8
--- /dev/null
+++ b/facenet_sandberg/validation/src/parser/pair.py
@@ -0,0 +1,23 @@
+from typing import Generic
+from typing import TypeVar
+
+T = TypeVar('T')
+
+
+class Pair(Generic[T]):
+    def __init__(self, image1: T, image2: T, is_match: bool) -> None:
+        self._image1 = image1
+        self._image2 = image2
+        self._is_match = is_match
+
+    @property
+    def image1(self):
+        return self._image1
+
+    @property
+    def image2(self):
+        return self._image2
+
+    @property
+    def is_match(self):
+        return self._is_match
diff --git a/facenet_sandberg/validation/src/parser/pair_parser.py b/facenet_sandberg/validation/src/parser/pair_parser.py
new file mode 100644
index 000000000..e22adf7d7
--- /dev/null
+++ b/facenet_sandberg/validation/src/parser/pair_parser.py
@@ -0,0 +1,56 @@
+import logging
+from os.path import isfile
+from os.path import join
+from parser.pair import Pair
+from parser.parser_base import ParserBase
+from typing import Dict
+from typing import Iterable
+
+
+class PairParser(ParserBase):
+
+    def __init__(self, pairs_fname: str, image_dir: str) -> None:
+        self.pairs_fname = pairs_fname
+        self._image_dir = image_dir
+
+    def compute_pairs(self) -> Iterable[Pair]:
+        with open(self.pairs_fname, 'r', encoding='utf-8') as f:
+            next(f)  # pylint: disable=stop-iteration-return
+            # skip first line, which contains metadata
+            for line in f:
+                try:
+                    pair = self._compute_pair(line)
+                except FileNotFoundError:
+                    logging.exception('Skipping invalid file')
+                else:
+                    yield pair
+
+    def compute_metrics(self) -> Dict[str, float]:
+        raise NotImplementedError()
+
+    def _compute_full_path(self, image_path: str) -> str:
+        exts = ['.jpg', '.png']
+        for ext in exts:
+            full_image_path = join(self._image_dir, f'{image_path}{ext}')
+            if isfile(full_image_path):
+                return full_image_path
+        err = f'{image_path} does not exist with extensions: {exts}'
+        raise FileNotFoundError(err)
+
+    def _compute_pair(self, line: str) -> Pair:
+        line_info = line.strip().split()
+        if len(line_info) == 3:
+            name, n1, n2 = line_info
+            image1 = self._compute_full_path(join(name,
+                                                  f'{name}_{int(n1):04d}'))
+            image2 = self._compute_full_path(join(name,
+                                                  f'{name}_{int(n2):04d}'))
+            is_match = True
+        else:
+            name1, n1, name2, n2 = line_info
+            image1 = self._compute_full_path(join(name1,
+                                                  f'{name1}_{int(n1):04d}'))
+            image2 = self._compute_full_path(join(name2,
+                                                  f'{name2}_{int(n2):04d}'))
+            is_match = False
+        return Pair(image1, image2, is_match)
diff --git a/facenet_sandberg/validation/src/parser/parser_base.py b/facenet_sandberg/validation/src/parser/parser_base.py
new file mode 100644
index 000000000..9de6901e0
--- /dev/null
+++ b/facenet_sandberg/validation/src/parser/parser_base.py
@@ -0,0 +1,16 @@
+from abc import ABC
+from abc import abstractmethod
+from parser.pair import Pair
+from typing import Any
+from typing import Iterable
+
+
+class ParserBase(ABC):
+
+    @abstractmethod
+    def compute_pairs(self) -> Iterable[Pair]:
+        pass
+
+    @abstractmethod
+    def compute_metrics(self) -> Any:
+        pass
diff --git a/facenet_sandberg/validation/src/parser/pipeline/__init__.py b/facenet_sandberg/validation/src/parser/pipeline/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/facenet_sandberg/validation/src/parser/pipeline/parser_pipeline.py b/facenet_sandberg/validation/src/parser/pipeline/parser_pipeline.py
new file mode 100644
index 000000000..d17433f35
--- /dev/null
+++ b/facenet_sandberg/validation/src/parser/pipeline/parser_pipeline.py
@@ -0,0 +1,38 @@
+from parser.container_parser import ContainerParser
+from parser.pair import Pair
+from typing import Callable
+from typing import Iterable
+from typing import Optional
+from typing import cast
+
+PipelineFunction = Callable[[Iterable[Pair]], Iterable[Pair]]
+
+
+class ParserPipelineEmptyException(Exception):
+    pass
+
+
+class ParserPipeline:
+    def __init__(self, container_parser: ContainerParser) -> None:
+        self._container_parser = container_parser
+        self._funcs: Optional[Iterable[PipelineFunction]] = None
+        self.__pairs: Optional[Iterable[Pair]] = None
+
+    @property
+    def _pairs(self) -> Iterable[Pair]:
+        if not self.__pairs:
+            self.__pairs = self._container_parser.compute_pairs()
+        return cast(Iterable[Pair], self.__pairs)
+
+    def build(self, funcs: Iterable[Callable[[Iterable[Pair]],
+                                             Iterable[Pair]]]) -> None:
+        self._funcs = funcs
+
+    def execute_pipeline(self) -> Iterable[Pair]:
+        if not self._funcs:
+            err = 'Pipeline must first be built before being executed'
+            raise ParserPipelineEmptyException(err)
+        pairs = self._pairs
+        for func in self._funcs:
+            pairs = func(pairs)
+        return pairs
diff --git a/facenet_sandberg/validation/src/parser/pipeline/parser_pipeline_funcs.py b/facenet_sandberg/validation/src/parser/pipeline/parser_pipeline_funcs.py
new file mode 100644
index 000000000..5c76693e6
--- /dev/null
+++ b/facenet_sandberg/validation/src/parser/pipeline/parser_pipeline_funcs.py
@@ -0,0 +1,31 @@
+from parser.pair import Pair
+from typing import Iterable
+
+from calculator.distance_calculator import DistanceCalculator
+
+
+def fill_empty(pairs: Iterable[Pair], embedding_size: int) -> Iterable[Pair]:
+    empty_embedding = [[0] * embedding_size]
+    return (Pair(pair.image1 or empty_embedding,
+                 pair.image2 or empty_embedding,
+                 pair.is_match) for pair in pairs)
+
+
+def remove_empty(pairs: Iterable[Pair]) -> Iterable[Pair]:
+    return (pair for pair in pairs if pair.image1 and pair.image2)
+
+
+def filter_target(pairs: Iterable[Pair],
+                  distance_metric: str) -> Iterable[Pair]:
+    return (_compute_target(pair, distance_metric) for pair in pairs)
+
+
+def _compute_target(pair: Pair, distance_metric: str) -> Pair:
+    possible_pairs = [Pair(image1, image2, pair.is_match)
+                      for image1 in pair.image1
+                      for image2 in pair.image2]
+    distance_calculator = DistanceCalculator(distance_metric)
+    distances = distance_calculator.calculate(possible_pairs)
+    distance_criteria = min if pair.is_match else max
+    index, _ = distance_criteria(enumerate(distances), key=lambda x: x[1])
+    return possible_pairs[index]
diff --git a/facenet_sandberg/validation/src/validate.py b/facenet_sandberg/validation/src/validate.py
new file mode 100644
index 000000000..3950a0f22
--- /dev/null
+++ b/facenet_sandberg/validation/src/validate.py
@@ -0,0 +1,88 @@
+from argparse import ArgumentParser, FileType, Namespace
+
+from evaluator.evaluator import Evaluator
+from metrics.metrics import DistanceMetric, ThresholdMetric
+
+
+def _parse_arguments() -> Namespace:
+    parser = ArgumentParser()
+    parser.add_argument('--image_dir',
+                        type=str,
+                        required=True,
+                        help='Path to the image directory.')
+    parser.add_argument('--pairs_fname',
+                        type=FileType('r', encoding='utf-8'),
+                        required=True,
+                        help='Filename of pairs.txt')
+    parser.add_argument('--model_path',
+                        type=str,
+                        required=True,
+                        help='Path to the facial recognition model')
+    parser.add_argument(
+        '--is_insightface',
+        action='store_true',
+        help='Set this flag if the model is insightface')
+    distance_metrics = [str(metric)
+                        .replace(f'{DistanceMetric.__qualname__}.', '')
+                        for metric in DistanceMetric]
+    parser.add_argument(
+        '--distance_metric',
+        type=str,
+        required=True,
+        choices=distance_metrics,
+        help=f"Distance metric for face verification: {distance_metrics}.")
+    parser.add_argument('--threshold_start',
+                        type=float,
+                        required=True,
+                        help='Start value for distance threshold.')
+    parser.add_argument('--threshold_end',
+                        type=float,
+                        required=True,
+                        help='End value for distance threshold')
+    parser.add_argument(
+        '--threshold_step',
+        type=float,
+        required=True,
+        help='Step size for iterating in cross validation search.')
+    threshold_metrics = [str(metric)
+                         .replace(f'{ThresholdMetric.__qualname__}.', '')
+                         for metric in ThresholdMetric]
+    parser.add_argument('--threshold_metric',
+                        type=str,
+                        required=True,
+                        choices=threshold_metrics,
+                        help='metric for calculating optimal threshold.')
+    parser.add_argument(
+        '--embedding_size',
+        type=int,
+        required=True,
+        help='Size of face vectors from face_verification_container.')
+    parser.add_argument(
+        '--remove_empty_embeddings_flag',
+        action='store_true',
+        help='Instead of a default encoding for images where\
+faces are not detected, remove them')
+    parser.add_argument(
+        '--prealigned_flag',
+        action='store_true',
+        help='Specify if the images have already been aligned.')
+    return parser.parse_args()
+
+
+def _main(args: Namespace) -> None:
+    evaluator = Evaluator.create_evaluator(args)
+    evaluation_results = evaluator.evaluate()
+    print('Evaluation results: ', evaluation_results)
+    parser_metrics = evaluator.compute_metrics()
+    print('Parser metrics: ', parser_metrics)
+
+
+def _cli() -> None:
+    args = _parse_arguments()
+    args.pairs_fname.close()
+    args.pairs_fname = args.pairs_fname.name
+    _main(args)
+
+
+if __name__ == '__main__':
+    _cli()
diff --git a/notebooks/Example Usage.ipynb b/notebooks/Example Usage.ipynb
new file mode 100644
index 000000000..03998597c
--- /dev/null
+++ b/notebooks/Example Usage.ipynb	
@@ -0,0 +1,547 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T15:46:06.403900Z",
+     "start_time": "2018-11-07T15:45:50.978981Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from facenet_sandberg import Identifier, get_image_from_path_rgb, get_image_from_path_bgr, \\\n",
+    "                            Detector, embedding_distance, DistanceMetric, get_images_from_dir, \\\n",
+    "                            get_dataset, tsne_sklearn, tsne_tensorboard\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "import numpy as np\n",
+    "import os\n",
+    "from sklearn.manifold import TSNE\n",
+    "import glob\n",
+    "import cv2\n",
+    "import PIL\n",
+    "\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T09:30:58.536355Z",
+     "start_time": "2018-11-07T09:30:58.395673Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "noam_chomsky_1 = get_image_from_path_rgb(\"noam-chomsky-1.jpg\")\n",
+    "noam_chomsky_2 = get_image_from_path_rgb(\"noam-chomsky-2.jpg\")\n",
+    "not_noam_chomsky = get_image_from_path_rgb(\"not-noam-chomsky.jpg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T09:30:58.614393Z",
+     "start_time": "2018-11-07T09:30:58.610963Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def plot_images(image_1, image_2):\n",
+    "    f = plt.figure()\n",
+    "    f.add_subplot(1,2, 1)\n",
+    "    plt.imshow(image_1)\n",
+    "    f.add_subplot(1,2, 2)\n",
+    "    plt.imshow(image_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T09:30:59.581428Z",
+     "start_time": "2018-11-07T09:30:58.618548Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "images = list(get_images_from_dir('people/noam_chomsky', recursive=False))\n",
+    "plt.imshow(images[3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Align"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T09:31:03.729522Z",
+     "start_time": "2018-11-07T09:30:59.584935Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "detector = Detector()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T09:31:05.301240Z",
+     "start_time": "2018-11-07T09:31:03.732423Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "faces = detector.find_faces(noam_chomsky_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T09:31:05.727674Z",
+     "start_time": "2018-11-07T09:31:05.303622Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plot_images(noam_chomsky_1, faces[0].image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bulk align"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T09:31:13.554953Z",
+     "start_time": "2018-11-07T09:31:05.729619Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "aligned_images = list(detector.bulk_find_face(images))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T09:31:13.742371Z",
+     "start_time": "2018-11-07T09:31:13.557184Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(aligned_images[3][0].image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Vectorize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T14:03:30.267305Z",
+     "start_time": "2018-11-07T14:03:27.088999Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "identifier = Identifier(model_path=os.path.join('models','facenet_model.pb'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get single face vectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-05T09:04:31.938275Z",
+     "start_time": "2018-11-05T09:04:28.815586Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# vectorize() returns an array of vectors, one for each face in a single image (we grab the first because there is only one\n",
+    "# face in these images)\n",
+    "vector_1 = identifier.vectorize(noam_chomsky_1)[0]\n",
+    "vector_2 = identifier.vectorize(noam_chomsky_2)[0]\n",
+    "vector_3 = identifier.vectorize(not_noam_chomsky)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-05T10:10:33.309081Z",
+     "start_time": "2018-11-05T10:10:33.299194Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "vector_1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize face encodings: Facenet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T14:51:17.533893Z",
+     "start_time": "2018-11-07T14:51:17.026358Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "noam_images = list(map(get_image_from_path_rgb, glob.glob(\"people/noam_chomsky/*.*\")))\n",
+    "other_people = list(map(get_image_from_path_rgb, glob.glob(\"people/foucault/*.*\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T14:51:46.222916Z",
+     "start_time": "2018-11-07T14:51:17.542278Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# We use the bulk encoding method vectorize_all() because it's much faster \n",
+    "# Filter out empty arrays and grab the first item from each array of vectors per image \n",
+    "noam_vectors = list(map(lambda x : x[0], filter(None, identifier.vectorize_all(noam_images, prealigned=False))))\n",
+    "other_vectors = list(map(lambda x : x[0], filter(None, identifier.vectorize_all(other_people, prealigned=False))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T14:51:46.236171Z",
+     "start_time": "2018-11-07T14:51:46.226300Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# combine into one features array\n",
+    "features = np.array(noam_vectors + other_vectors)\n",
+    "# label each feature (0 is noam and 1 is not noam)\n",
+    "labels = np.array(([0] * len(noam_vectors)) + ([1] * len(other_vectors)) )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T14:51:47.619724Z",
+     "start_time": "2018-11-07T14:51:46.240623Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# use tsne to reduce dimensionality for visualization\n",
+    "tsne = TSNE(n_components=2, random_state=0)\n",
+    "reduced = tsne.fit_transform(features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T14:51:47.956023Z",
+     "start_time": "2018-11-07T14:51:47.621815Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(20, 20))\n",
+    "colors = 'r', 'g',\n",
+    "for i, c, label in zip([0, 1], colors, ['noam_chomsky', 'foucault']):\n",
+    "    plt.scatter(reduced[labels == i, 0], reduced[labels == i, 1], c=c, label=label)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize face encodings: Insightface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-05T09:52:56.778083Z",
+     "start_time": "2018-11-05T09:52:56.445334Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "noam_images_bgr = list(map(get_image_from_path_bgr, glob.glob(\"people/noam_chomsky/*.*\")))\n",
+    "other_people_bgr = list(map(get_image_from_path_bgr, glob.glob(\"people/foucault/*.*\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-05T09:53:40.888531Z",
+     "start_time": "2018-11-05T09:52:57.252725Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "insightface = Identifier(model_path=os.path.join('models','insightface', 'insightface_ckpt'), is_insightface=True)\n",
+    "noam_vectors_insightface = list(map(lambda x : x[0], filter(None, insightface.vectorize_all(noam_images_bgr, prealigned=False))))\n",
+    "other_vectors_insightface = list(map(lambda x : x[0], filter(None, insightface.vectorize_all(other_people_bgr, prealigned=False))))\n",
+    "# combine into one features array\n",
+    "features_insightface = np.array(noam_vectors_insightface + other_vectors_insightface)\n",
+    "# label each feature (0 is noam and 1 is not noam)\n",
+    "labels_insightface = np.array(([0] * len(noam_vectors_insightface)) + ([1] * len(other_vectors_insightface)))\n",
+    "# use tsne to reduce dimensionality for visualization\n",
+    "tsne_insightface = TSNE(n_components=2, random_state=0)\n",
+    "reduced_insightface = tsne_insightface.fit_transform(features_insightface)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-05T09:53:41.184401Z",
+     "start_time": "2018-11-05T09:53:40.891554Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(20, 20))\n",
+    "colors = 'r', 'g',\n",
+    "for i, c, label in zip([0, 1], colors, ['noam_chomsky', 'foucault']):\n",
+    "    plt.scatter(reduced_insightface[labels_insightface == i, 0], reduced_insightface[labels_insightface == i, 1], c=c, label=label)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize face encodings: directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T14:55:43.367179Z",
+     "start_time": "2018-11-07T14:55:13.261710Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tsne_sklearn(\n",
+    "        img_dir=\"/Users/armanrahman/facenet_sandberg/notebooks/people\",\n",
+    "        model_path=\"/Users/armanrahman/models/facenet_model.pb\",\n",
+    "        is_insightface=False,\n",
+    "        prealigned=False,\n",
+    "        is_flat=False,\n",
+    "        log_dir=\"log_dir\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-07T15:46:31.597259Z",
+     "start_time": "2018-11-07T15:46:09.854515Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Encoding:100% |##########################| Time:  0:00:08 Elapsed Time: 0:00:08\n",
+      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
+      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 2160x2160 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt = tsne_sklearn(\n",
+    "        img_dir=\"/Users/armanrahman/facenet_sandberg/notebooks/people\",\n",
+    "        model_path=\"/Users/armanrahman/models/facenet_model.pb\",\n",
+    "        is_insightface=False,\n",
+    "        prealigned=False,\n",
+    "        is_flat=False,\n",
+    "        save_plt=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compare two faces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-05T09:04:50.839002Z",
+     "start_time": "2018-11-05T09:04:50.422416Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plot_images(noam_chomsky_1, noam_chomsky_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-05T09:04:52.676870Z",
+     "start_time": "2018-11-05T09:04:50.841114Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "identifier.compare_images(noam_chomsky_1, noam_chomsky_2).is_match"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-05T09:04:52.963315Z",
+     "start_time": "2018-11-05T09:04:52.678783Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plot_images(noam_chomsky_2, not_noam_chomsky)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-11-05T09:04:53.763808Z",
+     "start_time": "2018-11-05T09:04:52.965658Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "identifier.compare_images(noam_chomsky_2, not_noam_chomsky).is_match"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/faceboxes.ipynb b/notebooks/faceboxes.ipynb
new file mode 100644
index 000000000..bccb27f33
--- /dev/null
+++ b/notebooks/faceboxes.ipynb
@@ -0,0 +1,478 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sources\n",
+    "[Original Paper](https://arxiv.org/abs/1708.05234) \n",
+    "\n",
+    "[Tensorflow Implementation](https://github.com/TropComplique/FaceBoxes-tensorflow)\n",
+    "\n",
+    "[Dockerize Implementation](https://hub.docker.com/r/arrahm/faceboxes/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-20T16:53:28.939258Z",
+     "start_time": "2018-09-20T16:53:28.915868Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-20T16:53:31.737683Z",
+     "start_time": "2018-09-20T16:53:29.467029Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.10.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "# Needs tensorflow version 1.10 or greater \n",
+    "print(tf.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-20T16:53:32.050781Z",
+     "start_time": "2018-09-20T16:53:31.740171Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'\n",
+    "\n",
+    "import numpy as np\n",
+    "from PIL import Image, ImageDraw\n",
+    "import os\n",
+    "import cv2\n",
+    "import time\n",
+    "import urllib\n",
+    "from urllib.request import urlopen\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Face Detector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-20T16:53:32.097500Z",
+     "start_time": "2018-09-20T16:53:32.053459Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class FaceDetector:\n",
+    "    def __init__(self, model_path, gpu_memory_fraction=0.25, visible_device_list='0'):\n",
+    "        \"\"\"\n",
+    "        Arguments:\n",
+    "            model_path: a string, path to a pb file.\n",
+    "            gpu_memory_fraction: a float number.\n",
+    "            visible_device_list: a string.\n",
+    "        \"\"\"\n",
+    "        with tf.gfile.GFile(model_path, 'rb') as f:\n",
+    "            graph_def = tf.GraphDef()\n",
+    "            graph_def.ParseFromString(f.read())\n",
+    "\n",
+    "        graph = tf.Graph()\n",
+    "        with graph.as_default():\n",
+    "            tf.import_graph_def(graph_def, name='import')\n",
+    "\n",
+    "        self.input_image = graph.get_tensor_by_name('import/image_tensor:0')\n",
+    "        self.output_ops = [\n",
+    "            graph.get_tensor_by_name('import/boxes:0'),\n",
+    "            graph.get_tensor_by_name('import/scores:0'),\n",
+    "            graph.get_tensor_by_name('import/num_boxes:0'),\n",
+    "        ]\n",
+    "\n",
+    "        gpu_options = tf.GPUOptions(\n",
+    "            per_process_gpu_memory_fraction=gpu_memory_fraction,\n",
+    "            visible_device_list=visible_device_list\n",
+    "        )\n",
+    "        config_proto = tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False)\n",
+    "        self.sess = tf.Session(graph=graph, config=config_proto)\n",
+    "\n",
+    "    def __call__(self, image, score_threshold=0.5):\n",
+    "        \"\"\"Detect faces.\n",
+    "        Arguments:\n",
+    "            image: a numpy uint8 array with shape [height, width, 3],\n",
+    "                that represents a RGB image.\n",
+    "            score_threshold: a float number.\n",
+    "        Returns:\n",
+    "            boxes: a float numpy array of shape [num_faces, 4].\n",
+    "            scores: a float numpy array of shape [num_faces].\n",
+    "        Note that box coordinates are in the order: ymin, xmin, ymax, xmax!\n",
+    "        \"\"\"\n",
+    "        h, w, _ = image.shape\n",
+    "        image = np.expand_dims(image, 0)\n",
+    "\n",
+    "        boxes, scores, num_boxes = self.sess.run(\n",
+    "            self.output_ops, feed_dict={self.input_image: image}\n",
+    "        )\n",
+    "        num_boxes = num_boxes[0]\n",
+    "        boxes = boxes[0][:num_boxes]\n",
+    "        scores = scores[0][:num_boxes]\n",
+    "\n",
+    "        to_keep = scores > score_threshold\n",
+    "        boxes = boxes[to_keep]\n",
+    "        scores = scores[to_keep]\n",
+    "\n",
+    "        scaler = np.array([h, w, h, w], dtype='float32')\n",
+    "        boxes = boxes * scaler\n",
+    "\n",
+    "        return boxes, scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-20T16:53:33.087249Z",
+     "start_time": "2018-09-20T16:53:32.100085Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model_path = os.path.join('models', 'faceboxes_model.pb')\n",
+    "model_url = 'https://facialstorage.blob.core.windows.net/models/faceboxes_model.pb'\n",
+    "urllib.request.urlretrieve (model_url, model_path)\n",
+    "face_detector = FaceDetector(model_path, gpu_memory_fraction=0.25, visible_device_list='0')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Get an image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-20T16:53:33.134167Z",
+     "start_time": "2018-09-20T16:53:33.090222Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def download_image(url):\n",
+    "    req = urlopen(url)\n",
+    "    arr = np.asarray(bytearray(req.read()), dtype=np.uint8)\n",
+    "    image = cv2.imdecode(arr, -1)\n",
+    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "    return image\n",
+    "\n",
+    "def resize_image(image_array):\n",
+    "    image = Image.fromarray(image_array)\n",
+    "    desired_size = 768\n",
+    "    old_size = image.size\n",
+    "    ratio = float(desired_size) / max(old_size)\n",
+    "    new_size = tuple([int\n",
+    "                      (x * ratio) for x in old_size])\n",
+    "    image = image.resize(new_size, Image.ANTIALIAS)\n",
+    "    return image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-20T16:53:34.349108Z",
+     "start_time": "2018-09-20T16:53:34.135325Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "image = download_image('https://i.amz.mshcdn.com/WZUyjzT0PDhqKpbcSAi4QYV-TWU=/950x534/filters:quality(90)/https%3A%2F%2Fblueprint-api-production.s3.amazonaws.com%2Fuploads%2Fcard%2Fimage%2F792479%2Fae396389-d75b-4852-9a8a-7b34e28f71bd.jpg')\n",
+    "# !!! Image should be at least 768 pixels wide to find faces properly \n",
+    "image = resize_image(image)\n",
+    "image_array = np.array(image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-20T16:53:38.455380Z",
+     "start_time": "2018-09-20T16:53:38.096346Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x12c6c75f8>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Show detections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-20T16:53:39.487896Z",
+     "start_time": "2018-09-20T16:53:39.440615Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def draw_boxes_on_image(image, boxes, scores):\n",
+    "    print(image.size)\n",
+    "    image_copy = image.copy()\n",
+    "    draw = ImageDraw.Draw(image_copy, 'RGBA')\n",
+    "    width, height = image.size\n",
+    "\n",
+    "    for b, s in zip(boxes, scores):\n",
+    "        ymin, xmin, ymax, xmax = b\n",
+    "        fill = (255, 0, 0, 45)\n",
+    "        outline = 'red'\n",
+    "        draw.rectangle(\n",
+    "            [(xmin, ymin), (xmax, ymax)],\n",
+    "            fill=fill, outline=outline\n",
+    "        )\n",
+    "        draw.text((xmin, ymin), text='{:.3f}'.format(s))\n",
+    "    return image_copy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-20T16:53:40.997471Z",
+     "start_time": "2018-09-20T16:53:40.164446Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(768, 431)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=768x431 at 0x12A3D2358>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boxes, scores = face_detector(image_array, score_threshold=0.3)\n",
+    "draw_boxes_on_image(Image.fromarray(image_array), boxes, scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Measure speed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-20T17:26:55.079603Z",
+     "start_time": "2018-09-20T17:26:52.248625Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average time(sec):0.025305808419998358, std deviation: 0.0014581008719369262\n"
+     ]
+    }
+   ],
+   "source": [
+    "times = []\n",
+    "for _ in range(110):\n",
+    "    start = time.perf_counter()\n",
+    "    boxes, scores = face_detector(image_array, score_threshold=0.25)\n",
+    "    times.append(time.perf_counter() - start)\n",
+    "    \n",
+    "times = np.array(times)\n",
+    "times = times[10:]\n",
+    "print('average time(sec):{}, std deviation: {}'.format(times.mean(), times.std()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the Docker container"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-20T16:53:48.470301Z",
+     "start_time": "2018-09-20T16:53:45.053727Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Instructions and container are hosted here:\n",
+    "# https://hub.docker.com/r/arrahm/faceboxes/\n",
+    "import docker\n",
+    "import tempfile\n",
+    "import os\n",
+    "import io\n",
+    "import zlib\n",
+    "import base64\n",
+    "\n",
+    "# Create a temporary file from image array so we can use it with the docker container\n",
+    "with tempfile.NamedTemporaryFile(mode=\"wb\", dir=os.getcwd()) as image_file:\n",
+    "    f = io.BytesIO()\n",
+    "    image.save(image_file, 'png')\n",
+    "    image_name = os.path.basename(image_file.name)\n",
+    "    base_dir = os.path.dirname(image_file.name)\n",
+    "    command = '--image_path=/images/' + image_name\n",
+    "    volumes = {base_dir: {'bind': '/images', 'mode': 'ro'}}\n",
+    "    client = docker.from_env()\n",
+    "    stdout = client.containers.run('arrahm/faceboxes',\n",
+    "                                   command,\n",
+    "                                   volumes=volumes,\n",
+    "                                   auto_remove=True)\n",
+    "    json_obj = json.loads(stdout.decode('utf-8').strip())\n",
+    "    boxes = json_obj['boxes']\n",
+    "    scores = json_obj['scores']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-20T16:53:48.737912Z",
+     "start_time": "2018-09-20T16:53:48.472269Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(768, 431)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=768x431 at 0x12C682198>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "draw_boxes_on_image(Image.fromarray(image_array), boxes, scores)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/notebooks/log_dir/00000/default/metadata.tsv b/notebooks/log_dir/00000/default/metadata.tsv
new file mode 100644
index 000000000..5c2c11b53
--- /dev/null
+++ b/notebooks/log_dir/00000/default/metadata.tsv
@@ -0,0 +1,214 @@
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+foucault
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
+noam_chomsky
diff --git a/notebooks/log_dir/00000/default/sprite.png b/notebooks/log_dir/00000/default/sprite.png
new file mode 100644
index 000000000..9a1ba84b1
Binary files /dev/null and b/notebooks/log_dir/00000/default/sprite.png differ
diff --git a/notebooks/log_dir/00000/default/tensors.tsv b/notebooks/log_dir/00000/default/tensors.tsv
new file mode 100644
index 000000000..14ab92ff1
--- /dev/null
+++ b/notebooks/log_dir/00000/default/tensors.tsv
@@ -0,0 +1,214 @@
+0.033686477690935135	-0.020088590681552887	-0.06952820718288422	-0.011154617182910442	-0.037127964198589325	0.027675606310367584	-0.0063485270366072655	-0.029187960550189018	-0.06240008398890495	0.023013534024357796	-0.03989127278327942	0.05008380860090256	-0.007741665933281183	-0.03287876397371292	-0.037601739168167114	-0.11748761683702469	0.004521750379353762	0.028853019699454308	0.0517779104411602	-0.00796215794980526	-0.01320212334394455	0.04430721700191498	0.008175059221684933	0.018932420760393143	0.0035871469881385565	0.009131262078881264	0.03721753507852554	0.014984164386987686	0.06288659572601318	0.03397535905241966	0.07323484122753143	-0.07629911601543427	0.025469092652201653	0.007061793934553862	0.024168068543076515	-0.021153077483177185	-0.00932538416236639	0.020008398219943047	0.09124591946601868	-0.004324222449213266	-0.03978244960308075	0.002721556229516864	0.014402855187654495	-0.07305984199047089	-0.033653050661087036	0.018916836008429527	-0.09906667470932007	-0.0816487967967987	0.03786204010248184	-0.024345258250832558	-0.04038189351558685	0.037773434072732925	-0.013737428933382034	0.006962971296161413	-0.05075986683368683	-0.05853983014822006	0.021147212013602257	0.05474182218313217	-0.0023966969456523657	0.033427875488996506	0.003103101858869195	0.052188556641340256	0.02112867683172226	0.01912570931017399	0.040994882583618164	-0.046899404376745224	0.06041162461042404	-0.019902678206562996	-0.03430609777569771	0.055051736533641815	-0.02307363599538803	0.03799401596188545	0.008237716741859913	0.05246703326702118	0.012825925834476948	-0.07009980082511902	-0.0658215656876564	-0.026217026636004448	0.0034547538962215185	-0.010499485768377781	-0.03407277911901474	-0.015609469264745712	0.05904542654752731	0.010025382973253727	-0.007614569738507271	-0.014626451767981052	-0.020526742562651634	-0.0133888591080904	-0.04297048598527908	-0.027310442179441452	0.07672982662916183	-0.07654406875371933	-0.018452832475304604	-0.004770145285874605	0.003414884675294161	0.0015660864301025867	0.09974024444818497	0.021774563938379288	-0.011182659305632114	0.012542948126792908	0.007509208284318447	-0.011697988957166672	0.038831550627946854	-0.005357932299375534	-0.013190535828471184	-0.07232970744371414	-0.05544512718915939	0.037685517221689224	0.05604179948568344	-0.009318584576249123	0.07691653072834015	0.06407523155212402	-0.037204716354608536	-0.04187309741973877	0.09156667441129684	-0.015131950378417969	-0.009043064899742603	-0.03963371738791466	0.06665240973234177	0.043772805482149124	-0.02605883590877056	0.06191077455878258	-0.047607943415641785	-0.031202781945466995	-0.027755513787269592	0.03947842866182327	-0.03561769798398018	0.026931975036859512	-0.04703158512711525	0.031104153022170067	-0.02519323118031025	0.04382910951972008	0.0009743536356836557	0.04626869782805443	-0.06611140072345734	-0.050161369144916534	-0.04624262824654579	0.05271061137318611	0.018751198425889015	0.01964183896780014	-0.06158456206321716	0.04558531567454338	0.08054178208112717	0.06409871578216553	-0.030015263706445694	-0.06756670027971268	-0.05235038325190544	-0.003868826199322939	0.04159802570939064	-0.01327139139175415	0.013098297640681267	0.06295636296272278	0.07102881371974945	0.04222641512751579	0.04805096983909607	-0.008017653599381447	0.008607492782175541	-0.028615107759833336	0.00025720472331158817	-0.03297343850135803	-0.002686248393729329	0.045227162539958954	0.013339419849216938	-0.007405259646475315	0.038427866995334625	0.11682872474193573	0.03111659176647663	-0.04015909135341644	-0.027450164780020714	0.0921173170208931	-0.02965993620455265	-0.02082338184118271	0.019321126863360405	-0.014758187346160412	-0.022040149196982384	-0.039930086582899094	-0.0007684670854359865	-0.025311551988124847	0.06316789239645004	-0.042124319821596146	-0.00369357131421566	-0.09647668153047562	0.0272817499935627	0.017931073904037476	-0.00862825382500887	0.029031982645392418	0.009481054730713367	-0.04287506267428398	9.967866208171472e-05	-0.005291931331157684	-0.013227852992713451	-0.044986072927713394	-0.040255751460790634	-0.04440966621041298	-0.018871083855628967	-0.07196889817714691	0.022247519344091415	0.03359810262918472	0.08593057096004486	0.051991723477840424	-0.06735207140445709	0.01761963777244091	-0.09610722959041595	-0.011216271668672562	0.039883941411972046	0.019261177629232407	-0.045136239379644394	-0.06941553205251694	0.015080058015882969	-0.017515845596790314	-0.03446295112371445	0.0056818281300365925	-0.026902087032794952	0.003045464400202036	0.04445115476846695	-0.0059319534339010715	0.02342970296740532	0.020477814599871635	0.0343799851834774	-0.006907808128744364	-0.03625836968421936	-0.05038740485906601	-0.037359852343797684	-0.06436290591955185	0.008329243399202824	-0.07402340322732925	0.028958028182387352	-0.012583899311721325	0.0653679296374321	-0.05850660055875778	0.10571553558111191	0.08697237819433212	0.05220778286457062	0.016091041266918182	0.028692690655589104	-0.066811203956604	0.0273258276283741	0.0035335335414856672	-0.02273113839328289	-0.0813247486948967	0.04407616704702377	-0.026704896241426468	0.013449410907924175	0.03361193463206291	-8.715534931980073e-05	-0.02183370105922222	0.047015901654958725	0.019435152411460876	-0.10429835319519043	-0.02109808474779129	0.03005172312259674	-0.05587441101670265	0.06493590772151947	0.07543408870697021	0.019957732409238815	-0.07956808805465698	0.0518675372004509	-0.04144309088587761	0.08950231224298477	-0.0035674497485160828	0.0016930615529417992	0.00954573042690754	-0.047495000064373016	-0.07026299089193344	0.009458093903958797	-0.005730185192078352	0.04045373573899269	0.06208913400769234	-0.01683088392019272	-0.03058619797229767	0.10396815091371536	0.06679727882146835	0.023011015728116035	0.041412826627492905	0.013200763612985611	-0.004103848710656166	0.03669190779328346	0.05381504446268082	0.026392892003059387	-0.0548575185239315	0.039071083068847656	-0.025101708248257637	0.01201173011213541	0.011860114522278309	-0.03359382972121239	-0.07226955890655518	0.013618448749184608	-0.014475639909505844	0.0049719554372131824	0.015656625851988792	-0.030739545822143555	-0.07689791917800903	-0.06431017071008682	-0.00704206945374608	-0.02359503135085106	-0.017064956948161125	-0.017296407371759415	0.06980762630701065	-0.02241031639277935	0.03240674361586571	0.12853558361530304	-0.05348602682352066	-0.010462360456585884	0.04809032008051872	-0.02580576203763485	-0.07108891755342484	-0.02696169912815094	-9.525533096166328e-05	0.02901633270084858	0.02944873832166195	0.04968598484992981	-0.04918716102838516	0.008842665702104568	0.03567557781934738	-0.0027475510723888874	-0.03134135901927948	0.0007052678847685456	-0.0875142440199852	-0.02382475696504116	0.03414645045995712	0.03810206055641174	-0.00984866637736559	-0.008312695659697056	0.02197437360882759	-0.059107888489961624	-0.024098234251141548	0.03094359301030636	-0.12973549962043762	0.020708126947283745	-0.041137050837278366	0.0056761326268315315	-0.07496210187673569	0.006611967924982309	0.010882250964641571	0.004516376182436943	-0.04778457060456276	-0.06525760889053345	0.002653352916240692	0.005770179908722639	0.06235290318727493	0.07933548092842102	-0.05387920141220093	0.00563370855525136	-0.04533424228429794	-0.0015393514186143875	-0.023837901651859283	-0.038437407463788986	-0.1273638904094696	0.11352305859327316	0.027584169059991837	-0.0337967686355114	-0.04403024911880493	-0.01472635567188263	0.06894902139902115	-0.010051412507891655	-0.004975711926817894	0.022521594539284706	0.05804521590471268	-0.044002074748277664	0.021200327202677727	-0.02036074362695217	-0.046773843467235565	-0.01995112933218479	0.028135253116488457	0.04699203744530678	-0.022474296391010284	0.06061423942446709	0.020173024386167526	-0.050848424434661865	0.047900523990392685	0.017299441620707512	0.011741380207240582	-0.04072533920407295	0.007485767360776663	0.06053395941853523	-0.07185381650924683	0.01761084608733654	-5.361664079828188e-05	0.027313116937875748	0.025792060419917107	0.03112073615193367	0.025434521958231926	-0.04318702593445778	-0.008579947985708714	0.021635908633470535	0.013784971088171005	-0.0016635830979794264	-0.02225697599351406	0.04392676800489426	0.11644234508275986	-0.01395796425640583	0.12324615567922592	0.01737048476934433	0.03388059884309769	0.005864112637937069	0.09602998197078705	0.01587885245680809	0.041671790182590485	0.007324951700866222	0.02605397440493107	0.05491739138960838	-0.017389316111803055	0.03252071514725685	0.0734562873840332	0.022522417828440666	-0.044575490057468414	-0.04067184403538704	0.06767302006483078	-0.09198518097400665	-0.048399005085229874	0.024814093485474586	-0.016164207831025124	-0.017879579216241837	0.09196411073207855	-0.029411837458610535	-0.11392223089933395	0.041475169360637665	0.03474847227334976	-0.044868871569633484	-0.02571091614663601	-0.03179356828331947	0.05762745812535286	0.01071949489414692	-0.006608536466956139	-0.01809690333902836	0.043344736099243164	0.04504711925983429	0.024863161146640778	-0.05188506096601486	0.03747660294175148	0.06708794087171555	0.09257940202951431	-0.01614988036453724	0.06503241509199142	-0.023750655353069305	0.0177549347281456	-0.038070134818553925	0.025923609733581543	0.04194745048880577	0.04050571471452713	0.029351407662034035	0.02462327852845192	0.014487657696008682	-0.03330380469560623	0.003982705529779196	-0.001464424654841423	-0.0597103051841259	0.035736460238695145	0.02292044460773468	-0.038414012640714645	0.036931198090314865	0.004512122832238674	0.018924392759799957	0.030358299612998962	0.0072767361998558044	0.05694272369146347	-0.02203436754643917	-0.004698341712355614	0.03389536961913109	0.007125153671950102	-0.024938907474279404	-0.015947826206684113	0.013175456784665585	-0.011278168298304081	0.0009926046477630734	-0.009168806485831738	0.03457252308726311	0.0057123457081615925	-0.02337765321135521	0.023894505575299263	0.04349888116121292	-0.022797290235757828	0.0727100819349289	-0.018115326762199402	0.038124166429042816	0.039247434586286545	0.041633013635873795	-0.14755094051361084	0.028343109413981438	0.02348748780786991	0.06874869018793106	0.04445498809218407	0.05581562966108322	0.01578139327466488	0.033252179622650146	-0.02924170158803463	-0.06061366945505142	0.03961733356118202	0.00811768975108862	0.00992838479578495	0.009144622832536697	0.03131905198097229	-0.008032725192606449	0.04874623194336891	0.09416060149669647	0.062419407069683075	-0.026216600090265274	0.03620293736457825	0.004188932944089174	0.035280536860227585	0.04640804976224899	0.04413873702287674	0.04112519323825836	0.03036639653146267	0.007794488221406937	-0.017643896862864494	-0.013688373379409313	0.03024129383265972	-0.019136708229780197	-0.006171789485961199	0.05600028857588768	0.016535500064492226
+0.011027496308088303	-0.024405695497989655	-0.08386463671922684	0.020927319303154945	0.005689271725714207	0.05996856093406677	-0.0007956196204759181	0.009789375588297844	-0.0803603082895279	0.044801197946071625	-0.07180933654308319	-0.0029143900610506535	-0.00856136716902256	-0.03346320614218712	-0.007798569276928902	-0.11411905288696289	-0.03559922054409981	-0.03163212537765503	0.08382797986268997	-0.05168793350458145	-0.022534646093845367	0.06254357844591141	0.0358409509062767	0.07168295979499817	0.054749973118305206	0.06195983290672302	0.05129256844520569	0.005751613061875105	0.06759600341320038	0.020089156925678253	0.007372919004410505	-0.04961138218641281	-0.0289068054407835	-0.06689078360795975	0.049537088721990585	-0.004556132480502129	0.02437485381960869	0.04832742363214493	0.026185983791947365	-0.06681882590055466	-0.036847542971372604	0.07215116173028946	0.0061321319080889225	-0.06714709103107452	-0.017863335087895393	-0.020054230466485023	-0.03432276472449303	-0.05887506157159805	0.004439337644726038	-0.03531866893172264	-0.009271482937037945	0.003929611761122942	-0.03269802778959274	-0.04683336988091469	-0.057008031755685806	0.05312695354223251	-0.011464918032288551	0.07964036613702774	0.038238707929849625	0.04390713572502136	0.037616681307554245	-0.019184870645403862	0.006512091960757971	-0.04650275781750679	0.0002603734319563955	-0.005165216512978077	0.069388747215271	-0.007318341173231602	-0.009788683615624905	0.05898619070649147	-0.03379299119114876	0.03597832843661308	0.03770941495895386	0.05520223081111908	0.016187241300940514	-0.043834805488586426	-0.04015132784843445	-0.017848113551735878	0.06317564100027084	0.04395510256290436	0.04354774206876755	0.015665685757994652	0.06106394901871681	0.06698007136583328	-0.025323936715722084	0.029010998085141182	-0.03085332363843918	0.031015915796160698	-0.01323470938950777	-0.007057372480630875	0.02334076352417469	-0.03743452578783035	0.033210836350917816	0.043381862342357635	-0.01404672022908926	-0.0510500967502594	0.05336055904626846	-0.0114252595230937	0.014222022145986557	-0.012847556732594967	0.005534724332392216	-0.05897929146885872	0.05154038220643997	-0.01982218772172928	0.010757329873740673	-0.03445741906762123	-0.10222240537405014	-0.040029700845479965	0.05300113186240196	-0.06736285984516144	0.04006781801581383	0.08117178827524185	-0.030349010601639748	-0.03600214421749115	0.08787691593170166	-0.014367434196174145	0.04144081845879555	-0.03502441570162773	0.014278905466198921	0.030011653900146484	-0.015279010869562626	0.013456911779940128	-0.0048884800635278225	-0.008159703575074673	0.015587206929922104	0.011759160086512566	-0.014114554971456528	0.05875600501894951	0.00886007770895958	0.0725414976477623	0.017348572611808777	0.03597139194607735	0.047681912779808044	0.02057616226375103	-0.06379777938127518	-0.04453032463788986	-0.007904701866209507	0.08176549524068832	0.04509413614869118	0.06346899271011353	-0.08988972753286362	0.018553832545876503	0.1191159188747406	0.03577522933483124	-0.025228817015886307	-0.040783289819955826	-0.06134122237563133	-0.010301433503627777	0.07747052609920502	-0.022916484624147415	-0.009622317738831043	0.02897725999355316	0.08623713999986649	0.017387207597494125	-0.020511344075202942	-0.05336051434278488	0.0595477931201458	-0.014507070183753967	-0.022945662960410118	-0.047839026898145676	0.02875436656177044	-0.009936190210282803	0.008644885383546352	-0.08202296495437622	0.056146372109651566	0.05267763137817383	0.004561274312436581	-0.0858997330069542	0.010225529782474041	0.08658096939325333	0.0017882500542327762	-0.049074750393629074	0.011911476962268353	-0.02169891446828842	-0.05069905146956444	-0.04552295804023743	0.058659106492996216	0.005937221925705671	0.04627959057688713	-0.01059223897755146	0.00999767892062664	-0.05304649844765663	0.022749312222003937	0.018992718309164047	-0.022895770147442818	0.03829217329621315	0.013671902008354664	-0.029014961794018745	-0.018436912447214127	0.00972269382327795	-0.006911314558237791	-0.0051819817163050175	-0.005999334622174501	-0.029623810201883316	0.031398650258779526	0.007046937942504883	0.04939637705683708	0.03417319431900978	0.05921612307429314	0.07533663511276245	-0.08303217589855194	0.023197466507554054	-0.07045181840658188	-0.033225491642951965	-0.008032796904444695	0.05439569801092148	-0.00679369131103158	-0.011116976849734783	0.04314218834042549	-0.04247462376952171	-0.045436445623636246	-0.02030409500002861	-0.054272912442684174	0.024680836126208305	0.0169117022305727	0.05620743706822395	-0.04138171672821045	0.017215169966220856	-0.05698104575276375	0.008510543033480644	-0.05823785066604614	-0.017478201538324356	-0.03509422019124031	-0.03263314440846443	-0.02350476384162903	-0.02317221835255623	-0.05022844672203064	-0.005157540552318096	0.11789020895957947	-0.038473617285490036	0.05670633167028427	0.06260817497968674	-0.01492103561758995	-0.000930093927308917	-0.006334173958748579	-0.03532351180911064	0.013342667371034622	-0.04294842481613159	-0.0412198081612587	-0.08257514238357544	0.013106772676110268	-0.01871887966990471	-0.03841615840792656	0.023593630641698837	0.0076267109252512455	-0.0004288193304091692	-0.03237566351890564	0.03530694544315338	-0.06888159364461899	0.007275368086993694	0.07281385362148285	0.01923300325870514	-0.015439375303685665	0.04176897928118706	0.018193406984210014	-0.07567493617534637	-0.009410250931978226	0.006514210253953934	0.07961617410182953	-0.07042815536260605	0.027444444596767426	-0.018782692030072212	-0.023561565205454826	0.010046640411019325	0.024699829518795013	0.013567112386226654	0.07450196146965027	0.07026652246713638	0.02500319480895996	0.038343850523233414	0.029869480058550835	0.03123924881219864	-0.020638737827539444	0.04272882267832756	-0.04725426435470581	-0.03273483365774155	0.0181382168084383	0.06542918086051941	0.008929073810577393	-0.02631179429590702	0.05791493132710457	0.004842869006097317	-0.032599084079265594	-0.04038913547992706	0.010455594398081303	-0.029155686497688293	-0.012648070231080055	-0.009655945934355259	0.022379620000720024	-0.03043280355632305	0.02819252945482731	-0.07489082217216492	-0.06126808747649193	0.03658949211239815	-0.05717825889587402	0.04585150629281998	-0.009435566142201424	0.07482326030731201	-0.02465173602104187	0.03697523847222328	0.10137780010700226	0.013720766641199589	0.0532737672328949	0.023128436878323555	-0.03503548353910446	-0.09219510853290558	-0.008638063445687294	-0.003385993419215083	-0.016171114519238472	0.008659126237034798	0.040142688900232315	-0.03324892744421959	0.00023912242613732815	0.004504280164837837	0.0049490975216031075	-0.009261071681976318	-0.011175333522260189	-0.029809527099132538	-0.05945615470409393	0.0370158888399601	0.035792190581560135	0.006360624451190233	0.009025298058986664	0.02877216227352619	-0.05009933561086655	-0.008767453953623772	0.01804923638701439	-0.12969869375228882	0.02542601153254509	-0.03539208695292473	0.01311469916254282	-0.06604242324829102	0.01924707554280758	0.0016257448587566614	0.002657992532476783	-0.11810676008462906	-0.056099288165569305	0.04352828115224838	0.018325166776776314	0.048757679760456085	0.13347789645195007	0.01932278648018837	-0.026998858898878098	-0.022187205031514168	-0.047245170921087265	-0.040558334439992905	-0.06899242848157883	-0.06472506374120712	0.09387009590864182	0.03731588274240494	0.024837492033839226	-0.04784339293837547	-0.0460854209959507	-0.0032696188427507877	-0.07217615842819214	0.0051992544904351234	0.007992672733962536	0.01772133819758892	-0.06519826501607895	-0.022801928222179413	0.018423117697238922	-0.001568949199281633	-0.0306714940816164	0.022016309201717377	0.027082227170467377	-0.03475333750247955	0.12578339874744415	-0.049073006957769394	-0.06187346205115318	0.033735282719135284	-0.01525032240897417	0.02574183978140354	-0.02220715768635273	0.00871300045400858	0.07886338979005814	-0.07166767865419388	-0.03171280026435852	-0.033394187688827515	0.04359731823205948	0.0123476292937994	0.01956470124423504	0.019206881523132324	-0.015139013528823853	0.04576127976179123	0.028638947755098343	-0.007567723281681538	0.011097168549895287	-0.042651839554309845	0.033491350710392	0.050450585782527924	0.0024141198955476284	0.045230645686388016	0.017107803374528885	-0.04708098620176315	0.03740837797522545	0.08518330752849579	0.067903533577919	0.027905181050300598	-0.008288588374853134	0.029367217794060707	-0.08570055663585663	0.018481295555830002	-0.005816803779453039	0.0789542943239212	0.0007406279910355806	0.003498178208246827	-0.019510725513100624	0.05425161495804787	0.001415794133208692	-0.00012813603098038584	0.03996918722987175	-0.008020740933716297	-0.025082629173994064	0.04991648346185684	-0.02302737347781658	-0.021966975182294846	0.004050043411552906	0.03663934767246246	-0.07991335541009903	0.011525432579219341	-0.019916918128728867	0.004723855294287205	0.021567292511463165	0.0015560833271592855	-0.0359266921877861	0.019579874351620674	0.025131454691290855	-0.006585592404007912	-0.026161285117268562	0.017660589888691902	0.07109177857637405	0.04889138042926788	0.017199916765093803	0.08600446581840515	-0.09780380874872208	0.0004015046579297632	0.006130060646682978	0.061284132301807404	0.028684765100479126	0.06383467465639114	-0.0049594976007938385	0.006740662269294262	-0.0217656958848238	-0.04859449714422226	0.05877756327390671	0.03761683776974678	-0.10545378178358078	0.08152129501104355	0.04950597137212753	-0.09118515998125076	0.09842153638601303	0.028452595695853233	0.06019601970911026	-0.03319253772497177	0.0501684695482254	0.03132190927863121	0.000567608221899718	0.02053949050605297	0.052537865936756134	-0.027564913034439087	-0.004143833182752132	-0.0017296236474066973	0.00475951237604022	0.03506636619567871	0.014325786381959915	-0.07659345865249634	-0.029283804818987846	0.009068962186574936	-0.04640249162912369	-0.03106715716421604	-0.04256843030452728	-0.002295123413205147	0.027384106069803238	-0.023077497258782387	0.034485310316085815	0.023957470431923866	0.0803368017077446	-0.12103386968374252	-0.027424391359090805	0.056696441024541855	0.02994389645755291	0.06559807807207108	0.02285727672278881	0.010371631942689419	0.0824052169919014	-0.02739478461444378	-0.0981280729174614	0.02520403079688549	0.015463409945368767	0.012999027036130428	0.027222517877817154	-0.02314138226211071	-0.017432257533073425	0.050465263426303864	0.05445677787065506	0.04090394452214241	0.035675372928380966	0.0039504473097622395	-0.042143117636442184	-0.0034324442967772484	0.06230991333723068	0.0322134904563427	0.044305864721536636	0.02189972810447216	-0.016184916719794273	-0.05509840324521065	-0.005737069062888622	0.03307286649942398	0.01833866536617279	0.04008618742227554	0.053409986197948456	-0.016048483550548553
+-0.05447464436292648	0.0015701422234997153	-0.035708654671907425	0.009650718420743942	-0.009833269752562046	0.023168887943029404	0.006517108529806137	-0.021543314680457115	-0.02376026101410389	0.05322771891951561	-0.039178527891635895	-0.00045237442827783525	-0.04126407578587532	0.029432855546474457	0.007210437208414078	-0.07116975635290146	0.0015215196181088686	0.012584874406456947	0.03858897089958191	-0.06402870267629623	-0.07722891867160797	0.09589187055826187	0.0721656084060669	0.05164076387882233	0.01274065114557743	-0.014657692052423954	-0.01963573880493641	0.0037169053684920073	0.08010728657245636	0.004333301447331905	-0.061351750046014786	-0.007775614503771067	0.03328699991106987	-0.0422821044921875	0.07271265238523483	0.03560113534331322	0.04330626130104065	0.029245613142848015	0.03785192221403122	-0.030815133824944496	-0.0057713729329407215	0.04203687980771065	0.011182439513504505	-0.05280549079179764	-0.006018413696438074	-0.025842953473329544	-0.020032024011015892	-0.033031076192855835	0.021343903616070747	0.036601223051548004	-0.0029770839028060436	0.06589366495609283	-0.04110148921608925	-0.07235655188560486	-0.05169914290308952	0.02355298586189747	-0.0544300451874733	0.08879435807466507	0.038118984550237656	0.018786197528243065	0.03196929395198822	-0.02606651373207569	0.025502614676952362	-0.06550543755292892	0.011296724900603294	-0.022521503269672394	0.013904349878430367	0.07221657782793045	-0.018024984747171402	0.08216572552919388	-0.006700857542455196	0.08757185190916061	0.015684938058257103	0.030152833089232445	0.06823008507490158	-0.08447486907243729	-0.022714724764227867	0.02952549047768116	0.004507924895733595	0.01783353090286255	0.0055648996494710445	-0.009200569242238998	-0.004905016627162695	0.01920749433338642	-0.006732339970767498	0.018700724467635155	0.022970478981733322	0.030706189572811127	-0.05835817754268646	-0.0011794717283919454	0.06464138627052307	-0.07858966290950775	0.0459786020219326	-0.00648097787052393	-0.044102638959884644	-0.05668674409389496	0.08663441985845566	0.01829260215163231	0.008133761584758759	-0.012463931925594807	0.045141562819480896	-0.02630998194217682	0.02384362183511257	-0.053335465490818024	-0.018810877576470375	-0.030893441289663315	-0.04499989002943039	-0.0026923813857138157	0.05389247462153435	-0.04558578506112099	0.04413506016135216	0.028452664613723755	0.00011446007556514814	-0.03260605409741402	0.0428544282913208	-0.015096288174390793	0.07363025844097137	-0.03357277438044548	0.019363729283213615	0.012829400599002838	-0.009901526384055614	0.03706574812531471	-0.032637424767017365	0.030743366107344627	0.0036993843968957663	0.01951955445110798	0.006000794004648924	0.09096678346395493	-0.011247145012021065	0.06682237237691879	0.00018583379278425127	-0.03781081363558769	0.055516913533210754	0.004375263582915068	-0.05107564851641655	-0.0718032643198967	-0.04110637307167053	0.06591301411390305	0.05263252183794975	0.06641356647014618	-0.06964454799890518	0.06484154611825943	0.13708345592021942	0.05013914406299591	-0.016466567292809486	0.011048165149986744	-0.06172093376517296	-0.0021021030843257904	0.04395092651247978	-0.031032361090183258	0.015512664802372456	-0.003790442366153002	0.04770367592573166	-0.015971651300787926	-0.012769953347742558	-0.049562595784664154	0.009556776843965054	0.011183432303369045	0.007220401894301176	-0.05787578225135803	-0.000998402014374733	-0.0012062755413353443	-0.012400847859680653	-0.004768125247210264	0.05516284331679344	0.022941280156373978	0.026638437062501907	-0.08432121574878693	-0.0075964308343827724	0.06360482424497604	-0.014265376143157482	-0.05459537357091904	0.003950944170355797	-0.040565766394138336	-0.05085848271846771	-0.05451960489153862	0.04862810671329498	0.019522298127412796	0.022249555215239525	-0.012372403405606747	0.0046339877881109715	-0.08229571580886841	0.02793215587735176	0.05195432901382446	-0.0014625702751800418	0.014082865789532661	0.005051271989941597	-0.04708497226238251	-0.06502185761928558	0.02946351282298565	0.04093050956726074	-0.0023618254344910383	-0.009863495826721191	0.02164880931377411	0.011948072351515293	-0.03781462088227272	-0.014696325175464153	0.01170018594712019	0.02998478338122368	0.08518153429031372	-0.06982189416885376	0.034825630486011505	-0.05146745964884758	-0.06677810102701187	-0.012804652564227581	-0.019317559897899628	-0.017778148874640465	-0.05791381374001503	0.010269910097122192	-0.019249316304922104	-0.04333727806806564	-0.047982532531023026	-0.030942542478442192	-0.011204267852008343	0.008238471113145351	0.07351254671812057	0.020115986466407776	-0.03488979488611221	-0.03376523032784462	0.03725197911262512	0.004016368184238672	0.0004790254752151668	-0.05038997530937195	-0.08305706083774567	-0.026407388970255852	-0.009633926674723625	-0.014091551303863525	-0.0031561385840177536	0.09268698841333389	0.022710496559739113	0.05308890715241432	0.018576987087726593	-0.012817434966564178	0.02009746991097927	-0.0006271993624977767	0.011632428504526615	0.05971107631921768	-0.05389219522476196	-0.01744805835187435	-0.017112188041210175	-0.00038739407318644226	-0.0559384785592556	-0.007915141060948372	0.005766759160906076	0.01829361729323864	-0.017234068363904953	-0.020284563302993774	0.05306480824947357	-0.014334151521325111	-0.03247513622045517	0.08195631206035614	-0.0013328911736607552	-0.062347661703825	0.03867603465914726	-0.010213051922619343	-0.05401657521724701	0.007285366300493479	-0.052886225283145905	0.059849753975868225	-0.01230164896696806	-0.004995856899768114	-0.013616625219583511	-0.006831805687397718	0.011945685371756554	0.005910854786634445	-0.0073995948769152164	0.01709076575934887	0.03879360854625702	0.027675719931721687	0.023102520033717155	0.04956992343068123	0.06170496344566345	-0.03047899715602398	0.09521935135126114	-0.028281064704060555	-0.10154658555984497	-0.04811907932162285	0.06459984928369522	0.008524582721292973	-0.02428540587425232	0.07380836457014084	-0.03261467069387436	0.003484163898974657	-0.048839423805475235	0.03900987654924393	-0.07166711986064911	0.002665975596755743	0.025719596073031425	-0.020670734345912933	-0.0856081172823906	-0.026311729103326797	-0.029600534588098526	-0.01491005253046751	-0.026985878124833107	-0.05607471615076065	0.013840180821716785	-0.053998880088329315	0.07727456092834473	0.006094874814152718	0.03992043808102608	0.09911079704761505	0.029839761555194855	0.036502495408058167	0.06916794180870056	0.024957498535513878	-0.11865288764238358	0.04260721057653427	-0.029213085770606995	0.007463553920388222	0.03478569909930229	0.05236487835645676	0.02366098202764988	-0.016310138627886772	-0.02710634097456932	0.07708831876516342	-0.03157992660999298	0.021986357867717743	0.0006468811770901084	-0.08252815157175064	-0.03760571777820587	0.03630951792001724	-0.007672056555747986	0.015969889238476753	0.035842105746269226	-0.05767713114619255	0.0013631843030452728	-0.03941015526652336	-0.09688548743724823	0.08031528443098068	-0.01138681173324585	-0.04387267678976059	-0.0693429484963417	0.024316566064953804	0.026783645153045654	-0.039382219314575195	-0.03050997294485569	-0.07389850914478302	0.052699364721775055	0.015827111899852753	0.028941363096237183	0.09885769337415695	0.04223202168941498	-0.049259234219789505	0.0008677170844748616	0.029903562739491463	-0.005929502192884684	-0.09661674499511719	-0.050114478915929794	0.10806196182966232	0.019071748480200768	0.03648112714290619	-0.11485211551189423	-0.05508314073085785	0.08071300387382507	-0.024260371923446655	-0.0022741728462278843	-0.0019171185558661819	0.033654529601335526	-0.051287129521369934	0.00481414282694459	-0.03900543972849846	-0.005358510185033083	-0.07110944390296936	0.04102117195725441	-0.02299986593425274	-0.037918467074632645	0.08898575603961945	-0.006984849460422993	-0.06805022805929184	0.016355538740754128	-0.031013861298561096	0.05678567290306091	-0.04957558214664459	0.023193607106804848	0.07935738563537598	-0.0443992093205452	-0.04499679431319237	-0.04604440554976463	-0.0013356572017073631	0.017735680565238	0.0047842669300735	0.056751612573862076	-0.02839674800634384	0.05370659381151199	0.08212633430957794	0.025395439937710762	-0.03245624899864197	0.007231080438941717	0.030804725363850594	0.03785840421915054	-0.0035434330347925425	0.0776607096195221	0.03559943288564682	-0.05429539084434509	-0.012314565479755402	-0.0247708261013031	0.09220725297927856	-0.026645846664905548	-0.004639128688722849	0.06904103606939316	-0.01688038371503353	0.000463992910226807	-0.0030667518731206656	0.05437851697206497	-0.01623642072081566	-0.08132722973823547	-0.0025179910007864237	0.00829892698675394	-0.021121883764863014	-0.01833558827638626	-0.009203186258673668	-0.02648777700960636	-0.05766871199011803	0.10683717578649521	-0.017829641699790955	-0.03866391256451607	-0.02405725233256817	0.050909217447042465	-0.061275046318769455	-0.025392765179276466	0.02342887967824936	0.002341881860047579	0.04181287810206413	0.009477534331381321	-0.005592203699052334	0.001196176977828145	0.07667383551597595	0.003547350876033306	-0.13740919530391693	0.007072669919580221	0.036358993500471115	0.04377061501145363	-0.02152787335216999	0.02360944077372551	-0.08601030707359314	-0.012244787998497486	0.02441244199872017	0.02306338958442211	-0.01884814351797104	0.017596332356333733	0.007538131903856993	0.004669212270528078	0.04323182627558708	-0.05612771958112717	0.024501031264662743	-0.04194056615233421	-0.05831661447882652	0.06329059600830078	0.0453091636300087	-0.04313700646162033	0.09803268313407898	0.043255764991045	0.06003962457180023	0.010534935630857944	0.04567224159836769	0.004710991866886616	-0.007713996339589357	0.04940470680594444	0.05715392157435417	0.006948686204850674	-0.02421591617166996	-0.016762280836701393	-0.005433678161352873	0.008149794302880764	0.0290144644677639	-0.039102379232645035	0.028340602293610573	-0.011595604941248894	0.00036229038960300386	-0.03186863288283348	-0.03616717457771301	-0.01204015500843525	0.028347449377179146	-0.04982767999172211	0.03487319126725197	0.04370390623807907	0.06131565943360329	-0.11123586446046829	0.04969700425863266	0.06263165920972824	0.04399677738547325	0.10763082653284073	-0.009134558029472828	0.002265872433781624	0.07019053399562836	-0.028575174510478973	-0.024562561884522438	0.008187324739992619	0.024162478744983673	-0.014666353352367878	0.04438216984272003	0.025286678224802017	-0.03566599637269974	-0.01957003027200699	0.09036478400230408	0.04320146515965462	0.01000167801976204	0.0763283222913742	-0.027751239016652107	-0.01378415897488594	-0.007102640345692635	0.0780637338757515	0.07914768904447556	0.060931287705898285	0.012487723492085934	-0.042597174644470215	-0.046022266149520874	-0.006487582344561815	0.03467635065317154	0.018440470099449158	0.0011660783784464002	-0.01051325723528862
+0.016649676486849785	-0.028280075639486313	-0.0866527184844017	0.026405027136206627	-0.052983272820711136	0.08183588087558746	-0.00440096715465188	0.02641833759844303	-0.08887970447540283	0.016640255227684975	-0.03877842053771019	0.01373173575848341	-0.02415981888771057	-0.05220896378159523	0.014751848764717579	-0.1080712303519249	-0.007558582816272974	-0.04008330777287483	0.07617410272359848	-0.06113477051258087	-0.03938928246498108	0.06415663659572601	0.03745223209261894	0.07219957560300827	0.04203859344124794	0.026477226987481117	0.05218951776623726	0.045392487198114395	0.057772621512413025	0.04855026304721832	0.029545577242970467	-0.057886380702257156	-0.03384661301970482	-0.01554489228874445	0.020522233098745346	0.004291217308491468	0.020903071388602257	0.08570819348096848	0.0734039843082428	-0.05766165256500244	0.016471583396196365	0.04944629222154617	0.03542570397257805	-0.06451267749071121	-0.01038302667438984	-0.03539082407951355	-0.04126269370317459	-0.102022185921669	0.044362761080265045	-0.020518040284514427	-0.016090229153633118	0.07485157996416092	0.0075623951852321625	-0.057099662721157074	-0.06053761765360832	0.053331710398197174	-0.00531479949131608	0.054600972682237625	0.030058931559324265	0.06900833547115326	0.0025509530678391457	-0.015958499163389206	0.003512472379952669	-0.028259461745619774	0.058590542525053024	-0.0036976172123104334	0.05115363001823425	-0.0003162397479172796	-0.025010643526911736	0.024421894922852516	-0.04474634677171707	0.06982596218585968	0.00560098048299551	0.023750510066747665	0.03522108122706413	-0.052592549473047256	-0.06677066534757614	-0.05059793218970299	0.021338820457458496	0.007384305354207754	-0.02186909317970276	-0.02451610006392002	0.031889889389276505	0.034191276878118515	0.001304782461374998	0.014287503436207771	-0.05892563983798027	-0.00694896699860692	-0.015893684700131416	-0.016879547387361526	0.04978959262371063	-0.05512184649705887	-0.020240364596247673	0.0002593628305476159	-0.03516029193997383	-0.057784076780080795	0.0854564681649208	-0.032198548316955566	0.02910676598548889	-0.010186064057052135	-0.020702291280031204	-0.03896327689290047	0.054258204996585846	-0.022981533780694008	0.022333523258566856	-0.02113102190196514	-0.12005334347486496	-0.005274427589029074	0.06105780974030495	-0.031792208552360535	0.04500791057944298	0.09155896306037903	-0.02038627304136753	-0.020655661821365356	0.08682313561439514	-0.00023846793919801712	-0.0019637884106487036	-0.03390386328101158	0.011367361061275005	0.0064322748221457005	-0.04004841297864914	0.06507154554128647	0.0010855153668671846	-0.01748722791671753	0.02268507331609726	0.04106936603784561	-0.009105931967496872	0.06631473451852798	0.01056764181703329	0.03811473771929741	-0.02287178300321102	0.03660224750638008	0.07216811180114746	0.047154273837804794	-0.05425189435482025	-0.044288359582424164	-0.051276590675115585	0.06990078836679459	0.05101001262664795	0.05548788234591484	-0.08259014040231705	0.015092721208930016	0.1444176435470581	0.0331428088247776	-0.06809896975755692	-0.029202884063124657	-0.07852613180875778	0.007348096929490566	0.07361214607954025	0.002794271567836404	0.0012216983595862985	0.03900276497006416	0.09775043278932571	-0.013036462478339672	0.004968890454620123	0.011021101847290993	0.07001160085201263	-0.00905495323240757	0.019155895337462425	-0.019236797466874123	0.02541203424334526	-0.0015129863750189543	0.021856218576431274	-0.06475558131933212	0.03467247635126114	0.07351037859916687	0.017613660544157028	-0.0985308587551117	0.013613147661089897	0.0912565365433693	-0.00567318219691515	0.00021090982772875577	0.03559655323624611	-0.013904997147619724	-0.02822083607316017	-0.03161752596497536	0.0516447015106678	-0.009524496272206306	0.0553913339972496	-0.028720684349536896	-0.009467260912060738	-0.0653955489397049	0.04247189685702324	0.005246123299002647	-0.06341202557086945	0.01107309851795435	0.017351021990180016	0.006582917645573616	-0.023681681603193283	-0.005888720508664846	0.0052064950577914715	-0.03351139277219772	-0.023256314918398857	-0.041941870003938675	-0.018816884607076645	0.00199952838011086	0.06754910945892334	0.03552846610546112	0.04385862499475479	0.05225754901766777	-0.09293435513973236	0.033741746097803116	-0.03746912628412247	-0.06313060224056244	0.020958328619599342	0.04743039235472679	-0.037129901349544525	-0.055415667593479156	0.009616484865546227	-0.04592130333185196	-0.04517700895667076	0.013541447930037975	-0.02429047040641308	0.006767598446458578	0.02665223367512226	0.03633486106991768	-0.0027487282641232014	-0.032914068549871445	-0.030477259308099747	-0.02482563629746437	-0.0314289927482605	-0.03863433748483658	-0.0462951585650444	-0.05064871162176132	-0.0008231748943217099	-0.04625825211405754	0.0060257320292294025	-0.0031158272176980972	0.11924561858177185	-0.02848733216524124	0.0660245269536972	0.07800573110580444	0.011375537142157555	0.004932644311338663	0.011253890581429005	-0.04125593230128288	0.048105932772159576	-0.013178257271647453	-0.050351377576589584	-0.08634824305772781	-0.00401735445484519	-0.030883416533470154	-0.028310643509030342	-0.010383028537034988	0.016251683235168457	0.00018880091374740005	-0.006309375632554293	0.04710471257567406	-0.09306022524833679	-0.020382845774292946	0.04815363138914108	0.01647755689918995	0.0026992170605808496	0.06411535292863846	0.03283270448446274	-0.08191955089569092	0.022154999896883965	-0.06228732317686081	0.0774519219994545	-0.05769446864724159	0.05483796447515488	-0.03819901868700981	-0.03558417037129402	-0.0073436954990029335	0.011911274865269661	-0.0033525596372783184	0.004480582196265459	0.04388833045959473	-0.010633358731865883	0.010414338670670986	0.023780127987265587	0.10156722366809845	-0.008220712654292583	0.06290136277675629	-0.045009735971689224	-0.03722346946597099	-0.037924036383628845	0.02056736871600151	-0.0066718109883368015	-0.03284905105829239	0.03371119871735573	-0.016499249264597893	0.00012047636118950322	-0.03166244551539421	-0.021121732890605927	-0.024066073819994926	0.006655167788267136	0.0050033461302518845	0.011374746449291706	-0.015536103397607803	0.009087181650102139	-0.04902569577097893	-0.04229460656642914	0.045882999897003174	-0.03046596050262451	0.03243008628487587	-0.02934090606868267	0.07024694979190826	-0.013573416508734226	0.03821897879242897	0.10688231885433197	-0.0015016737161204219	0.02973351627588272	-0.007969514466822147	-0.01527683436870575	-0.09810460358858109	0.0056440201587975025	0.028257880359888077	0.023807425051927567	0.02563250996172428	0.06667394191026688	-0.06000954285264015	-0.018548795953392982	-0.007714600767940283	0.03096560388803482	-0.013261268846690655	0.030809570103883743	-0.029473256319761276	-0.05712141469120979	0.011444424279034138	0.04876008629798889	-0.02677845023572445	-0.0045453510247170925	0.01813877932727337	-0.006139306351542473	-0.004710656125098467	0.008134358562529087	-0.13735343515872955	0.05008172616362572	-0.012944711372256279	0.008054659701883793	-0.07525771856307983	0.006820036564022303	0.018680939450860023	0.008274976164102554	-0.05368034541606903	-0.0870828703045845	0.06698394566774368	0.02732297219336033	0.08583590388298035	0.11267527937889099	-0.04846417158842087	-0.00455052824690938	0.012820078991353512	-0.03404570743441582	-0.07887686789035797	-0.00957456137984991	-0.09331788867712021	0.07978998869657516	0.03472224995493889	-0.026675621047616005	-0.05059344321489334	-0.039498645812273026	0.0017198335845023394	-0.013626289553940296	0.010384452529251575	0.010451826266944408	0.00621569249778986	-0.09227938950061798	-0.02613455429673195	0.012544470839202404	-0.032034970819950104	-0.05953294038772583	-0.00019071124552283436	0.016317278146743774	-0.01631556823849678	0.07991212606430054	-0.026766540482640266	-0.04759661853313446	0.04007233679294586	0.010371843352913857	0.04154399782419205	-0.014677044935524464	-0.0118626793846488	0.06729814410209656	-0.10407133400440216	0.006200891919434071	-0.05506715178489685	0.05531923472881317	-0.03247500956058502	-0.026232829317450523	0.03644206002354622	-0.016683513298630714	-0.0005279026227071881	0.024620605632662773	0.005918159615248442	0.054307207465171814	-0.023011675104498863	0.06742704659700394	0.016554249450564384	0.0221325121819973	0.08879316598176956	0.0192645862698555	-0.029125770553946495	0.021550003439188004	0.05295969173312187	0.0485246405005455	0.03428586199879646	-0.018095631152391434	0.055212005972862244	-0.013022536411881447	0.03470358997583389	0.02350037917494774	0.06571580469608307	-0.029915155842900276	-0.0410793200135231	0.003551226807758212	0.06603606045246124	-0.045416612178087234	-0.011806418187916279	0.02801949717104435	0.0005086669116280973	-0.057543203234672546	0.06358059495687485	-0.012526958249509335	-0.0722498968243599	-0.025892943143844604	0.0036248413380235434	-0.04300734028220177	-0.01871192641556263	0.015480577014386654	0.031575534492731094	0.018607163801789284	0.011452513746917248	-0.022474322468042374	-0.028757940977811813	0.04347823187708855	0.01890970580279827	-0.021579040214419365	0.021663237363100052	0.05070553719997406	0.0600835420191288	0.03117961622774601	0.059494804590940475	-0.0651756301522255	-0.03698994964361191	0.03193538635969162	0.03857828304171562	0.006150487810373306	0.029546618461608887	-0.007231276948004961	0.030699850991368294	0.03364139422774315	-0.07915256172418594	0.04144204407930374	0.009174416773021221	-0.05369596183300018	0.06200327351689339	0.04953516647219658	-0.051721129566431046	0.05694799870252609	-0.002846956718713045	0.06722203642129898	0.0056552826426923275	0.027752622961997986	0.04544645920395851	0.024122854694724083	0.02725466713309288	0.026452986523509026	-0.03830942511558533	-0.010132724419236183	-0.002139449119567871	0.0026912325993180275	0.019400348886847496	-0.013758172281086445	-0.08979514986276627	0.02388867549598217	-0.010897837579250336	-0.04625226557254791	-0.03454195708036423	-0.010134326294064522	-0.024518631398677826	0.04129853472113609	-0.013002066873013973	0.03759010136127472	0.0276811346411705	0.0589236281812191	-0.10664310306310654	-0.007863304577767849	0.0038142709527164698	0.025910763069987297	0.05377217009663582	0.04017961770296097	-0.022221090272068977	0.050397053360939026	-0.020767811685800552	-0.0745619386434555	0.010845637880265713	-0.04068112000823021	0.022935546934604645	0.03833511099219322	0.01033235527575016	-0.02140093222260475	0.032543543726205826	0.0653340145945549	0.08022566884756088	0.027068808674812317	-0.007679740432649851	-0.015320118516683578	0.044717952609062195	0.06224376708269119	0.045648299157619476	0.04406179487705231	0.05350548401474953	-0.006930071860551834	-0.07626078277826309	-0.0015505412593483925	0.04065704718232155	0.04429077357053757	0.07298342883586884	0.01311756856739521	-0.01719764806330204
+-0.0031317949760705233	-0.003893353743478656	-0.033028505742549896	0.03805346414446831	-0.029674280434846878	0.04572527855634689	-0.037673283368349075	-0.0032951869070529938	-0.0581774078309536	-0.0037887184880673885	-0.032970868051052094	0.0009561381884850562	-0.045224446803331375	-0.007506716530770063	0.00826823990792036	-0.10080885142087936	0.01616472750902176	0.01494402252137661	0.07541506737470627	-0.07006414979696274	-0.03607964888215065	0.0932171419262886	0.05445070192217827	0.07381056994199753	0.022520676255226135	0.004758797120302916	0.03136194869875908	0.04714782163500786	0.08858169615268707	0.060970988124608994	-0.02205185405910015	-0.07271809875965118	0.01934289187192917	-0.03134581819176674	0.04339192807674408	0.02699236571788788	0.01157454028725624	0.06481937319040298	0.09599434584379196	-0.012150680646300316	-0.001144365407526493	0.019371187314391136	0.04166450351476669	-0.05394526571035385	0.006631039083003998	0.010038497857749462	-0.045567285269498825	-0.06791814416646957	0.03567983955144882	0.022805482149124146	0.0034237715881317854	0.08240798115730286	-0.020638205111026764	-0.03159891068935394	-0.018255086615681648	0.02131209895014763	0.012950016185641289	0.07459568977355957	0.06006889417767525	0.012554438784718513	0.008310210891067982	-0.05364938825368881	0.021816931664943695	-0.05744614452123642	0.0299665704369545	0.007511402014642954	0.030398527160286903	0.05124574527144432	-0.037417106330394745	0.047319862991571426	-0.009014859795570374	0.09326321631669998	0.007703632116317749	0.025560827925801277	0.04479830339550972	-0.0888938456773758	-0.05490034818649292	-0.01642506755888462	-0.018052788451313972	0.015415641479194164	-0.00869749765843153	-0.008642079308629036	0.04400070011615753	0.023607365787029266	-0.028801502659916878	0.032920774072408676	-0.018343664705753326	-0.014581914059817791	-0.02549564279615879	-0.006732881534844637	0.054300278425216675	-0.07201018929481506	0.004856578540056944	-0.004743613302707672	-0.0475015714764595	-0.05400664359331131	0.08097028732299805	0.03538547456264496	0.019605714827775955	-0.021282346919178963	-0.011803166940808296	-0.02855723723769188	0.03606583550572395	-0.04104769974946976	0.002984464168548584	-0.04329614341259003	-0.05490664765238762	-0.00010633601050358266	0.04103055223822594	-0.030791467055678368	0.034452326595783234	0.06420426815748215	-0.017226407304406166	-0.03139178827404976	0.09892875701189041	-0.010141965001821518	0.04595467448234558	-0.03678695484995842	0.007841051556169987	-0.021215610206127167	-0.036193232983350754	0.09545844048261642	-0.02640080265700817	-0.0025806406047195196	0.021756846457719803	0.037909843027591705	-0.014148388989269733	0.08063764870166779	0.014454209245741367	0.05222165957093239	-0.0015164472861215472	0.01855628937482834	0.03799308091402054	0.05352544039487839	-0.054917968809604645	-0.06773790717124939	-0.08255172520875931	0.08427304774522781	0.054816581308841705	0.05984390154480934	-0.06179988756775856	0.03909863159060478	0.13481327891349792	0.02802383527159691	-0.018639596179127693	-0.0007257075048983097	-0.037549734115600586	-0.014390941709280014	0.05022463575005531	-0.01942068338394165	0.04451330006122589	0.020968548953533173	0.05859895423054695	-0.003363274969160557	-0.02796567976474762	-0.03325796499848366	0.025878485292196274	-0.03778757527470589	0.019127557054162025	-0.030397213995456696	-0.012653306126594543	-0.008641622960567474	0.0003173564618919045	-0.0030187242664396763	0.020591313019394875	0.07617435604333878	0.01953468844294548	-0.09111116826534271	-0.029900627210736275	0.07833541929721832	-0.016123952344059944	-0.025426240637898445	0.03635221719741821	-0.023125724866986275	-0.019863415509462357	-0.02794741652905941	0.048008136451244354	-0.012161198072135448	0.06645739078521729	-0.019575441256165504	0.022552240639925003	-0.10066389292478561	0.028346050530672073	0.032254233956336975	-0.03210018202662468	0.016990775242447853	0.009157715365290642	-0.004408016335219145	-0.010602452792227268	0.022721292451024055	0.04890197142958641	-0.004080904182046652	-0.035209450870752335	0.0022018146701157093	-0.017057402059435844	-0.049113329499959946	-0.001669923309236765	0.009533097967505455	-0.0007851688424125314	0.06035260483622551	-0.0906170904636383	0.041740790009498596	-0.03761369735002518	-0.03565232828259468	0.016446715220808983	0.02488713711500168	-0.02824566513299942	-0.04603200405836105	0.00648921262472868	-0.045344121754169464	-0.013429264537990093	-0.010532940737903118	-0.020443974062800407	0.02097300812602043	-0.0023583900183439255	0.030123742297291756	0.0065961312502622604	-0.05031539872288704	-0.03343852236866951	0.00031653305632062256	-0.04956293851137161	-0.05825331434607506	-0.06915063410997391	-0.08230423182249069	-0.036521028727293015	-0.005393940024077892	-0.0027274899184703827	0.0295992624014616	0.12069668620824814	-0.019214777275919914	0.061489541083574295	0.03780018538236618	0.01711197942495346	0.04222378134727478	0.010369482450187206	-0.05208738148212433	0.043151360005140305	-0.009976493194699287	-0.018079938367009163	-0.0374782457947731	-0.017811166122555733	-0.02723325602710247	0.018661292269825935	0.002115276176482439	0.012694772332906723	-0.012751787900924683	-0.028840215876698494	0.048210445791482925	-0.06597913801670074	-0.0387747548520565	0.07321012765169144	0.001877836650237441	-0.050659094005823135	0.03373466432094574	0.04051816835999489	-0.05309217423200607	0.04263855144381523	-0.05966673046350479	0.07276120036840439	-0.03771001100540161	0.005318049341440201	-0.04251693934202194	-0.016803715378046036	0.001053389860317111	-0.021169563755393028	-0.0043858992867171764	0.04868187755346298	0.04027435928583145	-0.0056891897693276405	0.02472830004990101	0.03017251007258892	0.1030394583940506	0.006781811825931072	0.0763343945145607	-0.030213510617613792	-0.06708914786577225	-0.011380300857126713	0.06517517566680908	0.045221518725156784	-0.04975615069270134	0.04729044437408447	-0.04108657315373421	0.007983755320310593	-0.05352526158094406	0.029770979657769203	-0.040379080921411514	0.004779983311891556	0.011413177475333214	-0.0016672750934958458	-0.031381554901599884	0.0006289823795668781	-0.04129014536738396	-0.02337789349257946	-0.0053895930759608746	-0.040323950350284576	0.012199586257338524	-0.06643834710121155	0.0792175680398941	0.01871785894036293	0.006548893637955189	0.10031766444444656	-0.011384131386876106	0.024335557594895363	0.03811801224946976	0.004876873455941677	-0.11215212941169739	0.04731280356645584	0.007967083714902401	0.022022228688001633	0.055335499346256256	0.0718761682510376	-0.0167914517223835	0.013130969367921352	0.01613864116370678	0.03381126746535301	-0.009393679909408092	0.012361716479063034	-0.032247044146060944	-0.04259280115365982	0.042497314512729645	0.051505718380212784	0.004215158522129059	0.017143025994300842	0.03271624818444252	-0.029357802122831345	-0.008784392848610878	-0.00021442194702103734	-0.13407151401042938	0.0713491141796112	-0.010797369293868542	-0.031396977603435516	-0.11359619349241257	0.031211551278829575	0.014378674328327179	-0.012908133678138256	-0.05517973750829697	-0.08714774996042252	0.05446098372340202	0.028809847310185432	0.04213753342628479	0.08797236531972885	-0.003841087222099304	-0.03202013671398163	0.022264812141656876	-0.01651712693274021	-0.017367642372846603	-0.06320340931415558	-0.07179273664951324	0.10987521708011627	0.0200495645403862	0.018115252256393433	-0.0866246148943901	-0.05353386700153351	0.03970273211598396	-0.016909178346395493	0.004811473656445742	-0.004091242328286171	0.04543421417474747	-0.1004277691245079	-0.016824929043650627	-0.01350425649434328	-0.02346377819776535	-0.02927953377366066	0.04400268942117691	0.029047057032585144	-0.07228435575962067	0.0634244978427887	-0.018814081326127052	-0.0743245780467987	0.024613570421934128	-0.013361629098653793	0.01703350432217121	-0.05768527090549469	-0.05885844677686691	0.08657754212617874	-0.03892927244305611	-0.017843779176473618	-0.061601683497428894	0.011791594326496124	0.005689093843102455	-0.010175470262765884	0.027043474838137627	-0.037954699248075485	0.04591115564107895	0.05017079412937164	0.011151637881994247	-0.005216243676841259	-0.027152324095368385	0.04564365744590759	0.02903047949075699	0.0028905656654387712	0.10010814666748047	0.043297115713357925	-0.0241385567933321	-0.006408856716006994	0.021815769374370575	0.09541205316781998	0.02201569639146328	-0.018655473366379738	0.08148667216300964	0.008187719620764256	0.012143322266638279	0.014538619667291641	0.05683184787631035	0.006400127895176411	-0.06277848035097122	-0.032471705228090286	0.023781098425388336	-0.05358826741576195	0.0010359685402363539	-0.012677601538598537	-0.014056962914764881	-0.06854641437530518	0.1067403256893158	0.014513435773551464	-0.06963095813989639	-0.007763328030705452	0.048467375338077545	-0.02505648322403431	-0.02282710373401642	-0.0003783490101341158	0.007616856135427952	0.03800453618168831	-0.024431027472019196	-0.07440308481454849	-0.026160752400755882	0.06474123150110245	0.013316571712493896	-0.08226116746664047	2.459019742673263e-05	0.0658206194639206	0.09344623982906342	-0.011073444969952106	0.06517834216356277	-0.0662011206150055	-0.006333588156849146	0.026581617072224617	0.011991467326879501	0.03377002850174904	0.04538940638303757	0.03073771484196186	0.03410131111741066	0.020988093689084053	-0.07164328545331955	0.022993210703134537	0.018820593133568764	-0.05622547119855881	0.036833833903074265	0.050172675400972366	-0.04740939289331436	0.05119040608406067	0.04619241878390312	0.02971113845705986	0.032215412706136703	0.034262605011463165	0.04236006364226341	0.009719262830913067	0.007421264424920082	0.027996506541967392	-0.0015136738074943423	-0.0565764345228672	-0.054819364100694656	-0.022905951365828514	0.03066457062959671	-0.005827047396451235	-0.08315678685903549	-0.0013958424096927047	0.0018932491075247526	-0.0034859187435358763	0.01875690184533596	-0.010702804662287235	-0.0017238041618838906	0.06616277992725372	-0.04152875393629074	0.03661089763045311	0.02521873638033867	0.07372193038463593	-0.10549494624137878	-0.010570897720754147	0.05109307914972305	0.023011624813079834	0.07659442722797394	0.02670358680188656	-0.014792942441999912	0.03809653967618942	-0.015955615788698196	-0.032281432300806046	0.05346661061048508	-0.000294373749056831	0.0017740357434377074	0.028665801510214806	0.016555065289139748	-0.033561427146196365	-0.00259250751696527	0.05783316120505333	0.09786060452461243	0.03439534455537796	0.06887896358966827	-0.013603053987026215	0.037977539002895355	0.050798822194337845	0.06070296838879585	0.07568877935409546	0.050945721566677094	0.010821756906807423	-0.01922943815588951	0.005119310226291418	0.007804485969245434	0.010271403938531876	0.06109502166509628	0.03350374475121498	-0.04095594212412834
+0.02001814916729927	-0.014328667894005775	-0.0914124846458435	-0.0016279480187222362	0.012978650629520416	0.009963220916688442	-0.01773180440068245	0.00635930523276329	-0.05974815413355827	-0.02195006050169468	-0.05076470598578453	0.014279891736805439	0.0031736264936625957	-0.052010614424943924	-0.001738924183882773	-0.10166164487600327	-0.042205601930618286	-0.005568469408899546	0.04142056405544281	-0.012379094958305359	-0.027738794684410095	0.04164351150393486	-0.05618755891919136	0.018178220838308334	0.0246694628149271	0.033729929476976395	0.04887647181749344	-0.016912564635276794	0.07216998934745789	0.053032975643873215	0.018336180597543716	-0.05016428232192993	0.025957418605685234	-0.00499134324491024	0.00405045785009861	-0.025651773437857628	0.03262054920196533	0.050721973180770874	0.0729159265756607	0.001531371846795082	-0.0561404824256897	0.003830170026049018	-0.02618277817964554	-0.08452508598566055	-0.04657140374183655	-0.0054414402693510056	-0.05810585245490074	-0.06533779948949814	0.03171322122216225	-0.004157609306275845	-0.01987999863922596	0.0033293715678155422	-0.04810318350791931	-0.024659479036927223	-0.05725095048546791	0.005770612973719835	-0.005742142908275127	0.05445296689867973	-0.0011949194595217705	-0.013065870851278305	-0.013427125290036201	0.05421824008226395	-0.040531426668167114	-0.041322775185108185	-0.0030363183468580246	-0.011487763375043869	0.041512902826070786	0.010900440625846386	-0.030316807329654694	-0.002382064936682582	-0.025990163907408714	0.04136624187231064	-0.04517543688416481	0.039811499416828156	0.028321171179413795	-0.06605318933725357	-0.04488051310181618	-0.043057624250650406	-0.012958543375134468	-0.01724260486662388	-0.019917666912078857	0.007843904197216034	0.1377762258052826	-0.01761537417769432	-0.008639894425868988	-0.03847643733024597	-0.022803865373134613	-0.024982891976833344	-0.020661840215325356	-0.0019402002217248082	0.07585839927196503	-0.051142603158950806	0.023671045899391174	0.00025807259953580797	0.013364756479859352	-0.009638934396207333	0.09181296825408936	-0.03147045895457268	-0.05390980839729309	0.010304790921509266	-0.026940839365124702	-0.008416371420025826	0.09789718687534332	-0.015485002659261227	-0.028021160513162613	-0.07286982983350754	-0.06627601385116577	-0.0005622621974907815	0.029969047755002975	-0.04187377542257309	0.07228022813796997	0.08234651386737823	-0.06049271672964096	0.0005493282806128263	0.07830856740474701	0.01831645332276821	0.06246001273393631	0.008362670429050922	0.0757572278380394	0.044848181307315826	-0.037052642554044724	0.07744581252336502	-0.011654193513095379	-0.029040969908237457	0.003448960604146123	0.039201974868774414	-0.02143019251525402	-0.010830155573785305	-0.03568126633763313	-0.002188377780839801	0.007929522544145584	0.06194624304771423	0.017902521416544914	0.02608538791537285	-0.0573643259704113	-0.08403225988149643	-0.021062983199954033	0.04394594579935074	0.024841751903295517	0.02654240094125271	-0.07003553956747055	0.04509885609149933	-0.005763503722846508	0.06083286181092262	0.0368213914334774	-0.01151969563215971	-0.06679019331932068	-0.03856084495782852	0.06908497214317322	-0.02045053243637085	0.026679065078496933	0.020532840862870216	0.05404086038470268	0.009050018154084682	0.061606407165527344	0.003384689334779978	0.06958460807800293	0.018517006188631058	0.03790808469057083	0.017300261184573174	-0.008606141433119774	-0.02996172569692135	-0.015105393715202808	-0.03914366289973259	0.018898623064160347	0.11421952396631241	0.04764680191874504	-0.047309789806604385	-0.0259254053235054	0.10029028356075287	0.0077736154198646545	0.03676290065050125	0.014946095645427704	-0.015336032956838608	-9.899224096443504e-05	-0.035047344863414764	0.029454922303557396	-0.06272375583648682	0.023261742666363716	-0.012551079504191875	0.08094663918018341	-0.09693755209445953	-0.016142241656780243	0.037428390234708786	-0.006261199247092009	0.07176799327135086	-0.01613265834748745	0.004748417995870113	0.015775082632899284	0.02519858255982399	-0.01159853208810091	-0.043403688818216324	-0.024506311863660812	-0.03731180354952812	-0.0007638516835868359	-0.025648610666394234	0.04687443748116493	0.0884006917476654	0.0206135381013155	0.05338646471500397	-0.09684114158153534	-0.04193873703479767	-0.05263088643550873	0.006990564521402121	0.04212062805891037	0.05382087454199791	-0.02725975029170513	-0.028627650812268257	0.02808249182999134	-0.008676083758473396	-0.002151987049728632	-0.06280797719955444	-0.010194304399192333	0.036922719329595566	0.04725627601146698	-0.006630687043070793	-0.04513243958353996	0.017112379893660545	0.00941018108278513	-0.039421647787094116	-0.06362330913543701	-0.04679834842681885	-0.08683344721794128	-0.06956515461206436	-0.01351834088563919	-0.024154789745807648	0.005095020402222872	0.0027595465071499348	0.06576123833656311	-0.008407486602663994	0.03294985741376877	0.0798289105296135	-0.010998746380209923	-0.02904694154858589	0.025993410497903824	-0.019349049776792526	0.009559307247400284	-0.01664748601615429	-0.01801436021924019	-0.08484125137329102	0.025813987478613853	0.015797482803463936	-0.012906988151371479	0.015390298329293728	0.0004657499666791409	-0.010595898143947124	0.038737308233976364	0.017124012112617493	-0.09904444217681885	-0.011428270488977432	0.020128337666392326	-0.014496436342597008	0.010128866881132126	0.0201269444078207	0.049249716103076935	-0.08835718780755997	0.07184907793998718	-0.041789036244153976	0.07379044592380524	-0.044389791786670685	0.0026908330619335175	0.026000531390309334	-0.033629052340984344	-0.047587841749191284	0.00950403232127428	-0.0016014447901397943	0.05028524249792099	0.031741343438625336	-0.03836226835846901	-0.041624292731285095	0.008317435160279274	0.08498158305883408	0.004601022228598595	0.021251585334539413	-0.016408277675509453	-0.019529005512595177	0.043021827936172485	0.00968943815678358	0.027751408517360687	-0.06600771099328995	0.00942554883658886	0.009376686997711658	-0.007610066328197718	0.01887454465031624	-0.03350997343659401	-0.055733319371938705	0.01708541437983513	-0.036250725388526917	0.012793070636689663	-0.029343275353312492	-0.08753184974193573	-0.08439353108406067	-0.046973347663879395	0.00843001063913107	-0.028598550707101822	-0.036280762404203415	0.00506300013512373	0.04776440188288689	0.01324912067502737	0.07251706719398499	0.08879760652780533	-0.06250147521495819	0.021887557581067085	0.046646423637866974	-0.058564528822898865	-0.06849203258752823	-0.027156593278050423	-0.00405954010784626	-0.006295581813901663	0.04815034195780754	0.05635250732302666	0.013587819412350655	0.042363639920949936	-0.0012578382156789303	-0.014791100285947323	-0.023169847205281258	-0.012365514412522316	-0.04034535586833954	-0.04292808100581169	0.06539837270975113	0.0879550650715828	0.04067017883062363	-0.04285255819559097	0.020685318857431412	-0.02910398691892624	-0.04066403955221176	0.05210495367646217	-0.10864517837762833	0.003656156361103058	-0.04895201325416565	0.007629502099007368	-0.07850698381662369	0.0514344722032547	0.03172512352466583	-0.0625472292304039	-0.08529584109783173	-0.10408177226781845	-0.03817703574895859	0.046803951263427734	0.06662365049123764	0.09920398145914078	-0.0004222657880745828	-0.01126172300428152	-0.03884397819638252	0.009021904319524765	-0.03564227744936943	-0.039886876940727234	-0.08495811372995377	0.08708053082227707	-0.027128342539072037	-0.07419788837432861	-0.02124892920255661	-0.03503407910466194	0.006180110387504101	-0.02611522749066353	-0.003773408243432641	0.060887161642313004	0.010940712876617908	-0.059658899903297424	0.0011702029732987285	-0.015360732562839985	-0.02808474935591221	-0.02897365391254425	0.04689875990152359	0.048408739268779755	-0.06132584810256958	0.06941937655210495	-0.020807471126317978	-0.048874929547309875	0.04861528053879738	0.03160706162452698	-0.01846066676080227	-0.038665976375341415	-0.05974029749631882	0.010179468430578709	-0.1087009534239769	-0.005870808847248554	-0.040786776691675186	-0.01366452220827341	-0.03777848556637764	-0.017687896266579628	-0.05579804629087448	-0.015270755626261234	0.004178118426352739	0.011877579614520073	-0.008338332176208496	-0.02640904299914837	-0.038095906376838684	0.05554453656077385	0.057510118931531906	-0.020771922543644905	0.13087214529514313	0.036944642663002014	-0.03965708240866661	0.014519330114126205	0.12013832479715347	0.10508665442466736	0.052444539964199066	0.006054309196770191	0.03601985424757004	0.007011976093053818	0.055331867188215256	-0.009247295558452606	0.07150954753160477	-0.007210167124867439	-0.0468968041241169	-0.0639774352312088	0.0326974131166935	-0.07145670801401138	-0.013230942189693451	-0.009385070763528347	-0.05117383971810341	-0.02768164686858654	0.03472793847322464	-0.017990749329328537	-0.03827137127518654	0.04226136580109596	0.04597773402929306	-0.03318093717098236	-0.03313133120536804	-0.061363913118839264	0.03545675054192543	-0.005168496631085873	0.0020665160845965147	-0.053911298513412476	-0.005991530138999224	0.00987201277166605	0.02932855486869812	-0.0593109093606472	0.0459073968231678	0.09475843608379364	0.0478617325425148	-0.056765537708997726	0.09030313044786453	-0.028198042884469032	-0.014820406213402748	0.01556086353957653	0.01946384087204933	0.0600586012005806	0.019927021116018295	0.04630567133426666	0.013926840387284756	0.006777565460652113	-0.026607831940054893	0.009667851030826569	0.05777347832918167	-0.019084027037024498	0.016408491879701614	0.03257530927658081	-0.037700355052948	0.01997070014476776	0.038745418190956116	0.01111388299614191	-0.010206208564341068	0.05320729315280914	0.06020863354206085	0.015506792813539505	-0.03385097160935402	0.0172796081751585	0.006401341874152422	0.0293129850178957	-0.015295390039682388	-0.00662225903943181	-0.07605345547199249	0.009969322942197323	-0.036986663937568665	0.004982076119631529	0.02529354766011238	-0.010160263627767563	-0.018970921635627747	0.007711743004620075	-0.00340875331312418	0.04433244839310646	0.0018310791347175837	0.04718794673681259	0.0019481339259073138	0.03622777387499809	-0.13106940686702728	-0.005781612824648619	0.03370462730526924	0.03829734027385712	0.05517477169632912	0.06194538623094559	-0.00547373341396451	0.0766882672905922	-0.00851079449057579	-0.0731918215751648	-0.033866800367832184	-0.009583381935954094	0.0777832567691803	0.020983420312404633	0.03609148785471916	0.006223658099770546	0.029633279889822006	0.09934482723474503	0.03644793853163719	-0.0011224998161196709	-0.0029347711242735386	-0.00028533514705486596	0.02895057015120983	0.04812909662723541	0.01833326369524002	0.05013633146882057	-0.028756460174918175	0.0562911182641983	0.015876423567533493	-0.01250650454312563	0.037056200206279755	0.018457192927598953	0.050334442406892776	0.08085794001817703	0.007125887554138899
+0.06989748030900955	-0.0324060432612896	-0.06745049357414246	0.06244726479053497	-0.05357925966382027	0.05305182933807373	-0.12329724431037903	0.10488763451576233	-0.019528480246663094	-0.031126083806157112	-0.005243945401161909	-0.002356729470193386	0.011174499988555908	-0.022447962313890457	0.01817159168422222	-0.07335696369409561	-0.004684942774474621	0.02198193222284317	0.02680741436779499	-0.059501487761735916	0.01855401322245598	0.03919390216469765	0.008499512448906898	-0.020352762192487717	-0.01952982135117054	0.04740634933114052	0.019946688786149025	0.04559416323900223	0.05796528607606888	0.009500293992459774	-0.043629929423332214	-0.003180768806487322	0.02247200906276703	-0.07296302169561386	0.012648062780499458	0.03290150687098503	-0.008627131581306458	0.047492120414972305	0.03576795011758804	-0.05286569893360138	-0.032120704650878906	0.07170508056879044	-0.041666723787784576	-0.1360030174255371	-0.025961948558688164	-0.034333668649196625	-0.034930918365716934	-0.031591806560754776	-0.05385715514421463	-0.0262893196195364	-0.05040251836180687	0.002803587121888995	0.04482487589120865	0.014807503670454025	-0.07659605890512466	0.054775528609752655	0.05722539499402046	0.054741308093070984	0.03807447478175163	0.021637966856360435	-0.00616194773465395	0.012147129513323307	-0.07124453037977219	0.023537611588835716	-0.007206848356872797	0.0371108315885067	0.11529067903757095	-0.010294008068740368	0.04189819470047951	-0.034140706062316895	-0.030841533094644547	-0.010128408670425415	-0.003637651214376092	0.02094174548983574	0.03983395919203758	-0.0661507397890091	-0.09059464186429977	0.016490764915943146	-0.029852889478206635	0.01019818801432848	0.04681072384119034	-0.002293659606948495	0.10247714072465897	0.022016659379005432	0.0028751937206834555	-0.007583088241517544	0.0011323209619149566	0.03607575222849846	-0.06250249594449997	-0.004738035146147013	0.00814436562359333	-0.00030374646303243935	0.008319903165102005	-0.016891345381736755	-0.019495626911520958	0.09390836209058762	0.05584634467959404	-0.0844857320189476	-0.07361187040805817	0.022329945117235184	-0.045071687549352646	-0.056820716708898544	0.030135130509734154	0.012118019163608551	0.02298184297978878	-0.030859002843499184	-0.02211422473192215	-0.06752410531044006	0.0005792605224996805	-0.047984227538108826	0.0655243992805481	0.09211501479148865	-0.06014185771346092	-0.037100426852703094	0.040947746485471725	0.019597265869379044	0.045686300843954086	0.003910803236067295	0.03489308059215546	0.07128862291574478	-0.07222748547792435	-0.004304907284677029	-0.04437106102705002	-0.04531027376651764	0.011315644718706608	-0.004842871800065041	-0.0016557463677600026	-0.01035698875784874	-0.06943356245756149	-0.05670870468020439	-0.0237948689609766	0.03758974373340607	0.0206077229231596	-0.03690256178379059	-0.022446325048804283	0.07990442961454391	0.01715211756527424	0.04843783378601074	0.05924609303474426	0.009534281678497791	-0.06366729736328125	0.007367270067334175	0.02631148137152195	0.014504427090287209	0.02861638367176056	-0.00421207444742322	-0.09914648532867432	0.0132916159927845	-0.003009610576555133	0.009945785626769066	0.02378615364432335	0.004633451346307993	-0.022786926478147507	-0.0394025593996048	0.09261974692344666	0.05642123520374298	0.04180099070072174	0.04992925375699997	-0.00738511374220252	-0.04320763051509857	0.02822025492787361	0.0016108436975628138	0.019133133813738823	-0.043217696249485016	0.024932201951742172	0.046086784452199936	-0.009318812750279903	-0.04952804744243622	-0.027034034952521324	0.13464494049549103	-0.010095479898154736	-0.003459402360022068	0.030129361897706985	-0.06125498563051224	0.023458318784832954	-0.05397873744368553	0.03282991051673889	-0.05667389556765556	-0.05500781163573265	-0.02914733812212944	0.015017150901257992	-0.04740025848150253	-0.03601478785276413	-0.01921508088707924	-0.00400149542838335	0.048132721334695816	-0.043549567461013794	0.003935125656425953	0.024680152535438538	-0.025326205417513847	-0.033708326518535614	0.011714094318449497	0.04656568914651871	-0.03143574669957161	0.02793145179748535	0.05737506225705147	0.01108747348189354	0.04440119490027428	-0.008778693154454231	0.029638169333338737	-0.04227958619594574	0.07016632705926895	-0.015762848779559135	-0.0018541187746450305	-0.04051689803600311	0.03895234316587448	-0.06731577962636948	-0.040084779262542725	0.017726728692650795	0.03743497654795647	0.034878864884376526	0.026456354185938835	-0.04219340160489082	0.04707908257842064	0.012356357648968697	0.03702227771282196	-0.019624507054686546	0.02959386445581913	-0.03192216530442238	-0.04974550008773804	0.02045353874564171	0.008240342140197754	-0.06705472618341446	-0.03822963684797287	-0.017955979332327843	-0.028766676783561707	-0.0633319616317749	-0.017793646082282066	0.018553489819169044	-0.019863629713654518	0.05710132420063019	0.06928101927042007	0.008793774992227554	-0.09957114607095718	-0.0016850967658683658	0.010747065767645836	0.04937180504202843	-0.026323018595576286	-0.010523581877350807	-0.05941318720579147	-0.030506419017910957	0.04114904627203941	0.007730539888143539	-0.010762842372059822	-0.0037458264268934727	0.05720122903585434	-0.003124868730083108	-0.02922224812209606	-0.032391175627708435	-0.012327679432928562	-0.024186665192246437	-0.04050539433956146	0.013441812247037888	0.058212172240018845	0.07353097200393677	-0.04520879313349724	0.019605932757258415	-0.03412533178925514	0.05492658168077469	0.00586355198174715	0.027922455221414566	-0.033026617020368576	0.02120061032474041	-0.07913793623447418	-0.04891242831945419	-0.05979040265083313	0.049552857875823975	0.012798454612493515	-0.07550489157438278	-0.00476048095151782	-0.026728734374046326	0.0837530717253685	0.04463382065296173	-0.019957520067691803	0.007298066280782223	-0.00956074520945549	-0.02093898132443428	-0.046259600669145584	-0.018998807296156883	-0.019324159249663353	-0.0075571052730083466	0.054490234702825546	-0.06146765500307083	-0.02103910781443119	-0.069538414478302	0.08421860635280609	0.09477230906486511	0.00823262520134449	-0.02957305870950222	-0.06510554999113083	0.011185349896550179	-0.08159679919481277	0.015582224354147911	0.0745452493429184	-0.08661799877882004	-0.03650573268532753	0.0690959244966507	0.06302263587713242	-0.02564229629933834	0.045177359133958817	0.04809458553791046	-0.06760367751121521	0.025887221097946167	0.04022176191210747	-0.08664055913686752	-0.06604094058275223	-0.01561961229890585	-0.01593848317861557	0.005771024152636528	-0.034570205956697464	-0.0397515743970871	-0.04168185964226723	-0.006913770455867052	0.0042502968572080135	-0.0049001299776136875	0.05064791068434715	0.029926057904958725	-0.0630941092967987	0.0167348962277174	0.04781629890203476	0.03579338639974594	0.0009754145867191255	-0.03405467048287392	-0.0197971910238266	0.008882886730134487	-0.04313916712999344	0.030091337859630585	-0.042249519377946854	0.03366389498114586	0.019798316061496735	0.0848706066608429	-0.023924820125102997	0.004645711742341518	-0.02190217562019825	-0.005382841918617487	0.005422868300229311	-0.07583770900964737	-0.009814548306167126	0.007810201961547136	-0.007203422021120787	0.11054594069719315	-0.04807965084910393	0.01591915637254715	-0.008231543935835361	0.02194310910999775	-0.12004264444112778	-0.011053725145757198	0.013664879836142063	0.03960804268717766	0.005976046435534954	-0.04493134468793869	-0.017375530675053596	-0.08543356508016586	0.029949823394417763	-0.053865399211645126	-0.011179781518876553	0.037724997848272324	0.007265479303896427	-0.07716698199510574	0.057415418326854706	0.0326739102602005	0.04726636782288551	-0.06831110268831253	0.029011161997914314	-0.027911841869354248	0.027734776958823204	0.09452533721923828	0.009551653638482094	-0.0050709787756204605	0.015063977800309658	0.020475691184401512	0.042407382279634476	0.0031551020219922066	-0.019144350662827492	-0.014135208912193775	-0.047852545976638794	0.007327953353524208	-0.04114796593785286	0.00561358081176877	-0.07665111869573593	-0.05218105763196945	-0.01762707158923149	0.050734106451272964	0.018670592457056046	0.052129361778497696	-0.09402281045913696	-0.013506677933037281	-0.07541355490684509	0.0031855348497629166	-0.004161476623266935	0.06383639574050903	0.070267453789711	0.01088679488748312	-0.09877587109804153	-0.028144754469394684	0.09547385573387146	0.060352668166160583	0.025614410638809204	0.048249147832393646	0.04761473834514618	-0.07133158296346664	0.06732341647148132	-0.04921799153089523	0.05960693955421448	0.02909921482205391	-0.005613882560282946	-0.029913829639554024	0.02712039090692997	0.024851970374584198	0.014513680711388588	0.06839405745267868	-0.032861240208148956	0.015226051211357117	-0.00641106441617012	-0.061897773295640945	-0.01409863866865635	0.03760356456041336	-0.005550723057240248	-0.007725312374532223	-0.02034522220492363	0.01743823103606701	0.0212518610060215	0.023569656535983086	-0.03496439754962921	-0.06866234540939331	-0.012953205965459347	0.002815370913594961	-0.04160843417048454	-0.011779271066188812	-0.023027772083878517	0.07025041431188583	0.027086004614830017	0.019311901181936264	0.018852584064006805	-0.034893471747636795	-0.04057980328798294	0.031003549695014954	-0.015595152042806149	0.03446381166577339	-0.014888315461575985	0.051640331745147705	0.047192562371492386	-0.023852862417697906	0.006008077412843704	0.01326663140207529	0.05492641404271126	-0.09679406136274338	0.07781127095222473	-0.04287589713931084	-0.04963237792253494	0.00199327920563519	-0.010169998742640018	0.030972415581345558	0.0006064949557185173	0.06357493996620178	0.0010260887211188674	0.06441101431846619	0.01715845614671707	0.06231256574392319	0.04645422101020813	0.003466364461928606	0.020623011514544487	-0.013973810710012913	-0.056384194642305374	0.057320479303598404	-0.012026929296553135	0.0088896369561553	-0.012793481349945068	-0.06474824249744415	0.001850582892075181	-0.026402855291962624	-0.0852116271853447	0.06559097021818161	0.0009184377850033343	0.06308479607105255	0.006000287365168333	0.012568702921271324	-0.11813420802354813	0.017220379784703255	0.035490456968545914	0.08295252174139023	0.010783685371279716	0.04551488161087036	-0.05236527696251869	-0.01346317958086729	-0.020447444170713425	-0.044306427240371704	-0.018674688413739204	-0.01140312198549509	0.06424788385629654	0.009130418300628662	0.004161780700087547	0.026393825188279152	0.027503348886966705	0.06958486884832382	-0.004151659552007914	0.05488075688481331	-0.04086880758404732	-0.05197110399603844	-0.047331057488918304	0.0168728269636631	0.056232430040836334	0.015364413149654865	-0.012599476613104343	0.05839652940630913	-0.06783311814069748	-0.00770409544929862	0.017144721001386642	0.0190350953489542	0.03896347060799599	-0.011333524249494076	0.01767376996576786
+0.03831009566783905	-0.03476748242974281	-0.044267211109399796	0.028406957164406776	-0.04321662336587906	0.05821417644619942	-0.028500942513346672	0.018751507624983788	-0.07322027534246445	0.027077026665210724	-0.05079871788620949	0.04736006259918213	-0.011475469917058945	0.02054309844970703	-0.020297575742006302	-0.14304201304912567	0.00018020762945525348	-0.0077428193762898445	0.0453684963285923	-0.05646166950464249	0.007182079833000898	0.04330075904726982	0.04186828061938286	0.05633540078997612	0.015914147719740868	0.012174601666629314	0.04035688191652298	0.055063072592020035	0.06732317805290222	-4.220040136715397e-05	0.022616131231188774	-0.0798579528927803	-0.009553677402436733	-0.024690572172403336	0.011770188808441162	-0.03972266986966133	-0.03297822177410126	0.033733922988176346	0.04841797426342964	-0.047327250242233276	-0.0057299211621284485	0.07558337599039078	-0.0053606098517775536	-0.04082868993282318	0.0144647853448987	0.015865929424762726	-0.043122436851263046	-0.07818204909563065	0.009383508004248142	-0.035797420889139175	-0.04521894454956055	0.05683666467666626	0.028124945238232613	-0.02812030352652073	-0.0650532990694046	0.005110711790621281	-0.0005769755807705224	0.04719789698719978	0.005647890269756317	0.026630200445652008	-0.002507459372282028	0.011066245846450329	0.004830066580325365	-0.016196927055716515	0.06421738862991333	0.00015735412307549268	0.0690658763051033	0.01806892640888691	-0.011453221552073956	0.03889862820506096	-0.005056845489889383	0.08176196366548538	-0.006740626413375139	0.0763627216219902	0.055451106280088425	-0.042089689522981644	-0.03578973934054375	-0.014579759910702705	-0.012245750986039639	0.017426781356334686	0.0007468305411748588	0.013143530115485191	0.061394575983285904	0.04747546836733818	-0.030214795842766762	0.02368040941655636	0.006470685824751854	-0.02587803266942501	-0.03814227506518364	-0.0246686190366745	0.06661573797464371	-0.04753173142671585	-0.008751723915338516	0.0041625360026955605	-0.02801159955561161	0.015342853032052517	0.08053724467754364	-0.00940107461065054	0.012779454700648785	-0.010267730802297592	0.005575383547693491	-0.031765762716531754	0.040878862142562866	-0.022036949172616005	-0.01728132925927639	-0.0028986879624426365	-0.08427179604768753	0.0243255402892828	0.0005654815468005836	-0.04140118882060051	0.0967596098780632	0.07731649279594421	-0.042905356734991074	-0.007153655868023634	0.08290699869394302	-0.014001226983964443	-0.0029915941413491964	-0.01857876218855381	0.04635270684957504	0.036543093621730804	-0.016356835141777992	0.09206414967775345	-0.025097329169511795	-0.05082504823803902	0.0009630733984522521	0.04480584338307381	-0.01947413757443428	0.06433050334453583	0.004814344923943281	-0.012679917737841606	-0.023431949317455292	0.034151334315538406	0.026308003813028336	0.07012172788381577	-0.05306444317102432	-0.05592498183250427	-0.046969007700681686	0.04085182771086693	0.015430273488163948	0.032972391694784164	-0.11488280445337296	0.07665394991636276	0.12507525086402893	0.028311720117926598	-0.01647244021296501	-0.017866963520646095	-0.08064338564872742	0.012049582786858082	0.04690872132778168	-0.037066247314214706	-0.02671761065721512	0.020628975704312325	0.024742837995290756	0.025009216740727425	0.014804122038185596	-0.003064878983423114	0.026868902146816254	-0.027645710855722427	-0.01314997486770153	-0.04718950018286705	0.02100592479109764	0.040567852556705475	0.01231154054403305	-0.02599431574344635	0.05418545380234718	0.12401455640792847	0.02561071328818798	-0.06282005459070206	-0.03789882734417915	0.11149538308382034	-0.018284516409039497	-0.014828777872025967	0.01563236489892006	-0.05593284219503403	-0.028074871748685837	-0.02974054031074047	0.03647406026721001	0.009564211592078209	0.027885189279913902	-0.012709453701972961	0.050335273146629333	-0.09632138162851334	0.03086903691291809	0.01855522394180298	-0.011725794523954391	0.002492039930075407	-0.0492522306740284	-0.06523987650871277	0.021551040932536125	-0.039435919374227524	0.0009860557038336992	-0.05320964753627777	-0.022768080234527588	-0.03058232180774212	-0.016545254737138748	-0.016761530190706253	0.0319717638194561	0.010261069051921368	0.021663863211870193	0.09042557328939438	-0.05631112679839134	0.042333025485277176	-0.08468449860811234	-0.03178546205163002	-0.004771164618432522	0.02358551137149334	-0.06465022265911102	-0.07499378174543381	0.049382809549570084	-0.022024106234312057	0.01908555068075657	0.03624149411916733	-0.0417424775660038	0.02756383828818798	0.03157960623502731	0.03936220705509186	-0.012464388273656368	-0.0024428092874586582	-0.025921335443854332	-0.038783010095357895	-0.02065032161772251	-0.03532839193940163	-0.05684670805931091	-0.059974443167448044	-0.023926900699734688	-0.018624592572450638	0.009056302718818188	-0.00028084812220185995	0.10815521329641342	-0.07551165670156479	0.06254163384437561	0.07410743087530136	0.04838034138083458	-0.023181380704045296	0.013890466652810574	-0.008958607912063599	0.035051148384809494	-0.014097102917730808	-0.00736013101413846	-0.02008884586393833	0.028777141124010086	-0.0050482372753322124	-0.02565237134695053	0.021969087421894073	0.00483070919290185	-0.026582449674606323	0.040709238499403	0.029807856306433678	-0.09399648755788803	-0.02581828087568283	0.04962405934929848	-0.0016974823083728552	0.017849842086434364	0.07427376508712769	0.06665109843015671	-0.05976465716958046	0.03976745158433914	-0.03542283922433853	0.0411667637526989	-0.022643310949206352	0.02264861948788166	-0.002333767944946885	-0.045238133519887924	-0.05818659067153931	-0.03510665521025658	0.016704512760043144	0.041169825941324234	0.024917051196098328	0.006391675677150488	0.017393672838807106	0.04429253563284874	0.09300211071968079	0.034180011600255966	0.03365000709891319	0.014101915061473846	-0.05655718594789505	-0.0036202536430209875	0.03373534232378006	0.030921081081032753	-0.06917808204889297	0.07259917259216309	-0.04386086389422417	-0.015069372020661831	-0.029103916138410568	0.0005965523887425661	0.009773743338882923	0.03362046182155609	-0.0007455845479853451	-0.011967694386839867	-0.022624680772423744	-0.0019163854885846376	-0.07018427550792694	-0.018178647384047508	0.019429566338658333	-0.036907728761434555	0.002711816458031535	-0.03784206137061119	0.08493442833423615	-0.03086257539689541	0.02798963151872158	0.13357502222061157	-0.043847791850566864	0.044837966561317444	0.04093088582158089	-0.0022121190559118986	-0.0852276086807251	-0.017364101484417915	-0.015044931322336197	0.019745629280805588	0.045063771307468414	0.025717733427882195	-0.03634239360690117	0.02203289419412613	0.06696546822786331	0.01656719297170639	-0.025840898975729942	0.011211535893380642	-0.028461012989282608	-0.03787082061171532	0.0791645273566246	0.04448256641626358	-0.0010146968998014927	0.014977756887674332	-0.029785990715026855	-0.0056959218345582485	-0.07722552865743637	0.03922659903764725	-0.14598223567008972	0.04865928739309311	-0.03841423988342285	-0.004869888070970774	-0.0662267655134201	0.012992789037525654	0.056022439152002335	0.0160227008163929	-0.06582999974489212	-0.096920445561409	0.035714197903871536	0.01710669882595539	0.05610088258981705	0.10919029265642166	-0.016522889956831932	0.033747874200344086	-0.008020574226975441	-0.02332693710923195	-0.04488027095794678	-0.041970234364271164	-0.07770445197820663	0.09003268927335739	0.02908121608197689	-0.012422059662640095	-0.09447073191404343	-0.022227583453059196	0.057809893041849136	-0.024946708232164383	0.02206421084702015	0.007429259829223156	0.015561400912702084	-0.06466314941644669	0.04098792001605034	0.0022957457695156336	-0.024381570518016815	-0.02208535000681877	0.08103211224079132	-0.0008292790153063834	-0.02447856031358242	0.07211434841156006	-0.004683467093855143	-0.006341741420328617	0.032741185277700424	0.05464407801628113	0.05567051097750664	0.023131946101784706	-0.012636899948120117	0.04491214454174042	-0.059402529150247574	-0.00753807881847024	-0.0460398830473423	0.0414448007941246	-0.01382035855203867	0.006406425032764673	0.01691569946706295	-0.021027788519859314	0.0010674712248146534	-0.008734902366995811	0.04461784288287163	0.036206621676683426	-0.04668411239981651	0.075217604637146	0.03279896080493927	-0.026845872402191162	0.1101163923740387	0.03552559018135071	-0.009686269797384739	0.008073197677731514	0.10601628571748734	0.049390293657779694	0.02990957908332348	0.03293423727154732	0.04499764367938042	-0.0006923309410922229	0.058180857449769974	0.026499761268496513	0.04933872073888779	0.0017364389495924115	-0.051605530083179474	-0.012853935360908508	0.07079420983791351	-0.05483322963118553	-0.018005773425102234	0.017489556223154068	-0.010892856866121292	-0.030066268518567085	0.0903063714504242	0.004774624481797218	-0.09925160557031631	0.010144457221031189	0.007390959654003382	-0.019248545169830322	-0.02378028817474842	-0.025478003546595573	0.07408235967159271	0.026784822344779968	-0.013125617988407612	-0.01881255768239498	-0.03517170995473862	0.019474590197205544	-0.009342907927930355	-0.04824693500995636	-0.029700176790356636	0.0739915668964386	0.07977055758237839	0.0054870061576366425	0.07820951193571091	-0.07088030129671097	-0.042370378971099854	0.016027815639972687	0.07043912261724472	0.07807985693216324	0.018942290917038918	0.016625134274363518	0.0007505501853302121	0.008200207725167274	-0.0412757582962513	0.028134919703006744	0.020169230177998543	-0.06548751145601273	0.04334406554698944	0.03546540439128876	-0.047681815922260284	0.021210728213191032	0.014432955533266068	0.04152228683233261	0.007960140705108643	0.022854560986161232	0.059917811304330826	0.024938182905316353	0.013727335259318352	0.04022209346294403	-0.03888523206114769	0.0030903546139597893	-0.033450186252593994	-0.01573418267071247	-0.038997817784547806	0.033628806471824646	-0.06225839629769325	-0.007754455786198378	0.0205462034791708	-0.03319766744971275	-0.0030467105098068714	0.027242135256528854	-0.005735371727496386	0.07429695129394531	-0.02355887182056904	0.044881898909807205	0.021733896806836128	0.07622627168893814	-0.1386602222919464	0.029854822903871536	0.03338487073779106	0.05121289938688278	0.09629616141319275	0.01631401851773262	0.00553106190636754	0.013674749061465263	-0.0557873509824276	-0.05458984151482582	-0.007695553824305534	0.025888418778777122	0.03319459408521652	0.01123329158872366	0.028480879962444305	0.009268713183701038	0.016049670055508614	0.07725454866886139	0.020629601553082466	0.011840601451694965	-0.0036616045981645584	-0.02066674456000328	0.061033159494400024	0.031417280435562134	0.06494352966547012	0.03277019038796425	0.011270049959421158	0.001859181560575962	-0.036349106580019	-0.018287496641278267	0.044036731123924255	-0.005737278610467911	0.024319086223840714	0.02204783260822296	-0.02841821312904358
+0.023506857454776764	-0.037121906876564026	-0.04364355280995369	0.001746772089973092	-0.029247647151350975	0.032076627016067505	-0.01992982253432274	-0.02499265968799591	-0.052572377026081085	0.023235896602272987	-0.029232148081064224	-0.0060928924940526485	-0.04848554730415344	-0.003887372789904475	0.029799075797200203	-0.10832139104604721	0.009907432831823826	0.01658136583864689	0.06979797035455704	-0.045563701540231705	-0.04340248927474022	0.10869889706373215	0.06168666109442711	0.062069814652204514	0.0013598004588857293	-0.015982279554009438	0.0047949617728590965	0.026318034157156944	0.0804947018623352	0.020114343613386154	-0.011421234346926212	-0.058261409401893616	0.03387730196118355	-0.022677894681692123	0.06818012148141861	0.0010328823700547218	0.01947597600519657	0.07434860616922379	0.1107172891497612	-0.056783515959978104	0.009043853729963303	0.004595171194523573	0.01023906096816063	-0.06890879571437836	-0.022157222032546997	-0.014453543350100517	-0.05609402060508728	-0.09924963861703873	0.028016414493322372	0.045592889189720154	0.0045829894952476025	0.07266756147146225	-0.034410055726766586	-0.029886320233345032	-0.042629022151231766	-0.006696725729852915	0.013806932605803013	0.03829693794250488	0.02732996456325054	0.03891703858971596	-0.002203503390774131	-0.015161586925387383	0.012556900270283222	-0.019138576462864876	0.04920099303126335	-0.05188439041376114	0.07113859802484512	0.03332512453198433	-0.011583011597394943	0.05256994068622589	-0.01707606390118599	0.09360355138778687	-0.00976213626563549	0.021595075726509094	0.03552631288766861	-0.08595103025436401	-0.03773854672908783	-0.029490184038877487	-0.0011587102198973298	0.017101464793086052	-0.01064850389957428	-0.010516880080103874	0.017860151827335358	0.021743090823292732	-0.0010945888934656978	0.01953568309545517	-0.004069904331117868	-0.020594406872987747	-0.056022077798843384	-0.011717782355844975	0.07137427479028702	-0.0869096964597702	0.0062279547564685345	0.008102130144834518	-0.05990249663591385	-0.04069823771715164	0.08171423524618149	0.017271092161536217	-0.0016873189015313983	-0.0382218062877655	-0.005930062383413315	-0.00933822151273489	0.04256155714392662	-0.02259894646704197	-0.005156476050615311	-0.049417443573474884	-0.07747983187437057	0.01988200470805168	0.04608733952045441	-0.014147276058793068	0.039052367210388184	0.044402290135622025	-0.013566510751843452	-0.034947432577610016	0.0660051554441452	-0.009645636193454266	0.051510538905858994	-0.03633676841855049	0.043899327516555786	0.02813641168177128	-0.024101072922348976	0.06866569072008133	-0.047099240124225616	0.0018568782834336162	0.004134835675358772	0.0901813954114914	-0.0029247684869915247	0.04198271781206131	-0.020702416077256203	0.05245868116617203	-0.006444069091230631	0.024483613669872284	0.010460389778017998	0.03949424624443054	-0.05578236281871796	-0.074774831533432	-0.09498729556798935	0.07475759834051132	0.052596356719732285	0.04115356132388115	-0.09398499131202698	0.06648148596286774	0.15211224555969238	0.028538083657622337	-0.04348546639084816	-0.0012369516771286726	-0.058315835893154144	-0.01719541847705841	0.0340481735765934	-0.0372227244079113	0.01689133606851101	0.025191638618707657	0.07068947702646255	-0.030415501445531845	-0.00956237968057394	-0.029595568776130676	0.005651563405990601	-0.0006771082989871502	0.026013515889644623	-0.03223723918199539	-0.033645547926425934	-0.02511848509311676	0.009279687888920307	-0.02475900761783123	0.014473989605903625	0.06424283981323242	0.029981011524796486	-0.0804748684167862	-0.011413384228944778	0.07679412513971329	-0.018535209819674492	-0.02557951770722866	0.02738950215280056	-0.034375887364149094	-0.025165408849716187	-0.027819260954856873	0.027411730960011482	0.019515955820679665	0.06798652559518814	-0.020479971542954445	0.0240788497030735	-0.09837865829467773	0.02521950751543045	0.020842375233769417	-0.013870805501937866	0.00447677681222558	0.010890422388911247	-0.01526733860373497	-0.03965648636221886	0.009221750311553478	0.03222556412220001	-0.03552382066845894	-0.031522076576948166	0.011322913691401482	-0.019208692014217377	-0.04136944189667702	-0.009825416840612888	0.03184721991419792	0.015969328582286835	0.07412578165531158	-0.06818944215774536	0.05589841678738594	-0.04021801799535751	-0.04864967241883278	-0.015053481794893742	0.02165098860859871	-0.027871157974004745	-0.04587903618812561	0.02234376035630703	-0.013929410837590694	-0.02994389459490776	-0.005823143757879734	-0.062211763113737106	0.0018705035327002406	-0.00709496671333909	0.023093940690159798	0.03386131301522255	-0.050889015197753906	-0.01667504571378231	0.02266610786318779	-0.009573610499501228	-0.016083475202322006	-0.05888238549232483	-0.051812365651130676	-0.02942141704261303	-0.03779899328947067	0.003130923258140683	-0.005416812375187874	0.10311590880155563	-0.029274296015501022	0.07789765298366547	0.026079753413796425	0.04316020756959915	0.030362367630004883	0.006607183255255222	-0.04802119731903076	0.039761021733284	-0.011121802031993866	-0.009967985562980175	-0.03376327455043793	0.004372866824269295	-0.05794451758265495	-0.009464998729526997	-0.006442173849791288	-0.0036229765973985195	-0.02058919332921505	0.0015783635899424553	0.04858098924160004	-0.06526605039834976	-0.028149766847491264	0.06692050397396088	-0.035476990044116974	-0.030659127980470657	0.04608794301748276	0.014975061640143394	-0.08082321286201477	0.046140704303979874	-0.07582417130470276	0.05107741057872772	-0.04521484300494194	0.011254505254328251	-0.023636864498257637	-0.01595917157828808	-0.029641499742865562	0.01036931574344635	0.0050238147377967834	0.029359016567468643	0.048791706562042236	0.013511243276298046	0.011593694798648357	0.062269341200590134	0.10964018106460571	0.025684932246804237	0.058185476809740067	-0.0346161313354969	-0.04343840107321739	-0.03149203956127167	0.0332227386534214	0.027412721887230873	-0.05558372288942337	0.03221520781517029	-0.032024696469306946	0.0133142014965415	-0.03295730799436569	0.025441940873861313	-0.04381375014781952	0.00752912275493145	0.017531536519527435	0.009637071751058102	-0.04241504147648811	-0.03503032773733139	-0.054599661380052567	-0.010546739213168621	-0.008592478930950165	-0.04916180297732353	0.004220461938530207	-0.056712083518505096	0.0710500031709671	-0.0020073335617780685	-0.0049499766901135445	0.10224694013595581	-0.01871667243540287	0.01797129213809967	0.047463446855545044	-0.0004953946336172521	-0.1272037923336029	0.023417001590132713	0.0021016288083046675	0.01920018158853054	0.047971684485673904	0.07663092017173767	-0.0062556457705795765	0.006356849800795317	0.01992347277700901	0.04011838510632515	-0.029862046241760254	0.006459731608629227	-0.021854156628251076	-0.05533447116613388	0.01819162257015705	0.052325110882520676	-0.009466228075325489	0.041032370179891586	0.020356565713882446	-0.014448658563196659	-0.029980596154928207	-0.019605573266744614	-0.1348741054534912	0.07661642879247665	-0.03903816640377045	-0.03296637162566185	-0.10158973932266235	0.022563345730304718	0.02449493482708931	-0.018988797441124916	-0.0368022546172142	-0.09058266133069992	0.032955773174762726	-0.0037230676971375942	0.07014840841293335	0.10614057630300522	-0.01718774437904358	-0.05065002664923668	0.004772558342665434	0.01921462081372738	-0.035328458994627	-0.06879827380180359	-0.08764974772930145	0.09771265089511871	0.02519235759973526	0.0011996283428743482	-0.10858728736639023	-0.06560778617858887	0.07095983624458313	-0.00383983482606709	0.013952593319118023	-0.00777751998975873	0.02419189177453518	-0.09028506278991699	0.002704433398321271	-0.01223961915820837	-0.021120106801390648	-0.037801772356033325	0.06080390140414238	-0.007349954918026924	-0.061385247856378555	0.06330887228250504	-0.0030076762195676565	-0.0492817759513855	0.02374926395714283	-0.011049537919461727	0.03678318113088608	-0.06708090752363205	-0.03324228897690773	0.07177833467721939	-0.06731200218200684	-0.030535971745848656	-0.05292881280183792	0.017486728727817535	0.014426744543015957	0.01216723769903183	0.051267512142658234	-0.030821869149804115	0.045889053493738174	0.06166094169020653	0.004861616529524326	-0.0016575951594859362	0.02503078803420067	0.0554494634270668	0.030904315412044525	-0.005299271084368229	0.09267925471067429	0.025716129690408707	-0.01380900014191866	-0.0036939552519470453	0.008833300322294235	0.05506029352545738	0.010432751849293709	-0.0015475968830287457	0.06177402660250664	-2.9958315280964598e-05	0.013478852808475494	0.02355075627565384	0.05109851434826851	0.015168015845119953	-0.08922509849071503	-0.022149385884404182	0.020906737074255943	-0.07361942529678345	-0.0447416827082634	-0.006469134707003832	-0.00597231462597847	-0.06528766453266144	0.11331461369991302	0.0093922708183527	-0.09869088977575302	0.003065314143896103	0.04722410440444946	-0.023682881146669388	-0.05254356935620308	-0.014087958261370659	0.005212672986090183	0.014315586537122726	0.008992893621325493	-0.025689417496323586	-0.01358646247535944	0.05822994187474251	0.020516786724328995	-0.11903837323188782	-0.0026955034118145704	0.060767367482185364	0.07707226276397705	-0.006523079238831997	0.04580546170473099	-0.0618957094848156	-0.01962270587682724	0.040816836059093475	0.0053305611945688725	-0.004700534511357546	0.02685409225523472	0.0206875279545784	0.003231670940294862	0.03224322572350502	-0.0871422216296196	0.0027503676246851683	-0.01434355229139328	-0.022653456777334213	0.05530812591314316	0.032469045370817184	-0.02011786587536335	0.04086829349398613	0.04816288873553276	0.043986886739730835	0.04851677268743515	0.015415155328810215	0.026307037100195885	-0.0003511927498038858	0.02162924036383629	0.008442782796919346	0.017209799960255623	-0.051486194133758545	-0.04844019562005997	-0.022505465894937515	0.03460261970758438	-0.0010773830581456423	-0.07061000913381577	0.030833208933472633	-0.01833179220557213	-0.005750593263655901	-0.0043514627031981945	0.029112812131643295	-0.016787130385637283	0.05577220767736435	-0.02115039899945259	0.031168373301625252	0.020196583122015	0.07878226786851883	-0.13018286228179932	-0.00029073350015096366	0.031084856018424034	0.03383640944957733	0.07528545707464218	0.03957679867744446	-0.027809644117951393	0.025805458426475525	-0.020059168338775635	-0.019272422417998314	0.015362447127699852	0.0006239336216822267	0.0035327025689184666	0.02680971287190914	0.0020759887993335724	-0.004086988512426615	-0.003829310880973935	0.07101588696241379	0.0751156210899353	0.030046144500374794	0.039311688393354416	-0.05685605853796005	0.02593899704515934	0.0632341131567955	0.053776293992996216	0.06405574083328247	0.060680970549583435	0.05200778692960739	-0.036671847105026245	-0.03307891637086868	0.02543717995285988	0.009299373254179955	0.04255176708102226	0.007860330864787102	-0.04805402085185051
+-0.0018909182399511337	-0.044820114970207214	-0.07163918763399124	0.040148522704839706	-0.024100668728351593	0.03060447983443737	-0.010202106088399887	-0.020059561356902122	-0.048955127596855164	-0.020048461854457855	-0.03583501651883125	0.000949422363191843	-0.0535837784409523	-0.015000796876847744	0.011860433965921402	-0.05241715908050537	0.030414588749408722	0.0005083144642412663	0.0420965738594532	-0.0365186370909214	-0.04198957234621048	0.008989773690700531	0.0484716072678566	0.024975009262561798	0.014181813225150108	-0.021288897842168808	0.05109059810638428	0.029235752299427986	0.11856355518102646	0.05205254256725311	-0.008301329798996449	-0.03758396953344345	0.0021968604996800423	-0.04382101073861122	0.01333424262702465	0.016322100535035133	0.02829662151634693	0.056446366012096405	0.05845615640282631	-0.0066415476612746716	-0.0187129694968462	-0.007768165785819292	-0.044765446335077286	-0.09423409402370453	0.013070753775537014	-0.06697553396224976	-0.007071451283991337	-0.0750681459903717	0.05953264236450195	0.05706819146871567	-0.026492660865187645	0.03829507529735565	0.0037343245930969715	0.0029461702797561884	-0.06669114530086517	0.018020901829004288	-0.005554725881665945	0.094656802713871	0.07239227741956711	0.03295622766017914	0.024120861664414406	-0.06880514323711395	-0.0004335455596446991	0.020005328580737114	0.04376130923628807	0.038313500583171844	0.08552198112010956	0.006666417233645916	-0.04788011312484741	-0.019709691405296326	-0.08325302600860596	0.05530990660190582	0.03388413414359093	0.048142220824956894	-0.032680343836545944	-0.05925622209906578	-0.03565021604299545	-0.05236699804663658	0.015222525224089622	0.0609244629740715	-0.007747401017695665	-0.030496682971715927	0.06463219225406647	0.05127311125397682	0.06413701921701431	0.01025578286498785	-0.04486878588795662	0.04286313056945801	-0.03334140405058861	0.004904293920844793	0.0757235437631607	-0.08004801720380783	0.014482714235782623	-0.02864072658121586	-0.04323390871286392	-0.016412464901804924	0.07730145752429962	0.02418658137321472	0.025256190448999405	-0.009432461112737656	-0.009500333108007908	-0.06516203284263611	0.033058036118745804	-0.025761818513274193	0.031136665493249893	-0.02541785128414631	-0.06828494369983673	-0.03381151333451271	0.047071438282728195	-0.06895766407251358	0.04590458422899246	0.08032302558422089	-0.02854178287088871	-0.05554426088929176	0.054396700114011765	-0.0028279656544327736	0.025905100628733635	-0.08554232865571976	0.05299988389015198	0.029002878814935684	-0.013294350355863571	0.041683878749608994	-0.03260921314358711	0.027927709743380547	0.02086661197245121	-0.014501726254820824	0.018725844100117683	-0.0027638261672109365	-0.04052330181002617	0.02948848158121109	-0.013291019015014172	0.03960413113236427	0.040169186890125275	0.019112862646579742	-0.05741649866104126	-0.0764441266655922	-0.05329965054988861	0.04966019466519356	0.07504164427518845	0.04001578688621521	-0.054406002163887024	0.057056985795497894	0.07127469033002853	0.06777215003967285	-0.0267238337546587	0.010809036903083324	-0.07758999615907669	-0.006764566991478205	0.01769367605447769	0.0015048194909468293	0.022199874743819237	0.09031715989112854	0.06129208952188492	-0.03237209841609001	0.004783940967172384	0.00560713978484273	0.02916734851896763	0.00590055575594306	0.016991857439279556	-0.008955015800893307	0.041910745203495026	-0.022794226184487343	0.07619115710258484	-0.051124077290296555	-0.016377901658415794	0.0773569643497467	0.014580479823052883	-0.08533303439617157	0.026196274906396866	0.05746488645672798	-0.04631559178233147	-0.041450344026088715	0.061932049691677094	-0.07129103690385818	-0.0318310484290123	-0.03152480721473694	-0.0068153273314237595	-0.05762321501970291	0.06454260647296906	-0.05676047131419182	0.029335632920265198	-0.08730939775705338	-0.03088228963315487	0.03385291248559952	-0.03733561187982559	0.023870451375842094	-0.036768119782209396	-0.00010810525418492034	-0.06188717111945152	0.02946806699037552	-0.05942266061902046	-0.007317466661334038	-0.04864199832081795	-0.004663123283535242	-0.01384742185473442	0.009113366715610027	0.013043971732258797	0.03439464792609215	0.019695550203323364	0.04006330668926239	-0.0996699333190918	0.0186141487210989	-0.05071377754211426	-0.04787248745560646	0.0033619371242821217	-0.0050057461485266685	-0.025880511850118637	-0.022876020520925522	0.015032591298222542	-0.005783706437796354	-0.05611465871334076	0.008929742500185966	-0.026563286781311035	-0.0016058984911069274	-0.027423808351159096	0.05620531365275383	-0.031234493479132652	0.02413047105073929	-0.05065030977129936	0.04052756726741791	-0.040260590612888336	-0.009966649115085602	-0.05739545449614525	-0.0825614258646965	-0.03653340041637421	-0.008733549155294895	-0.0065642767585814	-0.0024037882685661316	0.04822957143187523	-0.01652209274470806	0.05027086287736893	0.007506104651838541	0.008204170502722263	-0.012204809114336967	-0.017451724037528038	-0.043546080589294434	0.09482070803642273	-0.01881350763142109	-0.04655006527900696	-0.06941016018390656	0.03074561059474945	-0.022466419264674187	-0.023904236033558846	-0.01584811694920063	-0.006674387492239475	-0.024040810763835907	-0.06479267030954361	0.010131077840924263	-0.09073537588119507	-0.07813657075166702	0.060598231852054596	0.006512967869639397	-0.017126280814409256	0.04203537479043007	0.010303831659257412	-0.0554659366607666	0.015322167426347733	-0.05513647943735123	0.09456553310155869	-0.002419346245005727	0.05700754001736641	-0.02137192152440548	-0.016160031780600548	0.020827030763030052	0.03857126459479332	0.01621590368449688	0.03679436445236206	0.03019641526043415	-0.012842683121562004	-0.03673461824655533	0.0002950042253360152	0.09735289961099625	0.0031672820914536715	0.045265860855579376	-0.04656021296977997	-0.020046867430210114	-0.025994110852479935	0.042586974799633026	0.012358295731246471	-0.035482194274663925	0.012348225340247154	0.005373972002416849	-0.00967987161129713	0.047255635261535645	0.023508794605731964	-0.05229117348790169	0.02727845311164856	0.05601564049720764	0.03368675336241722	-0.07103479653596878	-0.028841106221079826	-0.057779256254434586	0.0028815108817070723	-0.013530433177947998	-0.061871837824583054	0.05253385379910469	-0.04379314184188843	0.0983525738120079	0.03508548066020012	0.033151883631944656	0.09836351126432419	-0.009753908030688763	0.03030971810221672	0.028321104124188423	-0.029599517583847046	-0.10249456018209457	0.05478983372449875	-0.006143016275018454	-0.006388920824974775	0.0072428155690431595	0.03434772789478302	-0.02609316259622574	0.0036974234972149134	-0.05625222995877266	-0.012850807048380375	0.027349265292286873	-0.02686220221221447	0.006132322829216719	-0.0626898854970932	0.04505350813269615	0.03631511330604553	-0.012100080028176308	0.0268082395195961	0.05414735525846481	0.00032825025846250355	-0.0648391917347908	-0.021909138187766075	-0.09938358515501022	0.037886153906583786	0.01785682700574398	0.021476024761795998	-0.0574791319668293	0.01704673282802105	-0.015166797675192356	-0.04623487591743469	-0.040574412792921066	-0.07872608304023743	0.021729566156864166	0.043569378554821014	0.04988855496048927	0.10993462055921555	-0.04009813815355301	-0.050449952483177185	0.005351938772946596	0.005543883424252272	-0.051448311656713486	-0.06669628620147705	-0.08199301362037659	0.09531209617853165	0.021110132336616516	0.02411261387169361	-0.09268733859062195	-0.027880027890205383	-0.02780040167272091	-0.017913607880473137	0.018327413126826286	0.0016195684438571334	0.05666729062795639	-0.09641589969396591	0.017515363171696663	-0.0568295493721962	-0.030344991013407707	-0.054770056158304214	0.02666669897735119	0.05898912996053696	-0.06113143637776375	0.09019550681114197	-0.036771975457668304	-0.05908796936273575	0.028789794072508812	-0.0018480325816199183	0.027781549841165543	-0.04949386790394783	0.015104108490049839	0.09146660566329956	-0.07011082023382187	-0.01812271773815155	-0.04068014398217201	-0.02838044986128807	-0.020527765154838562	-0.052083685994148254	0.03331339359283447	-0.040840595960617065	0.012813142500817776	0.033236488699913025	-0.024017713963985443	0.001319817267358303	0.028178218752145767	0.025306953117251396	0.0519120953977108	0.020571086555719376	0.08189185708761215	-0.0006170039414428174	-0.0338323712348938	-0.0034178898204118013	0.021481797099113464	0.07888525724411011	0.007999200373888016	-0.0022883859928697348	-0.001951441983692348	-0.02920336090028286	0.03152419999241829	-0.0223360825330019	0.03328191488981247	-0.021287761628627777	-0.03470476716756821	-0.033589817583560944	-0.02506069839000702	-0.07745342701673508	-0.02742723375558853	0.005271803122013807	-0.05312445014715195	-0.09578367322683334	0.04064391180872917	-0.0030022193677723408	-0.01791125163435936	0.006787633057683706	0.06425253301858902	-0.07211655378341675	-0.03279513120651245	0.019220413640141487	0.03970932215452194	0.07463068515062332	0.04415879026055336	-0.06285779923200607	0.01952706277370453	0.03426346555352211	0.03783625364303589	-0.05574158579111099	0.0008922120905481279	0.019608622416853905	0.06089135631918907	0.03816451504826546	0.051950111985206604	-0.09617619961500168	-0.05489280819892883	0.010769556276500225	0.013516218401491642	0.002728204010054469	0.03719458729028702	0.030673034489154816	0.02819155715405941	0.0022049446124583483	-0.03398795798420906	-0.021120280027389526	0.07214262336492538	0.008994732983410358	0.030727684497833252	0.025789348408579826	-0.028673268854618073	0.08818969130516052	0.004805638454854488	0.05460357666015625	0.018191877752542496	0.07587960362434387	0.03709031641483307	0.043840136379003525	0.01764763705432415	0.04948961362242699	0.009454965591430664	-0.019879108294844627	-0.025805044919252396	-0.025472380220890045	0.023006726056337357	0.00924436841160059	-0.06572695076465607	0.031978968530893326	-0.019718749448657036	0.019396867603063583	0.0006044609472155571	-0.03360991179943085	-0.03087608516216278	0.04594862088561058	0.006405628751963377	0.0768875926733017	0.03418097272515297	0.05975446105003357	-0.13204142451286316	0.021703971549868584	0.052210867404937744	0.07495477795600891	0.05792122706770897	0.008107899688184261	-0.028510918840765953	0.05630346015095711	0.00016790190420579165	-0.016345851123332977	0.0192505344748497	0.037716981023550034	0.01580985262989998	0.02397555485367775	0.041933029890060425	-0.0518629290163517	0.012542656622827053	0.07847263664007187	0.07048030197620392	0.02724456787109375	0.01966981217265129	-0.01274657528847456	0.009641837328672409	0.017212899401783943	0.04535441845655441	0.017012158408761024	0.06989152729511261	0.011758283711969852	-0.01570926234126091	0.02380693517625332	0.011398138478398323	0.021031508222222328	0.04486308619379997	0.056182242929935455	0.00944552943110466
+0.01745554618537426	-0.041963234543800354	-0.10064536333084106	0.026184996590018272	-0.03905603289604187	0.01919611543416977	0.006068224553018808	-0.014416187070310116	-0.07737483829259872	0.035916347056627274	-0.06168188154697418	0.04503515735268593	-0.018984341993927956	-0.03774215653538704	-0.021471770480275154	-0.1041717529296875	0.010153996758162975	0.0007825469947420061	0.07439339905977249	-0.02539457380771637	-0.02372748591005802	0.038192447274923325	-0.018810924142599106	0.03765670210123062	0.018135732039809227	0.022143738344311714	0.048459623008966446	0.021250933408737183	0.06097937747836113	0.022117100656032562	-0.0031286824960261583	-0.06523628532886505	0.0068003227934241295	-0.02346431463956833	-0.026240097358822823	-0.057192154228687286	-0.02558325044810772	0.047866031527519226	0.0690498873591423	-0.02904990315437317	-0.041861895471811295	0.02464299276471138	-0.0385417677462101	-0.08358879387378693	-0.008881790563464165	0.016911661252379417	-0.06268367171287537	-0.08556056767702103	0.0398399792611599	0.008209068328142166	-0.02538413181900978	0.03850318118929863	-0.026537643745541573	-0.043701063841581345	-0.08853527903556824	-0.03218112513422966	-0.019599175080657005	0.07858406752347946	0.0011542452266439795	0.025126755237579346	-0.005496078170835972	0.023424198850989342	0.012112106196582317	-0.004800067748874426	0.020869920030236244	-0.00637140404433012	0.07132340967655182	-0.01742529682815075	-0.06449475884437561	0.03172801062464714	-0.023398054763674736	0.05464845150709152	0.0022705604787915945	0.09418559074401855	0.010446111671626568	-0.039835117757320404	-0.03283564746379852	-0.025458481162786484	0.030398959293961525	-0.0010249101324006915	-0.013887526467442513	0.020347174257040024	0.07413814961910248	-0.002349224640056491	-0.00803450308740139	-0.009969186969101429	0.0070697832852602005	-0.013660403899848461	-0.05207163468003273	-0.0399337075650692	0.04780147224664688	-0.0798497274518013	-0.006325765047222376	0.020354757085442543	-0.008100858889520168	0.009671605192124844	0.10737030953168869	-0.019520001485943794	-0.017242664471268654	0.01931823045015335	-0.018697848543524742	-0.054041117429733276	0.045477673411369324	-0.03739997372031212	-0.024263830855488777	-0.06650079041719437	-0.07494835555553436	0.03927566483616829	0.035418760031461716	-0.059112124145030975	0.0930803120136261	0.023051105439662933	-0.05023155361413956	-0.013917707838118076	0.10448803752660751	-0.001547476276755333	0.028283616527915	-0.02272895537316799	0.06138348951935768	0.062489643692970276	-0.014626632444560528	0.07378771156072617	-0.025920627638697624	-0.030768930912017822	0.003789435839280486	0.005681212060153484	-0.011699262075126171	0.04949052631855011	-0.04273971542716026	0.007828089408576488	-0.021698422729969025	-0.0021415979135781527	0.028507189825177193	0.04221169278025627	-0.09382596611976624	-0.0751577764749527	-0.02134062722325325	0.06933427602052689	0.04135860130190849	0.03740750253200531	-0.055058661848306656	0.07688606530427933	0.09139509499073029	0.066626638174057	-0.009810004383325577	-0.025475556030869484	-0.07047438621520996	-0.0018762756371870637	0.03847677260637283	-0.012127201072871685	-0.034745216369628906	0.07138585299253464	0.05184626951813698	0.004438364412635565	0.06234903633594513	-0.03016117960214615	0.05731409043073654	-0.015421444550156593	0.0037326794117689133	0.011219849810004234	0.012299664318561554	0.022886551916599274	0.037756603211164474	-0.01738070324063301	0.023929903283715248	0.11855030059814453	0.04444986209273338	-0.050598155707120895	-0.01918184757232666	0.10955779254436493	-0.016129983589053154	0.0125636737793684	0.04890019819140434	-0.051214102655649185	-0.046922918409109116	-0.021029973402619362	0.01908131130039692	-0.026063164696097374	0.06142429634928703	-0.007231483701616526	0.05217304453253746	-0.12311803549528122	0.029711829498410225	0.059022027999162674	-0.017325637862086296	0.01745670847594738	-0.04124953970313072	-0.04624181613326073	0.0049222013913095	0.011422310024499893	-0.027811236679553986	-0.051076482981443405	-0.016256343573331833	-0.02385755069553852	0.011597001925110817	-0.017434651032090187	0.05393582209944725	0.033765073865652084	0.03418030962347984	0.04958348721265793	-0.054872315376996994	-0.011533619835972786	-0.09102368354797363	-0.010500810109078884	0.015353785827755928	0.010934787802398205	-0.030097702518105507	-0.04961889982223511	0.01794457994401455	-0.03344570845365524	0.013279218226671219	0.0010291598737239838	-0.04013213515281677	0.01596936769783497	0.05617906525731087	0.015248327516019344	-0.014718576334416866	0.01713268831372261	-0.0172012560069561	-0.006485660094767809	-0.01250965241342783	-0.012332586571574211	-0.0570310577750206	-0.07937458902597427	-0.00496983015909791	-0.04901880398392677	0.01789472997188568	0.005536392331123352	0.0783386081457138	-0.0375412181019783	0.05093996226787567	0.08010298013687134	0.02693197689950466	-0.03866115212440491	-0.035015370696783066	0.019210945814847946	0.04370009899139404	-0.026734156534075737	-0.02647983655333519	-0.07799173146486282	0.04642225429415703	-0.00904526375234127	-0.0493590421974659	0.031458236277103424	0.01938837207853794	-0.00864364206790924	0.029127594083547592	0.0080821318551898	-0.06913585960865021	-0.03967449441552162	0.004327584523707628	0.014862586744129658	0.03922409191727638	0.07025830447673798	0.025772539898753166	-0.06167855113744736	0.023458566516637802	-0.06278675049543381	0.030299602076411247	-0.008528885431587696	0.009830920957028866	0.03853043168783188	-0.07831348478794098	-0.044557563960552216	-0.016833705827593803	0.031140746548771858	0.05420466512441635	0.001363383256830275	-0.011689605191349983	-0.02354847639799118	0.03589006885886192	0.09547040611505508	0.0067717405036091805	0.03151841089129448	-0.005730059463530779	-0.02484315074980259	0.0029533773194998503	0.007630211766809225	0.04269534349441528	-0.046502139419317245	0.05357268825173378	-0.01878655143082142	-0.023645522072911263	-0.009828117676079273	0.011021009646356106	-0.005840217228978872	0.03269423916935921	0.008021723479032516	-0.02566521428525448	-0.030154729261994362	-0.04202888160943985	-0.08233390003442764	0.005518619436770678	0.007646316662430763	-0.037461698055267334	-0.0251239612698555	-0.026458678767085075	0.09938238561153412	0.028029095381498337	0.052368808537721634	0.13444651663303375	-0.066330187022686	0.02927517332136631	0.05306105688214302	-0.06040981411933899	-0.05963161587715149	-0.03784095123410225	-0.021572882309556007	0.01589222624897957	0.03254227712750435	0.04935496300458908	-0.012295874767005444	0.03149782493710518	-0.0028754128143191338	-0.019310707226395607	-0.03388617932796478	0.010640808381140232	-0.053817711770534515	-0.0432608388364315	0.040828779339790344	0.07587949186563492	0.0368066132068634	-0.03168212249875069	-0.015884319320321083	-0.01580398716032505	-0.07217013835906982	0.019972678273916245	-0.15025679767131805	0.010126733221113682	-0.03510290011763573	-0.007956184446811676	-0.029244814068078995	0.032851483672857285	0.0547899529337883	-0.013481078669428825	-0.05652330070734024	-0.10741721838712692	-0.017412591725587845	0.020163027569651604	0.06175331026315689	0.11130353063344955	0.002542931353673339	0.022548584267497063	-0.03297073021531105	-0.012594211846590042	-0.043341029435396194	-0.05275275185704231	-0.10517075657844543	0.09767891466617584	0.015869343653321266	-0.043725334107875824	-0.05817451328039169	-0.06092371791601181	0.04675840213894844	-0.0486203096807003	0.02925873175263405	0.05109468102455139	0.02228379249572754	-0.04928942769765854	0.0325937420129776	-0.007302441634237766	-0.016865218058228493	-0.005504813976585865	0.07111949473619461	0.01633571833372116	-0.041407130658626556	0.0875038132071495	-0.015065968967974186	-0.04061252996325493	0.019260084256529808	0.0401277169585228	0.05986720696091652	-0.009846079163253307	0.004051475320011377	0.057222239673137665	-0.08840023726224899	-0.03152039647102356	-0.0069039673544466496	-0.017270857468247414	-0.044852666556835175	-0.0030667379032820463	0.030325761064887047	-0.02361098863184452	0.016797374933958054	0.03889021649956703	-0.0031416427809745073	0.001941644586622715	-0.03309670463204384	0.04305296018719673	0.07591964304447174	-0.008177360519766808	0.09914148598909378	0.039822012186050415	-0.044280145317316055	-0.015799015760421753	0.09516043961048126	0.04311886802315712	0.006837643217295408	0.008152177557349205	0.04123873636126518	0.006909474730491638	0.02995496615767479	-0.017748823389410973	0.08572697639465332	-0.00958272535353899	-0.05445701256394386	-0.018591834232211113	0.06211460754275322	-0.0739104226231575	-0.0475960448384285	0.0010195994982495904	-0.04905177652835846	-0.028534000739455223	0.060163285583257675	-0.004386222455650568	-0.03870736062526703	0.029391735792160034	0.062261488288640976	-0.06861455738544464	-0.019602302461862564	-0.023123737424612045	0.04277338832616806	0.05675240606069565	0.009602106176316738	-0.04591825231909752	-0.017913566902279854	0.04187972843647003	0.012522280216217041	-0.05022115632891655	0.007637814152985811	0.062371619045734406	0.04524474963545799	-0.0033514248207211494	0.05243803188204765	-0.0593932569026947	-0.016451789066195488	-0.02900092862546444	0.02215433493256569	0.08199770003557205	0.04510285705327988	0.018590467050671577	0.0382843054831028	0.014314788393676281	0.004949498921632767	0.0011500733671709895	-0.0019858109299093485	-0.018932858482003212	0.05044730752706528	0.021444788202643394	-0.05324582755565643	0.030974362045526505	0.009730623103678226	0.024944210425019264	-0.0024245993699878454	0.031722892075777054	0.04562966153025627	0.035175621509552	-0.015467406250536442	0.022817645221948624	-0.0008014042396098375	0.010038428008556366	-0.01729491725564003	0.013302529230713844	-0.03034358099102974	0.035339780151844025	-0.010096565820276737	0.0170765221118927	0.037880104035139084	0.0017218041466549039	-0.028165195137262344	0.02798604406416416	-0.027170604094862938	0.07086746394634247	0.005907696206122637	0.038518693298101425	0.03387351706624031	0.055527254939079285	-0.1319488137960434	0.047381237149238586	0.04449418932199478	0.04337315633893013	0.0518544502556324	0.03794519230723381	0.014215625822544098	0.07678186893463135	-0.04333803057670593	-0.07266455143690109	-0.0058820671401917934	0.02320966124534607	0.06235993281006813	0.036514610052108765	0.06422994285821915	-0.004062051884829998	0.008034955710172653	0.08933743834495544	0.029060807079076767	0.027369914576411247	0.0003828408080153167	-0.0012042435118928552	0.01604521833360195	0.061260588467121124	0.08272122591733932	0.02856971137225628	0.02086203545331955	0.022956296801567078	-0.00984262302517891	-0.035869717597961426	0.027761166915297508	-0.02211838588118553	0.018755005672574043	0.03169860690832138	0.013635615818202496
+0.019040286540985107	-0.024275783449411392	-0.038026243448257446	-0.051913969218730927	-0.06657101213932037	-0.01318675559014082	-0.00394065398722887	0.10751721262931824	-0.07738413661718369	-0.029015084728598595	-0.05239752307534218	0.029571734368801117	0.012461197562515736	-0.02048366144299507	0.010091086849570274	-0.07307352870702744	-0.029829606413841248	-0.0021489206701517105	0.04507913440465927	-0.030527586117386818	0.013638117350637913	0.08164256066083908	0.019650381058454514	0.02214409038424492	0.035388343036174774	-0.0004940729704685509	0.01716042123734951	0.04630027338862419	0.07491853833198547	-0.011528548784554005	-0.03421225771307945	-0.04647960513830185	0.034671612083911896	-0.0359426774084568	0.09838253259658813	0.0012363926507532597	-0.04492911696434021	0.06664656102657318	0.11041151732206345	-0.027830513194203377	0.020528754219412804	0.05984823778271675	0.04472909867763519	-0.07114274799823761	-0.009673946537077427	0.013263443484902382	-0.10236329585313797	-0.06994374096393585	-0.020463095977902412	0.010112650692462921	-0.12312482297420502	0.0361868180334568	0.008781044743955135	-0.006688740104436874	-0.02301323600113392	0.03940868377685547	0.04701724275946617	0.06833429634571075	0.01076100766658783	0.000720724870916456	-0.011170860379934311	0.016919216141104698	0.009499326348304749	0.004738672636449337	0.034503236413002014	-0.008837016299366951	0.05144381523132324	-0.02926652692258358	-0.009144777432084084	-0.022572044283151627	-0.02263309434056282	-0.014702747575938702	-6.605715498153586e-06	0.016514256596565247	0.014438632875680923	-0.028361594304442406	-0.011938451789319515	-0.006098905578255653	-0.036248281598091125	-0.06930699199438095	-0.03728915750980377	0.029438424855470657	0.005377587862312794	-0.03918324410915375	-0.037521567195653915	-0.014117859303951263	-0.015316273085772991	-0.026090575382113457	-0.07577308267354965	-0.05286816507577896	0.04357808828353882	-0.06673794239759445	0.002555410610511899	-0.10914944112300873	-0.0428696908056736	-0.009741093032062054	0.00848818477243185	-0.06414435058832169	-0.061658281832933426	-0.02237204648554325	-0.07071883231401443	-0.017594777047634125	0.042790092527866364	0.037738632410764694	-0.03912963718175888	-0.07345499843358994	0.01290018018335104	0.061595369130373	-0.011898018419742584	-0.016755132004618645	-0.037327952682971954	0.0516427718102932	0.006180478725582361	-0.02296106144785881	0.06254947930574417	-0.07134224474430084	-0.04153984412550926	0.10341029614210129	-0.0021930590737611055	-0.07335419952869415	-0.1004093661904335	0.03311437368392944	0.034451548010110855	-0.030329596251249313	-0.010006316937506199	0.11273963004350662	-0.012630454264581203	0.021315859630703926	-0.04659790173172951	-0.02592168003320694	-0.06436274200677872	-0.02964954264461994	0.009774644859135151	0.0007440852932631969	0.0029193672817200422	-0.006935205310583115	0.023064954206347466	0.11345954984426498	0.015352344140410423	-0.026152420789003372	-0.033942513167858124	-0.029205815866589546	0.07083398848772049	0.00579118262976408	-0.08914471417665482	0.013699527829885483	-0.08608146011829376	0.000392182293580845	0.029865071177482605	0.017946757376194	0.05706217512488365	-0.004574812948703766	-0.039322927594184875	-0.038668856024742126	0.028109675273299217	0.029145076870918274	-0.01660633087158203	-0.01268509030342102	-0.00324899610131979	0.0686870887875557	-0.0632929876446724	0.0006108413217589259	0.009423536248505116	0.0111519331112504	-0.002607796574011445	0.08461461216211319	0.029053619131445885	-0.060890037566423416	-0.0017613988602533937	0.08573666214942932	-0.055565327405929565	-0.01497635431587696	0.0009363082936033607	-0.09399847686290741	-0.0007238523685373366	-0.05159669741988182	0.02765272930264473	-0.026213334873318672	0.022380948066711426	-0.014328270219266415	-0.010890170931816101	-0.05222311243414879	0.07293964177370071	0.012919829227030277	-0.03687896206974983	-0.041053321212530136	-0.03440498560667038	-0.07136121392250061	0.019950591027736664	-0.04992883652448654	0.09052786231040955	0.0051828231662511826	-0.023559534922242165	0.002998798154294491	-0.02664272114634514	0.011137892492115498	-0.010860899463295937	-0.012372713536024094	-0.04876174405217171	0.009605618193745613	-0.04642585292458534	0.10008826106786728	-0.03924444690346718	-0.04479924216866493	-0.0069382525980472565	0.05444063991308212	-0.12399502843618393	-0.07589659839868546	-0.03882063180208206	-0.02621297910809517	0.10268432646989822	0.024931108579039574	-0.05262986198067665	-0.0033518564887344837	-0.0152702322229743	-0.02148227021098137	0.017166852951049805	-0.08429001271724701	-0.03130526468157768	-0.014782515354454517	-0.007895875722169876	0.0312198456376791	-0.07529427111148834	-0.03374890983104706	-0.014074459671974182	-0.0013525051763281226	0.087990902364254	0.013700242154300213	0.017740551382303238	-0.01720925234258175	0.010248984210193157	0.07036872208118439	0.0431581549346447	-0.030979951843619347	0.0004016839957330376	0.03237709030508995	0.08420676738023758	-0.01802818290889263	0.006752478424459696	-0.02414972148835659	-0.05953453481197357	-0.011756251566112041	-0.0014387744013220072	-0.004657336510717869	-0.03433464840054512	-0.0628470629453659	0.003792383475229144	-0.021027032285928726	-0.07278239727020264	-0.01820402406156063	0.024866728112101555	0.01934925839304924	-0.07457168400287628	0.08095446228981018	0.06411264091730118	-0.01879429630935192	0.03795919567346573	-0.011869031004607677	-0.03595587611198425	0.02036893367767334	0.06254793703556061	-0.02712422050535679	-0.03775980696082115	-0.008557402528822422	-0.08302255719900131	-0.008840780705213547	-0.00858200341463089	0.021608509123325348	-0.025543073192238808	0.02009456604719162	-0.00035327093792147934	0.06798397749662399	0.03882496803998947	0.04419591650366783	0.027286240831017494	-0.06387148052453995	-0.02688918448984623	-0.03396513685584068	0.05296029895544052	-0.01325830165296793	0.014575004577636719	-0.010743114165961742	-0.021157924085855484	-0.08577200770378113	-0.0046922750771045685	0.018632160499691963	0.09879277646541595	0.0022788660135120153	-0.005828541237860918	0.0021337626967579126	-0.0015150955878198147	-0.012340209446847439	0.024108510464429855	0.03426031023263931	0.02451438456773758	-0.030912093818187714	0.004042841959744692	0.029597017914056778	-0.04156307503581047	0.0317663736641407	0.02681460790336132	-0.0515630729496479	0.032846108078956604	0.00479496456682682	-0.04175986349582672	-0.029016239568591118	0.009621111676096916	-0.028846612200140953	0.03417801111936569	0.030963128432631493	0.002894127042964101	-0.03690320998430252	-0.009363973513245583	0.03809210658073425	-0.03461344540119171	0.06605058908462524	0.039205119013786316	-0.04127679765224457	-0.020423278212547302	-0.02698323503136635	0.004684329032897949	-0.024893024936318398	-0.03254155442118645	-0.032673802226781845	0.00993269495666027	-0.005149108823388815	-0.0014696500729769468	-0.10933496803045273	-0.038641512393951416	-0.03586813434958458	-0.025586100295186043	-0.05903063341975212	0.009643909521400928	0.01880738139152527	0.03684266284108162	-0.009849904105067253	-0.07610118389129639	0.05189133435487747	-0.02806433103978634	0.014364403672516346	0.02824866957962513	0.007369357161223888	0.0317649208009243	0.02067401446402073	-0.05612216889858246	-0.008588403463363647	0.059691689908504486	-0.02478748932480812	0.018209008499979973	0.0811312273144722	-0.05490213632583618	-0.0020055652130395174	0.024962903931736946	0.0675101950764656	-0.053211018443107605	-0.011021312326192856	0.03600846230983734	-0.0017581136198714375	0.0033245892263948917	-0.0043951417319476604	0.01944653131067753	0.016557445749640465	0.02498825080692768	0.06280501186847687	0.0034829031210392714	0.052316948771476746	0.05156693235039711	0.04173916205763817	-0.000776225992012769	0.031927332282066345	0.020626362413167953	0.0628228485584259	0.0032717606518417597	-0.0528787225484848	-0.011354254558682442	0.008725329302251339	0.04834131896495819	-0.07248424738645554	0.040754131972789764	-0.04975257068872452	0.005120539106428623	0.04859228804707527	0.07543157041072845	-0.02637767605483532	0.09930426627397537	-0.0004234114312566817	0.08454578369855881	-0.08767969906330109	0.02332346700131893	-0.01026429608464241	0.05504909157752991	0.09532714635133743	0.027095448225736618	-0.042113713920116425	0.05505555495619774	0.054295219480991364	0.05964306369423866	0.03375900909304619	-0.06439778953790665	0.1279052346944809	0.02668791450560093	-0.010871678590774536	-0.035753585398197174	0.061507001519203186	-0.03376083821058273	0.017922716215252876	-0.03864094987511635	0.006777924485504627	-0.005308396648615599	-0.013798392377793789	-0.04912569001317024	0.03450435400009155	-0.07950816303491592	0.013784537091851234	-0.005632329732179642	-0.06145818531513214	0.019062940031290054	-0.03595903515815735	-0.0017869881121441722	-0.024225208908319473	0.0010648395400494337	0.05240021273493767	0.027273692190647125	-0.0159718357026577	-0.013206375762820244	-0.10134195536375046	0.11777917295694351	-0.030425027012825012	-0.040469665080308914	0.018785294145345688	0.025662461295723915	0.09164571762084961	0.007752491161227226	0.02218652330338955	-0.0012120864121243358	-0.009377037174999714	0.05110987275838852	-0.01135302521288395	0.0928080677986145	-0.008892999961972237	0.03588125854730606	0.014636487700045109	0.02821953035891056	-0.06870416551828384	0.07754635810852051	-0.024053847417235374	0.019845010712742805	0.052532389760017395	-0.0467381477355957	0.022477610036730766	0.017703073099255562	-0.011548231355845928	0.028316646814346313	-0.005709795281291008	0.03486764430999756	0.03395233303308487	0.07496700435876846	-0.011381245218217373	-0.02969043329358101	-0.043400224298238754	-0.04131991043686867	0.01198579277843237	-0.03320956975221634	-0.029644791036844254	0.013082684949040413	0.029341788962483406	0.015612858347594738	0.015863172709941864	-0.005655577871948481	0.023603757843375206	-0.00848365668207407	0.05635332316160202	0.08558711409568787	-0.016891473904252052	0.08931820839643478	0.03798285871744156	-0.04146501049399376	0.02230796404182911	-0.05272596329450607	-0.03161930665373802	0.004457828123122454	-0.01870725303888321	0.08310993760824203	0.027371440082788467	0.02808871492743492	-0.02287655510008335	0.010421959683299065	-0.026987049728631973	-0.037544239312410355	0.06125463545322418	0.015397300943732262	0.006282567046582699	-0.0014667671639472246	-0.08370159566402435	0.04391653090715408	0.009132256731390953	0.05757899582386017	-0.012007624842226505	-0.05766662582755089	-0.003487531328573823	0.00398783665150404	0.03822164982557297	-0.011304815299808979	0.033743344247341156	-0.017722925171256065	-0.0382077656686306	-0.017049187794327736	0.043716128915548325	-0.04658328741788864	0.061226315796375275	-0.08633404970169067	-0.04749749228358269
+0.02055337280035019	-0.045284453779459	-0.08663482964038849	0.03277498111128807	-0.05387033522129059	0.02629619464278221	-0.020018713548779488	-0.00021582006593234837	-0.03615603968501091	0.044659484177827835	-0.03023628331720829	0.013403492048382759	0.0207564365118742	-0.031508125364780426	0.010554409585893154	-0.10032080858945847	0.001858401345089078	-0.027108049020171165	0.08159499615430832	-0.018085600808262825	-0.022782238200306892	0.058477193117141724	0.014385971240699291	0.06510122865438461	0.022828448563814163	0.038716137409210205	0.019361376762390137	0.020454412326216698	0.07193166762590408	0.045453477650880814	-0.005169848911464214	-0.022244034335017204	-0.01921786740422249	-0.0315004400908947	0.016872134059667587	0.0007865954539738595	-0.004247757606208324	0.053128138184547424	0.06399409472942352	-0.02486269921064377	-0.03859469294548035	0.05346726253628731	-0.00642580259591341	-0.06301746517419815	-0.01960543356835842	0.0200621597468853	-0.013848545029759407	-0.08583538234233856	0.04795488342642784	-0.01996881701052189	-0.002940157428383827	0.045457348227500916	-0.02132638543844223	-0.009831580333411694	-0.0773102194070816	0.007500786799937487	0.011116703040897846	0.0830097645521164	0.014627181924879551	0.05362020060420036	0.0199340358376503	-0.0002666761865839362	-0.013975669629871845	-0.05968533083796501	0.037817999720573425	0.02750103920698166	0.057085756212472916	0.0012679500505328178	-0.025020431727170944	0.06026167422533035	0.0002868532610591501	0.0678318589925766	0.01527414470911026	0.054275993257761	0.023808499798178673	-0.04416127875447273	-0.07647483050823212	0.0019975160248577595	-0.01872505061328411	0.02721579559147358	0.01033976674079895	-0.003463511122390628	0.06329172849655151	0.03289121016860008	-0.019672520458698273	-0.015728048980236053	-0.015958691015839577	0.02937672659754753	-0.02133663184940815	0.02550354413688183	0.05943932756781578	-0.03463563695549965	0.0016495066229254007	0.039241839200258255	-0.007668755948543549	-0.0325549878180027	0.09366168081760406	-0.008245965465903282	-0.03836921602487564	-0.007552397903054953	-0.01905008777976036	-0.06728067994117737	0.06372522562742233	-0.05070481076836586	0.007545952685177326	-0.03938732296228409	-0.06592395156621933	-0.008586878888309002	0.02825399674475193	-0.006937931291759014	0.03793718293309212	0.04586612805724144	-0.058164261281490326	-0.03503762558102608	0.060500338673591614	-0.022109078243374825	0.025395549833774567	-0.02688021957874298	0.02486974000930786	0.053742095828056335	-0.031684380024671555	0.041770871728658676	-0.015955850481987	-0.03219251707196236	0.0046211411245167255	0.014024115167558193	-0.04739857092499733	0.059983957558870316	-0.011801198124885559	0.017001736909151077	-0.0005291263805702329	0.02075294777750969	0.036256056278944016	0.04303095489740372	-0.06685817241668701	-0.03513864800333977	-0.039958883076906204	0.08335046470165253	0.037093423306941986	0.05096631124615669	-0.0808308869600296	0.04205746948719025	0.11944765597581863	0.048931654542684555	-9.522213804302737e-05	-0.017769386991858482	-0.06510479748249054	-0.0006941272877156734	0.07205390930175781	-0.014139208942651749	0.022590601816773415	0.03378139063715935	0.07642076909542084	0.0500405877828598	-0.01607711985707283	-0.025386301800608635	0.04595012962818146	0.0042063649743795395	0.012773367576301098	-0.05672662705183029	0.02581441029906273	-0.00023913750192150474	-0.027738727629184723	-0.0318731926381588	0.07250812649726868	0.04156636446714401	0.025645924732089043	-0.10921262949705124	0.048197269439697266	0.09586121886968613	-0.0016362783499062061	-0.012904836796224117	0.010154666379094124	-0.0401197113096714	-0.03773355111479759	-0.05431521311402321	0.06099153310060501	-0.023259637877345085	0.0615539513528347	-0.06127815693616867	-0.014352565631270409	-0.06600158661603928	-0.0031286682933568954	0.024426842108368874	-0.043799471110105515	0.05979163572192192	0.03472325578331947	-0.030745306983590126	-0.06722477823495865	-0.020290540531277657	0.018134111538529396	-0.0488908551633358	-0.0022790541406720877	0.0076036639511585236	-0.00553739070892334	-0.03882154822349548	0.03336375206708908	0.016965901479125023	0.0496821366250515	0.10381732881069183	-0.0556216761469841	0.06542880833148956	-0.08598300069570541	0.0028648991137742996	0.006256717722862959	0.04334978759288788	-0.01508795004338026	-0.08210945874452591	0.022432798519730568	-0.021528249606490135	-0.039448659867048264	-0.01668224297463894	-0.07098469138145447	0.02516988106071949	0.03993396461009979	0.034610554575920105	-0.004657234530895948	0.011441376991569996	0.008968262933194637	0.014801224693655968	-0.015737444162368774	-0.05701892450451851	-0.06968846917152405	-0.09491784125566483	-0.016850721091032028	-0.055730029940605164	-0.018506260588765144	0.028531350195407867	0.09341463446617126	0.008040878921747208	0.07690183818340302	0.03289928659796715	0.03087855875492096	-0.012754254043102264	0.0036839498206973076	0.000596282712649554	0.024834467098116875	-0.01710294559597969	-0.06305258721113205	-0.07061366736888885	0.038046710193157196	0.008738513104617596	0.013497144915163517	0.02627611719071865	0.016166558489203453	0.013251270167529583	-0.03844868019223213	0.058104079216718674	-0.04835443198680878	-0.014687630347907543	0.053639817982912064	0.007080697454512119	-0.013107500039041042	0.05909639969468117	0.0031101959757506847	-0.043305471539497375	0.04316236078739166	-0.059598978608846664	0.10337765514850616	-0.043798111379146576	-0.010600610636174679	-0.05330084636807442	-0.04364462569355965	-0.03949028626084328	0.007452002260833979	0.0010281576542183757	0.03819829225540161	0.056131307035684586	-0.02940697968006134	0.027450712397694588	0.04336157068610191	0.04716842994093895	-0.012236191891133785	0.10945310443639755	-0.02005986124277115	-0.039808791130781174	-0.0393173024058342	0.043668482452631	-0.04511905461549759	-0.042595695704221725	0.045994456857442856	-0.009865561500191689	-0.008378606289625168	-0.027996033430099487	-0.02137385867536068	-0.04543396830558777	-0.011853881180286407	-0.012265411205589771	-0.03922542184591293	-0.034406065940856934	0.007067237515002489	-0.044845160096883774	-0.04548404738306999	0.007288299035280943	-0.07638773322105408	-0.03245002403855324	0.01135591883212328	0.08014731109142303	-0.029327474534511566	0.04891600087285042	0.11462567746639252	-0.027234986424446106	0.030488768592476845	0.03443025052547455	-0.029958171769976616	-0.1075727790594101	-0.012116012163460255	0.024825509637594223	0.03283018246293068	0.015443339012563229	0.06338711827993393	-0.03174436464905739	-0.0062836529687047005	0.0005749426782131195	-0.0010190551402047276	-0.034336887300014496	0.02101953886449337	-0.04556431993842125	-0.018722927197813988	0.006403520703315735	0.014811727218329906	-0.017321942374110222	0.007279561832547188	0.020069943740963936	-0.04717390239238739	-0.007069599349051714	0.026082079857587814	-0.1167428195476532	0.04983539134263992	-0.0006682383245788515	0.018167821690440178	-0.07114140689373016	0.027422763407230377	-0.003489747876301408	0.01097328681498766	-0.06284799426794052	-0.09802868217229843	0.0009441553265787661	0.06113257259130478	0.04796444997191429	0.1325526088476181	0.008035250939428806	-0.030973218381404877	-0.045367613434791565	0.015136954374611378	-0.06856752932071686	-0.06624455004930496	-0.07205741852521896	0.07818427681922913	0.04935988783836365	0.028941623866558075	-0.04782082140445709	-0.07969043403863907	0.05402684956789017	-0.04318103566765785	0.01076999306678772	0.02934400551021099	0.04702417179942131	-0.055368099361658096	0.014335651881992817	-0.016730474308133125	-0.012105686590075493	-0.04291906952857971	0.032883550971746445	0.018734069541096687	-0.03580734133720398	0.11276886612176895	-0.02148609049618244	-0.059509918093681335	0.038973674178123474	-0.021372700110077858	0.04711830988526344	-0.038296669721603394	-0.01731453835964203	0.08535526692867279	-0.08365695178508759	0.009219232015311718	-0.04328445345163345	0.017542975023388863	-0.02557564154267311	0.017437832430005074	0.004841985180974007	-0.0208832286298275	0.01636001467704773	0.013795200735330582	0.02519659511744976	-0.03722206503152847	-0.03102806955575943	0.02913258597254753	0.045119646936655045	0.02242455817759037	0.0686606839299202	0.005393206607550383	-0.06997751444578171	0.030917756259441376	0.05554235354065895	0.06682218611240387	-0.006344127468764782	-0.007159266620874405	0.05417326092720032	-0.006403151899576187	0.03307327628135681	0.03530232980847359	0.07628169655799866	-0.023703603073954582	-0.06230791658163071	0.02006087452173233	0.06020098179578781	-0.06613359600305557	-0.04118574038147926	0.04461093246936798	-0.04352012649178505	-0.012331165373325348	0.07188469916582108	-0.031917717307806015	-0.04304797202348709	-0.006652581971138716	0.019526930525898933	-0.06313757598400116	0.004119119606912136	-0.0047125970013439655	0.055777888745069504	0.026644477620720863	0.030219826847314835	-0.037288814783096313	0.011225549504160881	0.02956882119178772	0.01428127195686102	-0.06694500893354416	0.008584779687225819	0.10917509347200394	0.05196550861001015	-0.008354554884135723	0.07758049666881561	-0.07409816235303879	0.019551491364836693	0.02358821965754032	0.04690152406692505	0.04140278697013855	0.02608836442232132	-0.0007231240742839873	0.010012613609433174	0.028864864259958267	-0.02893037535250187	0.041365496814250946	0.004679940175265074	-0.07218420505523682	0.045447904616594315	0.02121596224606037	-0.03192427009344101	0.0627225860953331	0.03694941848516464	0.03552519530057907	0.021418439224362373	0.05467267334461212	0.03702378645539284	0.008473941124975681	0.0336473248898983	0.04622545838356018	-0.0019191011087968946	-0.029658930376172066	-0.05981922522187233	-0.003777117934077978	-0.01733865588903427	-0.004160625394433737	-0.053974807262420654	-0.025609547272324562	0.015225927345454693	-0.031323857605457306	-0.03766150772571564	0.0033874711953103542	-0.016331177204847336	0.0761258602142334	-0.033558037132024765	0.058021701872348785	0.03832610324025154	0.0297506432980299	-0.13813018798828125	0.029794182628393173	0.04578344523906708	0.05951777473092079	0.07907788455486298	0.042243774980306625	-0.011538757011294365	0.05469962954521179	-0.053322866559028625	-0.09160555899143219	0.03404338285326958	-0.0035041356459259987	0.013898272998631	0.029136840254068375	0.009046582505106926	-0.011022777296602726	0.04012645408511162	0.07580789178609848	0.05938013643026352	0.01954963617026806	0.026546580716967583	-0.009417779743671417	0.0016996492631733418	0.0502508208155632	0.0509115569293499	0.05402377247810364	0.05452069267630577	0.005675846710801125	-0.06385279446840286	-0.03170172870159149	0.01043426152318716	0.022781027480959892	0.06462348997592926	0.03231639415025711	0.0001825450744945556
+0.02191905491054058	-0.012739676982164383	-0.015882501378655434	0.04290745407342911	-0.009411570616066456	0.12773388624191284	-0.03135543689131737	-0.00013837392907589674	-0.08519186079502106	-0.013499598950147629	-0.05575726181268692	0.029225943610072136	-0.002722675446420908	-0.0184470284730196	-0.029617197811603546	-0.10668648034334183	-0.028440311551094055	-0.010269831866025925	0.07195030152797699	-0.09966741502285004	-0.05137171223759651	0.04040239751338959	0.027840394526720047	0.039414916187524796	0.04725169762969017	0.03024371527135372	0.05801483616232872	-8.431687456322834e-05	0.04513091593980789	0.02408163994550705	0.0284900926053524	-0.014743941836059093	0.027447285130620003	-0.014976860955357552	0.00014640839071944356	-0.0011470310855656862	0.050207145512104034	0.04236285388469696	0.02484132908284664	-0.031002910807728767	0.0010584605624899268	0.019842231646180153	0.008053277619183064	-0.06028251722455025	0.010354995727539062	-0.015908880159258842	-0.04784444719552994	-0.05328477919101715	0.02237757295370102	-0.04501340538263321	-0.010273301042616367	0.049232568591833115	0.0029848753474652767	-0.004088054411113262	-0.04495133087038994	0.03539237380027771	0.03337816521525383	0.08939105272293091	0.036019906401634216	0.009639917872846127	0.017905309796333313	-0.028567325323820114	-0.003889769781380892	-0.07221223413944244	0.009584176354110241	0.05022987723350525	0.04160996899008751	0.0181767325848341	-0.01464865542948246	0.026034755632281303	-0.0031829937361180782	0.047283101826906204	0.0073464540764689445	0.08025186508893967	0.05571873486042023	-0.028886616230010986	-0.0552406832575798	-0.011609633453190327	-0.034481655806303024	0.018264133483171463	0.0017903480911627412	-0.0009110607206821442	0.08818494528532028	-0.004332587122917175	-0.009784352965652943	0.019727621227502823	-0.008923581801354885	-0.011045631021261215	-0.012742539867758751	-0.00039488408947363496	0.03577130287885666	-0.04979913309216499	0.014659428969025612	0.01235726848244667	-0.01106847170740366	-0.034598421305418015	0.06081882864236832	-0.011531481519341469	-0.025229575112462044	0.05493147671222687	0.01714518293738365	-0.042137518525123596	0.060897164046764374	-0.07030846923589706	6.764478166587651e-05	-0.01232646033167839	-0.06384154409170151	-0.04570356011390686	-0.006355246528983116	-0.08755926787853241	0.0631776675581932	0.09662952274084091	-0.09694699943065643	-0.031044039875268936	0.08046077936887741	0.04774526506662369	0.049351077526807785	-0.03941631689667702	0.06585075706243515	0.030032847076654434	-0.05012410134077072	0.08579863607883453	-0.013306938111782074	-0.010536951012909412	0.013334016315639019	-0.00023064248671289533	0.00942977238446474	0.043993089348077774	0.03327702730894089	-0.007797027472406626	0.014614642597734928	0.016787854954600334	0.03922540694475174	0.06499768048524857	-0.08829138427972794	-0.04644892364740372	-0.033780843019485474	0.03005482628941536	0.023764990270137787	0.008037056773900986	-0.07104954123497009	0.03439612314105034	0.05921965464949608	0.04089798778295517	0.01191385742276907	-0.0075348420068621635	-0.04790575057268143	-0.015542014501988888	0.08995933085680008	-0.02108347974717617	0.026370931416749954	0.018010837957262993	0.011602564714848995	0.0038523648399859667	0.032611723989248276	0.025044849142432213	0.038890987634658813	0.01962485909461975	0.044279687106609344	-0.013350720517337322	-0.002902938751503825	0.013970781117677689	-0.00629534013569355	-0.02466198056936264	0.03015798330307007	0.09554683417081833	-0.002812218153849244	-0.06013128533959389	-0.01473167072981596	0.13294868171215057	0.008440830744802952	0.0048134587705135345	0.010998878628015518	-0.033696066588163376	-0.00810062326490879	-0.051879409700632095	0.05333797633647919	-0.04135969281196594	-0.01791570708155632	-0.0011638400610536337	0.053605128079652786	-0.04497625678777695	0.011068650521337986	0.023408276960253716	0.00134690769482404	0.005982486996799707	0.011331324465572834	0.005798419006168842	0.03433895856142044	-0.0012019864516332746	0.001467329217121005	0.016186393797397614	-0.011831209994852543	-0.05278376489877701	-0.024477897211909294	-0.02577274851500988	0.06494330614805222	0.021858863532543182	0.033771853893995285	0.06237415224313736	-0.06419367343187332	0.0001336300338152796	-0.027003644034266472	0.01647922955453396	0.018971284851431847	0.03342944383621216	-0.0629240870475769	-0.03478752821683884	0.01973254233598709	-0.015103940851986408	-0.02542046271264553	-0.05197679251432419	-0.003957429435104132	0.0730740949511528	0.03474067896604538	0.045119453221559525	0.0008333678124472499	-0.02198519930243492	-0.007771690376102924	-0.012264869175851345	-0.06610224395990372	-0.0502530038356781	-0.04912012815475464	-0.08412153273820877	-0.04565507546067238	-0.0041732387617230415	-0.02335822768509388	0.002630108967423439	0.1009623110294342	-0.010064823552966118	0.06345804035663605	0.107890784740448	-0.01488508190959692	0.008052340708673	0.02410120889544487	-0.022188885137438774	0.053745023906230927	-0.032902173697948456	-0.014636466279625893	-0.06210309639573097	-0.00763884000480175	0.027031321078538895	-0.006997970398515463	0.02963697724044323	0.029475165531039238	-0.039274394512176514	0.020279536023736	-0.025033239275217056	-0.09044751524925232	-0.0229629036039114	0.0783371776342392	-0.01724720187485218	0.00431776512414217	0.04416046291589737	0.06763239949941635	-0.07342871278524399	0.033781155943870544	-0.027995288372039795	0.06872910261154175	0.004957327153533697	0.0336872935295105	0.03249272331595421	-0.012794810347259045	-0.018089525401592255	-0.06007995456457138	-0.01831815205514431	0.06799861788749695	0.003695692867040634	-0.02185884676873684	0.0010393226984888315	0.010662030428647995	0.11931899189949036	-0.029691195115447044	0.014440654776990414	-0.022944388911128044	-0.06329970806837082	-0.00032913594623096287	0.015065423212945461	0.054997943341732025	-0.047134581953287125	0.0557558573782444	0.00993333850055933	-0.06393630802631378	-0.02453131042420864	-0.02750736102461815	-0.041412610560655594	0.0009221567888744175	-0.040967606008052826	0.009298460558056831	-0.058329127728939056	-0.028174877166748047	-0.030963219702243805	-0.02090378664433956	0.021100016310811043	-0.001732198754325509	0.018581651151180267	-0.026756690815091133	0.10098548233509064	0.0002589579962659627	0.0706552118062973	0.11664604395627975	-0.036203134804964066	0.05501818284392357	0.06399121880531311	-0.02151782251894474	-0.10085529834032059	-0.01826135441660881	0.019915815442800522	-0.01579686813056469	0.02565668150782585	0.03250698745250702	-0.027074206620454788	0.026189083233475685	0.025050010532140732	0.002073106821626425	0.008963353000581264	0.020556725561618805	-0.044535860419273376	-0.04985998198390007	0.07348810881376266	0.12824861705303192	0.007667732425034046	-0.041542332619428635	0.0008882696274667978	-0.019182460382580757	-0.020260298624634743	0.05059191957116127	-0.14672565460205078	0.03580921143293381	-0.03979647904634476	0.012775719165802002	-0.0817931517958641	0.05487135052680969	0.010734856128692627	-0.02745206095278263	-0.030032766982913017	-0.06879022717475891	0.02536708302795887	0.05495604872703552	0.022479888051748276	0.0629904642701149	-0.017293840646743774	0.023289436474442482	-0.026857750490307808	-0.0401473268866539	-0.05890835076570511	-0.04454803466796875	-0.06361272186040878	0.08321793377399445	0.0016732303192839026	-0.049003876745700836	-0.03781396523118019	-0.05933237075805664	0.028615230694413185	-0.03506237640976906	0.024507224559783936	0.07109589874744415	0.030615217983722687	-0.10716187208890915	0.036205437034368515	-0.00914721842855215	-0.034798018634319305	-0.02188127487897873	0.04337328299880028	0.013496209867298603	-0.038146667182445526	0.057282377034425735	-0.0049785734154284	-0.04912607744336128	0.08309586346149445	0.05782899260520935	0.026957128196954727	-0.047072604298591614	-0.04087553545832634	0.03834708407521248	-0.08957543224096298	-0.01600569859147072	-0.08185993880033493	0.043983522802591324	-0.030014479532837868	-0.05111391842365265	-0.007476169615983963	-0.023705577477812767	-0.007320278324186802	-0.004980954807251692	-0.003525146981701255	-0.01500253938138485	-0.07493745535612106	0.054813191294670105	0.029649382457137108	-0.003685327246785164	0.10488643497228622	0.05916750058531761	-0.03859607130289078	0.0027998273726552725	0.0843210518360138	0.12016429752111435	0.04626623913645744	0.0013087448896840215	0.05874353274703026	-0.029137350618839264	0.06416063755750656	-0.012450270354747772	0.041718028485774994	-0.019516097381711006	-0.07622364163398743	-0.022389652207493782	0.027526061981916428	-0.027275171130895615	0.04631778970360756	0.02440684102475643	-0.06743364781141281	-0.04320053383708	0.05286603048443794	-0.003435974707826972	-0.055673278868198395	-0.03532977029681206	0.008192501030862331	-0.01420174352824688	0.0009258670033887029	-0.0414089635014534	0.03238973766565323	0.0191301591694355	-0.001379990833811462	-0.06275191158056259	-0.01695057563483715	0.008511949330568314	0.02484886161983013	-0.023443635553121567	-0.01810968853533268	0.08716019988059998	0.09331463277339935	-0.03696003183722496	0.06170203164219856	-0.06044730916619301	-0.04355614632368088	0.02819427102804184	0.029102010652422905	0.06428520381450653	-0.02489503100514412	0.021978285163640976	0.004528275225311518	0.004770003259181976	-0.04521806538105011	0.013152048923075199	0.03390903025865555	-0.07787306606769562	0.0360955148935318	0.02195628359913826	-0.07804132997989655	0.044585611671209335	0.029599232599139214	0.00563066266477108	-0.039049770683050156	0.042996857315301895	0.056569065898656845	0.011245235800743103	0.02206794172525406	0.04946712404489517	0.017153941094875336	-0.027820290997624397	0.011029169894754887	0.002878148341551423	-0.013912326656281948	-0.008716883137822151	-0.08092282712459564	-0.030846724286675453	0.03606148064136505	0.0006355809746310115	-0.04018513113260269	0.00040769600309431553	-0.04120347276329994	0.03234071657061577	-0.013295792043209076	0.033312808722257614	-0.019555915147066116	0.07102314382791519	-0.11556172370910645	-0.012525176629424095	0.055889904499053955	0.0340031199157238	0.06960088014602661	0.051522836089134216	-0.032016586512327194	0.06164153665304184	-0.020346516743302345	-0.05740802362561226	-0.01465599611401558	0.0008326179231517017	0.04341021925210953	-0.008432217873632908	0.03984358534216881	-0.011314347386360168	-0.005037502385675907	0.07704251259565353	0.03344852477312088	-0.008301387540996075	0.00977109931409359	0.0031781590078026056	0.0703115239739418	0.04240939021110535	0.038511574268341064	0.057703472673892975	0.021680200472474098	0.007544707041233778	-0.02445596270263195	0.026106150820851326	0.010265171527862549	0.018194854259490967	0.055833689868450165	0.012206148356199265	-0.015796173363924026
+0.004837268963456154	-0.03742794692516327	-0.05624079704284668	0.0406583808362484	-0.042603012174367905	0.04112572595477104	-0.013444192707538605	0.020501673221588135	-0.03201284632086754	0.01880400814116001	-0.06536559760570526	0.016672685742378235	-0.03919569030404091	0.02168896608054638	-0.0010946597903966904	-0.11207558214664459	-0.01781342923641205	0.0020039393566548824	0.08146964013576508	-0.07276728004217148	-0.064545638859272	0.09169886261224747	0.04977809265255928	0.04128115996718407	0.03193004056811333	0.005429596174508333	-0.017634090036153793	0.01155832689255476	0.07267456501722336	0.024741385132074356	-0.024990489706397057	-0.044491708278656006	0.02989051304757595	-0.026603829115629196	0.03629820793867111	0.004172289744019508	0.014307932928204536	0.08459059149026871	0.06710305064916611	-0.05213030427694321	0.017353486269712448	0.009566117078065872	-0.010802388191223145	-0.05559541657567024	-0.019809052348136902	-0.03698165342211723	-0.056160081177949905	-0.0743960291147232	0.012247316539287567	0.0035082052927464247	-0.014403079636394978	0.0431569404900074	0.005530843045562506	-0.01635904610157013	-0.07985544204711914	0.01436674315482378	-0.027758082374930382	0.09345550835132599	0.010813156142830849	-0.015917254611849785	-0.009514164179563522	-0.033772218972444534	-0.006921285763382912	-0.006600943859666586	0.009106457233428955	-0.017645930871367455	0.06373326480388641	0.045559294521808624	-0.012865830212831497	0.036283329129219055	-0.028507523238658905	0.07989876717329025	-0.0054658022709190845	0.0440673902630806	0.02646402083337307	-0.09311086684465408	-0.05454108491539955	-0.02105708047747612	0.012529926374554634	0.008119240403175354	0.026900313794612885	0.006715309340506792	0.03769969195127487	0.00862970482558012	-0.026242267340421677	0.08563142269849777	-0.01322764903306961	-0.04188274219632149	-0.06096181645989418	-0.01903989538550377	0.09401450306177139	-0.05203989893198013	0.010203207843005657	-0.00880785845220089	-0.012983063235878944	0.010890889912843704	0.1241399496793747	-0.0021052889060229063	-0.02675015665590763	-0.02410789765417576	-0.013914017006754875	-0.025156809017062187	0.06100352108478546	-0.03386832773685455	0.0019583951216191053	-0.05443662032485008	-0.05962182581424713	0.015920298174023628	0.018724577501416206	-0.04778238758444786	0.0747498869895935	0.06395029276609421	-0.012779422104358673	-0.05631349980831146	0.06325028091669083	-0.019320372492074966	0.06371237337589264	-0.01058559026569128	0.012217899784445763	0.02701532281935215	-0.04480981454253197	0.09072909504175186	-0.026885036379098892	-0.006171333137899637	-0.00940697267651558	0.04953633248806	0.0207429900765419	0.055169787257909775	-0.02792660892009735	0.005189293064177036	7.501457730541006e-05	0.031415022909641266	0.04612283781170845	0.046848393976688385	-0.040383897721767426	-0.07525540888309479	-0.05413832515478134	0.062068622559309006	0.0681493803858757	0.07678956538438797	-0.12615321576595306	0.06983199715614319	0.13537654280662537	0.0581776425242424	-0.028035299852490425	0.013993551954627037	-0.06673559546470642	0.01467818208038807	0.029629645869135857	-0.04784644395112991	0.03638124465942383	0.02176629565656185	0.036668021231889725	-0.04890477657318115	0.005070209503173828	0.015572400763630867	0.0163439754396677	0.016868295148015022	0.021337028592824936	-0.00044036738108843565	-0.012026391923427582	0.01087266020476818	0.0463545061647892	-0.031859397888183594	-0.01117730513215065	0.09744784981012344	-0.02208184450864792	-0.09426826238632202	-0.006170096341520548	0.13605399429798126	-0.02335604466497898	-0.03401704877614975	0.04333067312836647	-0.013605504296720028	-0.017242517322301865	-0.01961592212319374	0.01043136976659298	0.0013576566707342863	0.050383273512125015	-0.016079552471637726	0.03171452134847641	-0.0829988420009613	0.019262639805674553	0.0618521012365818	-0.0113380067050457	0.00883768405765295	0.0330580472946167	-0.00044113799231126904	0.03426031023263931	0.009224710054695606	0.06657381355762482	-0.035244181752204895	-0.053098052740097046	0.005070326384156942	-0.013450008817017078	-0.005930287763476372	0.0017238333821296692	0.03665773198008537	0.00016925869567785412	0.07452013343572617	-0.038605403155088425	0.03688586875796318	-0.04266059398651123	-0.028996413573622704	0.00699437502771616	0.03957319259643555	-0.0025132987648248672	-0.01728104054927826	0.05063307657837868	-0.05163072794675827	-0.03580731898546219	-0.013364036567509174	-0.01832255721092224	0.028753601014614105	0.023893816396594048	0.02430950477719307	0.022428547963500023	-0.03570466861128807	-0.044100940227508545	0.006878410466015339	-0.047904349863529205	-0.022070767357945442	-0.07903608679771423	-0.07469803839921951	-0.04478942230343819	0.0015185294905677438	0.022425012663006783	-0.0013304976746439934	0.09426520764827728	-0.0453672856092453	0.06345558166503906	0.06293565034866333	0.01840035244822502	0.014585944823920727	-0.04502341151237488	-0.003610811894759536	0.066815085709095	-0.024045085534453392	-0.018652649596333504	-0.03733748942613602	-0.03432926535606384	-0.01888827048242092	-0.04795015603303909	0.022097449749708176	-0.01030978187918663	-0.02365729957818985	0.05215870216488838	0.0277315191924572	-0.08912010490894318	-0.010295040905475616	0.05722218006849289	-0.02937481366097927	-0.007520958781242371	0.06628118455410004	0.02815823070704937	-0.04253765568137169	0.030019138008356094	-0.09684894233942032	0.030120981857180595	-0.023642433807253838	0.043032288551330566	0.013201023451983929	-0.015159494243562222	-0.016780894249677658	-0.019536228850483894	0.03166591003537178	0.04304414242506027	0.036109793931245804	0.0011147025506943464	0.006453515496104956	0.012281530536711216	0.1033269613981247	0.00937609001994133	0.089239202439785	-0.022038934752345085	-0.06439835578203201	-0.01137904729694128	0.016692379489541054	0.06596049666404724	-0.05120284855365753	0.022697726264595985	-0.011062942445278168	-0.006841658614575863	-0.03376954793930054	0.010714060626924038	-0.025281008332967758	0.004883102141320705	0.04071659594774246	0.017460094764828682	-0.028518391773104668	-0.01899850182235241	-0.06367264688014984	0.0307061355561018	0.02829841710627079	-0.06036080792546272	-0.017732102423906326	-0.06268231570720673	0.06904427707195282	-0.006960494909435511	0.03672439977526665	0.060173436999320984	-0.020123589783906937	0.048820480704307556	0.09242928773164749	0.005890166852623224	-0.10118676722049713	-0.025453342124819756	-0.02144293114542961	0.037246283143758774	0.04640968143939972	0.049030985683202744	0.0525476410984993	0.04372979328036308	0.026763305068016052	0.032445721328258514	-0.020905636250972748	0.017509547993540764	0.01895260252058506	-0.05431617423892021	0.03827867656946182	0.0576246939599514	0.020334279164671898	0.015316341072320938	0.021768543869256973	-0.026620974764227867	-0.023652277886867523	-0.02230958268046379	-0.15854494273662567	0.04559154435992241	-0.034637611359357834	-0.016933294013142586	-0.07393523305654526	0.018336232751607895	0.023128941655158997	-0.033918414264917374	-0.042053066194057465	-0.07727500051259995	0.05113803967833519	0.04257400333881378	0.009414924308657646	0.1303219497203827	-0.005945557728409767	-0.005737422965466976	-0.03504709526896477	-0.02717074565589428	-0.08327355980873108	-0.06335416436195374	-0.08250564336776733	0.06751079857349396	-0.0023517657537013292	-0.029828349128365517	-0.07049094885587692	-0.06237577274441719	0.02541988529264927	-0.0008889783057384193	0.01144446525722742	0.029178259894251823	0.020459163933992386	-0.06731121242046356	-0.00741723645478487	-0.020808489993214607	-0.011139393784105778	0.012319629080593586	0.0854799896478653	0.024397602304816246	-0.07436578720808029	0.05474449321627617	-0.012567441910505295	-0.01974491775035858	0.021252786740660667	0.02236972562968731	0.03828342258930206	-0.03139710798859596	-0.02812405303120613	0.05532209575176239	-0.058133289217948914	-0.030400501564145088	-0.027732647955417633	0.032623760402202606	0.0020648958161473274	-0.00012460497964639217	0.0383959524333477	0.0261222030967474	0.05084981396794319	0.07611602544784546	-0.01787017285823822	0.0005857013165950775	0.00932594295591116	0.06022261083126068	0.026466431096196175	0.00950230099260807	0.07902154326438904	0.08198351413011551	-0.016654692590236664	-0.025792516767978668	0.01469341292977333	0.06563166528940201	0.0343015156686306	-0.004849468823522329	0.058455970138311386	-0.020350871607661247	0.0042570410296320915	-0.010653111152350903	0.06011471524834633	0.007997872307896614	-0.06935255229473114	-0.01930239424109459	0.031571924686431885	-0.0601552352309227	-0.031163185834884644	-0.011874543502926826	-0.03314533457159996	-0.08232107013463974	0.05166531726717949	0.020420897752046585	-0.04517371580004692	0.004552188329398632	0.009440960362553596	-0.03349199891090393	-0.07819671928882599	0.004680757410824299	0.021512433886528015	0.07480596750974655	0.02433188259601593	-0.06678258627653122	-0.03238532319664955	0.04931648448109627	0.01981442980468273	-0.10542893409729004	-0.051945146173238754	0.08503589034080505	0.06739064306020737	0.002663795370608568	0.06386977434158325	-0.028209535405039787	-0.02010011300444603	0.030718019232153893	5.298242740536807e-06	0.013395140878856182	0.006844417657703161	0.008484452031552792	0.005978086497634649	0.042365919798612595	-0.021364187821745872	0.005502448882907629	0.018542209640145302	-0.04529966041445732	0.0442926399409771	0.06535749137401581	-0.03732887655496597	0.015435143373906612	0.03685824200510979	0.019078796729445457	0.004179522395133972	0.04953126981854439	0.03558255359530449	0.018685441464185715	0.0067338463850319386	0.04060753807425499	-0.03095013089478016	-0.003481257939711213	-0.013704564422369003	-0.021551895886659622	0.007039496209472418	-0.0007228286121971905	-0.06402478367090225	0.007417650893330574	-0.001182498293928802	0.013770997524261475	0.004143239930272102	0.008435795083642006	-0.04675358533859253	0.028660105541348457	-0.02072530798614025	0.06098749116063118	0.021417420357465744	0.05613383650779724	-0.10608397424221039	0.025917280465364456	0.07420636713504791	0.01999090053141117	0.07414497435092926	0.022082140669226646	-0.008727692998945713	0.038938526064157486	-0.05373527482151985	-0.023635443300008774	0.008003368973731995	0.009749716147780418	0.00713298050686717	0.027068210765719414	0.027093710377812386	-0.020866669714450836	-0.02842458337545395	0.08358114957809448	0.06409218162298203	0.028967346996068954	0.04308589547872543	-0.012345642782747746	0.02705604024231434	0.061403825879096985	0.05330536141991615	0.05762765556573868	0.05717639997601509	0.033015474677085876	-0.025048095732927322	-0.04179186001420021	-0.007048845291137695	0.029847001656889915	0.04002968221902847	-0.006903366651386023	-0.03083767555654049
+0.02272006683051586	0.010738067328929901	-0.044418059289455414	0.0011279885657131672	-0.06068818271160126	0.031423743814229965	-0.014168929308652878	-0.009820652194321156	-0.04995192214846611	0.023335957899689674	-0.04955894127488136	0.023349018767476082	-0.0029885703697800636	-0.057253625243902206	-0.0329105518758297	-0.1297311931848526	-0.0014333550352603197	-0.009735421277582645	0.07139652222394943	-0.04083796963095665	-0.00784207135438919	0.10903003066778183	0.02712852694094181	0.03640497848391533	0.018197985365986824	0.06030496954917908	0.03398618847131729	0.002861052518710494	0.05573509261012077	0.045720309019088745	0.03954952210187912	-0.020312894135713577	0.0404709167778492	-0.0668930932879448	0.05197320878505707	-0.0144694484770298	0.0008061140542849898	0.016008777543902397	0.11171276867389679	-0.039580151438713074	-0.03479103371500969	0.007573065813630819	0.043845467269420624	-0.0809376910328865	-0.06601590663194656	-0.004025558941066265	-0.09933210164308548	-0.057533346116542816	0.027548622339963913	-0.006509280297905207	-0.0038536558859050274	0.04331165552139282	-0.04595828801393509	-0.032401397824287415	-0.016189513728022575	-0.009735876694321632	0.047225337475538254	0.05623806267976761	-0.001578139839693904	0.031945228576660156	0.00183358252979815	0.06522183120250702	-0.0051137846894562244	-0.027423202991485596	0.016708120703697205	-0.027673261240124702	0.03165177255868912	-0.010898111388087273	-0.005503085441887379	0.04623202234506607	0.01588699035346508	0.09214039146900177	-0.017337355762720108	0.045299824327230453	0.020160699263215065	-0.05815422534942627	-0.06263604015111923	-0.042765818536281586	-0.023233434185385704	-0.0019117966294288635	-0.0212483461946249	-0.06185987964272499	0.051377154886722565	-0.031852878630161285	-0.007261181250214577	-0.04720025509595871	-0.03813283517956734	-0.03508142754435539	-0.027622580528259277	-0.023090975359082222	0.06554359942674637	-0.08439371734857559	0.03089573048055172	0.010876928456127644	-0.0031065535731613636	-0.006425600033253431	0.06913557648658752	-0.026710884645581245	-0.025514546781778336	0.03956107795238495	0.005977993831038475	-0.03769407048821449	0.057397838681936264	-0.008860229514539242	0.0008236647117882967	-0.04352448880672455	-0.059007566422224045	0.014343908056616783	0.06261105090379715	0.002156630391255021	0.0786643773317337	0.058231160044670105	-0.017292119562625885	-0.03803021088242531	0.044502634555101395	-0.03726130723953247	0.007610990200191736	-0.021215183660387993	0.05254822596907616	0.04770558327436447	-0.03257518634200096	0.0494358129799366	-0.05161885544657707	-0.0263514444231987	-0.0197471771389246	0.049554187804460526	0.0031438693404197693	0.03306145593523979	-0.02470480278134346	0.03778008371591568	0.0018513321410864592	0.021275468170642853	0.006517994217574596	0.03156973049044609	-0.09833192825317383	-0.0024494517128914595	-0.05815513804554939	0.07340175658464432	0.05847325921058655	0.03233502432703972	-0.07982175797224045	0.026313647627830505	0.08832179754972458	0.01941462606191635	-0.03891478851437569	-0.02873716875910759	-0.023641541600227356	-0.045288603752851486	0.04121851921081543	0.002013498917222023	0.031699273735284805	0.03881826624274254	0.06958786398172379	0.016086580231785774	0.05799731984734535	0.009730448946356773	0.022118771448731422	0.007134253159165382	0.019598152488470078	-0.012281573377549648	0.014769005589187145	-0.022108618170022964	-0.024024074897170067	-0.00803392007946968	0.051005322486162186	0.06503655761480331	0.08304136246442795	-0.06673376262187958	-0.04300558194518089	0.06920624524354935	-0.02277935855090618	-0.010758866555988789	0.01595594361424446	-0.017863048240542412	-0.023313745856285095	-0.0596182644367218	0.011731738224625587	-0.01796000637114048	0.05168653652071953	-0.022261779755353928	-0.009150723926723003	-0.10648518055677414	0.022615225985646248	-0.002855002647265792	-0.0358993336558342	0.03884654492139816	0.027518603950738907	-0.009940828196704388	-0.02546316385269165	0.025908567011356354	0.013206525705754757	-0.016868237406015396	-0.0208530705422163	-0.04758967086672783	-0.01233049388974905	-0.03958068788051605	0.035129476338624954	0.026789823547005653	0.0636659488081932	0.027073875069618225	-0.08194229751825333	0.05035177245736122	-0.07997840642929077	-0.02174333482980728	0.03306308388710022	0.01915726438164711	-0.03200613707304001	-0.08932727575302124	-0.02550462633371353	-0.03517769277095795	-0.0009092506370507181	-0.005302834324538708	0.0006328251911327243	0.012624706141650677	0.03045814298093319	-0.0009530910756438971	0.03833410143852234	-0.04766670987010002	0.014095480553805828	-0.004861889872699976	-0.014366304501891136	-0.05269972234964371	-0.03471457213163376	-0.04120885953307152	-0.016961729153990746	-0.07302901893854141	-0.021377239376306534	-0.03526847064495087	0.11173473298549652	-0.04059794917702675	0.12599942088127136	0.09158128499984741	0.00011654699483187869	0.02276536449790001	0.003047323552891612	-0.036695778369903564	0.0069777402095496655	0.01358538493514061	-0.04054557904601097	-0.0856318399310112	0.012499828822910786	-0.0337156280875206	-0.002111461479216814	0.0226877611130476	0.01942596770823002	0.0035561530385166407	0.03390952944755554	0.028162280097603798	-0.0669177919626236	-0.030391139909625053	0.01025579683482647	-0.06995826959609985	0.02217692695558071	0.06423242390155792	0.02014891617000103	-0.09116965532302856	0.04058893769979477	-0.061534445732831955	0.09132920950651169	-0.03002919629216194	0.017819063737988472	0.01534830592572689	0.007394934073090553	-0.040081754326820374	0.02324949949979782	-0.013273732736706734	-0.013894479721784592	0.06701882183551788	-0.023641439154744148	0.0009823673171922565	0.11008425056934357	0.09636805206537247	0.02845015935599804	0.05049245432019234	0.0016819584416225553	0.008693177253007889	0.031832315027713776	0.042914699763059616	0.007293424103409052	-0.058637700974941254	0.06410156190395355	-0.02570228837430477	-0.01165719609707594	-0.04806983470916748	-0.05426657944917679	-0.08392839878797531	-0.008687705732882023	-0.03069683164358139	-0.020797211676836014	0.012072532437741756	-0.03914909064769745	-0.08099094033241272	-0.089011050760746	0.0054478831589221954	-0.06284938752651215	-0.022117698565125465	-0.032689955085515976	0.008261644281446934	-0.04073489084839821	0.05402596294879913	0.09071558713912964	-0.04327546805143356	0.02922159433364868	0.010543040931224823	-0.025919632986187935	-0.09130733460187912	-0.021225623786449432	0.03335604816675186	0.01277790404856205	0.017524315044283867	0.07228612154722214	-0.051147837191820145	-0.012974988669157028	0.012005259282886982	0.0018548958469182253	-0.013188181445002556	-0.0010568154975771904	-0.0813947468996048	-0.07280123233795166	0.004850400146096945	0.06448843330144882	0.0015887069748714566	-0.02996067889034748	0.04409567639231682	-0.04788975045084953	0.010945013724267483	0.026708349585533142	-0.12762784957885742	0.02504863776266575	-0.051233112812042236	0.012701245956122875	-0.0651729479432106	0.01814885064959526	0.04070715606212616	-0.008327500894665718	-0.05098502337932587	-0.0892224907875061	0.012310865335166454	0.01894627884030342	0.04410158470273018	0.09539151936769485	-0.03221126273274422	-0.014675834216177464	-0.0289065670222044	0.005917039699852467	-0.04138149693608284	0.0002249737299280241	-0.11247800290584564	0.09584840387105942	-0.003030311781913042	-0.06357432901859283	-0.03052159771323204	-0.03645821288228035	0.03805229812860489	-0.03929098695516586	0.013518502935767174	0.050129666924476624	0.032623372972011566	-0.04242410883307457	0.005979885812848806	-0.0008758688345551491	0.000394467351725325	-0.04182227700948715	0.020761307328939438	0.0511113665997982	-0.004303696099668741	0.061491310596466064	-0.019281839951872826	-0.08708931505680084	0.07119366526603699	-0.008347814902663231	0.002539239590987563	-0.05028485506772995	0.0019576128106564283	0.05897587165236473	-0.07675900310277939	-0.01475767232477665	-0.008523257449269295	0.021275704726576805	0.015316218137741089	0.015520326793193817	0.037488434463739395	-0.004914140794426203	0.020769018679857254	0.05543907731771469	0.01069384254515171	0.006465749349445105	-0.0299812164157629	0.00441008759662509	0.08465895801782608	0.028334634378552437	0.06698893010616302	0.00357734109275043	-0.003276627976447344	0.03682452067732811	0.05257346108555794	0.04804036766290665	0.041318055242300034	-0.02979852445423603	0.04136868938803673	0.012234725058078766	-0.010635068640112877	-0.001050853170454502	0.1185426115989685	-0.023288868367671967	-0.06862412393093109	-0.04791377857327461	0.07075926661491394	-0.05826297774910927	-0.027897078543901443	0.004256341606378555	-0.03337891772389412	-0.028541725128889084	0.08204687386751175	-0.020659545436501503	-0.08247682452201843	0.03501226380467415	0.059630341827869415	-0.02217601053416729	-0.019791966304183006	-0.03105187602341175	0.027782466262578964	0.0110788494348526	-0.029834246262907982	0.00871796440333128	-0.00822109542787075	0.04543178528547287	0.03662099689245224	-0.026409557089209557	0.05028170719742775	0.049406297504901886	0.06888603419065475	-0.008531338535249233	0.05711156874895096	-0.04467998445034027	0.0023713703267276287	0.006663046777248383	0.02888433262705803	0.0337986946105957	0.021962076425552368	0.04096315801143646	0.024192053824663162	0.02045775204896927	-0.07076713442802429	0.023844916373491287	-0.01570064201951027	-0.0588558129966259	0.06785821169614792	-0.0050721559673547745	-0.04147632047533989	0.034642089158296585	0.01935293711721897	0.031283847987651825	-0.011100593954324722	0.009319677948951721	0.02156616933643818	-0.00878956075757742	-0.003756472608074546	0.027403859421610832	0.03231600299477577	-0.04365934059023857	-0.0033784660045057535	-0.009080938063561916	0.00931991171091795	0.0073781381361186504	-0.016894323751330376	0.020437458530068398	0.008152030408382416	-0.0012553526321426034	-0.047722626477479935	0.03972342610359192	0.0026608381886035204	0.09615007042884827	-0.021807584911584854	0.019212212413549423	0.002648330992087722	0.024441545829176903	-0.13154907524585724	-0.013714436441659927	0.030131544917821884	0.032170284539461136	0.05543278157711029	0.10736115276813507	0.039662763476371765	0.037366196513175964	-0.0095553332939744	-0.07078882306814194	0.05889873579144478	-0.038838744163513184	0.01212338823825121	0.06230713427066803	0.02024833858013153	0.00011323015496600419	-0.004225853830575943	0.1039779856801033	0.07777436822652817	0.004014094825834036	0.0035091338213533163	-0.028947195038199425	-0.012692843563854694	0.06300466507673264	0.032450173050165176	0.0746462270617485	0.04541287198662758	0.03338687866926193	-0.010056299157440662	-0.03160092607140541	0.0366193912923336	-0.0028127531986683607	0.03372384235262871	0.0126710319891572	-0.028810832649469376
+0.015210948884487152	-0.030587267130613327	-0.09507099539041519	0.02387291193008423	-0.037841036915779114	0.055034030228853226	0.0027285597752779722	0.03519432619214058	-0.07511484622955322	0.03430444002151489	-0.04513626545667648	0.012446064502000809	-0.03433024138212204	-0.03784022107720375	0.0042907241731882095	-0.11605405807495117	-0.011414886452257633	-0.04174201190471649	0.061893969774246216	-0.05947756767272949	-0.03975040465593338	0.06565485894680023	0.04459555447101593	0.0739416554570198	0.03470505028963089	0.016411026939749718	0.056887708604335785	0.0487365648150444	0.06989685446023941	0.03891855105757713	0.024493029341101646	-0.06778795272111893	-0.036389369517564774	-0.01779710128903389	0.028331374749541283	0.012981344014406204	0.0074673728086054325	0.08865310251712799	0.0567232109606266	-0.06698530167341232	0.007811189629137516	0.04449043050408363	0.041255220770835876	-0.057068463414907455	0.0010343710891902447	-0.0285600945353508	-0.046151529997587204	-0.10763037949800491	0.036577556282281876	-0.016666654497385025	0.0038938894867897034	0.05480942875146866	0.01773187331855297	-0.04531380534172058	-0.0767773911356926	0.048487842082977295	-0.031080126762390137	0.06414330750703812	0.029135102406144142	0.06687358021736145	0.0029885403346270323	-0.03420135751366615	0.009899752214550972	-0.014661405235528946	0.051348935812711716	-0.017436709254980087	0.05320045351982117	0.022113285958766937	-0.012263675220310688	0.04281860217452049	-0.04632796719670296	0.05799655616283417	0.021159686148166656	0.019294647499918938	0.031645022332668304	-0.0639873743057251	-0.06044233590364456	-0.045486532151699066	0.02893977425992489	0.028907597064971924	-0.0038511534221470356	0.0016483571380376816	0.0435771606862545	0.04436454176902771	-0.011861898005008698	0.03484470769762993	-0.053866174072027206	-0.016242310404777527	0.0002292461140314117	-0.0063450634479522705	0.04940903186798096	-0.03231976181268692	-0.010991975665092468	-0.0005409850855357945	-0.0444733202457428	-0.0475158765912056	0.08836746960878372	-0.010441181249916553	0.026631195098161697	-0.03888865187764168	-0.01668519526720047	-0.03015700727701187	0.054228659719228745	-0.014773326925933361	0.015495540574193	-0.013092339038848877	-0.11047890782356262	-0.0041675567626953125	0.06369224935770035	-0.03327977657318115	0.04010922089219093	0.08862747251987457	-0.005721105728298426	-0.024517472833395004	0.09845978021621704	-0.01482976134866476	0.001948110293596983	-0.036274947226047516	0.005209549330174923	0.0036555135156959295	-0.026077598333358765	0.06867958605289459	-0.01331364456564188	-0.029439713805913925	0.011139800772070885	0.03272693604230881	-0.020849838852882385	0.07268745452165604	0.008942724205553532	0.051979195326566696	-0.030108751729130745	0.04805070161819458	0.06748073548078537	0.04992823302745819	-0.04385480657219887	-0.059033289551734924	-0.05507967993617058	0.06382583826780319	0.04213310405611992	0.0734432190656662	-0.08903475105762482	0.030818363651633263	0.1490892469882965	0.042649637907743454	-0.07628984749317169	-0.02070789225399494	-0.07715208828449249	0.015252716839313507	0.07041945308446884	-0.004530661273747683	-0.006712092086672783	0.04064052179455757	0.09076018631458282	0.0018513171235099435	-0.01590586081147194	-0.007371861953288317	0.04114583507180214	-0.022472120821475983	0.0033015694934874773	-0.036461059004068375	0.04004504531621933	0.0028096758760511875	0.03449634090065956	-0.07588696479797363	0.04032719135284424	0.05987176299095154	0.009092948399484158	-0.10443808883428574	0.01791834644973278	0.09641725569963455	-0.010686046443879604	-0.023036954924464226	0.018515557050704956	-0.0010639623505994678	-0.03060259111225605	-0.0143375713378191	0.05220486596226692	0.005015885923057795	0.06043767184019089	-0.04795299097895622	-0.009633212350308895	-0.05757544934749603	0.027796655893325806	-0.0006736778886988759	-0.05379210785031319	0.00958823598921299	0.01310685370117426	-0.014696063473820686	-0.01953943818807602	0.005798493046313524	0.02013227716088295	-0.03579474240541458	-0.03223392367362976	-0.03694142773747444	-0.002050509676337242	0.0013135491171851754	0.05440329387784004	0.03864509239792824	0.04321194067597389	0.05366578698158264	-0.08981633186340332	0.03709230199456215	-0.04035721719264984	-0.060051701962947845	0.024810226634144783	0.051842257380485535	-0.000522880582138896	-0.04908788949251175	0.03048734925687313	-0.044872019439935684	-0.04436087980866432	0.021933546289801598	-0.03902783989906311	0.006716948468238115	0.028084825724363327	0.03680611401796341	-0.02295766770839691	-0.010758520103991032	-0.048693764954805374	-0.0076058111153542995	-0.03547968342900276	-0.029044954106211662	-0.04609363526105881	-0.04132001847028732	-0.007013982627540827	-0.043867841362953186	0.0025510776322335005	0.010647960938513279	0.1154339462518692	-0.037042222917079926	0.06404466181993484	0.05286919325590134	0.01944204978644848	0.006069148425012827	-0.0011240859748795629	-0.04373400658369064	0.030717963352799416	-0.018277710303664207	-0.060171257704496384	-0.07381542026996613	0.004673731978982687	-0.03205354884266853	-0.03278352692723274	-0.008983025327324867	0.0169875156134367	0.0049622515216469765	-0.008930613286793232	0.05407309532165527	-0.08466736227273941	-0.008675362914800644	0.044531580060720444	0.026935487985610962	-0.0023078941740095615	0.05741700902581215	0.01565375179052353	-0.08896280080080032	0.021568063646554947	-0.057286906987428665	0.0657750740647316	-0.06803766638040543	0.06335314363241196	-0.04021265730261803	-0.04475877434015274	-0.017161784693598747	0.026115650311112404	-0.002208828227594495	0.009468796662986279	0.059664200991392136	0.00018498777353670448	0.01174621470272541	0.026730289682745934	0.08295128494501114	0.0015954045811668038	0.0764501690864563	-0.035657480359077454	-0.04041053354740143	-0.021031837910413742	0.03765220567584038	-0.0025240425020456314	-0.031305573880672455	0.024871978908777237	-0.00983605720102787	0.004507496953010559	-0.045398373156785965	-0.011920230463147163	-0.025478355586528778	0.009824523702263832	0.038089435547590256	0.008350258693099022	-0.02748817205429077	0.009378419257700443	-0.05000489205121994	-0.044642433524131775	0.030191903933882713	-0.036737360060214996	0.028739364817738533	-0.01926356740295887	0.08239034563302994	-0.014950398355722427	0.029951198026537895	0.11204315721988678	0.011602348648011684	0.020442962646484375	-0.010122120380401611	-0.004407299216836691	-0.10019797831773758	0.010657772421836853	0.006261868868023157	0.03572048619389534	0.04186651483178139	0.06781977415084839	-0.03810717165470123	-0.018679672852158546	0.001224057748913765	0.04855531454086304	-0.022563017904758453	0.0318911038339138	-0.008535458706319332	-0.039345964789390564	0.005783617962151766	0.04136113449931145	-0.037525616586208344	0.026286330074071884	0.017552202567458153	-0.007999678142368793	-0.017126625403761864	-0.004607336595654488	-0.13487006723880768	0.05678514018654823	-0.029848171398043633	0.017806733027100563	-0.06838489323854446	0.00950329378247261	0.023981859907507896	0.013570291921496391	-0.06013328582048416	-0.09211816638708115	0.06947214901447296	0.023916475474834442	0.0706859901547432	0.12919646501541138	-0.028221730142831802	-0.013993202708661556	-0.004085940774530172	-0.019106755033135414	-0.07957955449819565	-0.04453089088201523	-0.07880745083093643	0.0914645791053772	0.03245483338832855	0.00013846580986864865	-0.07572095096111298	-0.04503586143255234	0.0010858079185709357	-0.024112334474921227	0.011438996531069279	-0.007989881560206413	0.018791915848851204	-0.08942800015211105	-0.026357831433415413	0.01065469067543745	-0.030437195673584938	-0.05087969824671745	0.021809857338666916	0.024445662274956703	-0.04922264814376831	0.0936693623661995	-0.02474992722272873	-0.025472309440374374	0.012133834883570671	0.005569634959101677	0.040983427315950394	-0.015625348314642906	-0.0015810386976227164	0.06123495474457741	-0.07995323091745377	0.0019277561223134398	-0.04053794592618942	0.039093345403671265	-0.02247651107609272	-0.00920360628515482	0.05139338970184326	-0.0186044592410326	0.003666521981358528	0.01844886504113674	0.006987674627453089	0.040230002254247665	-0.002552660880610347	0.05744096636772156	0.005262346938252449	0.015329992398619652	0.07952000945806503	0.01438197959214449	-0.029532156884670258	0.022171275690197945	0.03621498495340347	0.05332649499177933	0.03933405876159668	0.003936971537768841	0.049703456461429596	-0.01715649478137493	0.028522901237010956	0.03248829394578934	0.060466308146715164	-0.012437130324542522	-0.03302339091897011	0.007923642173409462	0.05301973596215248	-0.038667332381010056	-0.012470030225813389	0.0346151627600193	0.017664020881056786	-0.061551447957754135	0.06722726672887802	-0.005224287509918213	-0.07307310402393341	-0.021577075123786926	0.009299181401729584	-0.0708828940987587	-0.019778821617364883	0.029249541461467743	0.04334245249629021	0.03540064021945	0.012782325968146324	-0.032760363072156906	-0.012300977483391762	0.03475157171487808	0.015079706907272339	-0.035642340779304504	-0.0028587656561285257	0.03951245918869972	0.05308462306857109	0.028178226202726364	0.056614574044942856	-0.06570787727832794	-0.03650064766407013	0.02001691423356533	0.03992081806063652	0.009902947582304478	0.040540121495723724	-0.02603386715054512	0.011110016144812107	0.032696399837732315	-0.0670180469751358	0.02247222699224949	0.025971893221139908	-0.06669092923402786	0.06913717091083527	0.06928623467683792	-0.05608657747507095	0.0658344253897667	0.0008344090892933309	0.058804962784051895	0.011022211983799934	0.018531160429120064	0.06714047491550446	0.0010503055527806282	0.02206188440322876	0.031071534380316734	-0.050684623420238495	0.0009616952738724649	-0.01756998337805271	0.00659957155585289	0.025771209970116615	-0.011778721585869789	-0.09666652977466583	0.02557983808219433	-0.025182878598570824	-0.06464128941297531	-0.027850115671753883	-0.009259733371436596	-0.02272712253034115	0.040677860379219055	-0.02097795344889164	0.0341794453561306	0.06530460715293884	0.07789032906293869	-0.10218531638383865	0.010662215761840343	-0.004728479776531458	0.036312591284513474	0.0635969340801239	0.00729802530258894	-0.02511577308177948	0.036593083292245865	-0.04211271181702614	-0.08988910168409348	0.024524103850126266	-0.035853393375873566	0.006836825516074896	0.03705095872282982	0.001402254798449576	-0.013599765487015247	0.02483546733856201	0.07260792702436447	0.07778605818748474	0.02013728767633438	0.0006785328150726855	-0.01714743673801422	0.03594541177153587	0.05943923443555832	0.04495812579989433	0.04044616222381592	0.04191337898373604	0.012709863483905792	-0.06527654826641083	-0.014684007503092289	0.03160892054438591	0.04320194572210312	0.05615495890378952	0.03489794582128525	-0.021863067522644997
+0.0412849485874176	-0.04011998325586319	-0.026762930676341057	0.05799103155732155	-5.177722778171301e-05	0.07374855130910873	0.005251167342066765	0.01255565881729126	-0.05244360864162445	0.05183570459485054	-0.05393284931778908	-0.001900539849884808	-0.046175193041563034	0.007203893736004829	-0.03044542297720909	-0.13122010231018066	-0.003898206865414977	0.020249705761671066	0.05259358882904053	-0.04653508961200714	-0.051774051040410995	0.08789732307195663	0.050207819789648056	0.015884511172771454	-0.019035937264561653	0.03732561320066452	-0.0022309001069515944	0.02139260433614254	0.06477846205234528	-0.017633209004998207	-0.03579962998628616	0.0032096405047923326	0.009741426445543766	-0.04645946994423866	0.03933095932006836	-0.026113253086805344	0.0030191377736628056	0.03907112404704094	0.03124259039759636	-0.035323310643434525	0.012055175378918648	0.06749270856380463	-0.013380411081016064	-0.04838498309254646	0.010684852488338947	-0.01684233359992504	-0.016880512237548828	-0.02368529513478279	-0.030367383733391762	-0.029303021728992462	-0.023695845156908035	0.01281164214015007	0.037610527127981186	0.005387609358876944	-0.06613852828741074	0.030150799080729485	-0.02256230264902115	0.11093443632125854	0.027392461895942688	-0.010648506693542004	0.0031984043307602406	-0.03984588012099266	-0.015299956314265728	-0.05124283954501152	-0.025274012237787247	-0.008728164248168468	0.0659499391913414	0.050483547151088715	-0.018124718219041824	0.029133768752217293	-0.005377757828682661	0.06926305592060089	-0.020996443927288055	0.06567119807004929	0.0350746214389801	-0.09383285045623779	-0.03877980634570122	0.02263530343770981	-0.002506513614207506	-0.022341519594192505	0.026785990223288536	0.056555844843387604	0.01699981652200222	0.023997992277145386	-0.052444182336330414	0.08518979698419571	0.022996194660663605	-0.056012652814388275	-0.06303377449512482	-0.016437001526355743	0.059074338525533676	-0.06035821512341499	0.02706291899085045	-0.016095109283924103	0.01785784773528576	0.027485476806759834	0.09396886080503464	0.01922766864299774	-0.046448033303022385	-0.005394495092332363	0.003646318567916751	-0.008776642382144928	0.03361966460943222	-0.04438645392656326	0.020293183624744415	-0.055495817214250565	-0.052823346108198166	0.003467992413789034	-0.027590565383434296	-0.07652544230222702	0.11431148648262024	0.06463246047496796	-0.04247869551181793	-0.07017642259597778	0.0738639160990715	0.011403104290366173	0.09107166528701782	-0.010017202235758305	0.027112653478980064	0.02069239877164364	-0.04174795746803284	0.08925996720790863	-0.006319895852357149	-0.02457333356142044	-0.00913961511105299	0.013969308696687222	0.03808118775486946	0.0359489880502224	-0.0018331327009946108	-0.0015250886790454388	-0.014211020432412624	0.014259164221584797	0.029172813519835472	0.02861805632710457	-0.05958552658557892	-0.07730690389871597	-0.049239013344049454	0.04538816586136818	0.08624459058046341	0.09963731467723846	-0.09174609929323196	0.04571162536740303	0.09777295589447021	0.05938871204853058	-0.031037453562021255	0.015822995454072952	-0.06264989078044891	0.002271478297188878	0.0350283682346344	-0.03836480528116226	0.06219486892223358	0.01434373203665018	-0.03008444234728813	-0.03754248097538948	0.02364358864724636	0.01342000812292099	-0.004852912854403257	0.02291393280029297	0.0029267698992043734	-0.02468264102935791	-0.012122849002480507	0.029051972553133965	0.0056676315143704414	-0.03135170042514801	-0.0005563407321460545	0.1033463105559349	-0.010231240652501583	-0.08189471811056137	-0.01724008470773697	0.14971795678138733	-0.031693823635578156	-0.03405718505382538	0.01609095185995102	-0.06017008423805237	0.021016623824834824	0.024637671187520027	0.006974474061280489	0.027742337435483932	0.04900563508272171	-0.022041022777557373	0.02550327591598034	-0.047073256224393845	0.017018305137753487	0.0412740558385849	0.021010475233197212	0.030498117208480835	0.007394763175398111	-0.03280121088027954	0.0673540011048317	0.015708845108747482	0.0901748314499855	-0.02009432017803192	-0.0218727495521307	-0.006844045594334602	0.041273459792137146	-0.013994413428008556	-0.04239532724022865	0.0204622820019722	-0.007898740470409393	0.042699798941612244	-0.029183410108089447	0.02243368700146675	-0.045873191207647324	-0.032007183879613876	-0.0035135357175022364	0.04392721876502037	-0.03292612358927727	-0.01046490203589201	0.06197788566350937	-0.04228704050183296	-0.0216989666223526	-0.0545000396668911	-0.018761591985821724	0.02291625365614891	0.010870200581848621	0.05438362807035446	0.007497003301978111	-0.016863374039530754	-0.08568421006202698	0.016981452703475952	-0.02182205393910408	-0.027175825089216232	-0.06587696075439453	-0.05824338644742966	-0.0632862001657486	0.025470418855547905	0.018649179488420486	0.04208362102508545	0.10116183012723923	-0.01474882010370493	0.05447993054986	0.07615851610898972	-0.020943475887179375	-0.026047516614198685	-0.03799273818731308	0.0007852715207263827	0.023408427834510803	-0.05616818368434906	0.02952134981751442	-0.014978773891925812	-0.029721707105636597	-0.003604370867833495	-0.05123605951666832	0.035372693091630936	0.02039935253560543	-0.08050286769866943	0.028001444414258003	-0.024935537949204445	-0.0767919272184372	0.0045339688658714294	0.07516597211360931	0.019585052505135536	-0.005227986257523298	0.03365970775485039	0.04772234708070755	-0.030197028070688248	-0.004460863769054413	-0.04256831482052803	0.005201396532356739	-0.0004315874248277396	0.01836356706917286	0.026393799111247063	0.010663428343832493	0.008682860992848873	-0.0675806850194931	0.001004250138066709	0.08294209837913513	0.02444659359753132	0.030501559376716614	-0.011795842088758945	-0.002751846332103014	0.10562800616025925	0.012269408442080021	0.05727149918675423	-0.023689256981015205	-0.08272033929824829	-0.023025207221508026	0.03692387789487839	0.05188832804560661	-0.058188892900943756	0.03940345346927643	0.06395619362592697	-0.02093406952917576	-0.04659071937203407	-0.003155938582494855	-0.0006163729703985155	0.04586263746023178	0.021897820755839348	-0.02061554044485092	-0.05181506276130676	-0.022123204544186592	-0.04105006530880928	0.015015393495559692	0.0459139384329319	-0.017636196687817574	-0.004319588653743267	-0.038077570497989655	0.07566928118467331	-0.027232209220528603	-0.022235102951526642	0.08012711256742477	-0.0069966185837984085	0.033706001937389374	0.09223400056362152	-0.011868974193930626	-0.07747744023799896	-0.03873113915324211	-0.03545628860592842	0.02503608539700508	0.07490658015012741	0.03454296663403511	0.007521297316998243	0.020776374265551567	0.05351828411221504	0.030221035704016685	-0.025243505835533142	0.006852751597762108	0.02167893946170807	-0.013858240097761154	0.04208860918879509	0.0640232115983963	0.06729722768068314	0.01244578417390585	0.021054474636912346	-0.0077901375479996204	-0.05464024841785431	-0.017163701355457306	-0.13375352323055267	0.012475177645683289	-0.050584327429533005	-0.06797274202108383	-0.0447329506278038	0.05623338744044304	0.03438233956694603	-0.002186672994866967	-0.0611991360783577	-0.028827888891100883	0.02987094037234783	0.026971617713570595	-0.00713874027132988	0.11389251053333282	0.014068878255784512	0.030142024159431458	-0.042355719953775406	-0.05269988626241684	-0.052023470401763916	-0.05908913165330887	-0.0620865561068058	0.07874152809381485	-0.0021724237594753504	-0.0026960172690451145	-0.03327537700533867	-0.07036180049180984	0.024412011727690697	-0.03733766824007034	-0.005920811090618372	0.07238826155662537	0.01705889403820038	-0.060228392481803894	0.024306202307343483	0.004031066317111254	-0.0016834998968988657	0.012641913257539272	0.0827842578291893	-0.010768113657832146	-0.030455073341727257	0.050864338874816895	-0.012293068692088127	-0.004769578110426664	0.0037280407268553972	0.00433995109051466	0.04220743477344513	-0.041927825659513474	-0.020308950915932655	0.037868037819862366	-0.0030570179224014282	-0.046266842633485794	-0.018732797354459763	0.05616186559200287	-0.022011468186974525	-0.01746291294693947	-0.01056132186204195	-0.01742607355117798	0.04951748251914978	0.0658990889787674	0.009150374680757523	0.015565874055027962	-0.03365803509950638	0.03542127460241318	0.010409400798380375	0.009811527095735073	0.11772868037223816	0.05891149491071701	-0.016745690256357193	-0.02479134127497673	0.06436969339847565	0.06933585554361343	0.027850858867168427	0.04215502738952637	0.05041792616248131	-0.062485843896865845	0.018160376697778702	-0.029513785615563393	0.07674485445022583	0.029762284830212593	-0.055608950555324554	-0.06494621187448502	0.02167626842856407	0.016795175150036812	-0.028775669634342194	0.041894491761922836	-0.030427198857069016	-0.0740695595741272	0.012365829199552536	-0.01245211437344551	-0.02814464084804058	-0.018350042402744293	-0.00044568959856405854	-0.011247674934566021	-0.060222938656806946	0.012589785270392895	0.011101079173386097	0.05311091989278793	0.00827635545283556	-0.07933785766363144	-0.05194266512989998	0.07308640331029892	-0.03785399720072746	-0.11120088398456573	-0.03591674938797951	0.06873279064893723	0.08517186343669891	0.006175455637276173	0.04705938324332237	-0.038565922528505325	-0.023469598963856697	0.00694445101544261	0.025556201115250587	0.06476213783025742	-0.0032764854840934277	-0.021117808297276497	0.018960721790790558	0.004142245277762413	-0.024067776277661324	0.01862724870443344	0.0011495272628962994	-0.05712558701634407	0.06983382999897003	0.04364451766014099	-0.11812848597764969	0.02403124049305916	0.0181734561920166	0.017690086737275124	-0.022833164781332016	0.06185996159911156	0.049510933458805084	0.016994882375001907	0.013830688782036304	0.05238224193453789	-0.0442020557820797	0.014451343566179276	0.03129572793841362	-0.005779130384325981	0.010815925896167755	-0.008562426082789898	-0.04416193068027496	-0.0011064634891226888	0.019948672503232956	0.001515233307145536	-0.0013128577265888453	0.008720669895410538	-0.05543556809425354	0.016154421493411064	-0.007992677390575409	0.0464218407869339	0.0340120755136013	0.10887371748685837	-0.07779364287853241	-0.004715355578809977	0.07434368133544922	0.04697487875819206	0.11335857957601547	0.009124258533120155	-0.009490312077105045	0.05070647969841957	-0.06785199791193008	-0.04850956052541733	0.010097621008753777	0.04586775600910187	-0.003936518914997578	0.011499990709125996	0.010976666584610939	-0.02592228353023529	-0.03301136940717697	0.06362538039684296	0.028721673414111137	0.026798201724886894	0.014487599022686481	-0.011564279906451702	0.04241348057985306	0.02404322661459446	0.036388494074344635	0.07555601745843887	0.02203119546175003	0.016357921063899994	-0.03371204435825348	-0.021958664059638977	0.003513970645144582	0.019603358581662178	0.00392490066587925	-0.0419195182621479	-0.00318642845377326
+0.058779027312994	-0.05505208298563957	-0.11617984622716904	0.037572599947452545	-0.06849558651447296	0.04554033279418945	-0.0018318030051887035	0.01886778511106968	-0.048950206488370895	-0.026948794722557068	-0.030941318720579147	0.027105897665023804	-0.006340729538351297	-0.03582049533724785	0.04464088752865791	-0.07763749361038208	0.06527043879032135	0.025503098964691162	0.05346652492880821	-0.02957071363925934	-0.04523560404777527	-0.004004594869911671	0.011194181628525257	-0.04837212711572647	0.005412175320088863	-0.038992155343294144	-0.01332671195268631	0.036172013729810715	0.07715363055467606	0.08613034337759018	0.015242801047861576	-0.06274257600307465	-0.03134359419345856	-0.049781300127506256	-0.05939200520515442	-0.022411489859223366	-0.05101338401436806	0.040405236184597015	0.030886884778738022	-0.01887236163020134	-0.03156962990760803	0.009614759124815464	-0.07309480011463165	-0.0714501440525055	0.02391873672604561	0.011278875172138214	0.006053760647773743	-0.06274071335792542	0.02643129788339138	-0.048039283603429794	0.0033629240933805704	-0.010722249746322632	-0.0010816375724971294	-0.019633296877145767	-0.0868237093091011	-0.01023246068507433	0.020468655973672867	0.05860278010368347	0.02698298543691635	0.12479249387979507	-0.06211845204234123	-0.002311225514858961	-0.0017649338115006685	0.03527138754725456	-0.014716275967657566	0.039131905883550644	0.09896976500749588	-0.023654019460082054	-0.0641086995601654	-0.05862456560134888	-0.05328066274523735	0.04136636480689049	0.06602679938077927	-0.014474313706159592	-0.004682154860347509	-0.09528838098049164	-0.02844112738966942	-0.035187505185604095	0.05103860795497894	0.05896902084350586	-0.02708370052278042	-0.06388242542743683	0.08343365788459778	0.05053829401731491	0.018280550837516785	-0.057297490537166595	-0.07504051178693771	0.02273372747004032	-0.01426194328814745	0.015709638595581055	0.0491524264216423	-0.018631763756275177	-0.01005807239562273	-0.011532996781170368	-0.0024997566360980272	-0.019522491842508316	0.03125015273690224	0.018354713916778564	-0.04906897991895676	0.03345430642366409	0.01279100775718689	-0.04997451975941658	0.0549139529466629	0.02825774811208248	0.06448644399642944	0.0008517892565578222	-0.04625382274389267	-0.005172148812562227	0.03882346302270889	-0.009987344034016132	0.025719420984387398	0.05811988562345505	-0.019511941820383072	-0.026693301275372505	0.05689768120646477	0.0010429322719573975	0.01018613949418068	-0.0777176097035408	0.050033047795295715	0.05539017915725708	-0.007296363357454538	0.02735334075987339	0.002766067162156105	-0.05756329372525215	0.008454551920294762	-0.031138144433498383	-0.026207460090517998	-0.01139563973993063	-0.014133375138044357	0.01679125428199768	-0.014034328982234001	0.043881893157958984	0.00992184691131115	0.038620490580797195	-0.06175774708390236	0.02895822562277317	-0.02037694863975048	0.06862365454435349	0.08873198181390762	0.023593297228217125	-0.03911028802394867	0.027659403160214424	0.038417793810367584	0.02426433004438877	0.04073341563344002	-0.035701196640729904	-0.06972770392894745	-0.0489039272069931	-0.0028651077300310135	0.027400650084018707	0.015270865522325039	0.041548486799001694	0.050627004355192184	0.0014508628519251943	0.06967303156852722	-0.04025682061910629	0.0625624805688858	-0.05514396354556084	-0.056235019117593765	-0.007829471491277218	0.05163848400115967	-0.010936991311609745	0.018740838393568993	-0.07523009181022644	0.03493766859173775	0.06757562607526779	0.035498037934303284	-0.011295723728835583	-0.056211698800325394	0.07197926193475723	-0.03650927543640137	0.03344300389289856	0.09657090157270432	-0.0212064478546381	-0.025906896218657494	-0.03876291960477829	0.07769323140382767	-0.07968492805957794	0.07422218471765518	-0.05998147651553154	0.014400954358279705	-0.03272872418165207	-0.011078745126724243	-0.015657519921660423	-0.03783227130770683	0.03745342046022415	0.034223586320877075	-0.04474793002009392	-0.022043149918317795	-0.008746902458369732	-0.06723139435052872	-0.06887290626764297	0.039603084325790405	0.006801327224820852	0.045804351568222046	0.009911403059959412	0.01864989474415779	0.046950630843639374	0.03451957181096077	-0.01082641538232565	-0.05900917202234268	0.024438990280032158	-0.015080602839589119	-0.021477030590176582	-0.023119986057281494	-0.01944596879184246	0.014383397065103054	-0.007490772288292646	0.059139326214790344	0.035370416939258575	-0.050982385873794556	0.04514358565211296	-0.08912211656570435	0.009417249821126461	0.02158854529261589	0.014148706570267677	-0.03460900858044624	0.036768823862075806	0.006301988381892443	0.01891481503844261	-0.0045148166827857494	-0.000948550587054342	-0.048677217215299606	-0.09617399424314499	-0.016568517312407494	-0.049436286091804504	-0.002895854879170656	0.02261163294315338	-0.0026687418576329947	0.02502978779375553	0.03076573833823204	0.007715982384979725	-0.003460034029558301	-0.036299992352724075	-0.046046964824199677	-0.04495912417769432	0.004399114288389683	-0.012679572217166424	-0.04168861359357834	-0.10634680092334747	0.026378553360700607	-0.051365841180086136	0.043776631355285645	-0.03599192947149277	0.04918759688735008	0.04360221326351166	-0.06051488220691681	-0.003741772845387459	-0.0442570261657238	-0.04607231542468071	0.055856991559267044	0.04324768856167793	0.011882721446454525	0.029003236442804337	-0.03682166337966919	-0.020701296627521515	0.05098111927509308	-0.034164611250162125	0.09877677261829376	-0.010925575159490108	-0.016879387199878693	-0.008741657249629498	-0.04060611501336098	-0.02797193080186844	0.06518576294183731	0.021037476137280464	0.0485839806497097	0.027757111936807632	-0.05072706937789917	-0.014102202840149403	-0.0353214405477047	-0.0018554473062977195	0.03373086825013161	0.006346999201923609	-0.06469527631998062	-0.011625813320279121	0.0022006691433489323	0.0027030243072658777	-0.039009939879179	-0.029802676290273666	0.023944854736328125	0.09111329168081284	-0.04063666611909866	-5.484450957737863e-05	-0.055006660521030426	0.02137245610356331	-0.016235148534178734	0.015623509883880615	-0.015884090214967728	-0.03748365119099617	0.0004390768299344927	-0.09962945431470871	-0.036787014454603195	-0.004168482962995768	-0.06454640626907349	0.028886714950203896	0.047835905104875565	0.05022392421960831	-0.02551647648215294	0.00781727209687233	0.09740978479385376	-0.0681319609284401	0.012612965889275074	-0.043458931148052216	-0.09614169597625732	-0.03968275710940361	0.01581447571516037	-0.01809254288673401	0.014463305473327637	-0.004850635305047035	-0.012198364362120628	-0.06256919354200363	-0.0009220471256412566	0.005341039504855871	-0.019065195694565773	-0.06133405491709709	-0.06584478914737701	-0.09862159192562103	-0.009834395721554756	0.04306848719716072	0.004798511043190956	-0.006322597153484821	-8.725934458198026e-05	0.038961537182331085	0.056669820100069046	-0.08015621453523636	0.021824445575475693	-0.05090959742665291	0.015577616170048714	0.027976039797067642	0.05455682799220085	0.0039015456568449736	-0.007213737815618515	-0.03448617458343506	-0.017866501584649086	-0.0760926678776741	-0.013969868421554565	-0.005310146603733301	0.024245571345090866	0.07784417271614075	0.08013634383678436	-0.06791944056749344	-0.011339726857841015	0.006679769605398178	0.008454819209873676	-0.06376313418149948	-0.024221254512667656	-0.08019410818815231	0.04663088917732239	0.02544289268553257	0.07695029675960541	-0.010973908007144928	-0.05487707629799843	-0.036551665514707565	-0.037476107478141785	0.019637780264019966	0.0025725257582962513	0.0938170775771141	-0.07542620599269867	-0.01928458735346794	-0.02125304937362671	-0.022196687757968903	-0.08633555471897125	0.018465889617800713	0.0058126915246248245	0.003584742546081543	0.0804213136434555	-0.042471084743738174	-0.08965250849723816	0.032862816005945206	-0.04322643578052521	-0.041843753308057785	0.0007041212520562112	0.015956850722432137	0.07890120893716812	-0.04865240305662155	0.023580277338624	0.01238382700830698	-0.01723511889576912	-0.028934279456734657	-0.05681519955396652	-0.0324108749628067	-0.09159104526042938	0.015620047226548195	-0.029278969392180443	-0.018897810950875282	-0.032292772084474564	-0.02210036665201187	-0.022153237834572792	0.018535031005740166	0.07211127877235413	0.02782624214887619	-0.06005452573299408	-0.03467908129096031	0.034008514136075974	0.09311167150735855	0.02064593695104122	-0.03408528491854668	0.05369838327169418	0.04335048049688339	-0.06046922504901886	0.07265955954790115	-0.00650064880028367	0.08749222755432129	0.000555472681298852	-0.03455404192209244	0.005797079764306545	-0.010851365514099598	-0.04178488999605179	-0.00544240465387702	0.037858132272958755	0.0012318950612097979	0.005213738884776831	0.01584373600780964	-0.024565059691667557	-0.03727339580655098	0.03222537785768509	0.08016988635063171	0.02085849456489086	-0.050286319106817245	-0.03637635335326195	0.03805568069219589	0.005505864042788744	-0.008936223573982716	-0.02924432046711445	0.03389393538236618	-0.03097505308687687	0.021993951871991158	-0.020681805908679962	0.025474268943071365	0.04397311806678772	0.03281935676932335	0.043002426624298096	0.021000703796744347	-0.09863036870956421	0.0012794025242328644	-0.007323205936700106	0.02684330940246582	0.021732786670327187	0.045254915952682495	-7.651926352991723e-06	0.03207044675946236	0.014414126053452492	-0.04154282808303833	-0.0028664665296673775	0.05532519146800041	-0.03804675117135048	-0.017085906118154526	-0.02948473021388054	-0.066865473985672	0.026794927194714546	-0.022912606596946716	0.0026199501007795334	0.03323325142264366	0.013513858430087566	-0.02708599530160427	0.05756241828203201	0.028480635955929756	0.045481398701667786	0.04952351003885269	-0.05642883852124214	-0.05546600744128227	-0.008951899595558643	-0.0297275148332119	0.023313382640480995	-0.010651706717908382	-0.010156269185245037	-0.03263958543539047	-0.021486684679985046	0.013771435245871544	-0.0046648732386529446	-0.07853223383426666	0.1128692477941513	0.01660914346575737	0.025108706206083298	-0.02359621226787567	0.06327758729457855	-0.124755859375	0.04940453916788101	0.10289784520864487	0.07629457861185074	0.0013534463942050934	0.025029484182596207	-0.02946098893880844	0.02941623516380787	0.032766833901405334	-0.13070155680179596	-0.019715189933776855	0.0330299437046051	0.06928865611553192	-0.09376984089612961	-0.01329744327813387	-0.07770498096942902	0.006975164636969566	0.022791307419538498	0.039597444236278534	0.024539008736610413	0.024007482454180717	-0.007738662417978048	0.02640555612742901	0.025113767012953758	-0.02230609394609928	0.030811002478003502	0.032045334577560425	0.007426291238516569	-0.009465846233069897	0.04671454057097435	0.06507525593042374	0.01785396970808506	-9.834788215812296e-05	-0.0031427149660885334	-0.043383751064538956
+0.02335214614868164	-0.043639957904815674	-0.03448772802948952	-0.02219519205391407	-0.042864568531513214	0.009016493335366249	-0.015548923052847385	-0.007885098457336426	-0.08528794348239899	0.03091268241405487	-0.040814511477947235	0.027201907709240913	-0.018941247835755348	-0.05032479763031006	-0.012819594703614712	-0.13312114775180817	-0.06558850407600403	-0.022880811244249344	0.041587457060813904	0.00212029367685318	-0.012587730772793293	0.030511051416397095	0.004558747634291649	0.038363587111234665	0.002600159728899598	0.02533722296357155	0.038152825087308884	-0.023548893630504608	0.01189473271369934	0.0015051623340696096	0.031116249039769173	-0.04964372143149376	-0.020444834604859352	0.03906097635626793	0.008404375985264778	-0.053456369787454605	0.015843667089939117	0.044994160532951355	0.05302271246910095	-0.02486451342701912	0.008833948522806168	0.04168929159641266	0.007002720143646002	-0.016470227390527725	-0.031250327825546265	0.017248034477233887	-0.06797637790441513	-0.06109390780329704	-0.013527455739676952	-0.0353027805685997	-0.027216553688049316	0.07479020953178406	-0.00227874587289989	0.023056507110595703	-0.04088957980275154	-0.00756939547136426	-0.009574582800269127	0.07294980436563492	-0.026304097846150398	0.03269496560096741	-0.01197002548724413	0.02279089204967022	-0.008729641325771809	-0.03019668720662594	0.059672120958566666	-0.011381955817341805	0.02703458070755005	-0.008019007742404938	-0.018673717975616455	0.030053503811359406	-0.006431322544813156	0.041158467531204224	-0.009632239118218422	0.06161868944764137	0.02179032564163208	-0.029537729918956757	-0.06557885557413101	-0.0558355413377285	0.0065498934127390385	-0.009583154693245888	-0.009498249739408493	0.03844139352440834	0.06610023975372314	0.0012786919251084328	-0.051444124430418015	0.03868452459573746	-0.008506224490702152	-0.025743393227458	-0.02138170413672924	-0.00156155857257545	0.062384046614170074	-0.04152192920446396	-0.016992667689919472	-0.0014413531171157956	-0.05554245784878731	-0.013646996580064297	0.08636584877967834	0.008148073218762875	-0.006442833226174116	-0.024373793974518776	-0.024455150589346886	-0.022615471854805946	0.04410269111394882	-0.018126707524061203	-0.006823500618338585	-0.04424792528152466	-0.06489457935094833	0.023190556094050407	-0.031158801168203354	-0.07529997080564499	0.07930832356214523	0.08277915418148041	-0.07276101410388947	0.01570378988981247	0.10863454639911652	0.0555858351290226	0.01733383908867836	-0.002080070087686181	0.019325189292430878	0.008620870299637318	-0.045571163296699524	0.08657978475093842	0.012523478828370571	-0.0030215985607355833	0.04541490972042084	0.06253984570503235	-0.039948590099811554	0.03980594873428345	-0.010047555901110172	-0.014882144518196583	-0.041180044412612915	0.03441320359706879	0.047387998551130295	0.053550608456134796	-0.036684971302747726	-0.09637647867202759	-0.06256214529275894	0.029804304242134094	-0.02362600341439247	0.02495267242193222	-0.09913847595453262	0.03876040130853653	0.08078218996524811	0.08177753537893295	-0.04436441510915756	-0.018583374097943306	-0.06025515869259834	0.044351786375045776	0.06138327717781067	-0.00370490993373096	-0.009265720844268799	0.026167338714003563	0.01539769396185875	0.0013576061464846134	0.010648226365447044	0.006030979100614786	0.04955799877643585	0.002626719418913126	-0.00899715069681406	0.0023200621362775564	0.03359416499733925	0.009781638160347939	-0.011554595082998276	-0.044262029230594635	0.03373830020427704	0.15180668234825134	-0.03368619456887245	-0.053763002157211304	-0.00030504027381539345	0.10675686597824097	0.004733010660856962	-0.020749446004629135	-0.0017850283766165376	-0.034456223249435425	-0.060082096606492996	-0.04259857162833214	0.03822389990091324	-0.017448678612709045	0.008673790842294693	-0.010999258607625961	0.05588846653699875	-0.06229149550199509	0.08714991807937622	0.017208999022841454	-0.005556059535592794	0.023140251636505127	-0.01334270741790533	-0.028165852651000023	0.030289990827441216	-0.04375182092189789	0.030500954017043114	-0.029384339228272438	-0.022892413660883904	-0.04589405283331871	-0.029178155586123466	-0.04107880964875221	0.08358728885650635	0.012336588464677334	0.030725592747330666	0.06052852421998978	-0.04993957281112671	-0.017834965139627457	-0.07894112169742584	-0.014366132207214832	0.009874435141682625	0.06361745297908783	-0.04845330864191055	-0.014765733852982521	0.03245408087968826	-0.010865820571780205	0.004040378145873547	0.02066459320485592	-0.024190019816160202	0.019651547074317932	0.023530539125204086	-0.020230386406183243	0.025345677509903908	0.01729305274784565	-0.006807075347751379	-0.017162412405014038	-0.07543830573558807	-0.033297281712293625	-0.02747064083814621	-0.09266320616006851	0.01958085410296917	0.0003695615741889924	0.027632419019937515	0.03332056105136871	0.09410840272903442	-0.03931004926562309	0.05531325563788414	0.10888154804706573	0.011046064086258411	0.013572225347161293	0.02683141827583313	-0.000370254012523219	0.009520519524812698	-0.013958217576146126	-0.017427364364266396	-0.05911027640104294	0.028654782101511955	0.012717421166598797	-0.05656162649393082	0.08822624385356903	-0.007657734677195549	-0.03177783638238907	0.057944200932979584	0.039802491664886475	-0.09536367654800415	-0.0506984181702137	0.06510891765356064	0.009602888487279415	0.05774431303143501	0.046370137482881546	0.08340734988451004	-0.06842634826898575	0.0226843673735857	-0.03140079602599144	0.017181523144245148	-0.005649637430906296	0.07295403629541397	-0.026547882705926895	-0.07218609005212784	-0.008656938560307026	-0.06525074690580368	0.04155028238892555	0.06231176480650902	0.014606250450015068	-0.008908887393772602	0.015460970811545849	0.016890257596969604	0.08698683232069016	-0.010908465832471848	0.014894838444888592	0.0056507643312215805	-0.02040659263730049	0.014101670123636723	0.011478905566036701	0.08055052906274796	-0.04587211832404137	-0.0006800587871111929	-0.021051933988928795	-0.015870893374085426	-0.022692838683724403	-0.025970375165343285	-0.006682418752461672	0.03145474195480347	-0.03778887912631035	0.023489918559789658	-0.004061962943524122	-0.015330804511904716	-0.008118780329823494	0.015672234818339348	0.051317013800144196	0.009225742891430855	0.020138507708907127	-0.027243178337812424	0.04791498929262161	-0.0347617082297802	0.07868309319019318	0.09269977360963821	-0.027499787509441376	0.03922120854258537	0.06528428941965103	0.017203621566295624	-0.09666766226291656	-0.023622307926416397	-0.011276165954768658	0.00013330203364603221	0.0142688462510705	0.05719795078039169	-0.02563084475696087	0.037582531571388245	0.034026093780994415	0.01782691292464733	-0.046803560107946396	-0.008997614495456219	-0.05279720202088356	-0.02988598309457302	0.09057014435529709	0.046556491404771805	0.05272515490651131	-0.018129641190171242	0.03061803989112377	-0.03072270005941391	-0.0647796243429184	0.06658025830984116	-0.1464250087738037	-0.012235736474394798	-0.01448456384241581	-0.02296271175146103	-0.09052468091249466	0.010565308853983879	0.04909950867295265	0.030643973499536514	-0.07171420753002167	-0.06279157847166061	0.029917309060692787	0.03701295703649521	0.053279466927051544	0.07539036870002747	-0.04227997362613678	0.034543946385383606	-0.04252655804157257	-0.05257469415664673	-0.0621892474591732	-0.020007386803627014	-0.08135484904050827	0.10041605681180954	0.05894843861460686	-0.05368253216147423	-0.030738867819309235	-0.01501280814409256	0.038324471563100815	-0.0322229266166687	0.05403793230652809	0.024734940379858017	-0.009960467927157879	-0.075360968708992	0.03583960607647896	0.04419807717204094	-0.029490690678358078	-0.006492140702903271	0.051654718816280365	0.031353551894426346	-0.05354456230998039	0.04966464638710022	0.0285029299557209	-0.018462782725691795	0.029706913977861404	0.0777800977230072	0.03491179272532463	0.009726222604513168	-0.03239603340625763	0.021894333884119987	-0.06694130599498749	-0.003665720345452428	-0.0933178961277008	0.06665191054344177	0.0011292658746242523	0.029676377773284912	0.012016404420137405	0.01779475063085556	-0.017292706295847893	0.009337526746094227	0.009792698547244072	0.0561392717063427	-0.06321250647306442	0.0829545184969902	0.06442096829414368	-0.02137867361307144	0.10522837191820145	0.03981846943497658	0.00612458074465394	0.02309676818549633	0.08072857558727264	0.03685440495610237	0.04947130009531975	-0.031897690147161484	0.025310175493359566	0.0131304282695055	0.028124259784817696	0.04618997126817703	0.03985920175909996	-0.04846732318401337	-0.060920871794223785	0.007793074008077383	0.07171573489904404	-0.06834758073091507	-0.0024061137810349464	-0.0023160495329648256	-0.0217916090041399	-0.05327941104769707	0.06281213462352753	-0.05431424081325531	-0.055815763771533966	-0.045248307287693024	-0.0018197004683315754	-0.03210745379328728	-0.021427761763334274	-0.05949878320097923	0.05665786936879158	-0.012571698054671288	0.01637929491698742	-0.029718318954110146	-0.039371296763420105	0.02936607412993908	-0.031574495136737823	-0.0059695616364479065	-0.022256290540099144	0.09643720090389252	0.08687669038772583	0.060516513884067535	0.08818304538726807	-0.06008563190698624	-0.03543606400489807	0.019717561081051826	0.04600023105740547	0.03683227673172951	0.042436834424734116	-0.008900227956473827	0.022925274446606636	0.018766196444630623	-0.016694169491529465	0.039912693202495575	0.04048695042729378	-0.011030011810362339	-0.009519007056951523	0.04871238395571709	-0.06324580311775208	-0.021649114787578583	0.03741691634058952	0.05178249999880791	-0.03308595344424248	0.0003707646101247519	0.08708198368549347	0.025872791185975075	0.00131352455355227	0.004066187422722578	-0.06134761497378349	0.036825358867645264	-0.022879106923937798	0.042450278997421265	-0.060561925172805786	-0.026365112513303757	-0.0472562275826931	-0.015547557733952999	0.05548103526234627	-0.032202236354351044	0.0002364250976825133	0.048253320157527924	0.03685104474425316	0.021231818944215775	-0.011590030044317245	0.04360295459628105	0.025636667385697365	0.046448465436697006	-0.08597376197576523	-0.038962870836257935	0.025194870308041573	0.009916676208376884	0.03487998619675636	0.0768958106637001	0.03007625602185726	0.10162583738565445	-0.03695909306406975	-0.044859807938337326	-0.04461100324988365	0.036986466497182846	0.013585390523076057	-0.0368608795106411	0.013071772642433643	0.008348937146365643	0.04049273952841759	0.07187291979789734	0.028472675010561943	0.010697627440094948	-0.00918278843164444	-0.0020144509617239237	0.06845977902412415	0.009884168393909931	0.02755056507885456	-0.013592727482318878	0.041807498782873154	-0.0010996628552675247	-0.02061229571700096	-0.029797827824950218	0.03089258261024952	0.003964093513786793	0.03785335645079613	0.04923390969634056	-0.031196724623441696
+0.05113601312041283	-0.008989055640995502	-0.0654844120144844	0.011061678640544415	-0.015244776383042336	0.05591130629181862	-0.03606594726443291	0.013739006593823433	-0.06793849170207977	0.015392901375889778	-0.07710839807987213	0.04008286073803902	0.009781725704669952	-0.03193540498614311	-0.04675086587667465	-0.11180881410837173	0.003126623807474971	-0.004018406383693218	0.09864982217550278	-0.05845355987548828	-0.024334093555808067	0.038976240903139114	0.009841592982411385	0.0450122132897377	0.04912978410720825	0.041471924632787704	0.0651424303650856	0.027461403980851173	0.05804302543401718	0.02654350735247135	-0.005734969396144152	-0.08981004357337952	-0.0005150530487298965	-0.04928027465939522	0.004682417958974838	-0.042111836373806	0.0036247014068067074	0.04720398411154747	0.059077560901641846	-0.04624452069401741	-0.05526106059551239	0.05551575496792793	0.0067499433644115925	-0.05150524154305458	-0.015525873750448227	0.01319080125540495	-0.0796625018119812	-0.06423095613718033	0.008150449022650719	-0.015300760045647621	-0.02694585733115673	0.03369682654738426	-0.0284479521214962	-0.027802996337413788	-0.07241153717041016	0.019123997539281845	0.01925601251423359	0.06446926295757294	-0.010421893559396267	-0.0043621063232421875	0.017295420169830322	0.02739047072827816	-0.018989315256476402	-0.0304695013910532	0.006897646468132734	-0.00442341435700655	0.07119894027709961	0.006706747226417065	-0.008728970773518085	0.040532659739255905	0.0017034657066687942	0.04761244356632233	-0.011838061735033989	0.07458673417568207	0.013881128281354904	-0.05508548393845558	-0.04635629430413246	-0.019053544849157333	0.004074688069522381	0.0004322740714997053	-0.016569962725043297	0.011416683904826641	0.09126222133636475	-0.005709039978682995	-0.0426996611058712	0.014355240389704704	-0.026088185608386993	-0.027337780222296715	-0.044377800077199936	-0.009652817621827126	0.04738912358880043	-0.07495211809873581	0.018209634348750114	0.007578989490866661	-0.01701468415558338	0.0009514745906926692	0.0907764732837677	-0.027757273986935616	-0.019568096846342087	0.019107859581708908	-0.039459288120269775	-0.04558634012937546	0.03452279791235924	-0.02332628332078457	-0.04015583172440529	-0.039560116827487946	-0.08051854372024536	0.021117188036441803	0.026245612651109695	-0.04422631114721298	0.07477163523435593	0.030819155275821686	-0.04975854232907295	0.002892746590077877	0.09854549914598465	0.0002814904728438705	0.051889706403017044	0.0005354201421141624	0.06951861083507538	0.036866236478090286	-0.019228152930736542	0.07731474936008453	-0.011732694692909718	-0.043468210846185684	0.016363540664315224	0.03262719511985779	-0.015997856855392456	0.061069704592227936	-0.046730343252420425	0.020477021113038063	-0.03611515462398529	0.03515248745679855	0.01744881644845009	0.038424666970968246	-0.09511686116456985	-0.05950973927974701	-0.03699227795004845	0.05099456012248993	0.04372083768248558	0.023741014301776886	-0.08772199600934982	0.02134222909808159	0.0792962834239006	0.02615070901811123	-0.014219879172742367	-0.032296620309352875	-0.023421693593263626	-0.028862273320555687	0.06593654304742813	-0.03247218579053879	-0.005740474443882704	0.02707538567483425	0.050439268350601196	0.03867776319384575	0.04368199408054352	-0.010139819234609604	0.053447697311639786	-0.0013203239068388939	0.041384030133485794	0.011342824436724186	0.017498774453997612	0.013270445168018341	0.0012161353370174766	-0.023572685196995735	0.047156888991594315	0.11393334716558456	0.05398327857255936	-0.07535988092422485	-0.03147352114319801	0.12983906269073486	-0.0072498563677072525	0.024016696959733963	0.01625601015985012	-0.034270770847797394	-0.0018847337923943996	-0.048596788197755814	0.052963316440582275	-0.0004991047899238765	0.024749046191573143	0.001522997161373496	0.05124534294009209	-0.08650705218315125	-0.010564030148088932	0.030239639803767204	-0.0019254819490015507	0.016793357208371162	-0.040590401738882065	-0.01640063337981701	0.044659990817308426	0.014291101135313511	-0.006241407711058855	-0.022932983934879303	-0.03589606285095215	-0.05431597679853439	-0.01363099180161953	-0.009151432663202286	0.05642703175544739	0.039657022804021835	0.02657783403992653	0.06942462176084518	-0.04768276959657669	0.004697171505540609	-0.08414698392152786	-0.0011825166875496507	0.021072624251246452	0.059543024748563766	-0.057262759655714035	-0.052942488342523575	0.012127798981964588	-0.028257856145501137	0.01071021519601345	-0.02242778055369854	-0.013027915731072426	0.02575206756591797	0.05483315512537956	0.009771945886313915	-0.027401521801948547	-0.006292841397225857	-0.02260340005159378	-0.00866494607180357	-0.04346606507897377	-0.05762907490134239	-0.048988256603479385	-0.062054239213466644	-0.04800156131386757	-0.018520653247833252	-0.008878601714968681	-0.010283499024808407	0.09924844652414322	-0.06581401824951172	0.0729534700512886	0.08994249254465103	0.04085777327418327	-0.026760360226035118	0.013383965939283371	0.0007408225210383534	0.036776021122932434	-0.04530623182654381	-0.008673964999616146	-0.04815561696887016	0.02402019128203392	0.006226746831089258	-0.031778980046510696	0.01780993863940239	-0.0013316957047209144	-0.009931406937539577	0.036573514342308044	0.017631031572818756	-0.0927344486117363	0.0067975157871842384	0.01829306036233902	-0.013898128643631935	0.0344531387090683	0.06557174026966095	0.04935001581907272	-0.06641504168510437	0.04797197878360748	-0.0322868786752224	0.029619034379720688	-0.031220462173223495	0.006253524217754602	0.040661729872226715	-0.049909669905900955	-0.04829423129558563	-0.02692278102040291	0.014548562467098236	0.03599083796143532	0.0034230821765959263	-0.026369070634245872	0.022190062329173088	0.02759227715432644	0.10966532677412033	0.024993455037474632	0.010878604836761951	-0.02000577561557293	-0.02251225896179676	0.03389016166329384	0.013803413137793541	0.06812602281570435	-0.08319732546806335	0.049738820642232895	-0.019386272877454758	-0.025373971089720726	-0.018682993948459625	-0.0023892209865152836	-0.03883218392729759	0.02344733290374279	0.006584919989109039	-0.01178253348916769	-0.02759672701358795	-0.03713667392730713	-0.09275534003973007	-0.04719243198633194	0.03577326610684395	-0.01588372513651848	-0.03343295305967331	-0.03227800875902176	0.11720963567495346	0.010897382162511349	0.036858752369880676	0.1014365702867508	-0.05602949112653732	0.030422791838645935	0.04046839103102684	-0.03320131450891495	-0.062204908579587936	-0.02749774232506752	-0.006980384700000286	0.001812775619328022	0.049576837569475174	0.051036834716796875	-0.013558521866798401	0.007337211165577173	0.031786754727363586	0.002302509266883135	-0.010803231038153172	0.00973030086606741	-0.043988000601530075	-0.05582798644900322	0.056970447301864624	0.11574440449476242	0.01551663689315319	-0.024220876395702362	-0.0003781934501603246	-0.05689065530896187	-0.009931361302733421	0.026841560378670692	-0.1540805548429489	0.01362632680684328	-0.08055093139410019	0.006132663227617741	-0.07227244228124619	0.04193132743239403	0.0500345379114151	0.003502103267237544	-0.050513800233602524	-0.09760312736034393	0.007218270096927881	0.0342133566737175	0.05492885410785675	0.09199780970811844	0.030968470498919487	0.015318470075726509	-0.01804843731224537	-0.045604102313518524	-0.03938424959778786	-0.035822875797748566	-0.0834745392203331	0.10166258364915848	0.008222359232604504	-0.06557100266218185	-0.029612304642796516	-0.03556711599230766	0.03684883564710617	-0.06374601274728775	-0.007417778484523296	0.07530779391527176	0.017077617347240448	-0.060922812670469284	0.019154846668243408	0.020232481881976128	-0.01534708309918642	-0.006231399718672037	0.0612780787050724	0.007617434952408075	-0.040825121104717255	0.09172651916742325	-0.040591366589069366	-0.005019139498472214	0.05201322212815285	0.014427877962589264	0.04593697935342789	-0.019024057313799858	-0.024330686777830124	0.04735465347766876	-0.070156529545784	-0.026777274906635284	-0.03674750402569771	0.013254902325570583	-0.031720004975795746	0.019556252285838127	0.01599877141416073	-0.0036903091240674257	0.026145318523049355	0.027661306783556938	-0.0005938588292337954	-0.004133671056479216	-0.04339619725942612	0.05504649877548218	0.027275823056697845	-0.014350003562867641	0.10472152382135391	0.04488951712846756	-0.011651447042822838	-0.00951271504163742	0.10365056246519089	0.09854009002447128	0.04176389053463936	0.005381877068430185	0.04906272515654564	0.004075528122484684	0.042820852249860764	-0.022496068850159645	0.06674348562955856	0.004000911954790354	-0.06664810329675674	-0.04825004190206528	0.0625397339463234	-0.0433320626616478	-0.009220220148563385	0.00779627775773406	-0.01327128428965807	-0.043640732765197754	0.07728176563978195	0.030311359092593193	-0.06876223534345627	0.036930616945028305	0.04893263056874275	-0.026633458212018013	-0.005173627752810717	-0.034670207649469376	0.058348365128040314	0.02771272324025631	-0.0017188236815854907	-0.02675718255341053	-0.012029326520860195	0.02340792678296566	0.007378180976957083	-0.0442795529961586	0.014923186972737312	0.07090391963720322	0.06503380835056305	-0.04064777493476868	0.06877102702856064	-0.053816959261894226	0.0008986022439785302	0.01812329888343811	0.02448546513915062	0.08715945482254028	0.017782963812351227	0.0468752384185791	0.01663905382156372	0.001869669766165316	-0.02723519876599312	0.004793927539139986	0.0242595337331295	-0.05976129695773125	0.05055999755859375	0.03007952868938446	-0.05635758861899376	0.02826307713985443	0.055950894951820374	0.0038640748243778944	-0.033436451107263565	0.03997842222452164	0.0402413010597229	0.02159649133682251	-0.004374511074274778	0.024769892916083336	-0.024077963083982468	0.007682244759052992	-0.014759676530957222	-0.02571183256804943	-0.0015250189462676644	0.023775815963745117	-0.043844472616910934	-0.014734603464603424	0.043064288794994354	0.002798696281388402	-0.02307354100048542	0.0433126837015152	-0.00431224936619401	0.0714346170425415	-0.015512579120695591	0.02813738025724888	0.00029576299129985273	0.07022835314273834	-0.12756560742855072	-0.018272673711180687	0.026859140023589134	0.007562532089650631	0.0688728541135788	0.07670369744300842	0.004113691858947277	0.0346316359937191	0.0019148444989696145	-0.0776011273264885	0.018183209002017975	-0.004550237208604813	0.055908165872097015	0.05383133515715599	0.04210400953888893	-0.014311783015727997	0.002151811495423317	0.1016078069806099	0.03397369757294655	0.0028072574641555548	-0.014265462756156921	-0.04487461969256401	0.030831273645162582	0.07060130685567856	0.057248786091804504	0.028809232637286186	0.004338119179010391	0.044397104531526566	0.002722937148064375	-0.029199790209531784	0.03684079647064209	-0.027486562728881836	0.032893676310777664	0.027912238612771034	-0.02979135327041149
+-0.011807800270617008	-0.028644172474741936	-0.09242703765630722	0.023917576298117638	-0.024357616901397705	0.0590868815779686	0.054535988718271255	-0.025768756866455078	-0.07055170834064484	0.05090990290045738	-0.057299382984638214	-0.019180839881300926	-0.030956223607063293	-0.02471032179892063	0.032127536833286285	-0.08404599875211716	-0.008021370507776737	-0.021381327882409096	0.08015642315149307	-0.04269125685095787	-0.027742139995098114	0.06571463495492935	0.02662193402647972	0.06801100820302963	0.021819397807121277	0.06750330328941345	0.016080468893051147	0.021871816366910934	0.07841113954782486	0.014598794281482697	0.015924297273159027	-0.0329485684633255	-0.0026940538082271814	-0.05062435194849968	0.03194922208786011	0.0062290639616549015	0.03346763178706169	0.07774195820093155	0.08262879401445389	-0.0662994235754013	-0.033103521913290024	0.04054536297917366	0.032792624086141586	-0.07152178138494492	-0.013445917516946793	-0.0658189207315445	-0.028663169592618942	-0.07806037366390228	0.03247489035129547	0.0012816196540370584	-0.024310829117894173	0.07851715385913849	-0.009879705496132374	-0.04987527057528496	-0.0682060644030571	0.003910643979907036	0.011826113797724247	0.06005515903234482	0.017404254525899887	0.055733442306518555	0.039056383073329926	-0.011288560926914215	0.004792428109794855	-0.048927731812000275	0.0032211721409112215	-0.031171884387731552	0.05682212859392166	-0.015484524890780449	-0.042972199618816376	0.0713634118437767	-0.07046058773994446	0.04919186234474182	0.027070563286542892	0.021890100091695786	-0.013609004206955433	-0.035170018672943115	-0.03936902433633804	-0.03188261389732361	0.009218799881637096	0.017730996012687683	-0.02553957886993885	-0.019284769892692566	-0.003484410000964999	0.04911268129944801	0.004619193729013205	0.010041963309049606	-0.06768523156642914	0.021943502128124237	-0.07346197962760925	-0.02275094948709011	0.0541018545627594	-0.08213964104652405	0.00415214616805315	0.03338059410452843	0.042064160108566284	-0.028026536107063293	0.08140548318624496	-0.03130919486284256	0.03132570907473564	-0.02944246493279934	-0.01632898859679699	-0.06580384820699692	0.03962491452693939	-0.02979413792490959	0.013240416534245014	-0.0666632279753685	-0.11394033581018448	-0.00038188783219084144	0.0636499747633934	-0.024215033277869225	0.029782000929117203	0.06026454269886017	-0.013133320026099682	-0.08760124444961548	0.05222504213452339	-0.05596977099776268	0.010232246480882168	-0.07259872555732727	0.007988577708601952	0.018666667863726616	-0.02494385465979576	0.019133543595671654	0.005052718333899975	0.02126152068376541	-0.026522206142544746	0.010111870244145393	-0.012379461899399757	0.04301752150058746	-0.0016768878558650613	0.06973112374544144	0.00863763689994812	0.06093065068125725	0.07611529529094696	0.017083991318941116	-0.07619711011648178	-0.05497005954384804	0.006672488059848547	0.0715859979391098	0.05731794238090515	0.05284808203577995	-0.07194770872592926	0.0034930321853607893	0.14071844518184662	0.04508049413561821	-0.07206182926893234	-0.04415120929479599	-0.044625964015722275	-0.04906081035733223	0.026737602427601814	-0.006519796326756477	0.0014843676472082734	0.05727306008338928	0.09436504542827606	-0.028361735865473747	-0.0017110161716118455	-0.011870012618601322	0.02012876607477665	0.031196696683764458	0.0492745116353035	-0.033611372113227844	0.031767915934324265	-0.009619442746043205	0.04367402195930481	-0.040443453937768936	0.02134239859879017	0.054054152220487595	0.007250132504850626	-0.10792717337608337	0.06181517615914345	0.06713129580020905	0.0017882344545796514	-0.019257264211773872	0.03397315740585327	-0.05082698538899422	-0.04325919970870018	-0.050088103860616684	0.007097119465470314	-0.003821766935288906	0.09927248954772949	-0.03905054181814194	-0.06058233231306076	-0.06219872087240219	0.001896820729598403	-0.0010947400005534291	-0.06556448340415955	0.009598667733371258	0.034930869936943054	-0.009416467510163784	-0.03901654854416847	0.023656949400901794	-0.0048006195574998856	-0.04269427806138992	-0.041996069252491	0.0029767523519694805	-0.03194739669561386	-0.011455602012574673	0.023927312344312668	0.02695794589817524	0.020949823781847954	0.06631854176521301	-0.05765191838145256	0.06081923842430115	-0.07558538764715195	-0.07007136940956116	-0.020047703757882118	0.04548981785774231	-0.017544003203511238	-0.07615046203136444	-0.02990754134953022	-0.05330565944314003	-0.05177002400159836	-0.02194933034479618	-0.016390476375818253	-0.01994396559894085	0.030657291412353516	0.03242776170372963	0.008739890530705452	-0.023248689249157906	-0.028997762128710747	0.016407955437898636	-0.01677865721285343	-0.07174190133810043	-0.04952748119831085	-0.03981694579124451	-0.02667921595275402	-0.0898822620511055	0.037338607013225555	-0.027699416503310204	0.07941249758005142	-0.0432048924267292	0.08294393122196198	0.062464140355587006	0.00046203495003283024	0.01925327256321907	-0.02036169543862343	-0.052013710141181946	0.07499831169843674	-0.014326667413115501	-0.03996482491493225	-0.1065249890089035	0.011818330734968185	-0.07180730998516083	-0.040180422365665436	0.006785474717617035	-0.01999395154416561	-0.010377944447100163	0.012858550995588303	0.056552764028310776	-0.11246985197067261	0.018687184900045395	0.036599405109882355	-0.023966114968061447	0.007815812714397907	0.05932367965579033	-0.01500790286809206	-0.04164279252290726	-0.019494742155075073	-0.044517453759908676	0.11705868691205978	-0.06756149232387543	0.04041464626789093	-0.01881531812250614	-0.004670014604926109	0.026705950498580933	0.06088806688785553	0.030160797759890556	-0.007696946617215872	0.06181023269891739	0.018326053395867348	0.014950140379369259	0.07683905959129333	0.047249145805835724	-0.024963974952697754	0.10227862000465393	-0.00833608116954565	-0.03114180825650692	-0.03409070894122124	0.09417035430669785	-0.03921884670853615	-0.01286148838698864	0.026618359610438347	-0.0373549684882164	0.022083895280957222	-0.02558843046426773	-0.025030210614204407	-0.07321900874376297	-0.01353506464511156	0.017958732321858406	0.017501523718237877	-0.008799169212579727	0.03849635273218155	-0.07080432772636414	-0.060561854392290115	0.02889256179332733	-0.07669208943843842	0.01863226667046547	-0.0430615209043026	0.040069613605737686	-0.014098620042204857	0.007115613669157028	0.0647028461098671	-0.002362446393817663	0.04711678624153137	-0.002201312920078635	0.004522474482655525	-0.07680046558380127	-0.024716487154364586	0.04668104648590088	0.043728381395339966	0.005002256948500872	0.06693699210882187	-0.01702674850821495	-0.022032931447029114	-0.007161862216889858	-0.008306674659252167	0.016001954674720764	0.0029502769466489553	-0.016048824414610863	-0.08702830225229263	-0.014582119882106781	-0.006038333754986525	-0.009955527260899544	0.0019775733817368746	0.04839474707841873	-0.059437211602926254	0.02475643903017044	-0.050404224544763565	-0.1209651306271553	0.01109524816274643	-0.014520636759698391	-0.01638365164399147	-0.05419546738266945	-0.012449587695300579	0.009199924767017365	-0.04500196501612663	-0.08749888837337494	-0.05053061246871948	0.026181401684880257	0.022162502631545067	0.04417319595813751	0.1097680926322937	-0.018329432234168053	-0.024781404063105583	-0.008557222783565521	-0.02643655240535736	-0.07421186566352844	-0.025592025369405746	-0.11598268151283264	0.06839113682508469	0.019417915493249893	-0.027245204895734787	-0.013574494980275631	-0.02058943547308445	-0.010643422603607178	0.018308395519852638	0.010274536907672882	0.005891172215342522	0.03187700733542442	-0.03538135811686516	-0.021038668230175972	-0.030338207259774208	-0.004686381667852402	-0.02368921786546707	-0.022561052814126015	0.0030764646362513304	0.00920902844518423	0.07457148283720016	-0.03145495057106018	-0.026507757604122162	0.007960584945976734	-0.0056928847916424274	0.04298007860779762	-0.025448568165302277	0.037640441209077835	0.08661890774965286	-0.06830470263957977	-0.003058196045458317	0.008255040273070335	0.04071404039859772	-0.008049302734434605	0.02185075916349888	0.035239480435848236	-0.00496002659201622	0.02796613797545433	0.0653267428278923	0.008461288176476955	0.037914615124464035	-0.005858757998794317	0.02972816675901413	0.053603969514369965	0.028326699510216713	0.049631860107183456	0.04369541257619858	0.012590295635163784	0.005255112890154123	0.03365186229348183	-0.002742975950241089	0.04492705315351486	-0.02971324324607849	0.024411704391241074	-0.03029737062752247	-0.037760403007268906	-0.007316901348531246	0.07058358192443848	-0.04106714949011803	0.005860274191945791	-0.004696979653090239	0.04535924270749092	-0.0640723928809166	-0.045893698930740356	0.026279345154762268	0.005336855072528124	-0.052257101982831955	0.048595231026411057	-0.00048158966819755733	-0.02586374245584011	-0.012559364549815655	-0.004568134900182486	-0.05759337544441223	-0.038119230419397354	0.02272254228591919	-0.0003085313946940005	0.04327197000384331	0.007039172109216452	-0.019659124314785004	0.038458451628685	0.06171315163373947	0.06710918992757797	-0.030943825840950012	0.06459306180477142	0.03046513721346855	0.03903631120920181	0.005293401889503002	0.040347568690776825	-0.05015118792653084	0.01265665702521801	-0.008809222839772701	0.046215031296014786	0.00984957255423069	0.029077738523483276	0.0010495370952412486	0.04973185062408447	0.0004444006772246212	-0.07307792454957962	0.032912809401750565	-0.022194502875208855	-0.04992564395070076	0.09334398061037064	0.036469269543886185	-0.039154086261987686	0.07025991380214691	0.02230525016784668	0.03553694859147072	-0.02740609273314476	0.04451633617281914	-0.008621240966022015	0.015265412628650665	0.049831874668598175	0.06322930753231049	-0.018073083832859993	-0.028563575819134712	0.02772231586277485	-0.0088648097589612	0.076256163418293	0.009073375724256039	-0.04830518737435341	0.01126561313867569	0.008684951812028885	-0.012567860074341297	-0.020844293758273125	-0.03489978238940239	-0.0314597561955452	0.012183204293251038	-0.017585711553692818	0.044844258576631546	0.02334362268447876	0.05226609483361244	-0.0919794887304306	0.00448310561478138	0.024790821596980095	0.01738479733467102	0.037392716854810715	0.03688431531190872	-0.004241234622895718	0.05451381206512451	-0.004482943564653397	-0.06677659600973129	0.049806736409664154	-0.023921946063637733	-0.008456862531602383	0.0702727884054184	0.013786153867840767	-0.02436697483062744	0.033618029206991196	0.07348858565092087	0.08149558305740356	0.031999748200178146	0.0026608039624989033	-0.01669183000922203	0.004870635457336903	0.05705147981643677	0.03609869256615639	0.05082292854785919	0.08720719814300537	-0.03711685165762901	-0.050088364630937576	0.012699337676167488	0.015174458734691143	0.03740972280502319	0.059103768318891525	-0.0025371480733156204	-0.010021411813795567
+0.03532195836305618	-0.015420186333358288	-0.0662839487195015	-0.006144797429442406	-0.037906713783741	0.021172799170017242	-0.011339066550135612	-0.029338860884308815	-0.0682920441031456	0.015690449625253677	-0.05316690355539322	0.04953218623995781	-0.0030768958386033773	-0.01851026900112629	-0.033346109092235565	-0.1256408542394638	0.007311686407774687	0.024800799787044525	0.05137396603822708	-0.010184514336287975	-0.0063310847617685795	0.03331019729375839	0.003258890239521861	0.03316676989197731	0.021470708772540092	0.005127681419253349	0.0423576720058918	0.023152047768235207	0.0757259726524353	0.03672795742750168	0.07904374599456787	-0.07376234978437424	0.03233085945248604	-0.00860987976193428	0.01929590292274952	-0.028303995728492737	-0.026258936151862144	0.02650463581085205	0.09112664312124252	-0.005379191599786282	-0.05528199300169945	-0.0031528742983937263	0.007314577233046293	-0.07873526960611343	-0.02328847534954548	0.01866099238395691	-0.08799625933170319	-0.08808014541864395	0.04442073777318001	-0.018637817353010178	-0.03336981311440468	0.026184625923633575	-0.0066767241805791855	0.00991909671574831	-0.05628391355276108	-0.054717015475034714	0.01560442615300417	0.06868458539247513	0.0009840460261330009	0.024198612198233604	-0.003991620149463415	0.04073721915483475	0.016225827857851982	0.02099738083779812	0.0329880565404892	-0.03216207027435303	0.06122101470828056	-0.012870386242866516	-0.03933827206492424	0.054803162813186646	-0.01701962575316429	0.04305479675531387	0.0030014882795512676	0.06699370592832565	-0.006753258872777224	-0.06663346290588379	-0.06050022318959236	-0.029585158452391624	0.008097831159830093	0.007022315636277199	-0.02049732580780983	-0.0057364474050700665	0.07374139130115509	0.016961079090833664	-0.0011690499959513545	-0.01851728931069374	-0.023745309561491013	-0.010299395769834518	-0.03698905557394028	-0.018928855657577515	0.07792320847511292	-0.0666331872344017	-0.004449889063835144	-0.007985849864780903	0.018765566870570183	0.0016949913697317243	0.0890439972281456	0.022526592016220093	-0.003236148040741682	0.007913483306765556	0.007483669091016054	-0.002985382219776511	0.04823387414216995	-0.010368712246418	-0.001065693679265678	-0.05739329382777214	-0.06112590432167053	0.036963820457458496	0.05078151449561119	-0.017241571098566055	0.07468945533037186	0.06865675002336502	-0.03943423554301262	-0.046749815344810486	0.08591964095830917	-0.015626901760697365	-0.0007705482421442866	-0.0411054827272892	0.061667267233133316	0.04064071923494339	-0.025430232286453247	0.07467290014028549	-0.04999340698122978	-0.03513779118657112	-0.023853259161114693	0.04355081543326378	-0.02706390805542469	0.018489036709070206	-0.0417950265109539	0.02217015251517296	-0.014608120545744896	0.06409300118684769	0.0010492021683603525	0.04759879782795906	-0.058739449828863144	-0.06291937828063965	-0.043680619448423386	0.05352206155657768	0.017990823835134506	0.014622244983911514	-0.0697416141629219	0.046965423971414566	0.06813503056764603	0.04986293986439705	-0.024641677737236023	-0.05330447852611542	-0.0589892752468586	-0.014840924181044102	0.04687730222940445	-0.022082699462771416	0.013933474197983742	0.06600243598222733	0.07015132158994675	0.05214250460267067	0.042361997067928314	-0.004759387578815222	0.011215245351195335	-0.03607716038823128	0.004788422957062721	-0.030933236703276634	-0.0013377234572544694	0.04110822454094887	0.03443419560790062	-0.012192015536129475	0.033649396151304245	0.10785192996263504	0.035061586648225784	-0.04070308804512024	-0.029636550694704056	0.08942162990570068	-0.044507697224617004	-0.022414838895201683	0.014353903941810131	-0.028472958132624626	-0.03272513300180435	-0.0371842123568058	-0.002618608297780156	-0.03165478631854057	0.07118044793605804	-0.049029603600502014	0.007538085803389549	-0.10555405169725418	0.011859574355185032	0.02689380943775177	-0.004966876469552517	0.03451951593160629	-0.0017203427851200104	-0.03746188059449196	-0.002776079811155796	0.007751967757940292	-0.01639307104051113	-0.04638329893350601	-0.043092530220746994	-0.03790783882141113	-0.01645405776798725	-0.05622990429401398	0.01954607479274273	0.02391454391181469	0.07742899656295776	0.05138935521245003	-0.06711604446172714	0.017501041293144226	-0.09641626477241516	0.0015328052686527371	0.04408909007906914	0.01841331645846367	-0.048175323754549026	-0.06137480214238167	0.016918018460273743	-0.022871775552630424	-0.0339190773665905	0.012188993394374847	-0.03455362841486931	0.014035549946129322	0.044915661215782166	-0.0035288305953145027	0.014370203949511051	0.029273735359311104	0.02707572653889656	-0.010956204496324062	-0.04827434942126274	-0.03898542374372482	-0.050166286528110504	-0.05981408432126045	-0.004660547245293856	-0.06815169751644135	0.02953166700899601	0.004930631723254919	0.05742857977747917	-0.059579234570264816	0.09948822855949402	0.0702720582485199	0.05373536795377731	0.013070832937955856	0.02210269682109356	-0.0701073706150055	0.024048158898949623	0.0023223289754241705	-0.014895634725689888	-0.08736221492290497	0.04372866451740265	-0.015067183412611485	0.007956176996231079	0.03049338422715664	0.0028214631602168083	-0.032033078372478485	0.03847633674740791	0.019634665921330452	-0.11343784630298615	-0.01757785491645336	0.028343696147203445	-0.05280639976263046	0.0557217039167881	0.07272931188344955	0.022846093401312828	-0.07305599004030228	0.0683138445019722	-0.03683317452669144	0.08448448777198792	-0.005127863027155399	0.003340363036841154	0.007826550863683224	-0.039782360196113586	-0.06368138641119003	0.022952795028686523	-0.008202651515603065	0.04330027103424072	0.06918999552726746	0.0002811718441080302	-0.016065308824181557	0.08438917249441147	0.059370581060647964	0.032738812267780304	0.048382386565208435	0.0027012177743017673	-0.007633201777935028	0.048306889832019806	0.062031086534261703	0.029766885563731194	-0.06852547824382782	0.03358909860253334	-0.025992555543780327	0.009842733852565289	0.017142929136753082	-0.013225789181888103	-0.067290298640728	0.018657062202692032	-0.018995191901922226	-0.0025279803667217493	-0.005900698713958263	-0.03007083386182785	-0.07391049712896347	-0.05310120806097984	-0.02480079047381878	-0.03014015406370163	-0.019575469195842743	-0.011168108321726322	0.07488781958818436	-0.011056624352931976	0.03683524206280708	0.1333177089691162	-0.0635041743516922	-0.005628315731883049	0.03289336711168289	-0.019924523308873177	-0.06511367857456207	-0.03174664080142975	-0.00936190877109766	0.030657542869448662	0.034638661891222	0.03935135155916214	-0.039526112377643585	0.014291192404925823	0.04243384674191475	-0.018061121925711632	-0.03157547116279602	-0.01958160102367401	-0.0759332999587059	-0.03267281875014305	0.05461237207055092	0.03884150832891464	-0.007795040961354971	-0.009798436425626278	0.017650309950113297	-0.0516418032348156	-0.03276000916957855	0.024027124047279358	-0.13252094388008118	0.02048042230308056	-0.04842895641922951	0.0028066239319741726	-0.0645674541592598	0.010386545211076736	0.006669312249869108	-0.004668573383241892	-0.06798965483903885	-0.07765074074268341	-0.003669298253953457	0.008740115910768509	0.0620923787355423	0.07881660014390945	-0.0447760708630085	0.002055992605164647	-0.05081044137477875	0.0028590941801667213	-0.01412716694176197	-0.03643188625574112	-0.1231064647436142	0.12435652315616608	0.026091894134879112	-0.019772902131080627	-0.048413872718811035	-0.020047154277563095	0.04816187545657158	-0.009359636344015598	-0.0021462126169353724	0.017872650176286697	0.0623934231698513	-0.042717620730400085	0.01709980145096779	-0.022438427433371544	-0.05119679868221283	-0.015592698939144611	0.03495648503303528	0.062481839209795	-0.044667504727840424	0.0660601556301117	0.004362259525805712	-0.05122953653335571	0.04356248304247856	0.01684522069990635	0.00436924584209919	-0.03364704176783562	0.004605236928910017	0.08236875385046005	-0.06589749455451965	0.006410572212189436	0.0017342898063361645	0.012655588798224926	0.028374601155519485	0.026318510994315147	0.011546695604920387	-0.05211648717522621	-0.005463358014822006	0.0033278963528573513	0.006678819190710783	-0.004730268381536007	-0.0179704912006855	0.04319480434060097	0.11314765363931656	-0.026603538542985916	0.12188416719436646	0.025661107152700424	0.03412482514977455	0.01698700711131096	0.10182902961969376	0.021138224750757217	0.047260332852602005	0.010898279026150703	0.028580768033862114	0.04201385751366615	-0.008690663613379002	0.03196054697036743	0.05961316078901291	0.01874903403222561	-0.04317662864923477	-0.03813372552394867	0.0689183846116066	-0.09996798634529114	-0.05835256725549698	0.017296267673373222	-0.022383753210306168	-0.027693094685673714	0.08451711386442184	-0.02212323620915413	-0.09563475102186203	0.046159982681274414	0.046893566846847534	-0.05126301571726799	-0.0281208623200655	-0.03419799357652664	0.05686024948954582	0.008413910865783691	0.007073071785271168	-0.02944936975836754	0.03232165053486824	0.03814222291111946	0.027806974947452545	-0.04509621113538742	0.03238547220826149	0.06353140622377396	0.08579978346824646	-0.015008837915956974	0.0748971700668335	-0.02153559774160385	0.016015421599149704	-0.04488646239042282	0.02541149966418743	0.05726955831050873	0.02990388497710228	0.044469404965639114	0.010300506837666035	0.019623246043920517	-0.03110056184232235	0.0002311423741048202	0.027039892971515656	-0.04864467307925224	0.03956136479973793	0.033369146287441254	-0.030473701655864716	0.03440845385193825	0.011710905469954014	0.02098427712917328	0.01764889992773533	0.020386310294270515	0.07397037744522095	-0.01686910167336464	-0.007705932483077049	0.03642656281590462	-8.148638880811632e-05	-0.034934062510728836	-0.03389662504196167	-0.0022216616198420525	-0.01580427587032318	0.013735730201005936	-0.016418391838669777	0.012782298028469086	0.011848707683384418	-0.017903370782732964	0.03671645373106003	0.036534082144498825	-0.017694685608148575	0.06790739297866821	-0.013724259100854397	0.03905532881617546	0.03266453370451927	0.04756250977516174	-0.15489977598190308	0.029280055314302444	0.03693992272019386	0.0711294412612915	0.04069428890943527	0.04300583526492119	0.013845990411937237	0.033095940947532654	-0.038586705923080444	-0.057075317949056625	0.04435059055685997	0.033775459975004196	0.015884462743997574	0.0063689472153782845	0.036843352019786835	-0.009687256067991257	0.044844597578048706	0.08943824470043182	0.05335444584488869	-0.01919027604162693	0.02978464402258396	0.0004441938654053956	0.035064153373241425	0.0482805073261261	0.03278801962733269	0.041177909821271896	0.027294190600514412	0.01080898568034172	-0.0011412941385060549	-0.007862680591642857	0.03410695865750313	-0.021003903821110725	0.004650975111871958	0.07244619727134705	0.010747619904577732
+0.01194172166287899	-0.04855723679065704	-0.015730468556284904	-0.028513535857200623	0.0018965002382174134	-0.06341397017240524	-0.04540630429983139	0.0004073729505762458	-0.007037352304905653	0.0018667611293494701	0.0019026536028832197	0.051800042390823364	0.021694794297218323	0.0050009735859930515	-0.045787692070007324	-0.07652457058429718	0.0065213944762945175	-0.033417753875255585	0.0027231108397245407	0.02784811146557331	-0.0014325756346806884	0.09217855334281921	0.04047095403075218	-0.010328719392418861	-0.027071675285696983	0.019871240481734276	0.03748923912644386	-0.02570207603275776	0.05049428343772888	0.015358660370111465	-0.009560110978782177	-0.03642422333359718	0.02041422575712204	-0.02432055026292801	-0.006461878307163715	-0.01159593090415001	-0.060735154896974564	-0.003799751866608858	0.07852143794298172	0.010068572126328945	-0.007409926503896713	-0.03057904727756977	0.03625936061143875	-0.00947609730064869	-0.05467125028371811	0.05832855403423309	-0.06642568111419678	-0.04125654697418213	-0.01777447946369648	-0.0007054439629428089	0.007947196252644062	0.030205069109797478	-0.0017732942942529917	0.003642550203949213	-0.08124540001153946	-0.03426118195056915	-0.01286548562347889	0.04516897723078728	-0.02691579796373844	-0.056929029524326324	-0.01383547205477953	0.009866100735962391	-0.05063081532716751	0.014926979318261147	0.016712261363863945	-0.0023864321410655975	-0.0322146899998188	-0.0044265566393733025	-0.014074817299842834	0.04285921901464462	0.05056897923350334	0.04426209256052971	0.033023323863744736	0.01553046703338623	-0.03584799915552139	-0.05082978308200836	-0.031127113848924637	-0.07671264559030533	-0.05619295686483383	0.017269207164645195	-0.014776727184653282	0.023030715063214302	0.023162927478551865	-0.015946390107274055	0.027587270364165306	-0.022452151402831078	0.03874226287007332	-0.0347333699464798	-0.04072480648756027	0.010377833619713783	0.05775660648941994	-0.05368584021925926	0.05187663808465004	-0.021084250882267952	-0.028453541919589043	-0.0295015387237072	0.0642089992761612	0.05157589167356491	-0.022597692906856537	-0.024971799924969673	-0.01691364124417305	0.06856650114059448	0.06979309767484665	0.035026371479034424	0.01206850353628397	-0.06034861132502556	0.007085057906806469	0.07393389195203781	0.026608603075146675	-0.020356377586722374	0.07150456309318542	0.0030599699821323156	-0.04749289155006409	-0.004896385595202446	0.03013336844742298	-0.0501876138150692	-0.004517782479524612	0.03329337015748024	0.05353932827711105	-0.020838210359215736	0.020084882155060768	0.055074166506528854	-0.12451756000518799	-0.016148395836353302	0.01726098731160164	0.03371679037809372	0.01766768842935562	0.0750511884689331	-0.0115102119743824	-0.012681161984801292	-0.04591183364391327	0.021000578999519348	-0.03761480748653412	0.06983661651611328	-0.06037333980202675	-0.042739398777484894	-0.08319641649723053	0.04048650711774826	0.01375565305352211	0.0761556476354599	-0.067738376557827	0.05913042649626732	0.012810605578124523	0.0409415178000927	-0.009867321699857712	-0.003672368125990033	-0.029849911108613014	-0.031586576253175735	0.058728232979774475	-0.007671013940125704	0.049639612436294556	0.04473619908094406	0.025410298258066177	0.04326780140399933	0.06146188825368881	-0.0017642545280978084	0.05295182019472122	-0.03225533664226532	0.011946674436330795	0.04031362757086754	0.010371144860982895	-0.040983252227306366	0.009775184094905853	0.003263764549046755	0.04721759632229805	0.035681020468473434	0.08302678167819977	-0.03869834169745445	-0.04512941464781761	0.008716230280697346	-0.011947345919907093	-0.020311711356043816	-0.03749992698431015	-0.029206180945038795	-0.019476424902677536	0.00496554234996438	-0.029219741001725197	-0.029589258134365082	0.0563943013548851	0.04726182296872139	0.05617741122841835	-0.09162593632936478	-0.06741832196712494	0.011402083560824394	0.007138829678297043	0.08006024360656738	-0.07594200223684311	0.0062972609885036945	-0.05056143179535866	0.008321297354996204	0.04528217390179634	-0.06958527863025665	-0.08889026939868927	-0.06822638213634491	0.06392312049865723	-0.026646917685866356	-0.005007706582546234	0.01035368163138628	0.016192633658647537	0.04373391717672348	-0.028874870389699936	-0.002762407064437866	-0.12019860744476318	-0.01625332608819008	-0.0016798177966848016	0.07743123918771744	-0.005879608448594809	-0.06513328105211258	0.03778884559869766	-0.02102525904774666	0.03546588867902756	0.026347622275352478	0.0009142996277660131	0.01094752736389637	-0.002842891262844205	-0.02354961261153221	-0.018713485449552536	0.0064964559860527515	0.03528473526239395	-0.047811511904001236	-0.02776593156158924	-0.012257559224963188	-0.009692222811281681	-0.05148223787546158	0.047800347208976746	0.0038088371511548758	-0.05195670202374458	0.02716587856411934	0.11343377828598022	-0.016168611124157906	0.022071480751037598	0.0023931898176670074	0.0440385527908802	0.04158094897866249	-0.010010519064962864	-0.04661618545651436	-0.017697539180517197	-0.06050720065832138	0.011428609490394592	-0.0012906050542369485	0.06866559386253357	-0.028076712042093277	-0.00990569032728672	-0.010674119926989079	0.022943276911973953	-0.013800962828099728	-0.02840420976281166	0.05125611275434494	-0.03319157287478447	-0.044071804732084274	0.028738340362906456	-0.006145820952951908	-0.02966185472905636	-0.03545205667614937	0.1133698970079422	-0.035957660526037216	0.10769961774349213	-0.07546856254339218	-0.018678870052099228	-0.044281769543886185	-0.0248623788356781	0.012414422817528248	-0.05432811751961708	-0.008572906255722046	0.021767398342490196	0.01140571478754282	0.035237524658441544	-0.04217444732785225	0.00046904475311748683	0.027668992057442665	-0.005474174860864878	0.09840746223926544	0.06857001036405563	0.002053936244919896	0.005709262099117041	0.03887364640831947	0.06749682873487473	0.009269566275179386	0.0628466010093689	-0.08812028169631958	0.07021061331033707	0.006597229745239019	0.0693640410900116	-0.017586171627044678	0.011050347238779068	-0.04232579097151756	-0.01011716853827238	0.007025857921689749	0.02886337786912918	-0.0005069816252216697	-0.0487494170665741	-0.022208601236343384	0.029248783364892006	-0.059843968600034714	-0.00637260265648365	-0.04831058531999588	-0.03785945847630501	0.019523609429597855	-0.007995830848813057	0.06555744260549545	0.06763308495283127	-0.002252452075481415	-0.02277357131242752	-0.009704358875751495	-0.03230860084295273	-0.07960017770528793	-0.007207180373370647	-0.07216156274080276	-0.022281520068645477	0.01786154694855213	0.0502239353954792	-0.02460174821317196	-0.01920885033905506	-0.0529024638235569	-0.007760974112898111	-0.04755377769470215	0.01902778260409832	0.009340246208012104	0.023911919444799423	0.0628325566649437	0.05063733458518982	0.07534049451351166	-0.004706436302512884	-0.024874472990632057	-0.015071473084390163	-0.03858191892504692	0.016039656475186348	-0.04147539660334587	0.04672519117593765	-0.08563194423913956	-0.0001900668430607766	-0.0496349111199379	0.07598281651735306	0.08150308579206467	0.05351373553276062	-0.055987317115068436	-0.0904519259929657	-0.042500030249357224	0.017688937485218048	-0.01861017383635044	0.06621496379375458	0.026015790179371834	0.01653551496565342	-0.023193208500742912	0.0030094131361693144	0.043382514268159866	-0.02118891477584839	-0.11725308746099472	0.07918118685483932	-0.018932199105620384	-0.023674415424466133	-0.10338834673166275	-0.021967967972159386	0.005602401215583086	0.007943392731249332	-0.028850505128502846	-0.023870807141065598	0.002152529777958989	-0.053447362035512924	0.03398602083325386	0.061460305005311966	-0.015742680057883263	-0.00186049728654325	0.08352787047624588	0.10058941692113876	-0.14847753942012787	0.06226262077689171	-0.01356600970029831	-0.044312696903944016	-0.06377103179693222	0.012894222512841225	-0.03675750643014908	-0.02009573206305504	-0.03382771089673042	0.0539134219288826	-0.03874718025326729	-0.08448409289121628	0.036662932485342026	-0.05760743096470833	0.013365601189434528	-0.0037657662760466337	-0.0026251054368913174	-0.05346926301717758	0.024637935683131218	-0.002531708450987935	0.014012926258146763	-0.020113516598939896	0.03694478049874306	0.07871907949447632	0.0453178733587265	0.02523343823850155	0.054777685552835464	-0.0034569285344332457	0.003357427893206477	0.012362068518996239	0.030942482873797417	0.07610036432743073	-0.0032027638517320156	-0.04662643373012543	0.004704318009316921	0.03309349715709686	0.03069421835243702	0.00915618147701025	0.009214134886860847	0.001497388700954616	-0.014430400915443897	-0.05400438234210014	0.03617079555988312	-0.09902236610651016	-0.085642009973526	-0.005614692810922861	-0.010689152404665947	-0.02141394093632698	0.05648357793688774	0.014711433090269566	-0.030993016436696053	0.02609694004058838	0.09517538547515869	-0.04985050484538078	-0.01729840599000454	-0.022695237770676613	-0.009308191016316414	-0.007593984715640545	-0.02349572815001011	-0.041938431560993195	-0.06120161712169647	0.009470716118812561	-0.058464013040065765	0.003293723566457629	-0.0251496359705925	0.028926938772201538	0.062041640281677246	0.021252769976854324	0.1235232725739479	-0.01536922249943018	-0.07788798213005066	0.027736451476812363	0.004642750136554241	0.03688962012529373	0.03906889259815216	0.08882564306259155	0.03486216068267822	0.03641148656606674	0.025796538218855858	-0.05380964279174805	0.039964236319065094	-0.012186821550130844	-0.04120897501707077	0.07401343435049057	-0.0055184438824653625	-0.052946943789720535	0.058134689927101135	0.024217665195465088	0.0224735289812088	0.03356771171092987	0.09414587914943695	-0.024278724566102028	-0.059147659689188004	-0.044687818735837936	-0.01470553781837225	0.032559651881456375	-0.09909652173519135	0.01032173540443182	-0.07688611000776291	0.014707468450069427	-0.020218858495354652	-0.009562540799379349	0.040785688906908035	-0.0003432762168813497	0.009171527810394764	0.04976421594619751	0.07616174221038818	0.0747496485710144	-0.03920566663146019	0.012422049418091774	0.03575024753808975	0.033888690173625946	-0.08756446093320847	-0.044558484107255936	-0.015017518773674965	-0.03581627085804939	0.05299164354801178	0.043779272586107254	-0.02612297795712948	0.03311267867684364	-0.0423610545694828	0.009724278934299946	0.052026648074388504	0.00791000947356224	-0.047096699476242065	0.07799530774354935	0.009036066941916943	0.02552693895995617	-0.0027563264593482018	0.03739676624536514	0.042168028652668	-0.012203922495245934	-0.014892110601067543	0.028075775131583214	0.012163462117314339	-0.004831221885979176	0.06747613102197647	-0.0262035820633173	0.0054773385636508465	0.08637144416570663	0.07768703997135162	0.013461152091622353	-0.05943024903535843	-0.08706779032945633	0.018946068361401558	0.0861605554819107	0.0019084368832409382
+0.013870996423065662	0.003676116233691573	-0.03815576434135437	0.017322467640042305	-0.040097951889038086	0.06338861584663391	-0.015474732965230942	0.007501591462641954	-0.07443293929100037	0.030257277190685272	-0.06588190048933029	0.007159399334341288	-0.02203931286931038	-0.019163303077220917	0.007427295204252005	-0.11397075653076172	0.004464283585548401	-0.00046542289783246815	0.0843128114938736	-0.05920639634132385	-0.03232899680733681	0.06363918632268906	0.054876718670129776	0.07078321278095245	0.04943130537867546	0.05623846873641014	0.044548846781253815	0.04158782586455345	0.09537379443645477	0.029789861291646957	0.0021783942356705666	-0.06694617122411728	-0.0004172157496213913	-0.03652279078960419	0.055655088275671005	-0.012178539298474789	0.03211294859647751	0.04154268279671669	0.09034866094589233	-0.032339587807655334	0.003882244462147355	0.01275144424289465	0.037097301334142685	-0.04486852511763573	-0.0164061039686203	-0.0024945614859461784	-0.05175062268972397	-0.06571251899003983	0.045498818159103394	-0.007329254876822233	-0.010685879737138748	0.07309263944625854	-0.04215361550450325	-0.056351188570261	-0.024795671924948692	0.01575566828250885	0.01204740907996893	0.06203921139240265	0.028239281848073006	0.030903218314051628	-0.01590840332210064	-0.017937613651156425	-0.020621860399842262	-0.06096016615629196	0.009594982489943504	-0.024552777409553528	0.04768657684326172	0.026094211265444756	-0.02583473175764084	0.02509334683418274	0.009733648970723152	0.0817493349313736	-0.012464367784559727	0.0398380383849144	0.0246818196028471	-0.06498512625694275	-0.02955833077430725	-0.04086579754948616	-0.02539532072842121	-0.0004996043280698359	-0.031109018251299858	-0.030033547431230545	0.061593905091285706	0.014390390366315842	-0.05487276613712311	0.031484853476285934	-0.03164258226752281	-0.019899865612387657	-0.0329514816403389	-0.004534134175628424	0.03875168040394783	-0.09037803113460541	0.0012553896522149444	0.006103768479079008	-0.020380394533276558	-0.0375942699611187	0.06683996319770813	-0.009583000093698502	0.0027587017975747585	0.009965161792933941	-0.02908521518111229	-0.030304696410894394	0.05914531275629997	-0.026450004428625107	-0.026577642187476158	-0.050647810101509094	-0.07758158445358276	0.006148137617856264	0.03856746852397919	-0.03141864016652107	0.04109521955251694	0.03858167678117752	-0.028454549610614777	-0.013567201793193817	0.07455189526081085	-0.014618497341871262	0.036042943596839905	-0.03292803466320038	0.019689353182911873	-0.00520143611356616	0.007814237847924232	0.09357739239931107	-0.006367858499288559	-0.008776858448982239	0.01076347753405571	0.031576283276081085	-0.026350369676947594	0.0904686376452446	0.003491385607048869	0.03566181659698486	-0.0002185166085837409	0.02807191200554371	0.027149628847837448	0.06180217117071152	-0.06752758473157883	-0.05705352872610092	-0.06374947726726532	0.06028880178928375	0.05264956131577492	0.05063255503773689	-0.0762934684753418	0.002662683604285121	0.13540196418762207	0.014795004390180111	-0.022549837827682495	0.00319999479688704	-0.03037031926214695	-0.03501768782734871	0.08421485126018524	-0.04199209809303284	0.03952229395508766	0.051631975919008255	0.0969739556312561	0.012401723302900791	-0.020357869565486908	-0.03866073116660118	0.04191805049777031	-0.024505767971277237	0.02579415775835514	-0.011756484396755695	-0.012980598025023937	-0.000990320579148829	-0.016557933762669563	-0.0012178929755464196	0.052959591150283813	0.10293770581483841	0.038398806005716324	-0.10466597229242325	-0.03743594512343407	0.09388940036296844	-0.0009185587987303734	0.009787616319954395	0.016386741772294044	-0.030730240046977997	-0.024152716621756554	-0.07304523885250092	0.042609840631484985	-0.0077532571740448475	0.056065578013658524	0.001114038866944611	0.016605570912361145	-0.05966750904917717	0.03328098729252815	0.02864156849682331	-0.036261141300201416	-0.008131023496389389	-0.0004917205078527331	-0.033333826810121536	0.005634648259729147	0.0011953263310715556	0.0471927709877491	0.021137727424502373	-0.049251630902290344	-0.035739004611968994	-0.03724611923098564	-0.031599096953868866	0.014121434651315212	0.0034339758567512035	0.013555418699979782	0.06247670203447342	-0.08870675414800644	0.03924821689724922	-0.05609611049294472	-0.02460312657058239	0.014829689636826515	0.037538688629865646	-0.04901508986949921	-0.04393414035439491	-0.025047525763511658	-0.044548604637384415	-0.013154867105185986	-0.02868693321943283	-0.016709454357624054	0.02580847032368183	0.02696930058300495	0.021298564970493317	0.009018052369356155	-0.05867398902773857	-0.02789180353283882	0.006213354412466288	-0.0250739436596632	-0.06450348347425461	-0.05554515868425369	-0.08910983055830002	-0.07474058866500854	0.009967477060854435	0.00895817019045353	0.0033069520723074675	0.13179242610931396	-0.02834005281329155	0.06156569719314575	0.06263111531734467	0.012276092544198036	0.04743913561105728	0.00449725054204464	-0.02905997633934021	0.04784775897860527	-0.017665311694145203	-0.014231668785214424	-0.05940132215619087	-0.03252176195383072	-0.01718986965715885	0.008518078364431858	0.008879014290869236	0.014650342985987663	-0.024135654792189598	0.005549674853682518	0.0468703918159008	-0.07702597230672836	-0.012320488691329956	0.0686073824763298	-0.014281975105404854	-0.0031574764288961887	0.04208321124315262	0.027946798130869865	-0.05677959695458412	0.06465045362710953	-0.04911554604768753	0.06150900945067406	-0.04738140106201172	0.03444910794496536	-0.0007065852405503392	-0.0222211591899395	-0.011949737556278706	-0.00830305926501751	-0.014409919269382954	0.023637527599930763	0.03239016607403755	-0.0019161790842190385	0.02935747429728508	0.0401204489171505	0.10818160325288773	0.007201173342764378	0.06407558917999268	-0.03146688640117645	-0.06597203761339188	-0.028339272364974022	0.05649314820766449	0.061850059777498245	-0.02798650972545147	0.035498179495334625	-0.04697244614362717	-0.017643561586737633	-0.03064589388668537	0.0019486977253109217	-0.07130037993192673	0.004310814663767815	0.010894620791077614	0.0044001247733831406	-0.01812710613012314	0.008726127445697784	-0.04345148429274559	-0.04649012163281441	0.019390814006328583	-0.03618544712662697	0.015573808923363686	-0.06876063346862793	0.06964828073978424	-0.009214995428919792	0.02531656250357628	0.06917023658752441	-0.06027495861053467	0.029542116448283195	0.021146565675735474	0.006042345892637968	-0.1241646409034729	0.023233521729707718	0.032212432473897934	0.006874403450638056	0.04045522212982178	0.06390727311372757	-0.035845402628183365	-0.03441765159368515	0.0013017315650358796	0.0007681869901716709	0.017139675095677376	0.02593323215842247	-0.056976836174726486	-0.046614084392786026	0.0246146060526371	0.07744845747947693	0.00877934880554676	0.0008443191763944924	0.031167306005954742	-0.05192352086305618	0.0003481152234598994	-0.004034741781651974	-0.1457190066576004	0.030136704444885254	-0.04314305633306503	-0.0253199003636837	-0.10268083214759827	0.03617532551288605	0.017565572634339333	0.005153590347617865	-0.06121237576007843	-0.07098192721605301	0.07585181295871735	0.008441212587058544	0.041382841765880585	0.07056156545877457	-0.007299572229385376	-0.01962529495358467	0.017360011115670204	-0.02959585003554821	-0.02180398441851139	-0.024460792541503906	-0.07474897056818008	0.10598442703485489	0.036582037806510925	-0.013500907458364964	-0.05965184047818184	-0.03859860077500343	0.04560958966612816	-0.0015672839945182204	-0.0038399233017116785	0.017944373190402985	0.05131233483552933	-0.08796872943639755	-0.007440572138875723	0.016692595556378365	-0.008674287237226963	-0.020678456872701645	0.01792784407734871	0.015404850244522095	-0.05681397020816803	0.06221206113696098	-0.01791580207645893	-0.05119796842336655	0.05578508600592613	0.025816308334469795	0.020372683182358742	-0.03766672685742378	-0.051576659083366394	0.05739726126194	-0.0633327066898346	-0.013729768805205822	-0.027847303077578545	0.04634520411491394	-0.011740106157958508	-0.014645848423242569	0.04822901263833046	-0.015227962285280228	0.04130043089389801	0.05737186223268509	-0.0023794483859091997	-0.009231948293745518	-0.04387227073311806	0.031546127051115036	0.021588798612356186	-0.004023480229079723	0.10687758028507233	0.042231231927871704	0.0010214719222858548	0.013852975331246853	0.018244463950395584	0.07595860958099365	0.03655041381716728	-0.02969144657254219	0.07638967037200928	0.012332565151154995	0.011269701644778252	-0.0013812545221298933	0.09820389002561569	-0.03704657405614853	-0.06444894522428513	-0.0036088486667722464	0.03756091371178627	-0.05443611368536949	0.01151595264673233	-0.02513411082327366	-0.02573665790259838	-0.06297454982995987	0.105521060526371	0.024984708055853844	-0.06995467841625214	0.006836583837866783	0.023199506103992462	-0.015979772433638573	-0.028216945007443428	0.013762736693024635	0.028138116002082825	0.02313343994319439	-0.011339561082422733	-0.04704100266098976	-0.020694972947239876	0.05342445150017738	0.017542395740747452	-0.058583687990903854	0.0038677952252328396	0.08121431618928909	0.09603699296712875	5.8696379710454494e-05	0.07933346182107925	-0.05538320541381836	0.006833599880337715	0.0249605905264616	0.022840987890958786	0.045573264360427856	0.030878260731697083	0.02540571428835392	0.046206891536712646	0.00755266472697258	-0.06853516399860382	0.02691536396741867	0.014091298915445805	-0.03539113327860832	0.024616574868559837	0.04761511832475662	-0.04257983714342117	0.04554101824760437	0.06080744415521622	0.03408146649599075	0.004693127702921629	0.06327785551548004	0.05056420713663101	0.004497805144637823	0.013710271567106247	0.028115641325712204	-0.024548698216676712	-0.033465366810560226	-0.011136069893836975	-0.02862372249364853	0.027643827721476555	-0.00746520422399044	-0.07531197369098663	-0.01382603868842125	0.022922247648239136	0.021392887458205223	0.002710983157157898	0.02593834325671196	0.022039294242858887	0.07419024407863617	-0.012342100031673908	0.08174712210893631	0.034800633788108826	0.07180707156658173	-0.096623994410038	-0.03984088823199272	0.06614299863576889	0.0038285083137452602	0.07594283670186996	0.06117267161607742	0.008044067770242691	0.043882280588150024	-0.0006812763167545199	-0.05803944915533066	0.04475202038884163	-0.007216118276119232	0.011391806416213512	0.020457692444324493	0.02138257957994938	-0.04337948188185692	-0.009561898186802864	0.07631690800189972	0.07286684960126877	0.012513930909335613	0.02853965386748314	-0.039054352790117264	0.04035528749227524	0.07349741458892822	0.023008063435554504	0.06705483049154282	0.03722018748521805	0.006024801172316074	-0.02399216964840889	-0.03489359840750694	0.046289198100566864	0.0016288987826555967	0.06783805042505264	0.019179439172148705	-0.04367324337363243
+0.03790536895394325	-0.021420398727059364	-0.09630240499973297	0.029938358813524246	-0.0006802036077715456	0.02935721166431904	-0.03048793226480484	0.013469051569700241	-0.09984562546014786	0.011915160343050957	-0.06310275197029114	0.014554457738995552	-0.0003714332415256649	-0.050191037356853485	-0.01801159605383873	-0.10577854514122009	-0.002088588895276189	0.014021292328834534	0.08965922147035599	-0.029942378401756287	-0.015403015539050102	0.048694804310798645	-0.015301039442420006	0.04438455402851105	0.04970637708902359	0.03870093449950218	0.07022390514612198	0.015850668773055077	0.09227367490530014	0.04334290698170662	-0.012995122000575066	-0.08348644524812698	-0.020130539312958717	-0.028155356645584106	0.006253708619624376	-0.041529420763254166	-0.007119615096598864	0.04455935209989548	0.05799335241317749	-0.022456500679254532	-0.06381013989448547	0.05714903026819229	-0.009464088827371597	-0.07073269039392471	-0.01775578036904335	0.015789959579706192	-0.05961678549647331	-0.07704579830169678	0.015404850244522095	0.006741162855178118	-0.03299378231167793	0.006842611823230982	-0.016889257356524467	-0.02645103819668293	-0.07534419000148773	0.007591691799461842	-0.005855025723576546	0.08221772313117981	0.008685771375894547	-0.007958421483635902	0.028825514018535614	0.027534306049346924	-0.01046207919716835	-0.022495966404676437	0.01347194705158472	-0.007429320365190506	0.05998428165912628	0.003095789346843958	-0.04269428178668022	0.035857848823070526	-0.0320606492459774	0.06273848563432693	-0.020059021189808846	0.07217111438512802	0.019630424678325653	-0.06408458948135376	-0.05969861149787903	-0.018084095790982246	0.03244262933731079	-0.021950600668787956	0.01725005730986595	0.024153368547558784	0.08592234551906586	0.012489489279687405	-0.02674940787255764	0.014065146446228027	-0.02369590476155281	0.0034880100283771753	-0.028196167200803757	-0.005371888633817434	0.06986405700445175	-0.06875800341367722	0.005191680509597063	0.007431476842612028	0.00728612719103694	-0.012811013497412205	0.10630052536725998	-0.014157566241919994	-0.03192713484168053	-0.0020953495986759663	-0.030883852392435074	-0.05187370628118515	0.05332999303936958	-0.016708580777049065	-0.03499677777290344	-0.07725203782320023	-0.07797373086214066	0.0030371840111911297	0.027026943862438202	-0.04159482568502426	0.07095732539892197	0.05673183500766754	-0.037833213806152344	-0.01573815383017063	0.09654776751995087	-0.01279933750629425	0.034852828830480576	0.010112650692462921	0.04710423946380615	0.0572032555937767	-0.022417323663830757	0.06900877505540848	-0.010195550508797169	-0.04469339922070503	0.012168705463409424	0.0357486754655838	-9.596048039384186e-05	0.042257167398929596	-0.03331293538212776	0.023562034592032433	-0.008466058410704136	0.031343765556812286	0.02760039083659649	0.03231656923890114	-0.0689006894826889	-0.07686210423707962	-0.016297386959195137	0.07605132460594177	0.033053021878004074	0.060368917882442474	-0.08135272562503815	0.04386939853429794	0.06627843528985977	0.04531310126185417	-0.009503921493887901	-0.035073913633823395	-0.04495289921760559	-0.01777891255915165	0.07683717459440231	-0.02701401337981224	0.010426686145365238	0.03942720592021942	0.05164431780576706	0.023266257718205452	0.04614492133259773	-0.024621397256851196	0.047533489763736725	-0.010110290721058846	0.02704090066254139	0.010138574056327343	0.003316539106890559	-0.006657053250819445	0.007409330923110247	-0.020341210067272186	0.01959281414747238	0.11582299321889877	0.028756143525242805	-0.07860419899225235	-0.018334321677684784	0.1068354994058609	-0.016717806458473206	0.003193403361365199	0.031757041811943054	-0.02971506677567959	-0.01368911936879158	-0.011225136928260326	0.03876090049743652	-0.004494563676416874	0.04404137283563614	-0.010681983083486557	0.0469469279050827	-0.11488031595945358	0.0025329880882054567	0.058927085250616074	-0.006498355884104967	0.05084148421883583	-0.014511054381728172	-0.014945309609174728	0.02389305643737316	0.024241499602794647	-0.0010597542859613895	-0.033534254878759384	-0.012988290749490261	-0.03482901304960251	0.011001750826835632	-0.013329150155186653	0.014702805317938328	0.04737769812345505	0.024222223088145256	0.06875424832105637	-0.061136405915021896	-0.013896405696868896	-0.08812493830919266	-0.010533985681831837	0.023349367082118988	0.05688752979040146	-0.04041552171111107	-0.03070123679935932	0.0288678128272295	-0.047721054404973984	-0.009198581799864769	-0.030901622027158737	-0.0208396315574646	0.030186448246240616	0.03394542261958122	0.017514364793896675	-0.040750473737716675	0.026721108704805374	-0.03180985152721405	-0.0018881680443882942	-0.05895163118839264	-0.040947917848825455	-0.08226210623979568	-0.0496598519384861	-0.00972449965775013	-0.01872291788458824	-0.005299352575093508	0.0069531709887087345	0.08434285968542099	-0.04611000046133995	0.052702896296978	0.08589627593755722	0.03024856373667717	-0.04985269904136658	0.002709334483370185	-0.012417814694344997	0.0336020290851593	-0.03187516704201698	-0.00812020618468523	-0.060389768332242966	0.009456980042159557	0.003926743753254414	-0.05422520264983177	0.03292449563741684	-9.978745219996199e-05	-0.009603389538824558	0.00841617677360773	0.018690532073378563	-0.08164714276790619	0.00887402705848217	0.017064562067389488	0.011808041483163834	0.006096006371080875	0.0453842394053936	0.031848397105932236	-0.06241573393344879	0.03701408952474594	-0.021133769303560257	0.056105416268110275	-0.04343956336379051	-0.014700467698276043	0.0050891912542283535	-0.05238114669919014	-0.028699709102511406	-0.005907329265028238	0.005341037642210722	0.058898601680994034	0.019435808062553406	-0.015586486086249352	-0.005272447597235441	0.02866436168551445	0.08654046803712845	0.02822987735271454	0.03147350251674652	-0.029828129336237907	-0.019880658015608788	0.026650696992874146	0.02241198532283306	0.04186956211924553	-0.06482642889022827	0.03965795785188675	-0.014193376526236534	-0.011962002143263817	-0.01021970808506012	0.012114664539694786	-0.021646743640303612	0.023400194942951202	-0.014092030003666878	0.0026158345863223076	-0.026065051555633545	-0.03934826701879501	-0.10231620818376541	-0.04041215777397156	0.03749166056513786	-0.030215313658118248	-0.04057181254029274	-0.0162062831223011	0.10134272277355194	0.01461795810610056	0.03414778411388397	0.12077347934246063	-0.042675383388996124	0.03799736872315407	0.05581130459904671	-0.0317586325109005	-0.054131053388118744	-0.019683735445141792	-0.009577356278896332	-0.0041816052980721	0.05614800006151199	0.05129462108016014	0.009607345797121525	0.028522077947854996	0.03331951051950455	0.014206841588020325	-0.02595558390021324	-0.008724957704544067	-0.04233989119529724	-0.04384046047925949	0.05836053937673569	0.08877889066934586	0.019050437957048416	-0.031667616218328476	0.010135229676961899	-0.04379153251647949	-0.032317064702510834	0.038342736661434174	-0.13600169122219086	0.006946624722331762	-0.05170661211013794	-0.014586709439754486	-0.07095018029212952	0.02816895954310894	0.030619829893112183	-0.011909300461411476	-0.07488235086202621	-0.10216677933931351	-0.014279838651418686	0.025682341307401657	0.06860418617725372	0.11295570433139801	0.04147204011678696	0.009564111940562725	-0.0190346147865057	-0.0352240651845932	-0.01344373356550932	-0.04559652507305145	-0.08462800830602646	0.10988186299800873	0.019267994910478592	-0.0405735969543457	-0.034415826201438904	-0.03850306198000908	0.027719922363758087	-0.07083001732826233	-0.007296078838407993	0.06576354801654816	0.01468573696911335	-0.04508141800761223	-0.015523688867688179	0.0044775791466236115	-0.04291541501879692	-0.014463690109550953	0.06241210177540779	0.03734227269887924	-0.04677622392773628	0.10669467598199844	-0.05183616653084755	-0.043307747691869736	0.03424813598394394	-0.003745219437405467	0.026994433254003525	-0.02777351438999176	-0.03147111460566521	0.06251419335603714	-0.06993883848190308	-0.02179156430065632	-0.036657825112342834	-0.010107786394655704	-0.028113752603530884	0.022655362263321877	-0.016655150800943375	-0.009648299776017666	0.03271095082163811	0.042561545968055725	-0.003592079970985651	-0.007602985482662916	-0.03229890391230583	0.06358297914266586	0.05419508367776871	-0.023594293743371964	0.1212727427482605	0.0380236841738224	-0.038122646510601044	-0.0004956763004884124	0.11954532563686371	0.08108636736869812	0.052737705409526825	-0.007600541692227125	0.0434541180729866	-0.01737702637910843	0.038969486951828	-0.005576191935688257	0.07431429624557495	0.012847996316850185	-0.03824194148182869	-0.06284139305353165	0.07234805822372437	-0.03972574323415756	-0.03285153955221176	0.023331834003329277	-0.01640321873128414	-0.04927222430706024	0.062049590051174164	0.009670842438936234	-0.05942508950829506	0.04186335206031799	0.0631713792681694	-0.052502430975437164	-0.016376491636037827	-0.048904579132795334	0.042457688599824905	0.03302522748708725	0.011113565415143967	-0.05454546585679054	-0.008050279691815376	0.05803123489022255	-0.004967179615050554	-0.057625286281108856	0.028810210525989532	0.07874680310487747	0.0648743063211441	-0.0303366556763649	0.07891302555799484	-0.04541604220867157	0.018332505598664284	0.0035557053051888943	0.014924373477697372	0.08147044479846954	0.035152655094861984	0.03798072040081024	0.021440738812088966	-0.007299549877643585	-0.02429332584142685	0.022361015900969505	0.031534843146800995	-0.04571250453591347	0.07547761499881744	0.03892327472567558	-0.06770765036344528	0.05017431825399399	0.034709636121988297	0.033921439200639725	-0.019453292712569237	0.06651315093040466	0.05593548342585564	0.03158073127269745	-0.007059046067297459	0.02436082437634468	-0.025575323030352592	0.02024582400918007	-0.0061671496368944645	-0.00781251210719347	-0.003329038619995117	0.035243600606918335	-0.03240029886364937	0.003244710387662053	0.023871900513768196	-0.003226579399779439	-0.004075917880982161	0.013784972950816154	-0.012590643018484116	0.0601828508079052	-0.01578686758875847	0.03072477877140045	0.008865934796631336	0.06498942524194717	-0.12738826870918274	-0.012984083965420723	0.03492014482617378	0.031232405453920364	0.060752347111701965	0.06337308883666992	0.018156150355935097	0.06123707816004753	-0.02529171109199524	-0.07954848557710648	0.015738895162940025	0.01448923721909523	0.05785350874066353	0.046222832053899765	0.021998558193445206	-0.0170340146869421	0.02437875233590603	0.08723226934671402	0.02239060029387474	0.03247326985001564	0.00886739231646061	-0.010092089883983135	0.026076950132846832	0.06049497425556183	0.04713765159249306	0.042060498148202896	-0.008503259159624577	0.006513975560665131	0.0024701610673218966	-0.01942904107272625	0.03629148006439209	-0.020177120342850685	0.021077878773212433	0.06254512071609497	0.0005727786337956786
+0.02459423430263996	0.009734385646879673	-0.048051685094833374	0.013405807316303253	-0.04230835288763046	0.02582983672618866	-0.016518568620085716	-0.01619218848645687	-0.0394875630736351	0.034131310880184174	-0.05944512039422989	0.004301060922443867	-0.020418893545866013	-0.05064494535326958	-0.039988476783037186	-0.12363862246274948	-0.008320163935422897	0.002801488386467099	0.09813334792852402	-0.04509195685386658	-0.018419789150357246	0.11177069693803787	0.022271808236837387	0.03493287414312363	0.016370048746466637	0.05535784363746643	0.027719778940081596	0.0015843964647501707	0.056062839925289154	0.05014568194746971	0.02191762998700142	-0.029640572145581245	0.04751429706811905	-0.07149957865476608	0.05470525845885277	-0.026913002133369446	0.017455382272601128	0.03358343616127968	0.11004716902971268	-0.041211362928152084	-0.04908481612801552	0.022077493369579315	0.035265084356069565	-0.05669787526130676	-0.06078316271305084	-0.012483005411922932	-0.10332842171192169	-0.05264980345964432	0.01189133059233427	0.008116132579743862	0.0026805137749761343	0.031399209052324295	-0.05983360484242439	-0.03612139821052551	-0.02812018431723118	-0.0009187905816361308	0.043968796730041504	0.05604786425828934	-0.0051220618188381195	0.013929758220911026	0.010138454847037792	0.055206332355737686	-0.007733531761914492	-0.045035406947135925	-0.00279801432043314	-0.029596878215670586	0.04582000523805618	0.0055727711878716946	-0.009601602330803871	0.046756561845541	0.0172463059425354	0.08844993263483047	-0.021949753165245056	0.047336988151073456	0.014557314105331898	-0.06774292886257172	-0.05175970867276192	-0.021088287234306335	-0.007989310659468174	-0.0002759020426310599	-0.009203862398862839	-0.04458204656839371	0.05403854697942734	-0.021075528115034103	-0.018837928771972656	-0.015079489909112453	-0.03810259327292442	-0.03338266909122467	-0.04382045194506645	-0.020004067569971085	0.06259333342313766	-0.09961236268281937	0.036770809441804886	0.01879016123712063	-0.003305416088551283	-0.0073591736145317554	0.08263415843248367	-0.021891575306653976	-0.02952430583536625	0.0380888506770134	0.0004157836374361068	-0.036260977387428284	0.05021773651242256	-0.015691783279180527	-0.0042740339413285255	-0.04935065284371376	-0.06746121495962143	0.010913814418017864	0.06342007964849472	-0.00881237629801035	0.08445204794406891	0.046829704195261	-0.014276226982474327	-0.04238508641719818	0.05299825966358185	-0.022193294018507004	0.04377363249659538	-0.010927397757768631	0.06279101967811584	0.057555779814720154	-0.02821950428187847	0.05037963390350342	-0.041439950466156006	-0.009422555565834045	-0.02204889804124832	0.04637197032570839	0.004006498958915472	0.03544022887945175	-0.02862667664885521	0.057827387005090714	0.003662040689960122	0.022110264748334885	0.014810927212238312	0.02425263822078705	-0.09105779230594635	-0.022327501326799393	-0.05796486511826515	0.06335854530334473	0.07174227386713028	0.046381816267967224	-0.09120365232229233	0.030483068898320198	0.08857470750808716	0.021310955286026	-0.031642891466617584	-0.021733280271291733	-0.0116080641746521	-0.04331693798303604	0.03489891439676285	-0.017255449667572975	0.029815634712576866	0.03255149722099304	0.07000559568405151	-0.0005128555349074304	0.043544869869947433	-0.0003816796524915844	0.01763768307864666	0.022725170478224754	0.02667427249252796	-0.015477212145924568	0.0075356666930019855	-0.019266877323389053	-0.026383476331830025	-0.012205477803945541	0.04476143419742584	0.07315763831138611	0.06989214569330215	-0.07347063720226288	-0.037561725825071335	0.08874332159757614	-0.019095981493592262	-0.012600051239132881	0.020358016714453697	-0.009413326159119606	-0.014790681190788746	-0.05491651967167854	0.02184535190463066	-0.004065441899001598	0.057317063212394714	-0.02624962478876114	0.012783116661012173	-0.10092100501060486	0.02067852020263672	0.013557473197579384	-0.02195979841053486	0.04130961745977402	0.03142169117927551	-0.003623106051236391	-0.0029733027331531048	0.04022808372974396	0.022825056686997414	-0.014289375394582748	-0.021032601594924927	-0.024850286543369293	0.0012734015472233295	-0.03463902324438095	0.01883542165160179	0.05099378526210785	0.05473310500383377	0.049907803535461426	-0.07107487320899963	0.039787981659173965	-0.07142934203147888	-0.023968979716300964	0.02771357074379921	0.0347854346036911	-0.01572714000940323	-0.0625128373503685	-0.008758245036005974	-0.04075237736105919	-0.007322320714592934	-0.02899906411767006	0.0025173986796289682	0.01723301038146019	0.02934630773961544	0.0030236588791012764	0.029548317193984985	-0.05008842423558235	-0.002307300455868244	0.013063272461295128	-0.01577065698802471	-0.05813254788517952	-0.03354761749505997	-0.036190908402204514	-0.04530404508113861	-0.06484660506248474	-0.01523601170629263	-0.04345216974616051	0.1086193323135376	-0.04174989461898804	0.11868888139724731	0.0894370749592781	-0.003279597032815218	0.019755864515900612	-0.011366434395313263	-0.02567892335355282	0.012853266671299934	0.0009684900287538767	-0.035767797380685806	-0.06976175308227539	0.0076677678152918816	-0.03089725784957409	-0.016488028690218925	0.029056452214717865	-0.00017124172882176936	0.0036217395681887865	0.03314270079135895	0.041659366339445114	-0.07145554572343826	-0.012112447060644627	0.012848744168877602	-0.06577469408512115	0.021081728860735893	0.05734710767865181	0.0025760966818779707	-0.08141748607158661	0.027871210128068924	-0.060522813349962234	0.08659949898719788	-0.04172324016690254	0.002937107812613249	0.02497272379696369	0.004251923877745867	-0.019434165209531784	0.01563340425491333	0.010186312720179558	0.005092355888336897	0.05615462362766266	-0.01885034888982773	0.004598268307745457	0.09718738496303558	0.09326262027025223	0.02095082588493824	0.05573675408959389	-0.0130083579570055	0.0061713880859315395	0.03629572317004204	0.048471201211214066	0.01589859649538994	-0.0687127336859703	0.058881256729364395	-0.01796938292682171	-0.00783868320286274	-0.04525023326277733	-0.027843398973345757	-0.08678339421749115	-0.004463068675249815	-0.02201216295361519	-0.010590838268399239	0.0064989239908754826	-0.0431312657892704	-0.09854510426521301	-0.09237927198410034	0.018656611442565918	-0.06731963157653809	-0.027577796950936317	-0.04762812703847885	0.02839522249996662	-0.01892310380935669	0.040546391159296036	0.0683237835764885	-0.02908244915306568	0.027560492977499962	0.030410481616854668	-0.0314006581902504	-0.0813986137509346	-0.02072816528379917	0.035026732832193375	0.010672986507415771	0.029105518013238907	0.07450726628303528	-0.01611236296594143	0.003918098285794258	0.012612317688763142	0.00604484835639596	-0.019736653193831444	-0.003740296931937337	-0.06644187122583389	-0.06778916716575623	0.009783139452338219	0.0694255530834198	0.012718616053462029	-0.014504080638289452	0.049408044666051865	-0.06060789152979851	0.01953917369246483	0.01737579144537449	-0.13928993046283722	0.027655839920043945	-0.06224149093031883	-0.005088061559945345	-0.07439081370830536	0.012682205066084862	0.04985754191875458	-0.019465811550617218	-0.04967883229255676	-0.08933716267347336	0.006109864916652441	0.01971365511417389	0.03395364433526993	0.10371030122041702	0.001528010587207973	-0.017957080155611038	-0.02644927054643631	-0.017569581046700478	-0.045314811170101166	-0.030947387218475342	-0.109250508248806	0.09420750290155411	-0.0032888262066990137	-0.06387437134981155	-0.021792208775877953	-0.04389923810958862	0.041109148412942886	-0.04658317565917969	-0.0054753064177930355	0.05652394890785217	0.03396766260266304	-0.04857287555932999	-0.006821028888225555	-0.007136761210858822	0.008781754411756992	-0.027886146679520607	0.03283701837062836	0.04100271686911583	-0.01621282659471035	0.06379381567239761	-0.03000938519835472	-0.07548398524522781	0.0637180283665657	-0.02326163835823536	0.008719829842448235	-0.06498479843139648	0.0004101925005670637	0.05660807713866234	-0.07884908467531204	-0.029563764110207558	-0.012126199901103973	0.025640791282057762	0.012087525799870491	0.0369853675365448	0.031215686351060867	-0.002017608378082514	0.04542200639843941	0.05805037170648575	0.007278102915734053	0.0016772700473666191	-0.025234777480363846	0.0048074559308588505	0.06903962045907974	0.022848187014460564	0.06340909749269485	0.02106558531522751	-0.011587265878915787	0.027268392965197563	0.04841288551688194	0.059331707656383514	0.04521071910858154	-0.015784544870257378	0.026947880163788795	0.008754324167966843	-0.01721246726810932	-0.015942659229040146	0.11563622206449509	0.004479635506868362	-0.07436338067054749	-0.06624183803796768	0.0573599748313427	-0.04345664009451866	-0.032721422612667084	-0.0022224239073693752	-0.030895138159394264	-0.040264736860990524	0.07674802094697952	-0.001320074312388897	-0.06833083182573318	0.037802159786224365	0.05782880634069443	-0.02482607588171959	-0.017015231773257256	-0.028476478531956673	0.023257168009877205	0.028832828626036644	-0.024397417902946472	-0.00827768724411726	-0.0006185662932693958	0.04591115564107895	0.03616444766521454	-0.056664250791072845	0.055674467235803604	0.05417314171791077	0.055857203900814056	-0.031807564198970795	0.05576099082827568	-0.04880349338054657	0.017295317724347115	0.014699448831379414	0.02284243516623974	0.030007267370820045	0.027523543685674667	0.032599736005067825	0.01863248459994793	0.00670720636844635	-0.06680440902709961	0.024696027860045433	-0.01637207716703415	-0.06334710121154785	0.06978161633014679	0.00415540486574173	-0.05807497352361679	0.04369603097438812	0.048214249312877655	0.010688654147088528	-0.009034932591021061	0.014995284378528595	0.007143300957977772	-0.014790697023272514	-0.00038655323442071676	0.035417426377534866	0.03481586277484894	-0.019950492307543755	0.0005146866315044463	-0.018086804077029228	0.026544472202658653	0.005925797391682863	-0.020143520087003708	0.011672100983560085	0.006928854156285524	0.00893384963274002	-0.04362734034657478	0.04355334863066673	-0.004104575142264366	0.07964865863323212	-0.03466735780239105	0.012588565237820148	-0.00857431162148714	0.04312584921717644	-0.13384124636650085	-0.013662705197930336	0.04995861276984215	0.019683022052049637	0.06432647258043289	0.09746986627578735	0.047830719500780106	0.039227064698934555	0.0013772944221273065	-0.07534430176019669	0.06227045878767967	-0.03190355375409126	0.012459083460271358	0.07889550924301147	0.016496172174811363	-0.007873974740505219	-0.010852157138288021	0.10489068180322647	0.08154842257499695	0.01598307676613331	0.008310028351843357	-0.04215218499302864	-0.023027971386909485	0.07134315371513367	0.03887854143977165	0.0844196081161499	0.05232230946421623	0.04222667217254639	-0.004726483020931482	-0.038522906601428986	0.033880818635225296	-0.007654940709471703	0.027867306023836136	0.010812644846737385	-0.04011571779847145
+0.03138476982712746	-0.03431883826851845	-0.028332263231277466	0.0005299714975990355	-0.031718406826257706	0.041029080748558044	-0.046214718371629715	-0.03070133738219738	-0.049906015396118164	0.011669150553643703	-0.012714334763586521	-0.0018328985897824168	-0.032151833176612854	-0.005354093853384256	0.033814601600170135	-0.10278446227312088	0.013899095356464386	0.008609043434262276	0.07046394050121307	-0.035490576177835464	-0.038465969264507294	0.09827177226543427	0.0601227805018425	0.05525672808289528	-0.0030798499938100576	-0.01471660751849413	0.0020060488022863865	0.04238789156079292	0.07627309858798981	0.028870251029729843	-0.011898799799382687	-0.061929650604724884	0.021309589967131615	-0.03192173317074776	0.07060503959655762	0.008188829757273197	0.01926763728260994	0.0659470409154892	0.10640660673379898	-0.04953477531671524	0.0127556212246418	0.017002588137984276	0.02044367417693138	-0.07677152752876282	-0.02185700833797455	-0.011026724241673946	-0.04693799093365669	-0.09633702784776688	0.024708716198801994	0.03140094503760338	0.003441533539444208	0.06536997109651566	-0.040327928960323334	-0.030129456892609596	-0.03541608154773712	-0.0019145229598507285	0.017675964161753654	0.04303469881415367	0.038203489035367966	0.051006533205509186	0.009343710727989674	-0.010717219673097134	0.010840384289622307	-0.01689930632710457	0.052088722586631775	-0.029549354687333107	0.08020872622728348	0.021681951358914375	-0.014385365881025791	0.05601727217435837	-0.004521693103015423	0.09563914686441422	-0.0027591309044510126	0.017689213156700134	0.028280561789870262	-0.09460776299238205	-0.04544965177774429	-0.023160628974437714	0.004648882430046797	0.020581211894750595	-0.003989574499428272	-0.01256644818931818	0.014328889548778534	0.03426126763224602	0.010715154930949211	-0.00109946692828089	-0.0021763122640550137	-0.000431377295171842	-0.052485398948192596	0.0027369647286832333	0.07839345932006836	-0.07963918149471283	0.010716697201132774	0.0007949652499519289	-0.06505466997623444	-0.04695352166891098	0.0860840231180191	0.03128132224082947	0.008894490078091621	-0.038551680743694305	-0.0031993919983506203	-0.014813966117799282	0.03184007108211517	-0.012504964135587215	-0.00010860688053071499	-0.04093162715435028	-0.06310392916202545	0.020551901310682297	0.04801442101597786	0.005528345704078674	0.031684573739767075	0.0472644604742527	-0.01668773964047432	-0.019124602898955345	0.0623537115752697	-0.01031421683728695	0.049956053495407104	-0.03686963766813278	0.05137823522090912	0.0292131919413805	-0.031892355531454086	0.06060021370649338	-0.054089803248643875	-0.010469854809343815	0.021891776472330093	0.09229795634746552	-0.002273269696161151	0.048215966671705246	-0.030143020674586296	0.05756436288356781	-0.01564190536737442	0.023056820034980774	0.014384615235030651	0.038711246103048325	-0.061034563928842545	-0.05632730945944786	-0.09802929311990738	0.0879310667514801	0.055846262723207474	0.04004805162549019	-0.08165833353996277	0.04455651342868805	0.15213574469089508	0.027696365490555763	-0.04400298744440079	-0.018543172627687454	-0.06639795750379562	-0.012061627581715584	0.04307236522436142	-0.03008207678794861	0.029963873326778412	0.024531584233045578	0.07250640541315079	-0.014541249722242355	-0.00783724058419466	-0.03470597416162491	0.019049540162086487	-0.018476154655218124	0.015813469886779785	-0.03137465566396713	-0.028161928057670593	-0.03746340796351433	0.0061344243586063385	-0.02252604253590107	0.023087913170456886	0.05011589452624321	0.04282689839601517	-0.08980747312307358	-0.008123456500470638	0.07085064053535461	-0.02087150514125824	-0.021321313455700874	0.021478451788425446	-0.04220912232995033	-0.022861003875732422	-0.036611542105674744	0.049205511808395386	0.018559642136096954	0.06770458817481995	-0.02640151046216488	0.007480551488697529	-0.09427926689386368	0.009516431950032711	0.011386185884475708	-0.028904784470796585	0.02114635892212391	0.0009076594142243266	-0.0006928440416231751	-0.04873907193541527	0.008244209922850132	0.029271883890032768	-0.03234824910759926	-0.023214519023895264	0.004906704183667898	-0.013430404476821423	-0.03289257735013962	-0.0004170351312495768	0.02913844585418701	0.019385244697332382	0.06685526669025421	-0.07080670446157455	0.05560819432139397	-0.045514050871133804	-0.0438285693526268	-0.017700398340821266	0.028414392843842506	-0.043131664395332336	-0.041550129652023315	0.02178051322698593	-0.009043493308126926	-0.04223470017313957	-0.005110116209834814	-0.058185845613479614	-0.0013172400649636984	-0.005712482146918774	0.017239991575479507	0.024693282321095467	-0.03456287458539009	-0.015762977302074432	0.026533234864473343	-0.01418494526296854	-0.01834406889975071	-0.06179548799991608	-0.05841565504670143	-0.022586051374673843	-0.02896164543926716	-0.008726430125534534	-0.004558549262583256	0.09439824521541595	-0.020903052762150764	0.09237296879291534	0.022782888263463974	0.041093338280916214	0.023087356239557266	0.022837961092591286	-0.054234039038419724	0.0358399860560894	-0.010414370335638523	-0.015901824459433556	-0.03476253151893616	0.009294134564697742	-0.03952382877469063	0.012128904461860657	-0.007030125241726637	-0.003989757504314184	-0.02198730781674385	-0.011195075698196888	0.05573422834277153	-0.06191413104534149	-0.025327686220407486	0.06347521394491196	-0.038726236671209335	-0.039620451629161835	0.039570894092321396	0.022203980013728142	-0.06855137646198273	0.04537404328584671	-0.06826632469892502	0.06212260574102402	-0.04170806705951691	-0.007080208510160446	-0.037984348833560944	-0.006507131736725569	-0.03982843831181526	0.0216369666159153	-0.00014096021186560392	0.028267983347177505	0.06269481778144836	0.013133889995515347	0.016476772725582123	0.060446374118328094	0.10534269362688065	0.03445940092206001	0.05591285228729248	-0.047091417014598846	-0.04954398423433304	-0.027074823155999184	0.02630518563091755	0.02344992570579052	-0.06588516384363174	0.03306496515870094	-0.017872463911771774	0.01993079110980034	-0.03146246448159218	0.026562299579381943	-0.047026846557855606	0.0004467617836780846	0.01625138521194458	0.01153991837054491	-0.04773254320025444	-0.020626666024327278	-0.04653133079409599	-0.02145448699593544	-0.0026023644022643566	-0.04239994287490845	0.00512103084474802	-0.03571903333067894	0.07588721066713333	-0.008412299677729607	-0.010849331505596638	0.10695035010576248	-0.01588679477572441	0.010936633683741093	0.034413084387779236	-0.0022557424381375313	-0.11419832706451416	0.028651200234889984	0.004158818628638983	0.018386848270893097	0.049207597970962524	0.07705654203891754	-0.01891075074672699	-0.0024063321761786938	0.021070702001452446	0.04170950502157211	-0.037068095058202744	-0.002136851195245981	-0.034106072038412094	-0.06136319786310196	0.0236921776086092	0.05034172907471657	-0.013173654675483704	0.02865263633430004	0.023237215355038643	-0.01634064130485058	-0.02836562879383564	-0.00958267506211996	-0.11256930232048035	0.08205604553222656	-0.02804039791226387	-0.022719791159033775	-0.10396324843168259	0.021457545459270477	0.0038506765849888325	0.00026871549198403955	-0.04245907440781593	-0.09345801174640656	0.033963676542043686	-0.00615279097110033	0.07886312156915665	0.10544686764478683	-0.01889793761074543	-0.05638866871595383	0.009736185893416405	0.01593226194381714	-0.023256998509168625	-0.06360577046871185	-0.09388044476509094	0.102473184466362	0.031145278364419937	0.010978824459016323	-0.11436277627944946	-0.06314289569854736	0.08277757465839386	-0.021434247493743896	0.0004787395882885903	-0.0016519831260666251	0.030269652605056763	-0.0812353640794754	0.005975666921585798	-0.007657467853277922	-0.010751795023679733	-0.058181166648864746	0.04351596161723137	-0.013698439113795757	-0.06115159019827843	0.06358157843351364	-0.015670713037252426	-0.06646642833948135	0.030664943158626556	-0.023378698155283928	0.02745349332690239	-0.07864411920309067	-0.02265464887022972	0.08855266124010086	-0.06219503656029701	-0.017487866804003716	-0.05779422074556351	0.022223209962248802	0.024586008861660957	0.012985109351575375	0.03622955456376076	-0.04219996929168701	0.04198722913861275	0.05953860655426979	0.0019731454085558653	-0.0026080894749611616	0.018475733697414398	0.05682817101478577	0.02955123968422413	0.00401554349809885	0.09591212868690491	0.019153792411088943	-0.01882394775748253	0.008188841864466667	0.022363048046827316	0.058805592358112335	0.0030261646024882793	-0.007987337186932564	0.07135067880153656	-0.0005477350205183029	0.024773651733994484	0.031212622299790382	0.04379979148507118	0.016399048268795013	-0.08705398440361023	-0.019518809393048286	0.02048558183014393	-0.060310568660497665	-0.04347258061170578	0.010586541146039963	-0.00219731405377388	-0.046259693801403046	0.11477792263031006	0.00965733453631401	-0.10112041980028152	-0.000701426004525274	0.06470657885074615	-0.02658734656870365	-0.037758808583021164	-0.016290074214339256	8.96535930223763e-05	0.001537523465231061	0.006382612977176905	-0.018104420974850655	-0.008284720592200756	0.05460479110479355	0.00689247902482748	-0.11352039873600006	0.003463721601292491	0.06570521742105484	0.0864189863204956	0.006015851628035307	0.05208520218729973	-0.06163054704666138	-0.0026011266745626926	0.0433887280523777	0.011299951933324337	-0.01288811769336462	0.02956940047442913	0.04032334312796593	0.0038827715907245874	0.03948453068733215	-0.08452709019184113	0.007014170289039612	-0.007451510988175869	-0.028483133763074875	0.05223511904478073	0.02952461875975132	-0.013415760360658169	0.03854546323418617	0.03668124973773956	0.05206821486353874	0.052880413830280304	0.024325111880898476	0.032559119164943695	0.003447642782703042	0.009748658165335655	-0.0012402462307363749	0.004252809565514326	-0.06113511323928833	-0.06832603365182877	-0.02506055310368538	0.02516247145831585	-0.001742273336276412	-0.08430422842502594	0.029298190027475357	-0.01467394270002842	-0.02528192289173603	0.005204217974096537	0.02737990953028202	-0.013747182674705982	0.06742866337299347	-0.03025718405842781	0.033931344747543335	0.01838032901287079	0.07789833098649979	-0.1382390260696411	-0.0074233124032616615	0.03603387996554375	0.029945190995931625	0.0800408273935318	0.04537513107061386	-0.025059260427951813	0.01841631904244423	-0.013208008371293545	-0.022023800760507584	0.020245173946022987	0.0028410693630576134	0.0051419478841125965	0.018407752737402916	0.00941342581063509	-0.015755904838442802	0.013885407708585262	0.059202890843153	0.07448950409889221	0.031032744795084	0.030298376455903053	-0.0650881826877594	0.025814319029450417	0.053781066089868546	0.06361906975507736	0.04775938019156456	0.048275239765644073	0.042589470744132996	-0.048269402235746384	-0.029941493645310402	0.012501345947384834	0.0037283203564584255	0.05651940777897835	0.020887093618512154	-0.03383054956793785
+0.02920173667371273	-0.07249482721090317	-0.0529782697558403	-0.0059825824573636055	-0.04858693853020668	0.01192227192223072	-0.006958978250622749	0.0017041817773133516	-0.061240505427122116	0.05719481781125069	-0.05551464483141899	0.038014285266399384	-0.02080489508807659	-0.04683626443147659	-0.04096073657274246	-0.11242519319057465	-0.009523934684693813	-0.017853589728474617	0.03319583088159561	-0.01410943828523159	-0.04657936096191406	0.07811767607927322	-0.016812371090054512	0.031215794384479523	-0.01864658296108246	0.023101631551980972	0.010104854591190815	0.02063680812716484	0.06750212609767914	0.011683405376970768	0.0416874960064888	-0.06334087252616882	0.007508702110499144	-0.023115249350667	-0.016076866537332535	-0.04815421625971794	-0.04056970402598381	0.028818177059292793	0.07356849312782288	-0.021711478009819984	-0.007202640641480684	0.046298425644636154	0.02362741529941559	-0.03241640329360962	0.006644790526479483	0.033282049000263214	-0.0552949532866478	-0.09447719156742096	0.024113014340400696	-0.020495114848017693	0.02614167518913746	0.07020290940999985	-0.005934889428317547	-0.024494610726833344	-0.06660295277833939	-0.0004431638808455318	-0.020518457517027855	0.014634673483669758	-0.03720461577177048	0.05706439167261124	-0.011977738700807095	0.012253111228346825	0.008141663856804371	0.00263383612036705	0.006880342960357666	-0.015372229740023613	0.0287725031375885	0.04687607288360596	-0.03721936047077179	0.027153268456459045	0.02354605123400688	0.05634339153766632	0.010912696830928326	0.05292702093720436	0.0054856897331774235	-0.0411965548992157	-0.02893250621855259	-0.005132702179253101	0.007793092168867588	-0.023451395332813263	-0.009449529461562634	0.01718599535524845	0.03651795908808708	0.0283255185931921	-0.017462337389588356	-0.005547873210161924	-0.004924970678985119	-0.043149061501026154	0.004224139731377363	0.002361482009291649	0.0641137957572937	-0.07273828983306885	0.008820406161248684	0.04196135699748993	-0.05976502224802971	-0.010409897193312645	0.06488647311925888	-0.0030850814655423164	-0.004057800397276878	-0.021328086033463478	0.009790757670998573	-0.004197452217340469	0.07167132943868637	-0.02814682386815548	0.023494049906730652	-0.03861368075013161	-0.08911743015050888	0.017712054774165154	0.04082728177309036	-0.040120430290699005	0.08704113215208054	0.06825252622365952	-0.03340594097971916	0.03743736445903778	0.12447131425142288	0.03841983899474144	-0.002509459387511015	-0.030122507363557816	0.06306949257850647	0.022282402962446213	0.018056204542517662	0.05329468473792076	-0.030394870787858963	-0.04997767508029938	0.01575547829270363	0.03965285047888756	-0.040681757032871246	0.05624986067414284	0.02184760384261608	0.04876568540930748	-0.016204440966248512	0.024097692221403122	-0.009418491274118423	0.06288306415081024	-0.06371680647134781	-0.09210654348134995	-0.077397920191288	0.016404157504439354	0.0443585030734539	0.048558127135038376	-0.0944671481847763	0.06219363585114479	0.10539799928665161	0.031050488352775574	-0.041230328381061554	-0.05524611473083496	-0.031549122184515	0.003998529631644487	0.025208784267306328	-0.01915864832699299	-0.016665829345583916	0.023107362911105156	0.0720343142747879	0.01644941233098507	0.03865080326795578	-0.00941991712898016	0.05320592597126961	-0.03834034129977226	-0.0034893453121185303	-0.032300617545843124	0.017797263339161873	0.0014061840483918786	-0.027905335649847984	-0.10042921453714371	0.0837533101439476	0.08574578166007996	0.05573320761322975	-0.029200250282883644	-0.015923889353871346	0.08633991330862045	-0.0361226350069046	-0.01022463571280241	-2.978336669912096e-05	-0.005338574759662151	-0.02087673731148243	-0.013952651992440224	0.0447356253862381	0.009008614346385002	0.07751991599798203	-0.03701086342334747	0.02788127027451992	-0.07393348217010498	0.04463394731283188	-0.014381134882569313	-0.008379186503589153	0.025372883304953575	-0.026875726878643036	-0.08665399253368378	-0.020353808999061584	-0.012262863107025623	0.011374191381037235	-0.05266127735376358	-0.029549801722168922	-0.018121387809515	0.032031334936618805	-0.05067163333296776	0.044918548315763474	0.032758235931396484	0.07727328687906265	0.035655636340379715	-0.0727851539850235	0.005542498081922531	-0.07459305226802826	-0.049067407846450806	-0.030163416638970375	0.0019137819763273	0.005833165720105171	-0.02378036640584469	0.056943438947200775	-0.026624290272593498	-0.023271413519978523	0.026989443227648735	-0.03508463874459267	0.01523127593100071	0.06260720640420914	-0.010041206143796444	-0.033900294452905655	0.032208435237407684	-0.01960771344602108	-0.02879399247467518	-0.001333559979684651	-0.012983471155166626	-0.011023261584341526	-0.05107890069484711	-0.014064286835491657	-0.09047416597604752	-0.003764423541724682	0.008922290056943893	0.1372791826725006	-0.03308319300413132	0.06834954768419266	0.034670088440179825	0.037851907312870026	0.009578783996403217	-0.012920471839606762	-0.03145657107234001	-0.020582616329193115	-0.031737957149744034	-0.01922949217259884	-0.033610448241233826	0.051890529692173004	-0.03131257742643356	-0.02669656090438366	0.040607620030641556	0.005160521250218153	-0.0321170948445797	0.023976946249604225	0.04255456477403641	-0.09389127790927887	-0.045036401599645615	0.056554216891527176	0.026817938312888145	0.04708382487297058	0.05532408133149147	0.010153206996619701	-0.06851132959127426	0.03545279800891876	-0.07144410163164139	0.027517013251781464	-0.02785124070942402	0.005876932758837938	-0.01415122952312231	-0.08988583087921143	-0.02413819171488285	0.006913663353770971	0.037883009761571884	0.05396479740738869	0.011717402376234531	-0.01297257374972105	0.02661879174411297	0.024687113240361214	0.06127157807350159	0.03315608948469162	0.010119032114744186	0.014036180451512337	-0.012331994250416756	0.02101518213748932	0.036264825612306595	-0.00838460586965084	-0.050588659942150116	0.03128669783473015	0.002118077129125595	0.018659740686416626	-0.01815485581755638	-0.009935025125741959	0.008122256025671959	0.009183547459542751	0.00535657349973917	0.004287674557417631	-0.023484401404857635	-0.0268880445510149	-0.03374871611595154	-0.05831533297896385	0.004154440946877003	-0.0038716373965144157	0.041851967573165894	-0.01135538425296545	0.04833333194255829	-0.029497165232896805	0.0561961755156517	0.11313353478908539	-0.015215273946523666	0.029505478218197823	0.008630684576928616	-0.04741206765174866	-0.09206322580575943	-0.019681692123413086	-0.01551827136427164	0.01978541910648346	0.020399294793605804	0.06281068176031113	-0.0646727904677391	-0.006970854941755533	0.0356290340423584	0.025535760447382927	-0.10795744508504868	0.00941511057317257	-0.052103497087955475	-0.00931328721344471	0.05428994819521904	0.030178247019648552	0.032173026353120804	0.022024918347597122	0.003933030180633068	-0.018839843571186066	-0.06309890002012253	0.021962663158774376	-0.12770472466945648	0.01688246615231037	-0.04785517603158951	-0.03497900813817978	-0.07008468359708786	0.032169949263334274	0.06179672107100487	0.04829215258359909	-0.07084500789642334	-0.06150674447417259	0.018927959725260735	0.013771927915513515	0.08975885063409805	0.07865782827138901	-0.028268247842788696	0.0424092561006546	-0.036339640617370605	-0.003128211945295334	-0.062034863978624344	-0.033245749771595	-0.13315333425998688	0.11772971600294113	0.04281838983297348	0.01990382932126522	-0.054390113800764084	-0.05710934102535248	0.04367822781205177	-0.029687244445085526	-0.0029481183737516403	-0.002489923033863306	-0.011990237981081009	-0.07733719795942307	0.006417388562113047	0.022445334121584892	-0.05041295289993286	-0.008205650374293327	0.08577897399663925	0.039466362446546555	-0.03874634951353073	0.09296252578496933	0.025872845202684402	-0.03294164314866066	0.006649942137300968	0.030470119789242744	0.014917652122676373	-0.001123621710576117	-0.010910125449299812	0.04656950756907463	-0.07459603250026703	-0.02455112710595131	-0.03144712746143341	0.06165667250752449	-0.01364001352339983	0.010947293601930141	0.0347316674888134	-0.05407944694161415	0.01712772250175476	-0.026053614914417267	0.04187164455652237	0.010108668357133865	-0.03779371827840805	0.033202484250068665	0.015132448635995388	0.01162837352603674	0.0688345655798912	-0.013467446900904179	-0.026249995455145836	0.02149207331240177	0.0884113609790802	0.03404877334833145	0.0018026891630142927	0.016272680833935738	0.02590927854180336	0.003976003732532263	0.01793001778423786	0.022799696773290634	0.08616774529218674	-0.01360106561332941	-0.06964582949876785	0.0020632618106901646	0.09831581264734268	-0.052520982921123505	-0.06900156289339066	0.041827570647001266	0.007189507130533457	-0.006043403409421444	0.03730739653110504	-0.024032732471823692	-0.056594569236040115	-0.031229598447680473	0.028678027912974358	-0.020878799259662628	-0.004748586565256119	-0.04083961993455887	0.07046444714069366	0.00919515360146761	-0.033896952867507935	-0.030870525166392326	-0.03438301384449005	0.009470964781939983	-0.02259378507733345	-0.04959236457943916	0.0159821268171072	0.07824940979480743	0.06250976026058197	0.014471346512436867	0.10065993666648865	-0.0798521339893341	-0.024568472057580948	0.03326897323131561	0.066037617623806	0.03634003922343254	0.08908197283744812	-0.008334352634847164	0.019985802471637726	-0.0038586065638810396	-0.0928259789943695	-2.0452238459256478e-05	-0.00799038726836443	-0.04644379764795303	0.005786709487438202	0.06403852999210358	-0.08936984091997147	-0.007061153184622526	0.012983706779778004	0.046007703989744186	0.033450543880462646	-0.0026754215359687805	0.06424032896757126	-0.007266830187290907	-0.024234311655163765	-0.050750307738780975	-0.015735726803541183	0.008254020474851131	-0.09017123281955719	0.051220644265413284	-0.018875103443861008	0.003481058869510889	-0.04121147096157074	0.03253927081823349	-0.014226422645151615	-0.08703245222568512	-0.03082050196826458	0.03233852982521057	0.006115531548857689	0.037663597613573074	0.004219127353280783	-0.0015692826127633452	0.03683223947882652	0.08088329434394836	-0.14840668439865112	0.015269538387656212	0.025600945577025414	0.058910541236400604	0.0566292442381382	0.011832449585199356	0.012801249511539936	0.06195409223437309	-0.044289033859968185	-0.09087436646223068	-0.01716816984117031	-0.0004074921307619661	0.03009280376136303	-0.0008337704348377883	0.017490258440375328	0.03469248488545418	0.006115044467151165	0.06206880882382393	0.030132872983813286	-0.011389296501874924	-0.014496389776468277	-0.027767375111579895	0.037884436547756195	0.03451838344335556	0.03943415358662605	0.015779772773385048	0.02193889394402504	0.04503641277551651	-0.023040931671857834	-0.029682358726859093	0.03546639159321785	0.017424119636416435	-0.024236362427473068	0.035743605345487595	-0.013185241259634495
+0.021102299913764	-0.03774121403694153	0.00940243061631918	-0.09063518792390823	-0.03917989134788513	0.030780477449297905	-0.03167782723903656	-0.054298389703035355	-0.05170904099941254	-0.043565522879362106	0.019174912944436073	-0.005567826796323061	0.012445039115846157	-0.000509553705342114	0.04080992192029953	-0.048209048807621	-0.029280278831720352	-0.022573573514819145	-0.018020296469330788	-0.006274827755987644	0.051077403128147125	-0.05862456187605858	0.03463805094361305	-0.00944923423230648	-0.0077010211534798145	0.055079370737075806	-0.033266108483076096	0.02067182958126068	0.047087520360946655	0.04073703661561012	0.013915296643972397	-0.027952183037996292	0.02789694257080555	-0.013010498136281967	0.009432874619960785	0.0007826962391845882	-0.013189371675252914	0.02114521712064743	0.03807741031050682	0.03921228647232056	-0.02700698934495449	-0.0379767008125782	-0.0605323426425457	-0.014227013103663921	-0.02821679413318634	-0.009756265208125114	-0.0037232222966849804	0.004801300819963217	0.000981263816356659	-0.006774746812880039	-0.07143696397542953	-0.03853905200958252	-0.058006737381219864	-0.0012327332515269518	0.07372907549142838	0.024219995364546776	0.08071865886449814	-0.015092035755515099	0.02810005284845829	0.08132503181695938	-0.05206400528550148	0.028371816501021385	-0.0703522115945816	0.0012279392685741186	0.05040304735302925	-0.008506988175213337	0.0538780502974987	-0.0587938092648983	-0.0921015590429306	-0.06204359233379364	-0.012102995999157429	0.041237469762563705	0.016127871349453926	-0.018820960074663162	-0.04845065623521805	-0.015732476487755775	0.018010592088103294	-0.017500391229987144	-0.009253893978893757	0.04175068065524101	-0.07922311127185822	-0.09630924463272095	0.11613406240940094	0.05551881343126297	0.05330758914351463	-0.10127908736467361	-0.07998093962669373	0.034601155668497086	-0.0362454429268837	-0.025861088186502457	0.06544206291437149	-0.06628356128931046	-0.0026100759860128164	-0.029368270188570023	0.024293839931488037	-0.026493338868021965	-0.011849022470414639	0.020079780369997025	0.06408814340829849	0.05879828706383705	0.0024814626667648554	-0.012571766972541809	0.048303257673978806	0.11271324753761292	0.018919112160801888	-0.00635235570371151	-0.030806202441453934	0.022029919549822807	-0.017112331464886665	-0.023805206641554832	-0.028712239116430283	0.0800572857260704	0.015477594919502735	-0.014612490311264992	-0.04163896292448044	0.005669095553457737	-0.021642938256263733	-0.037619929760694504	0.08642342686653137	-0.016992846503853798	0.046039700508117676	-0.005924393888562918	-0.025374872609972954	0.0032068095169961452	0.04246854409575462	0.03728105500340462	-0.037632666528224945	-0.044798292219638824	0.015850506722927094	-0.05043213069438934	-0.007280129007995129	0.07569189369678497	-0.042358461767435074	0.07463562488555908	-0.03595948591828346	-0.0037042193580418825	0.01323667075484991	0.03780136629939079	-0.012638791464269161	-0.042805176228284836	-0.02069701813161373	-0.05886778607964516	-0.04503792151808739	-0.06166290491819382	0.016727229580283165	-0.011841628700494766	-0.07048500329256058	-0.07497593760490417	-0.04633854329586029	0.0037952580023556948	0.02583620510995388	0.048334382474422455	0.010897897183895111	-0.0022056051529943943	0.016961829736828804	-0.004061572719365358	0.003838370321318507	-0.0033511221408843994	-0.04028564691543579	-0.0037127616815268993	-0.03395778685808182	-0.05307461693882942	-0.0273613128811121	-0.02645917423069477	0.008473810739815235	0.06466567516326904	0.12208713591098785	-0.020009925588965416	-0.09185970574617386	-0.006532320752739906	-0.004263715352863073	0.016299385577440262	0.025157228112220764	-0.09381032735109329	0.014756901189684868	-0.07110656052827835	0.03640797734260559	-0.063896045088768	-0.014838428236544132	-0.03559719771146774	0.10214870423078537	-0.0010274868691340089	-0.013493322767317295	0.010501277633011341	-0.031165149062871933	0.006355512887239456	-0.04125605523586273	-0.010710306465625763	-0.027509845793247223	-0.04053961858153343	-0.05099557340145111	0.005059539806097746	0.008039558306336403	-0.04458971694111824	-0.06737976521253586	0.03780543431639671	-0.0060387165285646915	0.08368705213069916	-0.07097933441400528	-0.022295230999588966	-0.08064769208431244	0.05856741964817047	-0.011394057422876358	-0.02701752446591854	-0.049437616020441055	0.009663469158113003	-0.07370442897081375	-0.020059792324900627	0.027433158829808235	0.018454493954777718	0.05423928052186966	-0.008942260406911373	-0.024946795776486397	0.017829790711402893	0.010781395249068737	-0.04940884932875633	-0.019172165542840958	-0.01171826384961605	0.019962014630436897	-0.0065432097762823105	-0.03355026617646217	-0.020923545584082603	-0.023234808817505836	-0.10286401212215424	-0.08346247673034668	-0.002406952902674675	-0.0049914042465388775	0.012625614181160927	-0.037100665271282196	0.0017272585537284613	-0.030204519629478455	0.06351584941148758	-0.11624739319086075	0.017737235873937607	0.06780190020799637	-0.09657905995845795	0.019203810021281242	0.008945907466113567	0.003807262284681201	-0.07052838057279587	-0.006812634412199259	0.031513918191194534	0.031456850469112396	-0.021351750940084457	-0.025991365313529968	-0.017760466784238815	-0.019976625218987465	0.03662285953760147	-0.06510747969150543	-0.02848886512219906	0.07957146316766739	-0.0011055717477574944	0.005273345857858658	-0.055145859718322754	0.034057728946208954	0.02438160590827465	0.0266315508633852	0.0328536257147789	0.05608950927853584	0.03374074772000313	0.025224940851330757	-0.03054799512028694	0.01214780006557703	0.0540284626185894	0.011256484314799309	-0.03172631189227104	0.03745127469301224	0.04350912198424339	-0.012745406478643417	-0.03523493930697441	0.010680926963686943	0.02809200994670391	0.020500345155596733	-0.022922350093722343	-0.02626791223883629	-0.013152757659554482	0.06736911088228226	0.04285889118909836	-0.01742696762084961	-0.04465160891413689	0.008257991634309292	0.0329102948307991	-0.04981183633208275	0.04169946163892746	-0.008232702501118183	-0.030649058520793915	0.01849442347884178	-0.0637999027967453	0.03783557564020157	-0.0419456772506237	0.0248287171125412	-0.03954111412167549	-0.022819800302386284	0.016584573313593864	0.007607577368617058	0.02866673655807972	0.02274368330836296	-0.02951277792453766	-0.028103629127144814	0.010334310121834278	-0.009654317982494831	-0.08866328746080399	0.011434448882937431	-0.053523950278759	-0.05504535883665085	-0.006036856677383184	0.0387142188847065	0.03732205182313919	0.0047684055753052235	-0.00527603505179286	-0.0821259543299675	-0.09179450571537018	0.005524050444364548	-0.013391174376010895	-0.08231978863477707	0.0659630298614502	-0.11415684223175049	-0.06648723781108856	-0.03183373436331749	0.10397446900606155	-0.0487094409763813	0.05663856863975525	-0.04874683916568756	-0.021289750933647156	0.0023173729423433542	-0.05013620853424072	-0.025714650750160217	0.014157244935631752	-0.035880159586668015	0.0037777209654450417	0.04829420894384384	-0.04138577729463577	0.0018857672112062573	-0.02735726535320282	-0.000559574575163424	-0.10417108237743378	-0.03140281140804291	0.037854086607694626	-0.1044062003493309	0.07677335292100906	-0.042510319501161575	-0.04538625851273537	0.0036500971764326096	0.02600236050784588	0.01595245487987995	0.02834152989089489	0.015565501525998116	-0.03697245195508003	0.052401646971702576	0.0048850891180336475	0.008450220339000225	-0.024846043437719345	0.025198280811309814	-0.037009287625551224	-0.004185768775641918	-0.029624497517943382	-0.03029523231089115	-0.0006684754625894129	-0.04768333584070206	0.014682664535939693	-0.03933844342827797	0.003489541821181774	-0.0521295890212059	-0.05469304695725441	-0.007015311624854803	-0.020933648571372032	0.018145712092518806	-0.006906962022185326	-0.08164370059967041	0.015977434813976288	0.052194539457559586	-0.052765995264053345	-0.03360521048307419	-0.02253376878798008	0.008808273822069168	-0.060501713305711746	0.07108106464147568	-0.0237620547413826	0.0032144661527127028	-0.011030548252165318	-0.040494877845048904	-0.10822111368179321	-0.06249348074197769	-0.003180334810167551	-0.03587332367897034	-0.06895825266838074	0.021959561854600906	-0.0752810537815094	-0.026216240599751472	0.046999432146549225	0.03735131025314331	0.06946032494306564	0.035873401910066605	0.044661641120910645	0.08100342005491257	0.05058114230632782	0.03931330144405365	0.007704888004809618	0.03262003883719444	-0.013017131946980953	-0.018810614943504333	0.11579108983278275	0.01164289005100727	0.02427941933274269	-0.006974908988922834	0.03344258293509483	-0.01877247542142868	-0.07669515907764435	-0.06087852269411087	0.07565222680568695	-0.06290745735168457	0.0029700743034482002	0.0002520190319046378	-0.008535291068255901	0.02716660685837269	-0.03824462369084358	0.09103754162788391	0.01876196265220642	0.03884332254528999	-0.048384130001068115	-0.030321722850203514	-0.006355616729706526	-0.06848479807376862	-0.04136621579527855	0.0028697995003312826	0.030067576095461845	0.014714401215314865	-0.030979735776782036	0.03462941199541092	0.05772345885634422	0.0759408250451088	0.08319159597158432	0.013800090178847313	0.05579577758908272	-0.0046822805888950825	0.00908602587878704	0.04666003957390785	0.049313124269247055	0.008845972828567028	0.029630374163389206	0.0593251995742321	0.051974743604660034	-0.037636179476976395	-0.060356687754392624	0.04016799479722977	0.06449766457080841	0.027060041204094887	-0.044006604701280594	-0.0743618756532669	0.0007429976831190288	-0.0006426267791539431	0.018678057938814163	0.047376204282045364	0.0196306724101305	0.07556648552417755	0.02321454882621765	0.04723893478512764	-0.018622374162077904	0.0309618953615427	-0.021048329770565033	-0.039405543357133865	-0.0009638344054110348	-0.05883423984050751	-0.06026778742671013	0.08815425634384155	-0.06068813055753708	-0.042526815086603165	0.0032565814908593893	-0.015506206080317497	0.03179815784096718	-0.013491912744939327	-0.0004925867542624474	0.06365753710269928	0.04562578722834587	0.05332005396485329	-0.05056074261665344	0.07939465343952179	-0.0633910745382309	-0.06373991817235947	0.07895436137914658	0.05510684847831726	-0.002914618467912078	0.02533474564552307	0.010889609344303608	-0.0007158673251979053	0.07420726865530014	-0.002830336568877101	-0.02107994630932808	-0.0008011097088456154	0.04736429452896118	-0.08600345999002457	0.029929379001259804	0.0007078784401528537	0.01592629961669445	0.008220040239393711	0.04475658759474754	-0.04025508835911751	-0.013463497161865234	-0.03597329556941986	-0.02845189906656742	-0.05185139924287796	-0.047762181609869	0.03386535122990608	-0.05990417301654816	0.028476078063249588	0.01634547859430313	0.015136380679905415	0.09586413949728012	-0.021581968292593956	0.055811814963817596	0.039361558854579926	-0.04339384287595749
+0.041053999215364456	-0.06726380437612534	-0.0970761626958847	-0.011934494599699974	-0.02798834629356861	0.01628004014492035	0.018174374476075172	-0.009921304881572723	-0.051758475601673126	0.033417709171772	-0.08311577886343002	0.05628886818885803	-0.019308868795633316	-0.037639960646629333	-0.030532926321029663	-0.12961916625499725	-0.01102499756962061	0.001301343785598874	0.06570664793252945	-0.018854111433029175	-0.038030561059713364	0.03439292684197426	-0.02948595955967903	0.04962526634335518	0.0022990205325186253	0.027048230171203613	0.014640779234468937	0.017053956165909767	0.04147541522979736	0.028998859226703644	0.03352360799908638	-0.05459773540496826	0.026280611753463745	-0.017337722703814507	-0.011287967674434185	-0.05533333122730255	-0.03493152931332588	0.07759184390306473	0.06111950799822807	-0.011663266457617283	-0.052198540419340134	0.010717405937612057	-0.03968466445803642	-0.054964736104011536	-0.014629550278186798	0.0019474630244076252	-0.05956365540623665	-0.08566538244485855	0.033532798290252686	-0.013815411366522312	-0.02408626116812229	0.04038553312420845	-0.00238342909142375	0.011133741587400436	-0.08973345905542374	0.001477111130952835	-0.01256821770220995	0.08625612407922745	-0.01925586722791195	0.012574001215398312	-0.03355737775564194	0.043006133288145065	-0.010283726267516613	-0.015346561558544636	0.017939792945981026	-0.012394089251756668	0.05223746597766876	0.009822015650570393	-0.04681891202926636	0.036769989877939224	-0.019357286393642426	0.04191276431083679	-0.01702488213777542	0.06320574879646301	-0.011384583078324795	-0.04747770354151726	-0.0520576648414135	-0.033436670899391174	0.004386776592582464	-0.009440237656235695	-0.03768786042928696	0.03534163162112236	0.0971934050321579	0.007247611414641142	-0.02596302330493927	-0.017039360478520393	-0.023913424462080002	-0.04595357924699783	-0.036932818591594696	-0.02039862424135208	0.0846143513917923	-0.04637838527560234	0.02579301968216896	0.010591561906039715	0.021944882348179817	0.003980569541454315	0.09035952389240265	-0.014429941773414612	-0.04025329649448395	-0.0023169158957898617	-0.010873674415051937	-0.014212613925337791	0.06822600960731506	-0.029360031709074974	0.009377419017255306	-0.05020492151379585	-0.08300000429153442	0.0511762835085392	0.02110709436237812	-0.04514295235276222	0.07568029314279556	0.06606458872556686	-0.0814267247915268	-0.022360794246196747	0.0877280905842781	0.0036007757298648357	0.05149782449007034	-0.008710971102118492	0.053180329501628876	0.037131812423467636	-0.04652443528175354	0.07847005873918533	-0.01007566973567009	-0.04231816902756691	-0.013878578320145607	0.05627959966659546	-0.03436131030321121	0.011034405790269375	-0.04028766229748726	-0.009509905241429806	-0.04842615872621536	0.05204683542251587	0.03204510360956192	0.03367850184440613	-0.07835401594638824	-0.10388761013746262	-0.0466410331428051	0.048286255449056625	0.040726520121097565	0.02462179586291313	-0.08770161122083664	0.05768384039402008	0.058411043137311935	0.07364911586046219	-0.017988895997405052	-0.030791200697422028	-0.07094378024339676	-0.020398346707224846	0.04628603160381317	-0.0038721296004951	0.005625717807561159	0.03686555474996567	0.0524834506213665	0.027112789452075958	0.0432356633245945	0.007866891101002693	0.03519958630204201	0.01361105591058731	0.03817189484834671	-0.004990779794752598	0.002172830980271101	0.012772795744240284	0.0006322154658846557	-0.030545342713594437	0.028967255726456642	0.11579500883817673	0.043319206684827805	-0.05444351211190224	0.0064069046638906	0.13469821214675903	-0.016581354662775993	0.008826053701341152	-0.00731118256226182	-0.03681740164756775	-0.025608722120523453	-0.0019655833020806313	0.0165384653955698	-0.010579034686088562	0.05963790416717529	-0.055684201419353485	0.029614359140396118	-0.08463024348020554	0.003951326943933964	0.04202428460121155	-0.0012408395996317267	0.027071159332990646	-0.010852301493287086	-0.02554992586374283	0.016598600894212723	-0.0016271224012598395	0.03858252242207527	-0.08087120205163956	-0.04080778360366821	-0.02903738059103489	-0.012153202667832375	-0.03322439268231392	0.05464368686079979	0.066083624958992	0.02602618746459484	0.05935303121805191	-0.03864799812436104	0.02310747653245926	-0.08098108321428299	-0.00940476544201374	0.04126974195241928	0.04391711577773094	-0.018291179090738297	-0.05125066265463829	0.04906514659523964	-0.025566115975379944	-0.007523945067077875	-0.02746335230767727	-0.04552338272333145	0.0276121124625206	0.059915944933891296	-0.025874732062220573	-0.023114049807190895	0.03443409129977226	-0.005731253419071436	-0.0010135184274986386	-0.05063149705529213	-0.03304444998502731	-0.06164014711976051	-0.07590176165103912	-0.029758455231785774	-0.07174805551767349	0.038942936807870865	0.031044168397784233	0.05267791822552681	-0.024936676025390625	0.06677188724279404	0.06487862020730972	0.01969262585043907	-0.03785372152924538	0.006947789806872606	-0.010412915609776974	0.02181771583855152	-0.0108652887865901	-0.03258473426103592	-0.09416139870882034	0.043377432972192764	-0.003579074749723077	-0.019614731892943382	0.04704225808382034	0.0002942996798083186	-0.035414181649684906	0.07400813698768616	0.024278609082102776	-0.11898200958967209	0.004195801913738251	0.032061588019132614	-0.003735738107934594	0.061258163303136826	0.08443823456764221	0.020357267931103706	-0.06253167986869812	0.05206079035997391	-0.04990217834711075	0.03668283671140671	-0.010374455712735653	0.034569818526506424	0.03381624445319176	-0.05171696096658707	-0.03548808768391609	-0.009363394230604172	0.013820572756230831	0.048712003976106644	0.033748138695955276	-0.022505173459649086	-0.00796844344586134	0.012917725369334221	0.05774005502462387	-0.00042697403114289045	0.05724658444523811	-0.0055351583287119865	-0.042695820331573486	0.01722310669720173	0.007188849616795778	0.01753806881606579	-0.08914107829332352	0.010639204643666744	0.006234353873878717	-0.01927327550947666	-0.011254473589360714	-0.031319569796323776	-0.0260603167116642	0.013268742710351944	-0.0031416912097483873	-0.01681608334183693	-0.04432886838912964	-0.061547618359327316	-0.052361756563186646	-0.002188851358368993	-0.007683607749640942	-0.009229219518601894	-0.05586227774620056	0.022257963195443153	0.06318768858909607	0.009168725460767746	0.051187798380851746	0.1144464984536171	-0.056762807071208954	0.02896583080291748	0.0489359125494957	-0.03417316824197769	-0.05429794266819954	-0.06780825555324554	-0.021364014595746994	0.05126680061221123	0.0557970367372036	0.06451793015003204	0.003974166698753834	0.030540315434336662	0.039594266563653946	-0.012075231410562992	-0.04616720601916313	-0.010712972842156887	-0.017069261521100998	-0.027129894122481346	0.030903687700629234	0.06783688813447952	0.02987593226134777	-0.013019507750868797	0.009794236160814762	-0.03429631143808365	-0.0443914458155632	0.01888062246143818	-0.15786385536193848	-0.007469641976058483	-0.058619312942028046	-0.017653262242674828	-0.041792407631874084	0.04140878841280937	0.049505431205034256	-0.013807808049023151	-0.07472096383571625	-0.09183664619922638	-0.0143300024792552	0.02632676437497139	0.05618160218000412	0.09621689468622208	-0.0003841108991764486	0.029232436791062355	-0.08334758132696152	-0.015802767127752304	-0.0694388672709465	-0.03309599310159683	-0.09188473224639893	0.10256354510784149	0.009722214192152023	-0.04649613797664642	-0.01108850259333849	-0.06738061457872391	0.010487199760973454	-0.03640624135732651	0.020593512803316116	0.08111263066530228	0.012866169214248657	-0.03342615067958832	0.021458540111780167	-0.03574829176068306	-0.010613045655190945	0.017800187692046165	0.0809306874871254	0.030823279172182083	-0.04435647279024124	0.08192077279090881	0.005633850581943989	0.009754045866429806	0.018716465681791306	0.022898728027939796	0.040856119245290756	-0.014313760213553905	-0.011296603828668594	0.03877004235982895	-0.07714644074440002	-0.012290826067328453	-0.01630537584424019	0.015476478263735771	-0.04393642023205757	0.01578780636191368	-0.004842268768697977	-0.029386254027485847	0.01618337631225586	0.009425892494618893	0.018924061208963394	0.015267409384250641	-0.04328394681215286	0.03876383230090141	0.044277772307395935	-0.013969118706882	0.11612705886363983	0.05570634827017784	-0.017992306500673294	0.015334929339587688	0.10472780466079712	0.0483233705163002	0.02400067448616028	0.025653457269072533	0.037590574473142624	0.030900875106453896	0.025195864960551262	0.004464497324079275	0.06667577475309372	-0.011776212602853775	-0.07110966742038727	-0.02079293504357338	0.047407131642103195	-0.09075893461704254	-0.053700752556324005	0.00811118632555008	-0.014350321143865585	-0.04238830879330635	0.0289854034781456	-0.011008748784661293	-0.04397576302289963	0.027649451047182083	0.01268577016890049	-0.05511598661541939	-0.039102703332901	-0.02496165968477726	0.06767021119594574	0.014032281003892422	0.04519563913345337	-0.034846093505620956	-0.03160882741212845	0.036367423832416534	0.03788134828209877	-0.07216615229845047	0.004804708994925022	0.07952317595481873	0.0538342110812664	-0.029399048537015915	0.0610937774181366	-0.035249385982751846	-0.005205077584832907	0.0002941775892395526	0.04248055815696716	0.08595899492502213	0.005662787239998579	0.002146276878193021	-0.004808393307030201	0.03339839354157448	-0.02583368495106697	0.0012522328179329634	0.021030576899647713	-0.013832468539476395	0.03443049266934395	0.012563451193273067	-0.03582824021577835	0.005239530466496944	0.05716361850500107	0.008829889819025993	-0.005368989892303944	0.020711196586489677	0.060547713190317154	-0.007311806082725525	-0.010948778130114079	0.025320008397102356	-0.029645953327417374	0.004752099514007568	-0.02324586734175682	-0.004615363199263811	-0.0494769848883152	0.010433775372803211	-0.028781915083527565	-0.011876795440912247	0.03474152833223343	-0.0148297268897295	-0.02790813148021698	0.03318998962640762	-0.023214131593704224	0.04993700981140137	0.002417877083644271	0.026486702263355255	0.02924848161637783	0.056192547082901	-0.12149780988693237	0.0325605683028698	0.013695941306650639	0.050647471100091934	0.04560329392552376	0.047531355172395706	-0.0031188204884529114	0.08145318180322647	-0.03765425458550453	-0.10561877489089966	-0.03142551705241203	0.014188268221914768	0.03419208154082298	0.013693282380700111	0.046428143978118896	0.0070968992076814175	0.011923957616090775	0.10529287159442902	0.03313843160867691	-0.014850153587758541	-0.01327100396156311	-0.023967644199728966	0.04348062351346016	0.03744972497224808	0.043144289404153824	0.02808281220495701	0.02811315655708313	0.059760015457868576	-0.006483564618974924	-0.04102632403373718	0.04813501238822937	0.004977806471288204	0.03066113591194153	0.022467834874987602	-0.02469624951481819
+0.10559633374214172	-0.05558424070477486	0.0011349889682605863	0.08471108227968216	-0.06196915730834007	0.08279727399349213	-0.02504146844148636	0.023117244243621826	-0.08306160569190979	-0.04954324662685394	-0.011105685494840145	0.05190755054354668	0.008020124398171902	-0.003363873576745391	-0.03675968199968338	-0.0936322882771492	0.0374978668987751	0.025957267731428146	0.0429299958050251	-0.03750765323638916	-0.026564141735434532	0.03264055401086807	0.048100847750902176	-0.007404404226690531	0.007194960489869118	-0.0247968677431345	0.06271708011627197	0.035708118230104446	0.04652567207813263	0.02502071112394333	0.04767351970076561	-0.05813860148191452	-0.012630632147192955	-0.013086802326142788	-0.04620375111699104	-0.01717386208474636	-0.020458579063415527	0.040890950709581375	0.049199413508176804	-0.04859883338212967	0.023731671273708344	0.02161148563027382	0.0316866971552372	-0.07295085489749908	0.03838484734296799	0.040578585118055344	-0.04622122645378113	-0.06592584401369095	0.023100802674889565	-0.03814685344696045	0.015102419070899487	0.06174261122941971	0.05015893280506134	0.049372538924217224	-0.06687004119157791	-0.041491247713565826	0.04048905149102211	0.08523096889257431	0.018106967210769653	0.036829859018325806	-0.018020836636424065	-0.05019392818212509	0.02170938439667225	0.018525149673223495	0.034815870225429535	0.051907844841480255	0.06946588307619095	-0.008508255705237389	0.02853040210902691	-0.004186662845313549	-0.013230929151177406	0.07354524731636047	0.029971571639180183	0.060810524970293045	0.026002220809459686	-0.06894493103027344	-0.04219084605574608	-0.05754393711686134	-0.04664973169565201	0.021142538636922836	-0.009104140102863312	0.008259635418653488	0.06077883020043373	-0.024018965661525726	-0.0073903570882976055	0.05393132194876671	0.024617725983262062	-0.06680503487586975	-0.010995249263942242	0.01564965397119522	0.02500206045806408	-0.039885614067316055	-0.034140411764383316	-0.015197932720184326	-0.053201090544462204	0.004715059883892536	0.07758072018623352	0.018339194357395172	-0.04212390258908272	0.019650906324386597	-0.029269272461533546	-0.028891509398818016	0.025350794196128845	-0.03036327287554741	0.03167511895298958	-0.019813453778624535	-0.012910360470414162	0.008339164778590202	-0.010107685811817646	-0.031551335006952286	0.08074193447828293	0.06318005174398422	-0.06179282069206238	-0.04231652617454529	0.10219819843769073	-1.4563080185325816e-05	0.0011545217130333185	-0.0763784721493721	0.04458950087428093	0.022606490179896355	-0.04369498789310455	0.13392508029937744	-0.04197008162736893	-0.07442232966423035	-0.0019564172253012657	-0.007704122923314571	0.00585115747526288	0.02665519528090954	0.01703295111656189	-0.021677033975720406	-0.00014715119323227555	0.02101292461156845	-0.018857965245842934	0.1116369441151619	-0.08790905028581619	-0.015464778989553452	-0.0934775099158287	0.0404137447476387	0.06020841747522354	0.017858168110251427	-0.019443631172180176	0.06643160432577133	0.0878254696726799	0.05532106012105942	-0.017396356910467148	-0.0040630074217915535	-0.013881243765354156	-0.003364303382113576	0.04781211540102959	0.012313498184084892	0.040994059294462204	0.018391484394669533	-0.024202700704336166	0.021892638877034187	0.03217742219567299	0.018119066953659058	0.010747512802481651	-0.04075463116168976	-0.013669862411916256	0.01896311342716217	0.010930348187685013	0.03080368973314762	0.034163132309913635	-0.01856771856546402	0.008797576650977135	0.12371993064880371	-0.03676525875926018	-0.0459822379052639	-0.04107705131173134	0.09452299028635025	-0.028170013800263405	0.010075709782540798	0.04212018474936485	-0.023880288004875183	0.04100365564227104	-0.0009326644358225167	0.011247698217630386	-0.03806295618414879	0.04961465671658516	0.02059503085911274	0.011780974455177784	-0.05453188717365265	0.012730286456644535	-0.03779293969273567	-0.0010719922138378024	-0.007815266028046608	0.006877533625811338	-0.027833830565214157	0.05824274197220802	-0.01567653939127922	-0.001997665734961629	-0.022793777287006378	-0.04621376097202301	-0.04846477136015892	0.00015690989675931633	-0.00840380135923624	0.016410864889621735	-0.018220093101263046	0.03335560858249664	0.01496119238436222	-0.009848818182945251	0.02832796797156334	-0.0526311993598938	0.030544094741344452	0.03887425363063812	0.021388154476881027	-0.0608152337372303	-0.019386915490031242	0.0072010960429906845	-0.013370556756854057	-0.011603305116295815	0.03099193423986435	0.007458540145307779	0.036689478904008865	-0.003905810881406069	0.03765434771776199	0.02942829020321369	-0.012308172881603241	0.0016335129039362073	0.0038569404277950525	-0.04473118111491203	-0.06568083167076111	-0.051642417907714844	-0.07188446819782257	-0.019247496500611305	0.017231695353984833	0.0022593766916543245	0.047867096960544586	0.09198600053787231	-0.07103124260902405	0.10166963934898376	0.06772959232330322	0.06551547348499298	-0.009503250010311604	-0.018202148377895355	-0.03795541822910309	0.011715494096279144	0.0061585609801113605	0.013485084287822247	-0.03480983525514603	0.025747355073690414	-0.0069673433899879456	0.021864380687475204	0.01601802557706833	0.06149156764149666	-0.02762753702700138	0.00595571706071496	-0.0406976118683815	-0.09519153088331223	-0.04881656542420387	0.03512798994779587	-0.010024686343967915	0.021586008369922638	0.058477986603975296	0.07423210889101028	-0.053980693221092224	0.03663644567131996	-0.07592421025037766	0.026637624949216843	0.012258755043148994	0.01397813018411398	0.009189087897539139	-0.034675925970077515	-0.07347344607114792	-0.05717974528670311	0.003921153023838997	0.04006648436188698	-0.007611985318362713	-0.01597440242767334	-0.003532010829076171	0.008011341094970703	0.1480335295200348	0.06535416841506958	-0.009329581633210182	0.0009353943751193583	-0.022117795422673225	-0.0028766815084964037	-0.023230379447340965	0.07222680002450943	-0.046533603221178055	0.020738817751407623	0.006474165711551905	-0.04142405837774277	-0.040091436356306076	-0.0339471697807312	-0.014634300023317337	0.01476092729717493	0.023946121335029602	-0.02137739770114422	0.002270644763484597	-0.025003597140312195	-0.04785008728504181	-0.0018828775500878692	0.021266406401991844	0.021415000781416893	0.016683010384440422	-0.07528136670589447	0.10062988847494125	-0.03671693056821823	-0.015631327405571938	0.14180298149585724	-0.06521429866552353	0.04146547242999077	0.03912302479147911	-0.00965270958840847	-0.07289726287126541	-0.0369846448302269	-0.001066351542249322	0.01815272681415081	0.05250469967722893	0.05833059921860695	-0.03481193631887436	0.016950611025094986	0.0751948431134224	0.006406267173588276	-0.010144265368580818	0.014599834568798542	-0.04947982728481293	-0.01211032085120678	0.09106016159057617	0.10243823379278183	-0.013129177503287792	0.012821350246667862	0.013127646408975124	0.03533592075109482	-0.057316768914461136	0.0195272546261549	-0.13103802502155304	0.03482533246278763	-0.05856061726808548	0.017748834565281868	-0.05852854996919632	0.057799894362688065	0.006012310739606619	0.053110599517822266	0.006973702926188707	-0.020445579662919044	0.015018533915281296	0.05645471438765526	0.015067261643707752	0.07499761134386063	-0.06237419322133064	0.011712618172168732	-0.021970968693494797	-0.03136063739657402	-0.05544153228402138	-0.011733701452612877	-0.07704745978116989	0.07270032912492752	-0.012142092920839787	-0.00443667359650135	-0.05872127786278725	-0.005653660744428635	0.03225385770201683	-0.017629804089665413	0.05642177537083626	0.028037477284669876	0.06377944350242615	-0.10965020954608917	0.07881295680999756	0.01605994999408722	-0.04292383790016174	0.02141101472079754	0.07287010550498962	0.019036535173654556	-0.04880586639046669	0.008966160006821156	-0.027855059131979942	-0.01507326029241085	0.04835687577724457	0.03454471752047539	0.01255461573600769	-0.03345903381705284	-0.041934411972761154	0.04240736365318298	-0.004681691527366638	-0.00805822852998972	-0.02491236850619316	0.03133115544915199	0.009320736862719059	-0.06035153940320015	0.04089382663369179	-0.05073416233062744	-0.014704599045217037	0.013529786840081215	0.019087037071585655	0.007588690146803856	-0.02039441466331482	0.04092220216989517	0.0152382617816329	0.014290799386799335	0.08221419155597687	-0.011382817290723324	0.014824873767793179	-0.02711785025894642	0.08727096021175385	0.07095126807689667	0.04323187470436096	0.012183654122054577	0.04246240854263306	0.011272656731307507	0.04135984182357788	0.014674687758088112	0.031953275203704834	-0.0011674915440380573	-0.09484652429819107	-0.04458801448345184	0.015141033567488194	-0.07230239361524582	0.0034217939246445894	0.06020043417811394	-0.05683562159538269	-0.04495939984917641	0.062210917472839355	0.009832999669015408	-0.10960173606872559	-0.02703244425356388	0.04534367099404335	0.033582527190446854	-0.027187306433916092	-0.029719868674874306	0.06168939918279648	0.024863507598638535	-0.024416867643594742	-0.06635910272598267	-0.033268071711063385	-0.006914914585649967	0.023473400622606277	-0.0041383602656424046	-0.06406852602958679	0.05076923593878746	0.1123121976852417	0.009919032454490662	0.03794408217072487	-0.05903872102499008	-0.021894235163927078	-0.009285452775657177	0.02109428681433201	0.08586777746677399	-0.009273520670831203	0.009946085512638092	0.018105613067746162	0.029811477288603783	-0.01157008484005928	-0.029418066143989563	0.019676703959703445	-0.031935952603816986	0.008769423700869083	0.03406701236963272	-0.04937208443880081	-0.01564028114080429	-0.034317128360271454	-0.016643844544887543	0.01051070261746645	0.019218675792217255	0.058177247643470764	0.0459287166595459	-0.021742600947618484	0.003169119590893388	0.015766356140375137	-0.057980768382549286	-0.048979826271533966	0.024843458086252213	0.005149717442691326	-0.054461896419525146	-0.048246074467897415	0.02280586026608944	0.021628698334097862	0.011830375529825687	0.024844257161021233	0.08017153292894363	-0.034133750945329666	0.09019797295331955	-0.01658599078655243	0.0038784919306635857	-0.009207295253872871	0.05619603022933006	-0.09537401795387268	-0.003408225253224373	0.026143960654735565	0.009970227256417274	0.06121905893087387	0.07433747500181198	-0.043534375727176666	-0.0175565667450428	-0.029459210112690926	-0.026328446343541145	0.04375613480806351	0.019015660509467125	0.03248376399278641	-0.03584110736846924	0.03218308836221695	-0.03093915805220604	-0.013744323514401913	0.06301524490118027	0.046117085963487625	0.007646351121366024	0.02978876419365406	0.03739769384264946	0.10152676701545715	0.07496651262044907	0.023134689778089523	-0.010065036825835705	0.022371754050254822	0.00945619773119688	-0.0029458394274115562	0.05536455288529396	0.0029115499928593636	-0.017422419041395187	0.03644745796918869	-0.0011866253335028887	-0.03759831190109253
+0.03893241286277771	-0.06379051506519318	-0.08954379707574844	0.0568121001124382	-0.06253500282764435	0.033952746540308	-0.025081781670451164	0.03652453422546387	-0.09218690544366837	0.0014922235859557986	-0.00814860314130783	0.01151325274258852	0.01923130266368389	-0.05741681531071663	-0.04675375670194626	-0.10572835803031921	-0.023913729935884476	-0.0022720645647495985	0.06831087172031403	-0.06164376810193062	-0.03353352099657059	0.03651874512434006	-0.024827200919389725	0.053955547511577606	0.014551877975463867	0.00619271257892251	0.025128090754151344	0.014610049314796925	0.03935974836349487	0.06143227964639664	0.016623076051473618	-0.03161587938666344	-0.0025114102754741907	-0.013581057079136372	-0.049193818122148514	-0.02189604379236698	-0.04025333374738693	0.063410185277462	0.031155863776803017	-0.023688768967986107	-0.07677651941776276	0.07612059265375137	0.0372936949133873	-0.040246035903692245	0.026487695053219795	0.03612036630511284	-0.04962316155433655	-0.054836273193359375	0.02936820685863495	-0.0067390999756753445	-0.006550977006554604	0.06327283382415771	0.07468871772289276	0.045657116919755936	-0.05500584468245506	0.03126294165849686	0.023702146485447884	0.09988900274038315	-0.015052488073706627	0.021882563829421997	0.021402116864919662	0.008984372951090336	0.007963220588862896	-0.04922834038734436	0.0572148859500885	0.04348912462592125	0.013233286328613758	0.020601216703653336	-0.02074885368347168	0.05516810715198517	-0.0285154078155756	0.07391279190778732	-0.007814921438694	0.05989659205079079	0.03261386603116989	-0.03821511194109917	-0.0842314213514328	-0.01585005596280098	-0.02359868958592415	0.009944836609065533	-0.014216700568795204	0.042156096547842026	0.08342470973730087	0.012668594717979431	-0.011447645723819733	0.0032424048986285925	-0.016528451815247536	-0.024589762091636658	0.019536888226866722	-0.008083213120698929	0.08000879734754562	-0.02439524605870247	-0.0006079912418499589	0.005237678065896034	-0.00841975212097168	0.013437069952487946	0.10691741108894348	-0.02986542135477066	-0.019983457401394844	0.04287255182862282	-0.03290941193699837	-0.07390216737985611	0.055318355560302734	-0.051195431500673294	-0.009229526855051517	-0.017560753971338272	-0.08195215463638306	0.004783981945365667	-8.488987077726051e-06	-0.04024185985326767	0.07917845994234085	0.11371723562479019	-0.06743234395980835	-0.045009795576334	0.06857828050851822	0.027519579976797104	0.017774729058146477	-0.006868754513561726	0.007838066667318344	0.05236251279711723	-0.07826501131057739	0.07618092745542526	0.00033106893533840775	-0.0409509614109993	0.018088171258568764	0.04631520435214043	-0.028828158974647522	0.02986838109791279	0.025707636028528214	-0.013401820324361324	-0.02855966053903103	0.0060405973345041275	0.06471380591392517	0.08368503302335739	-0.06508442014455795	-0.041116777807474136	-0.03944094106554985	0.067463219165802	0.030930295586586	0.03318263590335846	-0.093411885201931	0.04997297748923302	0.06179036572575569	0.02348213456571102	-0.010626283474266529	0.011340088210999966	-0.044015005230903625	0.01368112862110138	0.05024069547653198	0.03151177987456322	0.023804379627108574	0.0013634136412292719	-0.014274480752646923	0.039524536579847336	0.024258200079202652	0.030492372810840607	0.01375338714569807	0.024154553189873695	0.04470917209982872	-0.02939346432685852	0.03184982016682625	-0.009359261021018028	-0.010932370088994503	-0.028874928131699562	0.031091876327991486	0.0735190361738205	-0.002421394921839237	-0.057744741439819336	0.0018830616027116776	0.11675342172384262	0.00851984228938818	0.001492093550041318	0.021880976855754852	-0.025666026398539543	-0.01329038292169571	0.015578619204461575	0.06910604983568192	0.0093817338347435	-0.0024957102723419666	-0.06678488105535507	0.011114035733044147	-0.08112867921590805	0.019514892250299454	-0.0002494125219527632	-0.007208024617284536	0.04721590131521225	0.0327228344976902	-0.01530529372394085	-0.0013390772510319948	-0.020252985879778862	0.014908815734088421	-0.06053118035197258	0.015987742692232132	-0.040952060371637344	-0.03723398596048355	-0.007851681672036648	0.049474094063043594	0.03188686817884445	-0.0005641716998070478	0.06266381591558456	-0.028245193883776665	0.06907618045806885	-0.05774317681789398	-0.02184876799583435	0.042228251695632935	0.03838299587368965	-0.03634483367204666	-0.08416703343391418	0.014552242122590542	-0.02343863435089588	0.013341792859137058	0.0022186904679983854	-0.004287388641387224	0.054765116423368454	0.034050893038511276	0.055054113268852234	0.002141825156286359	0.014112446457147598	0.011358254589140415	-0.03615904599428177	-0.038379967212677	-0.0718129426240921	-0.058705419301986694	-0.05865142121911049	-0.006315741688013077	-0.07305114716291428	0.002236538100987673	0.04975327104330063	0.06782448291778564	-0.02846028283238411	0.0931011214852333	0.0973002016544342	0.018439078703522682	-0.04800081253051758	0.01043749786913395	-0.0017718436429277062	0.009342161938548088	0.015454387292265892	-0.02125973254442215	-0.07672999054193497	0.019890811294317245	0.01716112717986107	-0.047605548053979874	0.031665682792663574	0.03130871802568436	-0.0028796689584851265	0.016721850261092186	0.02872425504028797	-0.06646892428398132	0.0014126166934147477	0.004046773537993431	0.009741697460412979	0.011877220124006271	0.08040519803762436	0.057341061532497406	-0.04480139911174774	0.017251793295145035	-0.06234268844127655	0.0823625773191452	-0.01321559026837349	0.005349071696400642	-0.048278726637363434	-0.026536446064710617	-0.03315043821930885	-0.048882149159908295	-0.03757651150226593	0.006861649453639984	0.016668668016791344	-0.05094527825713158	0.0006177929462864995	0.010376430116593838	0.09345387667417526	-0.0047458200715482235	0.0726124569773674	-0.002052397932857275	-0.033855900168418884	0.02163802646100521	0.014274675399065018	-0.01812492311000824	-0.05568554252386093	0.033784497529268265	-0.015110207721590996	-0.03680926561355591	-0.06378975510597229	-0.030022403225302696	0.007445015944540501	0.025686418637633324	-0.02241404913365841	-0.07102961838245392	-0.05017083138227463	-0.022378183901309967	-0.034034643322229385	-0.02232394926249981	0.05668516457080841	-0.0033965911716222763	-0.039579879492521286	-0.0014909873716533184	0.07210490107536316	-0.005989684723317623	0.0660582035779953	0.13563190400600433	-0.00430196151137352	0.08664344251155853	0.0282431710511446	0.02099410444498062	-0.033351849764585495	-0.02501205913722515	0.020732365548610687	0.03133058175444603	0.05196497589349747	0.03121466189622879	0.0007069361745379865	0.045040734112262726	0.06243642419576645	0.04752491042017937	-0.01936841756105423	0.002156540984287858	-0.052411872893571854	-0.019728001207113266	0.053646087646484375	0.06448104977607727	-0.005945459008216858	-0.010965777561068535	-0.006929370574653149	-0.0058519672602415085	-0.018796125426888466	0.024781445041298866	-0.14145907759666443	0.013418618589639664	-0.0326172299683094	0.0276806578040123	-0.03519322723150253	0.02123136818408966	0.056078244000673294	0.01424909196794033	-0.02021479234099388	-0.11484527587890625	0.013057056814432144	0.04054979979991913	0.037377290427684784	0.11597581952810287	0.02334100566804409	0.05184819921851158	-0.05769705772399902	-0.00606395211070776	-0.09738776087760925	-0.03463974967598915	-0.05440331995487213	0.12558308243751526	0.01452545914798975	-0.04473410174250603	-0.031398531049489975	-0.06178879737854004	0.02036368101835251	-0.05061677470803261	0.02915753610432148	0.06886450946331024	-0.004470518324524164	-0.050122473388910294	0.017062494531273842	-0.025022653862833977	-0.0404362753033638	-0.024205831810832024	0.0661512240767479	0.01667902246117592	-0.02810480259358883	0.07106908410787582	-0.02415730431675911	-0.029882565140724182	0.01393851637840271	0.039566848427057266	0.06379280239343643	-0.03295945003628731	-0.0350150503218174	0.01614234410226345	-0.04834534972906113	-0.006114855874329805	-0.08116165548563004	0.020139211788773537	-0.07025475800037384	0.009521679021418095	-0.01572590135037899	-0.007585717830806971	-0.0065249972976744175	-0.023530583828687668	0.01968655176460743	0.015042874030768871	-0.08028920739889145	0.035686127841472626	0.01972147822380066	0.03126978129148483	0.0781945064663887	0.05175580456852913	-0.06382758170366287	0.020401205867528915	0.09146318584680557	0.08574916422367096	0.04801662638783455	0.03712882846593857	0.03778079152107239	0.022008884698152542	0.06591975688934326	0.023616936057806015	0.03619003668427467	-0.01233929954469204	-0.07483398914337158	-0.020580215379595757	0.06840227544307709	-0.06990563869476318	-0.014819455333054066	0.05414818599820137	0.0025396891869604588	-0.05680661275982857	0.009511196985840797	-0.002306732116267085	-0.07257277518510818	-0.009667606092989445	0.03401926904916763	-0.028549402952194214	0.03616826981306076	-0.03099846839904785	0.0594867579638958	0.010930475778877735	0.018659736961126328	-0.028690382838249207	-0.020912984386086464	0.06488490849733353	0.046247441321611404	-0.009427910670638084	0.0011316313175484538	0.07850795239210129	0.06121869757771492	-0.03832859918475151	0.03022700734436512	-0.04595416784286499	-0.013470618985593319	0.034574199467897415	0.014855033718049526	0.06695783883333206	-0.013502787798643112	-0.0001315442204941064	-0.022695600986480713	0.01373946201056242	-0.015005508437752724	0.023436453193426132	0.024028386920690536	-0.06404842436313629	0.08070739358663559	0.0075180563144385815	-0.0764528214931488	0.03060215152800083	0.032248321920633316	0.01613452285528183	-0.030894948169589043	0.03135591372847557	0.04091906547546387	0.01681010238826275	-0.0028083608485758305	0.039578285068273544	-0.012441565282642841	-0.025539455935359	-0.013441483490169048	-1.1838215868920088e-05	-0.009004197083413601	0.00974385254085064	-0.04353133216500282	0.026876986026763916	0.01763550378382206	-0.06205461174249649	-0.041271910071372986	0.01853424496948719	-0.029547885060310364	0.04415713623166084	-0.03483948856592178	-0.022629348561167717	-0.004926073830574751	0.008049039170145988	-0.1159098818898201	0.044433679431676865	-0.00631079962477088	0.01050128135830164	0.031033582985401154	0.07392346113920212	-0.014119938015937805	0.007333455141633749	-0.05863980948925018	-0.1119987741112709	0.02402653731405735	-0.0337587334215641	0.02925129048526287	0.028810447081923485	0.05046216398477554	0.013312294147908688	0.006433384958654642	0.10658206045627594	0.030099693685770035	0.021694889292120934	0.012653051875531673	0.016527000814676285	0.0351509228348732	0.02117972820997238	0.04757200554013252	0.02782043069601059	0.025756753981113434	0.024450693279504776	-0.002725929254665971	-0.01038291584700346	0.02496064081788063	0.025884458795189857	0.041140779852867126	0.012538031674921513	-0.04839012771844864
+-0.008200710639357567	-0.014708519913256168	-0.05602620169520378	0.018692495301365852	-0.026082653552293777	0.05232645198702812	-0.0036979743745177984	-0.012867062352597713	-0.07202740758657455	0.02313336916267872	-0.044268541038036346	0.0021508471108973026	-0.07181026041507721	-0.004722110461443663	0.002428284613415599	-0.08947587013244629	0.0335647277534008	-0.002015330595895648	0.06104891374707222	-0.06675294786691666	0.0014933183556422591	0.07252635061740875	0.053367093205451965	0.05701998993754387	0.02777683548629284	-0.0007370848907157779	0.05422640219330788	0.04066219925880432	0.10662508755922318	0.03293769806623459	0.004795372020453215	-0.07466209679841995	0.013942163437604904	-0.018480470404028893	0.04265431687235832	0.009567170403897762	0.014905101619660854	0.03596490994095802	0.10478941351175308	-0.03455556184053421	-0.011974322609603405	-0.008204837329685688	0.022621693089604378	-0.04340311139822006	0.0010111629962921143	-0.021446632221341133	-0.043623678386211395	-0.11441319435834885	0.07173316925764084	0.050162095576524734	0.01226471271365881	0.0723072811961174	-0.011318838223814964	-0.03617354482412338	-0.034542266279459	0.006441380828619003	0.0021815106738358736	0.05129484459757805	0.04456658661365509	0.01025333907455206	0.019242826849222183	-0.021088799461722374	-0.0072649684734642506	-0.020573990419507027	0.04176769033074379	-0.00419045751914382	0.04483108967542648	0.037389110773801804	-0.01932292990386486	0.031236745417118073	-0.06167365983128548	0.08780983835458755	0.009433962404727936	0.037903349846601486	0.025111833587288857	-0.03040367364883423	-0.03458365797996521	-0.05332133173942566	-0.029395760968327522	0.03477824479341507	-0.027305014431476593	-0.04759196937084198	0.04253285005688667	0.029525158926844597	0.01857380010187626	0.02827361971139908	-0.044014886021614075	-0.006272163242101669	-0.02344956062734127	0.012110277079045773	0.05281201750040054	-0.09251897037029266	0.007336837239563465	0.019533313810825348	-0.0399758443236351	-0.028501341119408607	0.06548319011926651	0.02205519936978817	0.0341486893594265	-0.02947254292666912	0.02006589248776436	-0.03520416468381882	0.06654820591211319	-0.042529474943876266	0.00016330837388522923	-0.03651571273803711	-0.08488538861274719	-0.03260194510221481	0.038271162658929825	-0.04029131680727005	0.031066274270415306	0.06113468110561371	-0.004398970864713192	-0.05336039140820503	0.04694977402687073	-0.04686598479747772	0.0028350900392979383	-0.06361270695924759	0.04517272859811783	0.026271335780620575	-0.00194209860637784	0.06586951017379761	-0.049265116453170776	0.0275762639939785	-0.0073025948368012905	0.024644382297992706	0.01670360565185547	0.05625252053141594	0.017017565667629242	0.045666322112083435	-0.004729076288640499	0.06152340769767761	0.019294751808047295	0.03864828497171402	-0.047001637518405914	-0.08141329884529114	-0.05710062012076378	0.04932047054171562	0.03621451184153557	0.05960655212402344	-0.07915989309549332	0.0687231719493866	0.12177779525518417	0.02811160311102867	-0.045663803815841675	-0.012195829302072525	-0.03765343502163887	-0.030184855684638023	0.048222772777080536	-0.007824246771633625	0.01471247710287571	0.04849345237016678	0.08046316355466843	-0.012193894013762474	-0.00724148890003562	-0.010684855282306671	0.009161489084362984	0.002102385275065899	0.045246873050928116	-0.03256507217884064	0.02844119817018509	-0.03454776853322983	0.007067245431244373	-0.0012910241493955255	0.03198205307126045	0.08138711005449295	0.04607285186648369	-0.1010284572839737	0.005202914588153362	0.0601307712495327	-0.01795552857220173	-0.015111589804291725	0.0220401082187891	-0.04197782278060913	-0.04365681856870651	-0.03375128284096718	-0.004536198452115059	-0.0016337118577212095	0.06240146979689598	-0.028400087729096413	0.02789091132581234	-0.11327226459980011	-0.003445655107498169	0.03144102916121483	-0.051116593182086945	0.0019304767483845353	-0.0036494757514446974	-0.015113448724150658	-0.04868802800774574	0.03273294121026993	0.009483416564762592	-0.004617179743945599	-0.05732332542538643	-0.011670072562992573	-0.03209804743528366	-0.044546980410814285	-0.0021041955333203077	0.029668699949979782	0.009507416747510433	0.06971653550863266	-0.09335777908563614	0.05547624081373215	-0.04998873546719551	-0.06296051293611526	0.009142016991972923	0.01800420507788658	-0.028163503855466843	-0.06694167852401733	-0.0004402245976962149	-0.029159853234887123	-0.016708897426724434	0.0022920577321201563	-0.002468446269631386	-0.002558581531047821	-0.0316433347761631	0.01976253278553486	-0.022666744887828827	-0.03872045874595642	-0.030617041513323784	0.027118174359202385	-0.01778963766992092	-0.043175697326660156	-0.03272261470556259	-0.03832594305276871	-0.026478756219148636	-0.03610893338918686	-0.011035972274839878	-0.03974152356386185	0.11433473974466324	-0.03873787820339203	0.07572069019079208	0.022803988307714462	0.008619981817901134	0.038403987884521484	0.020960720255970955	-0.06571634858846664	0.08218549191951752	-0.02288113348186016	-0.04537661001086235	-0.020326819270849228	0.016569223254919052	-0.04269726201891899	-0.012972133234143257	0.010101599618792534	-0.0189286507666111	-0.009513302706182003	-0.025994688272476196	0.05147706717252731	-0.08179373294115067	-0.046276647597551346	0.04218890517950058	-0.014173146337270737	-0.02068801410496235	0.028708012774586678	0.011314954608678818	-0.08818720281124115	0.040316011756658554	-0.04997618868947029	0.10784941166639328	-0.057872701436281204	0.03697873279452324	-0.004833213984966278	-0.004615561570972204	0.016779372468590736	0.037210363894701004	0.020800217986106873	0.012421202845871449	0.022790633141994476	-0.0029819272458553314	-0.005514577962458134	0.06175415962934494	0.10220418870449066	0.005444889422506094	0.06630388647317886	-0.001570404740050435	-0.022298503667116165	-0.03454548120498657	0.05682937428355217	-0.0022603555116802454	-0.05703761801123619	0.04137718304991722	-0.0630950778722763	0.014074699021875858	-0.02140158787369728	0.014437947422266006	-0.07079191505908966	-0.015578268095850945	0.02753482386469841	0.030043426901102066	-0.015208657830953598	-0.008915296755731106	-0.05584145337343216	-0.04853324219584465	-0.032538507133722305	-0.07499315589666367	0.02472531422972679	-0.07540778815746307	0.06384341418743134	0.014156513847410679	0.014093627221882343	0.10057857632637024	0.0011417956557124853	0.03334454074501991	0.021597443148493767	0.024415021762251854	-0.12737630307674408	0.04861912503838539	0.029530009254813194	0.011667096056044102	0.017094891518354416	0.06976379454135895	-0.02248951606452465	-0.009739604778587818	-0.02605978399515152	0.03796356916427612	-0.01251941453665495	-0.0001300503936363384	-0.006155055947601795	-0.05369286239147186	0.02732061594724655	0.05153125151991844	-0.02333192341029644	0.024170706048607826	0.016340654343366623	-0.022753272205591202	-0.024555468931794167	0.008067856542766094	-0.14098495244979858	0.0592690110206604	-0.013952626846730709	-0.02992652729153633	-0.09162138402462006	-0.013895712792873383	0.0450022891163826	-0.034563980996608734	-0.039253320544958115	-0.0930585041642189	0.01757138781249523	0.017187748104333878	0.043378252536058426	0.11004766076803207	-0.005063633434474468	-0.02137773670256138	0.020592378452420235	-0.018623681738972664	-0.011479486711323261	-0.04073135182261467	-0.10054261982440948	0.08440781384706497	0.018683552742004395	-0.0025461807381361723	-0.10135356336832047	-0.01452852226793766	0.0162259042263031	0.02100062184035778	0.014205975458025932	0.0006050749798305333	0.05103239044547081	-0.12040829658508301	-0.02291450835764408	-0.04234723374247551	-0.04214390739798546	-0.0367068387567997	0.042264729738235474	0.062255214899778366	-0.059256188571453094	0.08863596618175507	-0.022227054461836815	-0.0680650845170021	0.029347114264965057	-0.007251957897096872	0.018314016982913017	-0.033027589321136475	-0.008765527978539467	0.061241380870342255	-0.07544191181659698	-0.036635786294937134	-0.03100494295358658	-0.025559084489941597	0.012450842186808586	-0.001579503295943141	0.053860846906900406	-0.03261425718665123	0.03674693405628204	0.04764171689748764	0.027319272980093956	0.015048326924443245	0.02582196705043316	0.03164992108941078	0.05188393592834473	-0.02048404887318611	0.09261096268892288	0.010554690845310688	0.001296902191825211	-0.0026893913745880127	0.021603714674711227	0.05366767942905426	0.030130892992019653	-0.0154243940487504	0.030590029433369637	0.010282468982040882	-0.007967022247612476	0.02252952568233013	0.042957551777362823	-0.02735944278538227	-0.03671775385737419	-0.01888958364725113	0.029752468690276146	-0.06806818395853043	-0.03595496341586113	-0.014768211171030998	-0.025595352053642273	-0.07385558634996414	0.11965293437242508	0.006651001982390881	-0.08562707155942917	0.01611013151705265	0.04138968884944916	-0.04100700095295906	-0.03629208356142044	0.002522445283830166	0.043304692953825	0.05584733933210373	-0.00830933079123497	-0.02487078681588173	0.00024115109408739954	0.04064628481864929	0.05159863457083702	-0.048092711716890335	-0.008440626785159111	0.03162922337651253	0.06555897742509842	0.004602223634719849	0.05667494982481003	-0.06402482092380524	-0.04036463797092438	0.04147743433713913	0.037402667105197906	0.02239937148988247	0.015735723078250885	0.02143838070333004	0.023880666121840477	0.010758547112345695	-0.0798480361700058	-0.00023255552514456213	0.029801512137055397	-0.010785582475364208	0.061671867966651917	0.0398307628929615	-0.0351228341460228	0.07980994135141373	0.052457764744758606	0.06137750297784805	0.01770573854446411	0.010352500714361668	0.017342619597911835	0.009120924398303032	0.045794129371643066	0.044829111546278	0.01922544650733471	-0.031503550708293915	-0.0115105751901865	-0.00733925448730588	0.040431275963783264	0.009322036989033222	-0.07449643313884735	0.00018175745208282024	-0.0073417495004832745	0.024180060252547264	-0.030325809493660927	-0.007542209699749947	0.006716473959386349	0.07296957820653915	-0.020669717341661453	0.03975820168852806	0.014715973287820816	0.07335516065359116	-0.12501823902130127	0.0031592200975865126	0.005143428221344948	0.039682574570178986	0.07839889079332352	0.027915259823203087	-0.005984538234770298	0.03784622997045517	-0.0032728160731494427	-0.03664393350481987	0.043269433081150055	-0.007732688914984465	-0.0101991668343544	0.060556989163160324	0.0026693406980484724	-0.001698168576695025	0.024544833227992058	0.07971511781215668	0.09042546153068542	0.04117991402745247	0.017459383234381676	-0.009676080197095871	0.03955010324716568	0.04717934504151344	0.05783230811357498	0.06743650883436203	0.0668640211224556	0.00919106975197792	0.00012225739192217588	0.018121810629963875	0.034543897956609726	-0.0006492654792964458	0.03682837262749672	0.03220042958855629	-0.025315506383776665
+0.04903428256511688	-0.07590302079916	-0.08936519920825958	0.00443049892783165	-0.02081098034977913	0.021696308627724648	0.00864363182336092	-0.014404227957129478	-0.0657910704612732	0.038199588656425476	-0.06724588572978973	0.05968635901808739	-0.017983118072152138	-0.03805459290742874	-0.03216519579291344	-0.12363419681787491	0.0019852938130497932	0.014462439343333244	0.07074660807847977	-0.013090494088828564	-0.037129808217287064	0.061687834560871124	-0.01758795790374279	0.013157819397747517	-0.011464543640613556	0.01956966146826744	0.01774032786488533	0.005709442310035229	0.05817050859332085	0.02165934629738331	0.02354331873357296	-0.05738518759608269	0.021980728954076767	-0.014220093376934528	-0.014407075010240078	-0.04868820309638977	-0.034352775663137436	0.05927010998129845	0.06085371971130371	-0.016070596873760223	-0.04595257714390755	0.011837353929877281	-0.02152276039123535	-0.05701234191656113	-0.022693276405334473	0.011140289716422558	-0.056699566543102264	-0.08387196063995361	0.01620730198919773	-0.017496353015303612	-0.015818262472748756	0.04079233482480049	0.018035609275102615	0.019568918272852898	-0.09578423202037811	-0.02665509656071663	-0.0010316657135263085	0.10600098967552185	-0.023301279172301292	0.016616301611065865	-0.012157997116446495	0.03617120906710625	-0.006665096618235111	-0.010567876510322094	0.006577452179044485	-0.008693033829331398	0.05927830561995506	0.010742378421127796	-0.04297277703881264	0.04275215044617653	-0.019021304324269295	0.045280154794454575	0.00895067397505045	0.057948555797338486	-0.008403089828789234	-0.06349124014377594	-0.05990241467952728	-0.028965113684535027	-0.0008863513940013945	-0.012945032678544521	-0.03090292029082775	0.02886582538485527	0.07419814169406891	0.00637509161606431	-0.02833942510187626	-0.012196263298392296	-0.015449830330908298	-0.03669530153274536	-0.04844386503100395	-0.003141340333968401	0.09142349660396576	-0.049812693148851395	0.011716388165950775	0.012281765230000019	0.01697770319879055	0.0026346028316766024	0.10752951353788376	0.0114205963909626	-0.06853646039962769	-0.006034108344465494	-0.005133462604135275	-0.02010984532535076	0.06974011659622192	-0.02877180464565754	0.012224046513438225	-0.07355006039142609	-0.05588965117931366	0.04314456880092621	0.016492260619997978	-0.034336157143116	0.08185205608606339	0.05848310887813568	-0.0804116278886795	-0.03586415573954582	0.08988815546035767	-0.009989237412810326	0.04702431708574295	-0.01397494226694107	0.06631599366664886	0.048523083329200745	-0.04376674443483353	0.07165902107954025	-0.02651187963783741	-0.04398330673575401	-0.025735531002283096	0.05107065290212631	-0.019849738106131554	0.020132172852754593	-0.029899364337325096	0.002096275333315134	-0.04152483120560646	0.04930941388010979	0.02345520816743374	0.038991041481494904	-0.09458997845649719	-0.08936917781829834	-0.05294821411371231	0.06792819499969482	0.04755105450749397	0.04305136576294899	-0.0873299390077591	0.0578853003680706	0.0676732137799263	0.07938597351312637	-0.026024313643574715	-0.040332671254873276	-0.06039682403206825	-0.03453030809760094	0.05624186620116234	-0.0006165630184113979	0.031317997723817825	0.04067317768931389	0.028816288337111473	0.01796938292682171	0.061288539320230484	-0.0006860780413262546	0.02444632537662983	0.007966194301843643	0.02587037906050682	0.008945374749600887	0.0028653189074248075	-0.0003834956150967628	0.004850384779274464	-0.022902293130755424	0.02635607123374939	0.11738830804824829	0.028883542865514755	-0.059418871998786926	0.00550040602684021	0.13322094082832336	-0.019424226135015488	-0.00412612734362483	0.0015445247991010547	-0.03717474639415741	-0.02178863435983658	-0.004717474803328514	0.01669970341026783	-0.009342078119516373	0.07502458989620209	-0.047301147133111954	0.008431906811892986	-0.08228879421949387	-0.006740253418684006	0.027776367962360382	-0.004017077852040529	0.04219823330640793	0.007513062562793493	-0.028257066383957863	0.009939962066709995	0.003923261538147926	0.041420575231313705	-0.08317498862743378	-0.0425291582942009	-0.020207082852721214	0.005709588062018156	-0.040009938180446625	0.02431851625442505	0.06089429184794426	0.029435278847813606	0.06144753471016884	-0.029816970229148865	0.02006104402244091	-0.10205239057540894	-0.012421171180903912	0.016344262287020683	0.04456503689289093	-0.015392479486763477	-0.04851900413632393	0.04319104924798012	-0.02903745323419571	-0.027007637545466423	-0.026275476440787315	-0.03867393732070923	0.031566642224788666	0.04259265586733818	-0.018794531002640724	-0.01435053814202547	0.04303080588579178	-0.0009642424411140382	0.016861846670508385	-0.05493145436048508	-0.04078871011734009	-0.05662580952048302	-0.08098503202199936	-0.0064858742989599705	-0.06461575627326965	0.023611294105648994	0.021687466651201248	0.06862878799438477	-0.017880141735076904	0.08523314446210861	0.0657372698187828	0.03600923344492912	-0.03394847735762596	-0.006201330106705427	-0.029184652492403984	0.021141257137060165	-0.023610038682818413	-0.019599417224526405	-0.08615982532501221	0.039616674184799194	-0.025904716923832893	-0.015069619752466679	0.049300093203783035	0.011447462253272533	-0.034466128796339035	0.057615071535110474	0.012557631358504295	-0.10319101065397263	-0.007774869911372662	0.038231708109378815	-0.012081801891326904	0.04600222036242485	0.0683375895023346	0.018510522320866585	-0.05616919323801994	0.05345810577273369	-0.049517180770635605	0.05331585183739662	-0.013973983004689217	0.010766854509711266	0.03363683819770813	-0.05950689688324928	-0.03411538526415825	-0.0043336693197488785	0.0168035626411438	0.05865764617919922	0.021287014707922935	-0.015801813453435898	0.0014102980494499207	0.027799120172858238	0.06642895936965942	0.01357903890311718	0.04877496510744095	-0.006517421454191208	-0.02941644750535488	0.017388302832841873	0.012475679628551006	0.018234362825751305	-0.08387982845306396	0.027664538472890854	0.024222420528531075	-0.010332576930522919	-0.020781565457582474	-0.043613068759441376	-0.02992340922355652	-0.00764875952154398	-0.006289830431342125	-0.0005650186212733388	-0.028410427272319794	-0.056145165115594864	-0.06328780204057693	-0.007615912239998579	-0.005576244555413723	-0.0201072059571743	-0.05265110731124878	0.016617506742477417	0.06939074397087097	-0.010898087173700333	0.04111873358488083	0.12528096139431	-0.047682441771030426	0.030209967866539955	0.0649937242269516	-0.03267541155219078	-0.06930579245090485	-0.06394226104021072	-0.025557497516274452	0.032752394676208496	0.04661395028233528	0.07534658908843994	0.007067565806210041	0.02110672928392887	0.0493602454662323	0.005501736421138048	-0.05845537409186363	-0.008811527863144875	-0.0355813093483448	-0.015759911388158798	0.03772326186299324	0.07570014894008636	0.03312085568904877	-0.018278401345014572	0.024317918345332146	-0.024418318644165993	-0.04593127965927124	0.027377214282751083	-0.1465185433626175	-1.101760244637262e-05	-0.053150590509176254	-0.018902085721492767	-0.04554896056652069	0.03552188724279404	0.036660175770521164	-0.006027701776474714	-0.06997814029455185	-0.0713268294930458	-0.03522641584277153	0.026283621788024902	0.048100922256708145	0.10596710443496704	0.0006928592920303345	0.03208068013191223	-0.07917328178882599	-0.020717957988381386	-0.05781630799174309	-0.0476011261343956	-0.1177557185292244	0.1038324162364006	0.00843776948750019	-0.03840233013033867	-0.017462724819779396	-0.06520002335309982	0.026381706818938255	-0.038418110460042953	0.013816261664032936	0.08033639192581177	0.041574422270059586	-0.0392664410173893	0.024387190118432045	-0.02382729761302471	-0.02529873698949814	0.006714940071105957	0.08267487585544586	0.032319359481334686	-0.047632474452257156	0.0770905613899231	0.0010724255116656423	-0.012167753651738167	0.010502761229872704	0.004799284506589174	0.025869132950901985	-0.033319782465696335	-0.008526040241122246	0.054158419370651245	-0.06330262124538422	-0.024525610730051994	-0.003119892440736294	0.009029506705701351	-0.02601654827594757	0.01720389910042286	0.0015374424401670694	-0.03536805883049965	0.014679189771413803	0.025271758437156677	0.021523306146264076	-0.0014630125369876623	-0.032294128090143204	0.04305855184793472	0.054236602038145065	0.001330966711975634	0.1111592948436737	0.037217769771814346	-0.01330031082034111	0.007779472507536411	0.10717838257551193	0.041122328490018845	0.027030931785702705	0.02123005874454975	0.0473208911716938	0.015485594049096107	0.020975183695554733	0.009326702915132046	0.06440099328756332	-0.005738041363656521	-0.06893490254878998	-0.02760782465338707	0.049176789820194244	-0.08267724514007568	-0.060725100338459015	0.03339431434869766	-0.012067295610904694	-0.03876963257789612	0.045461397618055344	-0.01646779291331768	-0.06163506209850311	0.014863339252769947	0.03347570821642876	-0.04363272711634636	-0.05087471753358841	-0.037709686905145645	0.06234689801931381	0.02277371473610401	0.031595174223184586	-0.04132041335105896	-0.018278775736689568	0.04040956497192383	0.03022317960858345	-0.07117056846618652	0.0028983834199607372	0.07949310541152954	0.07381556928157806	-0.019088279455900192	0.06270752847194672	-0.04161457344889641	-0.002566447015851736	-0.0011023032711818814	0.035927727818489075	0.07977908849716187	0.009358882904052734	0.013722631148993969	0.006479273084551096	0.02876162715256214	-0.031511783599853516	-0.01673656702041626	0.013811375014483929	-0.029988255351781845	0.03965238109230995	0.014781189151108265	-0.05964956805109978	0.008700679056346416	0.05153066664934158	0.0015903525054454803	0.0032223139423877	0.02309928834438324	0.047306183725595474	-0.0019887122325599194	-0.005807612091302872	0.021827332675457	-0.013189579360187054	0.0003933013940695673	-0.033279646188020706	0.010888762772083282	-0.03220292180776596	0.005980967078357935	-0.018704965710639954	-0.0005047488957643509	0.030534885823726654	-0.015226038172841072	-0.019007449969649315	0.042589206248521805	-0.032000407576560974	0.07113505154848099	-0.010570894926786423	0.01501680351793766	0.022842634469270706	0.0667196661233902	-0.12603998184204102	0.019628554582595825	0.022768966853618622	0.04406975582242012	0.053070515394210815	0.06586254388093948	-0.011094307526946068	0.07561288774013519	-0.041849322617053986	-0.09739092737436295	-0.017729992046952248	0.013050233013927937	0.01880832202732563	0.0034316701348870993	0.03447410836815834	-0.008742576465010643	0.010402625426650047	0.09703531116247177	0.03384508565068245	-0.0026517780497670174	0.00996505469083786	-0.01038677990436554	0.05574190616607666	0.0309857539832592	0.05166425555944443	0.026519497856497765	0.03940274566411972	0.04538155347108841	-0.004973904695361853	-0.01716512441635132	0.02032024972140789	-0.016613421961665154	0.016477713361382484	0.019854281097650528	-0.01730331964790821
+-0.003757934086024761	-0.05996017903089523	-0.0899183452129364	0.029453309252858162	-0.06236733868718147	0.04786329343914986	0.0470750667154789	-0.0036442207638174295	-0.08446823060512543	-0.04585932195186615	0.013005057349801064	-0.0023127980530261993	-0.03809897601604462	-0.04605157673358917	0.052011217921972275	-0.040483634918928146	-0.0022709830664098263	0.013109605759382248	-0.02773098275065422	-0.023884447291493416	-0.0014065636787563562	-0.013503670692443848	0.011269466951489449	0.02147427387535572	-0.030746569857001305	-0.007211014162749052	0.029703740030527115	0.04598129913210869	0.04514075443148613	0.0338464230298996	0.02707386389374733	-0.007852703332901001	0.008692332543432713	-0.058180071413517	-0.04813602939248085	0.02811632677912712	0.019229678437113762	0.07385830581188202	0.09478925168514252	-0.02680102176964283	-0.0050388057716190815	0.02044614590704441	-0.07959682494401932	-0.13102956116199493	0.08984965831041336	-0.037250932306051254	-0.009789811447262764	-0.06640762090682983	0.06800836324691772	0.03171872720122337	-0.066624715924263	0.032789114862680435	-0.018467366695404053	-0.016139820218086243	-0.05822361260652542	0.012390317395329475	0.040649283677339554	0.013035688549280167	0.05625374987721443	0.08098545670509338	-0.01477727945894003	-0.032927680760622025	0.014368056319653988	0.006853835191577673	0.05437562242150307	0.024680623784661293	0.0849723219871521	-0.05722983181476593	-0.07745592296123505	-0.0386166013777256	-0.08854051679372787	0.04096280410885811	0.02278951369225979	0.016506843268871307	0.010185964405536652	-0.026627860963344574	-0.0027327761054039	-0.027662111446261406	-0.017816396430134773	0.021313287317752838	-0.06462948769330978	-0.006890919990837574	0.0718594342470169	0.005740494467318058	0.07526743412017822	-0.07082722336053848	-0.02556220069527626	0.02423821948468685	-0.0420413576066494	-0.062127918004989624	0.00899936817586422	-0.07574868202209473	-0.038301944732666016	-0.02242819033563137	-0.020323170349001884	-0.006867953110486269	0.02016400173306465	-0.06062602624297142	0.02180366963148117	0.0405297614634037	-0.033695753663778305	-0.07106576859951019	0.015699032694101334	-0.009181106463074684	0.025550156831741333	-0.020974034443497658	-0.08638420701026917	0.0015613007126376033	0.01893831603229046	-0.06124826520681381	0.040359608829021454	0.09726843237876892	-0.012119082733988762	-0.03729567304253578	0.0942644253373146	0.002987137297168374	-0.03680280223488808	-0.09505993127822876	0.04750123247504234	0.0028441986069083214	-0.034664303064346313	0.00656714104115963	0.01411332655698061	0.015374870039522648	0.014541218988597393	-0.035278044641017914	-0.048415761440992355	-0.03051309660077095	-0.015600045211613178	-0.043234653770923615	0.022852081805467606	-0.028307821601629257	0.0025330306962132454	0.03220776468515396	-0.044432252645492554	-0.0062381187453866005	0.027772720903158188	0.062154620885849	0.02779422700405121	-0.04075102508068085	0.013651636429131031	0.05043433979153633	0.021864667534828186	0.057081304490566254	-0.010835608467459679	0.011822706088423729	-0.08509430289268494	-0.018491564318537712	-0.05002767592668533	0.05246932804584503	-0.08049120754003525	0.0657946988940239	0.008808041922748089	-0.0957685187458992	0.07290517538785934	0.020817818120121956	0.034950003027915955	0.0064942678436636925	-0.009246555157005787	0.00045518288970924914	0.0019663297571241856	-0.003324354300275445	0.05767663195729256	-0.06450076401233673	-0.04254010319709778	0.05198705941438675	0.03649749606847763	-0.01825067587196827	0.01924695447087288	0.005103963427245617	-0.04629684239625931	0.00709268543869257	0.08310848474502563	-0.08332988619804382	-0.005898586008697748	0.0001164058776339516	0.001910955528728664	-0.09441352635622025	0.04518899694085121	-0.02672225423157215	0.03995248302817345	-0.09051887691020966	0.041202105581760406	-0.013252782635390759	-0.03558823838829994	-0.04532137140631676	-0.055887579917907715	-0.047986678779125214	-0.034831780940294266	-0.028391003608703613	-0.13332578539848328	-0.063237264752388	0.023345937952399254	0.020157454535365105	-0.011762917041778564	0.012440614402294159	0.040370550006628036	0.019057156518101692	-0.004175905138254166	-0.011080930009484291	-0.10993055254220963	0.029964540153741837	-0.021301649510860443	-0.04192865639925003	-0.01141347922384739	-0.03896526247262955	-0.051857635378837585	-0.0744880735874176	-0.0041068303398787975	0.008661437779664993	0.04826395958662033	0.04768197983503342	-0.054155200719833374	-0.036364153027534485	-0.005409597884863615	0.0589480884373188	-0.023860499262809753	-0.017095912247896194	0.006496312562376261	-0.046975597739219666	0.04124956578016281	0.023883497342467308	-0.0581122562289238	-0.047682248055934906	-0.011276287026703358	-0.07955388724803925	0.03641805425286293	0.02529464103281498	-0.008654676377773285	-0.009721808135509491	-0.02514464221894741	0.058792710304260254	-0.013693109154701233	-0.029705742374062538	-0.017265530303120613	-0.003047862323001027	0.05731642618775368	0.02478151209652424	-0.03986503556370735	-0.09148330986499786	0.021021900698542595	-0.01460952591150999	0.0028682409320026636	-0.04169899597764015	0.030477354303002357	-0.03756014257669449	-0.0497736819088459	-0.04271024838089943	-0.06969837099313736	-0.07704591751098633	0.021034616976976395	0.03565454110503197	-0.031230106949806213	0.04468081146478653	0.049914006143808365	-0.0713748037815094	-0.020320914685726166	-0.04391825199127197	0.06006300449371338	0.036237869411706924	0.07311613857746124	-0.02114432491362095	-0.049710702151060104	0.006507468409836292	-0.0015964092453941703	0.00534885935485363	0.016271017491817474	-0.0019548162817955017	-0.03591761738061905	-0.08547466993331909	-0.01807594485580921	0.06785982102155685	-0.01010863482952118	-0.012551359832286835	0.0195943433791399	-0.029658665880560875	-0.03036029450595379	0.004374019335955381	-0.03192014619708061	-0.0029665075708180666	0.01593622751533985	0.007880719378590584	-0.03272533416748047	0.017048481851816177	0.0013054810697212815	0.04390823096036911	0.1079382374882698	0.024799369275569916	-0.03195425868034363	-0.06696520745754242	-0.017804326489567757	-0.05455750599503517	0.0023568153847008944	0.015061081387102604	-0.051193077117204666	0.06887175142765045	-0.00027602692716754973	0.06291453540325165	0.006257609464228153	0.006379118654876947	0.09215780347585678	-0.042213182896375656	0.021199852228164673	-0.03379182517528534	-0.0981210395693779	-0.031141551211476326	0.009412064217031002	0.011104173958301544	0.019957732409238815	0.008715752512216568	0.028199730440974236	-0.10121850669384003	0.0252436064183712	-0.03654903173446655	-0.08253247290849686	0.07277842611074448	-0.010073624551296234	-0.05446219444274902	-0.0771884024143219	0.038777098059654236	-0.013744408264756203	-0.01642986759543419	-0.009297862648963928	0.001532750902697444	0.09934040904045105	-0.11446575075387955	-0.005653698928654194	-0.07607129961252213	0.012429242953658104	0.02038872241973877	0.037623729556798935	-0.020437048748135567	0.03741373494267464	0.013189643621444702	-0.04728180170059204	-0.03312535211443901	-0.06445743888616562	0.02226567268371582	0.018225982785224915	0.055190954357385635	0.05499587580561638	-0.104749895632267	-0.002135197864845395	0.06423782557249069	0.041225891560316086	-0.0577743761241436	0.012770255096256733	-0.05194281041622162	0.03580630570650101	0.008806447498500347	0.007031396962702274	-0.03684793412685394	-0.02246735990047455	-0.021643657237291336	0.010916939936578274	0.08244506269693375	-0.036601025611162186	-0.0005972926155664027	-0.05540290102362633	0.04056498035788536	-0.037452101707458496	-0.007113845553249121	-0.04038913920521736	0.006617818493396044	-0.009566651657223701	0.038444191217422485	0.0636834055185318	0.010058702901005745	-0.04308655112981796	0.003391699632629752	0.06594188511371613	0.029969144612550735	0.015372570604085922	-0.005009023938328028	0.0189555361866951	-0.07271092385053635	0.05788847431540489	-0.036156006157398224	-0.020885135978460312	-0.10597303509712219	-0.07778675109148026	0.0040074908174574375	-0.02575920894742012	-0.03240218386054039	-0.011548757553100586	-0.023389272391796112	0.040333762764930725	-0.06358156353235245	-0.024280061945319176	0.0517340749502182	0.06203015148639679	0.04083150997757912	-0.022561784833669662	-0.07739906013011932	-0.005483061075210571	0.07974742352962494	0.03147940710186958	-0.032329197973012924	0.008491878397762775	0.031015686690807343	-0.034947194159030914	0.05337299406528473	-0.0503731332719326	0.05891912803053856	-0.017376089468598366	0.017992183566093445	-0.03307736665010452	-0.03563270717859268	-0.062310073524713516	0.007207481190562248	0.0005812736344523728	-0.04625707492232323	-0.04282532259821892	-0.009602449834346771	-0.010511496104300022	-0.00539355305954814	0.013476589694619179	0.02697494998574257	-0.019888093695044518	-0.03330439329147339	0.0025479779578745365	0.007002260535955429	0.021816996857523918	-0.05062222480773926	-0.049987953156232834	0.004958335310220718	0.047602612525224686	0.03333782032132149	-0.011556508019566536	0.05368856340646744	-0.013941068202257156	0.04860187694430351	0.02622509002685547	-0.023924048990011215	-0.10421731323003769	-0.07754576951265335	-0.023943444713950157	0.045856673270463943	0.03630295768380165	0.03171785920858383	0.006406079977750778	0.07492690533399582	-0.04322832077741623	-0.04413684085011482	-0.0018173344433307648	-0.018862908706068993	0.025537243112921715	0.03559272363781929	-0.0450325571000576	-0.007890960201621056	0.07209759950637817	-0.08746889233589172	0.013279053382575512	0.048006534576416016	0.025850193575024605	0.013380017131567001	0.09923133254051208	0.009796886704862118	0.03428180143237114	0.04157543554902077	-0.06683621555566788	0.0006014718092046678	-0.012271764688193798	-0.014670184813439846	0.030369648709893227	0.023721804842352867	0.047148849815130234	0.014180446043610573	-0.00908520258963108	-0.014793009497225285	-0.044513434171676636	-0.043051689863204956	0.047702010720968246	0.032686442136764526	0.05683521181344986	-0.011587733402848244	0.003447685856372118	-0.07153508067131042	0.05583126097917557	0.0036394759081304073	0.09103749692440033	-0.01093774102628231	-0.0069403816014528275	-0.031704556196928024	0.032364122569561005	0.02278744988143444	-0.0007861653575673699	-0.05484519153833389	0.02769913524389267	0.08403830230236053	0.004512235522270203	0.06443333625793457	-0.01492395717650652	-0.013399315066635609	0.03667222708463669	0.04271406680345535	0.020543178543448448	-0.024364136159420013	0.0034254335332661867	0.0015741289826110005	0.01296585239470005	0.025252144783735275	0.025355342775583267	0.021678006276488304	0.0018535665003582835	-0.03165845945477486	0.05948084592819214	0.07211768627166748	0.036801163107156754	0.029955066740512848	-0.010846144519746304	0.012431994080543518
+0.01184793934226036	-0.07054077088832855	-0.08522239327430725	0.022613050416111946	-0.03795836493372917	0.02548304572701454	0.020110633224248886	-0.030306287109851837	-0.04122248291969299	0.030481668189167976	-0.05586826056241989	0.042691294103860855	-0.015093796886503696	-0.040036872029304504	-0.012161646969616413	-0.09614289551973343	0.0028812880627810955	0.008411378599703312	0.058183085173368454	-0.007322931662201881	-0.00748217711225152	0.04603128135204315	-0.011549797840416431	0.03868027403950691	-0.002672429196536541	0.026640666648745537	0.00328688882291317	0.03476056084036827	0.061589643359184265	0.03695574402809143	0.05147436261177063	-0.039755143225193024	0.05293619632720947	-0.08159426599740982	0.011624974198639393	-0.03482408449053764	-0.0046292869374156	0.05378764122724533	0.10085184872150421	-0.048671308904886246	-0.03958756476640701	0.0008180044824257493	-0.026855092495679855	-0.11711650341749191	-0.04447707161307335	-0.022137224674224854	-0.03778411075472832	-0.08874671161174774	0.048657070845365524	0.000499306304845959	-0.01797535829246044	0.030708221718668938	-0.008876354433596134	0.0021078058052808046	-0.0641782134771347	-0.029975298792123795	0.02802453748881817	0.0803312435746193	-0.006017305888235569	0.053023360669612885	-0.029815347865223885	0.03718872368335724	-0.025500744581222534	0.018782872706651688	-0.014294099062681198	-0.0017621342558413744	0.09872283786535263	-0.02530723251402378	-0.024105951189994812	0.015739956870675087	-0.03067512810230255	0.038250092417001724	0.020671840757131577	0.06522414088249207	-0.058549199253320694	-0.035960134118795395	-0.039786677807569504	-0.03592758625745773	0.007276250049471855	0.0384884849190712	-0.02093801647424698	-0.026248008012771606	0.08416305482387543	0.039311353117227554	0.03846564143896103	-0.03976716846227646	-0.03258824720978737	-0.0041005490347743034	-0.06307036429643631	-0.022702479735016823	0.055459242314100266	-0.04819205403327942	0.0235653817653656	0.04876576364040375	0.03392311558127403	0.025296129286289215	0.06860736012458801	-0.029892347753047943	-0.013942760415375233	0.025806734338402748	-0.02949618175625801	-0.022675439715385437	0.06561487913131714	-0.00822463445365429	0.055186156183481216	-0.06471480429172516	-0.08798743039369583	0.01634211279451847	0.0720471441745758	-0.02172747254371643	0.0772373154759407	0.08314846456050873	-0.06757393479347229	-0.06843207031488419	0.045922379940748215	-0.01459352858364582	0.04057172313332558	-0.05385797843337059	0.06648851186037064	0.06025968864560127	-0.019241387024521828	0.019195113331079483	-0.032835446298122406	-0.02091921865940094	-0.012234996072947979	0.05062074959278107	-0.00749753275886178	-0.019918572157621384	-0.05513390898704529	0.0019483541836962104	0.010659214109182358	0.0867297574877739	0.014428711496293545	0.017085019499063492	-0.08388523757457733	-0.04097197949886322	-0.0421152338385582	0.06953214108943939	0.07149659842252731	0.012869907543063164	-0.09244739264249802	0.028412112966179848	0.07406044751405716	0.05851934850215912	-0.02948964387178421	-0.04051240533590317	-0.09563005715608597	-0.049017284065485	0.020846039056777954	-0.014963141642510891	0.017992287874221802	0.059026606380939484	0.08767195791006088	-0.002334759570658207	0.04451262578368187	0.009281719103455544	0.04221201315522194	0.02673836424946785	-0.00890825130045414	0.00011932609777431935	0.015605706721544266	-0.004495490342378616	0.04210267961025238	-0.05679734796285629	0.019818630069494247	0.08051102608442307	0.04221368581056595	-0.06050623208284378	0.01550040952861309	0.09305360168218613	-0.049736686050891876	-0.012404643930494785	0.01902875490486622	-0.06450580060482025	-0.03817185014486313	-0.039062224328517914	0.009290183894336224	-0.03957042843103409	0.10225148499011993	-0.0690307691693306	-0.013907727785408497	-0.0819888561964035	-0.00416373647749424	0.01753298006951809	-0.04010070115327835	0.03577039763331413	0.001475076307542622	-0.022033443674445152	-0.03985370695590973	0.011046313680708408	-0.032084837555885315	-0.057132501155138016	-0.044320248067379	-0.015049710869789124	0.0037917974404990673	-0.0027583737391978502	0.040562957525253296	0.06096677854657173	0.04451665282249451	0.054202161729335785	-0.06443440914154053	0.055994611233472824	-0.08292309939861298	-0.03549453616142273	-0.008281661197543144	0.032372623682022095	-0.037747953087091446	-0.03013126738369465	0.009327932260930538	-0.02592177502810955	-0.03223864734172821	0.012622988782823086	-0.047256384044885635	0.007310859393328428	0.02374163828790188	0.0013612965121865273	-0.015291274525225163	0.04002093896269798	-0.012917705811560154	-0.008111800067126751	-0.022832168266177177	-0.018869847059249878	-0.04103619232773781	-0.0664416253566742	-0.044620346277952194	-0.09099266678094864	0.01168623473495245	-0.005449273623526096	0.06153218075633049	-0.03200605511665344	0.09431152790784836	0.035095732659101486	0.00890089850872755	-0.007689992897212505	-0.027009740471839905	-0.04924655333161354	0.03305172920227051	0.005854097194969654	-0.053127117455005646	-0.1038651317358017	0.04258131980895996	-0.017508702352643013	-0.0025165975093841553	0.032740477472543716	-0.001357704633846879	-0.02737039513885975	0.02593401074409485	0.03747594356536865	-0.12465967983007431	-0.03533967211842537	0.028614932671189308	-0.05786775052547455	0.05828909948468208	0.086116723716259	0.015031328424811363	-0.049308836460113525	0.0329085998237133	-0.064009889960289	0.1029270738363266	-0.01624837890267372	0.030077215284109116	0.01430539507418871	-0.015703031793236732	-0.015035262331366539	0.04933609440922737	0.017266709357500076	0.03138618916273117	0.08014562726020813	0.016813619062304497	0.004055683966726065	0.02246217243373394	0.030860723927617073	0.020802585408091545	0.0497121624648571	-0.0017668120563030243	-0.0005639743758365512	0.007580365985631943	0.04483667016029358	-0.03105965256690979	-0.06718073785305023	0.001497734454460442	0.024109413847327232	-0.01951589249074459	0.017014512792229652	-0.034769248217344284	-0.046845391392707825	0.006840202491730452	-0.0013662162236869335	0.0015140186296775937	-0.04003296047449112	-0.021088458597660065	-0.08811375498771667	-0.03710446506738663	-0.010433366522192955	-0.07297707349061966	0.007168489508330822	0.02585684135556221	0.03171355649828911	-0.02584029920399189	0.040093500167131424	0.0957464724779129	-0.05677744746208191	0.05160707235336304	0.024469425901770592	-0.055144019424915314	-0.06925027817487717	-0.06579084694385529	-0.00905095785856247	0.027881788089871407	-0.04155699163675308	0.07105713337659836	-0.024982381612062454	0.006141267251223326	0.0043739317916333675	-0.05389218032360077	-0.007752195466309786	-0.04012089967727661	-0.029320159927010536	-0.04981326311826706	0.0400691032409668	0.01718670129776001	0.022088181227445602	0.006625038105994463	0.053980182856321335	-0.011008646339178085	-0.0402027927339077	0.0026045269332826138	-0.13451316952705383	0.014478275552392006	-0.039193179458379745	0.008499300107359886	-0.04269622266292572	0.02089180238544941	0.004016471561044455	-0.02356177568435669	-0.09038525819778442	-0.03982383385300636	-0.019870927557349205	0.0006844568415544927	0.06450292468070984	0.13248009979724884	-0.06227034702897072	-0.01738041639328003	-0.06902918964624405	0.00945444405078888	-0.09846743941307068	-0.02155548520386219	-0.10771729797124863	0.08917836844921112	0.006743737030774355	-0.00010278112313244492	-0.002409482141956687	-0.061087775975465775	-0.00212355749681592	0.0008689817041158676	0.022560998797416687	0.02672710455954075	0.05334480479359627	-0.04349590092897415	0.027155205607414246	-0.029985088855028152	0.017995381727814674	-0.006861710920929909	0.018586434423923492	0.048016637563705444	-0.01922445558011532	0.05397207289934158	-0.01927514374256134	-0.05502905324101448	0.03248560056090355	0.011395719833672047	-0.0036897945683449507	-0.028120391070842743	0.019206268712878227	0.09613405913114548	-0.0816216990351677	-0.01509083341807127	0.010148536413908005	0.018208181485533714	0.011915144510567188	0.0013390611857175827	0.028951790183782578	-0.02487221546471119	0.03473474457859993	0.020724602043628693	-0.018840746954083443	0.016190791502594948	-0.02419826202094555	0.013455009087920189	0.08096998929977417	0.013789315707981586	0.06344868242740631	0.010486660525202751	-0.020360834896564484	0.04064945504069328	0.09318475425243378	0.016299981623888016	0.008454672992229462	0.02695220150053501	0.018092023208737373	-0.02391788922250271	-0.009016323834657669	-0.014143481850624084	0.0641312301158905	-0.019150475040078163	-0.034840963780879974	-0.00302682351320982	0.05134839564561844	-0.08706435561180115	-0.08114930987358093	0.0223089586943388	-0.05404432862997055	-0.010277003049850464	0.01965051144361496	-0.04685598239302635	-0.016076430678367615	0.03400808572769165	0.04816258326172829	-0.04852202534675598	-0.042878057807683945	-0.003281257813796401	0.0219680555164814	0.008009743876755238	0.02392534352838993	-0.03737393766641617	0.00993385910987854	0.010924598202109337	0.05526188760995865	-0.037823401391506195	0.03455478698015213	0.06330794841051102	0.060871049761772156	0.029690107330679893	0.09523037075996399	-0.060024525970220566	-0.017395632341504097	-0.01587590016424656	0.05286649242043495	0.02789965644478798	0.004113874863833189	0.0239814855158329	0.020953504368662834	0.008409242145717144	-0.05339372530579567	-0.005323577672243118	0.03799978271126747	-0.012623650021851063	0.051015764474868774	0.00023319096362683922	-0.026519348844885826	0.023408083245158195	0.007829579524695873	0.047315746545791626	0.0017446150304749608	0.03256582096219063	0.016622131690382957	0.028367795050144196	-0.004999636206775904	0.02573404647409916	0.018705153837800026	-0.03685583546757698	-0.03643525764346123	-0.02715316228568554	-0.0182410329580307	0.03422779589891434	-0.011963429860770702	-0.014073380269110203	-0.010960426181554794	-0.01978394389152527	-0.02411140687763691	0.01043927576392889	-0.03813520446419716	0.03787665814161301	0.010025743395090103	0.03726784139871597	0.01417482178658247	0.05344967916607857	-0.13718634843826294	0.01991250365972519	0.06831318140029907	0.07410450279712677	0.015542935580015182	0.037387602031230927	-0.01499998476356268	0.05720093473792076	-0.01710628904402256	-0.06687044352293015	-0.005222162697464228	0.022105149924755096	0.026276426389813423	0.011119666509330273	0.04027215763926506	-0.015102044679224491	0.047167956829071045	0.10380107909440994	0.05014251917600632	0.01028030551970005	-0.01631951704621315	-0.061541277915239334	0.005727316718548536	0.0436781607568264	0.017079079523682594	0.03924795985221863	0.06296340376138687	0.042508646845817566	-0.03364953398704529	0.0024828368332237005	0.05243334919214249	0.040436986833810806	0.05228671431541443	0.024416733533143997	-0.016528218984603882
+0.06057637557387352	-0.022423937916755676	-0.02776644006371498	0.07752149552106857	-0.06194780021905899	0.029679585248231888	-0.0033492634538561106	-0.034441035240888596	-0.07194557040929794	0.01518068928271532	-0.0018462726147845387	-0.042809925973415375	-0.02648092433810234	-0.04750368371605873	-0.014773106202483177	-0.12902341783046722	0.007006922736763954	-0.01027008332312107	0.10323074460029602	-0.016471143811941147	-0.02563443034887314	0.058105651289224625	0.0020682872273027897	0.026923615485429764	-0.027294166386127472	0.04142695292830467	0.029350562021136284	0.00145714299287647	0.05032157897949219	0.06178317591547966	-0.021937565878033638	-0.010376611724495888	-0.007677972316741943	-0.051286011934280396	-0.011484808288514614	-0.06899417191743851	-0.03677692264318466	0.046028826385736465	0.10916893184185028	-0.032287660986185074	-0.05060876905918121	0.058247338980436325	-0.006238099653273821	-0.035162415355443954	0.01562928967177868	0.0225903932005167	-0.05159839615225792	-0.04476610943675041	0.016703179106116295	-0.0015937320422381163	-0.013054857030510902	0.043104372918605804	-0.006875991355627775	0.045705344527959824	-0.04592793434858322	-0.01976061426103115	0.06388012319803238	0.07972239702939987	0.00768465967848897	0.05461026728153229	0.03488566726446152	-0.040738143026828766	0.034517403692007065	-0.05723082274198532	0.022237177938222885	0.042113617062568665	0.06415362656116486	-0.02985740453004837	-0.05340016633272171	0.028354888781905174	-0.002089478075504303	0.10296901315450668	0.004664952866733074	0.07926081866025925	-0.03612750396132469	-0.04082454368472099	-0.06382567435503006	-0.03663160651922226	0.009554235264658928	0.03939719870686531	0.01419808343052864	0.02593855746090412	0.01784251630306244	0.03428063914179802	-0.007853583432734013	0.01918320171535015	-0.009216796606779099	-0.033047866076231	-0.009593525901436806	-0.012729834765195847	0.04993061721324921	-0.08752971887588501	-0.009464273229241371	0.010615537874400616	0.011155716143548489	0.008835447952151299	0.06615000218153	0.03347213938832283	-0.030060768127441406	0.0001947045384440571	-0.027848368510603905	-0.08674552291631699	0.011972358450293541	-0.0305534265935421	0.032007068395614624	-0.03715955838561058	-0.05548207089304924	0.028251077979803085	-0.029204770922660828	-0.06706321984529495	0.08727850764989853	0.04791402816772461	-0.05740197375416756	-0.0728912502527237	0.06931544095277786	0.007268113549798727	0.030404336750507355	-0.03410230949521065	0.030644742771983147	0.0339910127222538	-0.06395038962364197	0.080474853515625	0.008504216559231281	-0.01407540775835514	0.017281150445342064	-0.0006101545295678079	-0.008435003459453583	0.009201296605169773	0.004700715187937021	0.039026353508234024	-0.02253357321023941	0.00714496523141861	0.03581897169351578	0.06051440164446831	-0.08766242116689682	-0.05381583794951439	-0.07058252394199371	0.07985018938779831	0.06249769777059555	0.09071491658687592	-0.05426516756415367	0.057549212127923965	0.08171546459197998	0.026749085634946823	-0.0430394746363163	0.021247809752821922	-0.01638619415462017	-0.000542016641702503	-0.015679430216550827	0.016088679432868958	0.05233359709382057	0.0631759762763977	-0.018407747149467468	-0.014059819281101227	0.00030175631400197744	0.014208853244781494	0.010721791535615921	0.0017259361920878291	0.010297928936779499	-0.014069114811718464	0.04283326491713524	-0.009167050942778587	0.007725506089627743	-0.02831685170531273	-0.016444159671664238	0.08099231123924255	0.018407924100756645	-0.053085897117853165	0.01628757268190384	0.1106862723827362	-0.0119093032553792	-0.0007690639467909932	0.06910926103591919	-0.08557375520467758	-0.029989611357450485	0.004081751219928265	0.018872611224651337	-0.03724084421992302	0.11655447632074356	-0.05539049208164215	0.023688672110438347	-0.08559367060661316	0.026384485885500908	0.00601097010076046	-0.035298269242048264	0.052540648728609085	0.03238823637366295	-0.004064669832587242	-0.006272351834923029	0.0020845055114477873	0.011618888936936855	-0.04512219503521919	0.011300542391836643	0.004515036009252071	0.00969487614929676	-0.0547785721719265	0.002464381279423833	-0.03303772583603859	0.0014626382617279887	0.035255324095487595	-0.026778550818562508	0.0478367805480957	-0.0725344866514206	0.012904859147965908	0.0013683661818504333	0.06019677221775055	-0.03525349497795105	-0.05672068148851395	0.0030370804015547037	-0.03045264258980751	0.0023121198173612356	-0.017616650089621544	-0.03503939509391785	0.006924232002347708	0.0013024467043578625	0.017808297649025917	0.03586364910006523	-0.005720146000385284	-0.011966525577008724	0.039004962891340256	-0.028550107032060623	-0.07596945017576218	-0.05902630463242531	-0.07219172269105911	-0.01867983303964138	-0.02425524778664112	0.040826473385095596	0.06158846616744995	0.06555400043725967	-0.022291431203484535	0.10066503286361694	0.07712557911872864	-0.012527886778116226	-0.01089845784008503	-0.05847146362066269	-0.009855329059064388	0.005840939469635487	0.007031656336039305	-0.014530441723763943	-0.054113537073135376	0.04202532023191452	-0.005145177245140076	-0.054005689918994904	0.04449063539505005	0.023937290534377098	-0.033384229987859726	-0.04039314016699791	0.006912602577358484	-0.05504661798477173	-0.05596945807337761	0.03665662556886673	0.019346904009580612	-0.004529691766947508	0.056712888181209564	0.032856740057468414	-0.0031642818357795477	-0.01574649102985859	-0.03438272327184677	0.06069124862551689	-0.006746670696884394	0.009175262413918972	-0.034354954957962036	0.008033418096601963	0.00910379458218813	-0.042097657918930054	0.06867408752441406	0.07472467422485352	0.02276449091732502	-0.012080186977982521	-0.005977238528430462	0.040779776871204376	0.08466587960720062	0.033747125416994095	0.08517907559871674	-0.01938246376812458	0.016921015456318855	0.0028494533617049456	0.05486224219202995	0.012386279180645943	-0.07900924235582352	0.03315191715955734	0.04327487200498581	-0.01527421735227108	-0.045559726655483246	-0.029817337170243263	-0.00876695942133665	0.0238087959587574	-0.007245873566716909	-0.04590657353401184	-0.0026360535994172096	-0.006927382200956345	-0.07237578183412552	-0.016086209565401077	0.04197894036769867	-0.04334680363535881	-0.012545734643936157	-0.06875275820493698	0.04993976652622223	-0.017852626740932465	-0.01820051483809948	0.084009550511837	-0.03992947190999985	0.051271792501211166	0.01594075746834278	-0.04264097288250923	-0.05323056876659393	-0.016213010996580124	0.037052083760499954	0.015720903873443604	0.05357648432254791	0.04869496077299118	-0.04980722814798355	0.06992029398679733	0.04168160259723663	-0.022839009761810303	-0.04547126963734627	-0.05070064589381218	-0.052024003118276596	-0.03933590650558472	0.08463357388973236	0.00821085274219513	0.08112424612045288	0.005495154298841953	0.033005330711603165	0.015359318815171719	-0.05967378616333008	0.029577234759926796	-0.12226475030183792	0.0025773742236196995	-0.00395215442404151	-0.00598943280056119	-0.05545162037014961	0.0014762887731194496	0.05600947141647339	0.01756034605205059	-0.07815411686897278	-0.07052582502365112	-0.01830989494919777	0.06899067759513855	0.01632021553814411	0.12156710773706436	-0.004773118533194065	0.034198448061943054	-0.041612014174461365	-0.0722750574350357	-0.06796851009130478	-0.054467275738716125	-0.11746188253164291	0.05995718017220497	0.032202959060668945	-0.025526637211441994	-0.00946364551782608	-0.060467708855867386	0.00020131330529693514	-0.06941016018390656	0.04804264381527901	0.06807966530323029	0.042543504387140274	-0.06115460395812988	0.004298478830605745	-0.01834009401500225	-0.0002835544291883707	0.00981004349887371	0.06137138977646828	0.018624864518642426	-0.04152342304587364	0.05164231359958649	-0.04158676788210869	-0.08080067485570908	0.041044995188713074	-0.0021361769177019596	0.041853830218315125	-0.04187610745429993	-0.026870857924222946	0.08960691839456558	-0.012718304060399532	-0.005665924400091171	-0.04690695181488991	0.021574050188064575	-0.024253690615296364	0.026173759251832962	-0.03454951196908951	-0.044315118342638016	0.01723291352391243	0.027167202904820442	0.020131858065724373	0.029689712449908257	-0.03152391314506531	0.012357141822576523	0.06832906603813171	0.0613662451505661	0.04919534921646118	-0.004315977916121483	-0.029310541227459908	0.014700300991535187	0.06900964677333832	0.03504428640007973	0.01378112006932497	-0.030151035636663437	0.022152435034513474	-0.04720892757177353	0.012808946892619133	-0.012067520059645176	0.09131881594657898	-0.005886297207325697	-0.04722168296575546	-0.053490135818719864	0.028436319902539253	-0.05592401698231697	-0.04648891091346741	0.02219126932322979	-0.02529866434633732	-0.07392296195030212	0.023281199857592583	0.00026197973056696355	-0.0328855998814106	-0.04337144270539284	0.06701590865850449	-0.0070609706453979015	0.011074461974203587	-0.041312720626592636	0.005926330108195543	0.05072014778852463	-0.010523652657866478	-0.05932537466287613	-0.006513823289424181	0.05263443663716316	0.0046460493467748165	-0.016030356287956238	0.016943946480751038	0.08713237196207047	0.060455139726400375	0.025926806032657623	0.05425253137946129	-0.0859188586473465	-0.002063723746687174	-0.01729532703757286	0.010435670614242554	0.08231066912412643	0.06021469086408615	-0.01655530370771885	0.044346414506435394	-0.022317105904221535	-0.018232114613056183	0.019280128180980682	0.03700797259807587	-0.03151305392384529	0.04660996049642563	-0.004302866756916046	-0.11411948502063751	0.010723138228058815	0.012882505543529987	0.015302808955311775	-0.03579734265804291	0.01631229743361473	0.011533663608133793	0.047743260860443115	0.020133720710873604	0.025030849501490593	0.012848257087171078	-0.041078198701143265	-0.035989146679639816	-0.005791484843939543	0.046586669981479645	-0.03547288477420807	-0.032041195780038834	-0.005665245931595564	0.04125891253352165	0.013103478588163853	-0.03345468267798424	0.02588481269776821	-0.03684762120246887	0.07546006888151169	-0.03802356868982315	0.02215636894106865	-0.0039450181648135185	0.05321068689227104	-0.0928003117442131	-0.03421024978160858	0.0634940043091774	0.025960104539990425	0.07420294731855392	0.0878717377781868	0.022799570113420486	0.030109116807579994	-0.02451934479176998	-0.08177962154150009	0.07002348452806473	0.0239807590842247	0.015365813858807087	0.0029127290472388268	0.024302663281559944	-0.04175732284784317	-0.02972320094704628	0.03783190995454788	0.07131728529930115	0.059542812407016754	0.0007791543030180037	0.007828060537576675	0.006103808991611004	0.06535787880420685	0.03688152879476547	0.047288939356803894	0.08070074766874313	-0.008405409753322601	0.000289273913949728	0.015994755551218987	0.005012009758502245	-0.01480442751199007	0.030145986005663872	-0.012064514681696892	-0.03911370784044266
+0.041498471051454544	-0.002516461070626974	0.006534512620419264	0.07336071133613586	-0.07110471278429031	-0.027576111257076263	-0.04037613794207573	-0.0003624232194852084	0.019383251667022705	-0.06304243952035904	-0.018014846369624138	-0.02513088472187519	-0.009703579358756542	-0.00030238140607252717	0.05559226870536804	-0.006684558931738138	-0.03520513325929642	0.03826582804322243	-0.02706635184586048	-0.013887724839150906	0.021371187642216682	-0.014715765602886677	-0.09434837102890015	0.040564849972724915	-0.015128650702536106	0.03824806958436966	-0.056019313633441925	0.04742416366934776	0.004853209480643272	0.022237956523895264	0.027082255110144615	-0.02340528927743435	0.026424134150147438	-0.09854699671268463	-0.06518200039863586	0.03389435634016991	-0.012933077290654182	0.08255874365568161	0.04482069984078407	-0.029029691591858864	-0.054229822009801865	0.019467754289507866	-0.0728035420179367	-0.10175493359565735	0.034554146230220795	-0.01986069418489933	0.027127590030431747	-0.019599972292780876	0.06364645063877106	0.035192638635635376	0.07111073285341263	0.01262402068823576	-0.05137481540441513	-0.04739883169531822	-0.012845195829868317	0.009963873773813248	0.053454019129276276	-0.09449620544910431	-0.040756989270448685	0.05295218154788017	-0.08493631333112717	-0.0007899054326117039	0.0047666653990745544	0.03161394223570824	-0.04356983304023743	-0.0077938721515238285	0.020647283643484116	0.07617682963609695	-0.009807238355278969	-0.06557949632406235	0.017635585740208626	0.09384043514728546	-0.06135348975658417	-0.04279264807701111	0.02452148124575615	-0.08506250381469727	-0.016376694664359093	0.04453442245721817	0.00104693160392344	-0.006875273771584034	-0.003505473956465721	-0.06148657575249672	0.1049523800611496	-0.0030271434225142	-0.019115524366497993	-0.06121935322880745	-0.006313146557658911	-0.03333127871155739	0.03422371298074722	0.039265524595975876	0.011708511039614677	0.06048828363418579	-0.03960472717881203	0.06301385164260864	0.017128216102719307	0.04924736171960831	0.023261617869138718	-0.12414009869098663	-0.021742289885878563	0.0013383878394961357	-0.030797000974416733	-0.02846830151975155	0.017257723957300186	0.0039403787814080715	0.051261018961668015	0.0016773848328739405	-0.06098345294594765	0.003508826484903693	0.04147658124566078	0.05785832181572914	0.05958951637148857	0.05249470844864845	0.06380841135978699	0.10591716319322586	0.04148807004094124	0.0025893407873809338	0.02318520098924637	-0.06847748160362244	-0.02331562526524067	0.02546018362045288	0.014821585267782211	0.016781015321612358	-0.0003243583778385073	-0.06063796207308769	0.005872679408639669	0.011178083717823029	-0.08396800607442856	-0.037041790783405304	0.006857625208795071	-0.014219827950000763	0.10393049567937851	0.04882654547691345	-0.022640636190772057	-0.0036312539596110582	0.034959983080625534	0.05384476110339165	0.00020035586203448474	0.022787639871239662	0.018368400633335114	-0.0221061073243618	0.0065332879312336445	0.022012988105416298	-0.008138524368405342	-0.06378540396690369	0.06371200084686279	0.010082692839205265	0.016506904736161232	-0.03868831321597099	-0.07522284239530563	-0.007614646572619677	-0.07070495188236237	-0.031604766845703125	0.04314371198415756	-0.09161001443862915	0.0363406240940094	0.04431220516562462	0.07355630397796631	0.02188248001039028	-0.018018463626503944	0.003214774187654257	-0.041658349335193634	-0.04809488356113434	-0.0006101959152147174	-0.08555662631988525	-0.05676763877272606	-0.03870851919054985	0.03483283147215843	0.031437747180461884	-0.04493489861488342	-0.03159709274768829	-0.024825694039463997	0.040987394750118256	0.062347136437892914	0.043555524200201035	0.0666111558675766	-0.007875320501625538	0.014481932856142521	-0.05265376344323158	0.031309325248003006	-0.01247838418930769	0.006607207003980875	-0.04123511537909508	-0.007941081188619137	-0.058412957936525345	-0.03286154568195343	0.0005338154151104391	-0.017568986862897873	-0.00813942588865757	0.0032188966870307922	0.021507911384105682	-0.09962674230337143	-0.048080820590257645	0.04262557998299599	0.07850329577922821	-0.02772466279566288	0.04970457777380943	-0.026350734755396843	0.043837979435920715	-0.005390318110585213	-0.04770846664905548	-0.06892314553260803	0.017698289826512337	-0.0103715555742383	0.059031892567873	-0.007499516475945711	-0.09276223182678223	0.04168940335512161	0.038979366421699524	0.0017954579088836908	-0.03870423510670662	-0.0009001391590572894	0.01449489127844572	0.01895010843873024	0.019765658304095268	0.0736331194639206	-0.018025556579232216	-0.04560534283518791	0.03644344583153725	0.03798339515924454	-0.06761269271373749	0.0451938733458519	0.0063570342026650906	-0.08185777813196182	0.02137291617691517	-0.047113630920648575	-0.06716659665107727	-0.02841183915734291	0.018099315464496613	-0.020256461575627327	-0.013698093593120575	0.007640901952981949	-0.011557603254914284	-0.0032976181246340275	-0.04950091615319252	-0.01007936429232359	-0.01031278446316719	-0.08607293665409088	0.10342923551797867	0.020210009068250656	-0.062090881168842316	-0.06164226308465004	0.005761908367276192	0.04416566714644432	-0.022160949185490608	0.013610858470201492	0.04084248095750809	0.0772058516740799	0.03174121677875519	-0.0003276550560258329	-0.0008813152089715004	-0.06869829446077347	-0.07683837413787842	0.017245041206479073	0.04261266440153122	-0.032796792685985565	-0.002089639427140355	-0.001800641301088035	-0.04946911707520485	0.011836565099656582	0.0016545376274734735	-0.006383280735462904	-0.0003591624554246664	-0.016567133367061615	-0.03985380008816719	0.08248074352741241	-0.025606026872992516	-0.04480883106589317	0.0143109280616045	-0.0186294075101614	0.053990039974451065	-0.05411650985479355	-0.04175416752696037	0.065937340259552	-0.02450691908597946	0.03153342008590698	-0.01405791100114584	0.030345341190695763	-0.025605246424674988	-0.08163122087717056	-0.008195309899747372	-0.09599143266677856	-0.03026271052658558	-0.01390276663005352	0.029180195182561874	-0.042231712490320206	0.10632485896348953	0.007588198408484459	-0.011327072978019714	-0.03789553418755531	-0.01897977665066719	-0.018358226865530014	-0.0853058397769928	-0.02320188283920288	0.004086395259946585	-0.07103582471609116	-0.010433022864162922	0.04311807453632355	-0.043302375823259354	-0.01369007583707571	-0.025214925408363342	-0.05860653519630432	-0.08285883814096451	0.04618905112147331	-0.034348905086517334	-0.06342355906963348	0.017445357516407967	-0.052142415195703506	0.03346974402666092	0.07932845503091812	0.004618037026375532	0.034095682203769684	0.01910308562219143	0.018087752163410187	0.07647264003753662	-0.010023082606494427	-0.02948055975139141	-0.018015872687101364	-0.059116121381521225	-0.0776643306016922	0.06758348643779755	-0.02773119881749153	0.00597486924380064	-0.00512916874140501	0.027108702808618546	0.08729694038629532	-0.035155508667230606	-0.02243301458656788	0.011699838563799858	0.01680944673717022	0.011177724227309227	0.011822781525552273	-0.006173965986818075	0.013024701736867428	-0.03119088150560856	-0.06509269028902054	-0.050158001482486725	-0.016204744577407837	0.01779833994805813	-0.0061903540045022964	0.06409994512796402	-0.019321059808135033	-0.0811358317732811	0.0008522715652361512	0.02305290661752224	0.11021289229393005	-0.06407128274440765	0.03828098997473717	0.029326921328902245	0.01219871174544096	-0.07707924395799637	-0.003053003689274192	0.0450141578912735	-0.05457393452525139	-0.04264683648943901	0.01265048049390316	0.013559161685407162	-0.06067598611116409	-0.014868197962641716	0.020174775272607803	-0.040350425988435745	0.009240206331014633	0.03518969938158989	0.008766252547502518	-0.006352477706968784	-0.03413520008325577	-0.010304066352546215	-0.009287445805966854	-0.020845206454396248	0.005481102038174868	-0.021076150238513947	0.015226536430418491	-0.04479733854532242	0.055409807711839676	-0.03589471057057381	-0.007913823239505291	-0.04822598025202751	0.061112500727176666	0.021637097001075745	-0.030910087749361992	-0.023232560604810715	-0.04737289994955063	-0.017797322943806648	0.014313564635813236	0.048842400312423706	-0.0461946465075016	-0.04941728338599205	-0.06902820616960526	-0.03939735144376755	-0.07099690288305283	-0.041555255651474	0.008754841051995754	-0.05687345564365387	0.0012040345463901758	-0.045842405408620834	-0.028465187177062035	0.03304010257124901	-0.029477501288056374	-0.008065245114266872	0.02146478183567524	0.03345680981874466	-0.040240343660116196	0.01834702305495739	-0.06381699442863464	0.05318458005785942	0.023553894832730293	-0.03985476866364479	0.009375021792948246	0.04493935778737068	-0.0365498885512352	0.033113062381744385	-0.0255631934851408	0.01738923043012619	0.032148752361536026	-0.014219655655324459	0.035927530378103256	0.018332336097955704	0.004036357626318932	0.0015053397510200739	0.05142593011260033	-0.05963423103094101	-0.036159273236989975	0.00511739868670702	-0.04713161662220955	-0.1087077185511589	-0.02920985408127308	0.004098269622772932	-0.038711123168468475	0.02318512462079525	-0.022330038249492645	0.055963121354579926	0.030585329979658127	-0.0609545037150383	-0.019727127626538277	-0.015331041067838669	-0.04103104770183563	0.046479057520627975	-0.012336141429841518	0.042977944016456604	-0.0014575235545635223	0.016965506598353386	0.05663779005408287	0.058502402156591415	-0.02583777904510498	-0.1028592586517334	-0.03733362257480621	-0.007425839081406593	-0.0036794499028474092	0.005638584028929472	-0.003226298140361905	0.018890444189310074	-0.06500961631536484	-0.043033283203840256	-0.02000376023352146	0.09619127959012985	0.006180102936923504	-0.025792544707655907	0.06870479881763458	-0.04178516939282417	-0.004728600382804871	0.02850014716386795	-0.04638771712779999	-0.09233289211988449	-0.03149670735001564	-0.0280182845890522	0.04256393387913704	0.031184108927845955	0.031974174082279205	-0.0361107736825943	-0.04982965067028999	0.009476926177740097	-0.008842038922011852	-0.07954514771699905	0.0034085477236658335	0.026080435141921043	-0.027982201427221298	-0.0774756520986557	-0.0038087288849055767	-0.07443757355213165	0.05513331666588783	0.044585999101400375	0.04610287398099899	-0.011625138111412525	-0.0023026035632938147	-0.0024137997534126043	-0.049951884895563126	0.03947191685438156	0.010189070366322994	-0.07047062367200851	-0.007088940124958754	0.09826985001564026	-0.0420474074780941	0.05052947252988815	0.028722591698169708	-0.016905920580029488	0.0266913753002882	0.022756924852728844	-0.043936919420957565	0.018648769706487656	-0.04669518768787384	-0.03431793674826622	0.07173100858926773	-0.06055639311671257	0.06284978985786438	-0.06251407414674759	0.09376873075962067	-0.015234649181365967	-0.01646243967115879	0.07580678910017014	0.09747617691755295	-0.014368501491844654	0.026678597554564476	-0.030464807525277138
+0.04859809949994087	-0.011476660147309303	0.013945144601166248	0.06836928427219391	-0.07620767503976822	-0.019309151917696	-0.0030305797699838877	0.037050411105155945	-0.0008925320580601692	-0.03315205127000809	-0.05435315892100334	0.014763498678803444	0.01404746063053608	0.047312669456005096	0.02768867276608944	-0.012229097075760365	-0.045772455632686615	0.017072254791855812	-0.029361329972743988	-0.06034068763256073	0.03426850214600563	-0.025647489354014397	-0.04326868802309036	0.07045182585716248	0.03490027040243149	0.021665658801794052	-0.060526292771101	0.00984277855604887	0.0016719221603125334	0.016286766156554222	0.03941170498728752	-0.01260155625641346	0.026378491893410683	-0.06186181306838989	-0.0562000647187233	0.04003637656569481	-0.019914686679840088	0.0935930386185646	-0.032459989190101624	-0.022282332181930542	-0.05191294103860855	0.02245485410094261	-0.0602731853723526	-0.03618476167321205	0.019603293389081955	0.01359851285815239	0.04692176729440689	0.0034113696310669184	0.04441114887595177	0.014066942036151886	0.06494685262441635	0.0029956065118312836	0.006021074950695038	-0.04164544865489006	-0.01773577183485031	0.019760293886065483	0.04865780472755432	-0.03626496344804764	-0.045326944440603256	0.04331853985786438	-0.11964162439107895	-0.029354026541113853	-0.004220093600451946	-0.009858262725174427	-0.03434661403298378	-0.003501538187265396	-0.01942032016813755	0.11003854125738144	0.028521323576569557	-0.05689651146531105	0.024420462548732758	0.09488549083471298	-0.046104732900857925	-0.020104506984353065	0.06220578774809837	-0.06402799487113953	-0.02309209108352661	0.05984385311603546	-0.0165207851678133	0.021287910640239716	-0.006017219740897417	-0.08307957649230957	0.1275411993265152	-0.015161523595452309	-0.08252182602882385	-0.015636296942830086	-0.011206421069800854	-0.0374872088432312	0.04090510681271553	0.027167312800884247	0.0017179137794300914	0.09793040156364441	-0.058976639062166214	0.046079959720373154	0.041930001229047775	-0.0005781108047813177	0.022404175251722336	-0.12927162647247314	-0.04587852582335472	0.036232076585292816	-0.005302363075315952	0.0010413499549031258	0.048439230769872665	-0.006850229110568762	0.07091525942087173	0.026512444019317627	-0.06096753105521202	-0.02271391451358795	0.015867197886109352	0.014282707124948502	0.04500128701329231	0.07671003043651581	0.05121677368879318	0.041359852999448776	0.024646414443850517	-0.015973882749676704	0.03285067528486252	-0.06335677206516266	-0.06071273982524872	-0.008816511370241642	0.028133928775787354	0.0792248323559761	0.00025883910711854696	-0.08703568577766418	-0.01672728732228279	0.0160696841776371	-0.08985286206007004	-0.007712171413004398	0.09801773726940155	-0.05068053677678108	0.11571966111660004	0.06220106780529022	0.0006285817362368107	0.04657489061355591	0.055370595306158066	0.032529931515455246	-0.007023236248642206	0.022641798481345177	0.013254747726023197	-0.018384400755167007	-0.018406489863991737	0.036647286266088486	0.009596026502549648	-0.08115041255950928	0.07824898511171341	0.06421659141778946	0.004279436077922583	-0.04255833849310875	-0.010444832034409046	-0.011758703738451004	-0.04192022979259491	-0.08102632313966751	0.0075587322935462	-0.05246301740407944	-0.01966133713722229	0.047615356743335724	0.018763890489935875	0.04906658083200455	-0.044264208525419235	-0.02228982001543045	-0.0644860565662384	-0.011646447703242302	-0.02060936577618122	-0.04627987742424011	-0.025424500927329063	0.004136408679187298	-0.018801700323820114	0.018208393827080727	-0.06822256743907928	-0.014675070531666279	0.0013579907827079296	-0.01092302892357111	0.03607802465558052	0.07075198739767075	0.072972871363163	-0.014018288813531399	0.02274184301495552	-0.04594383388757706	-0.002446704776957631	-0.01789400540292263	0.008348721079528332	-0.007909403182566166	0.030622029677033424	-0.04668179526925087	-0.007976081222295761	-0.016977157443761826	0.044021084904670715	-0.03496799245476723	0.01804945059120655	-0.02227492444217205	-0.028125181794166565	-0.026532717049121857	0.042144279927015305	0.06377892196178436	-0.04386540502309799	0.0637575089931488	-0.03965209051966667	0.04459585249423981	-0.018354669213294983	-0.0060537587851285934	-0.047395799309015274	0.05452054366469383	0.008547812700271606	0.0607592947781086	0.031051229685544968	-0.07076939195394516	0.054589517414569855	0.03836081176996231	0.02043920010328293	-0.08284156769514084	-0.012341784313321114	0.012232640758156776	0.0171009860932827	0.08527561277151108	0.09007253497838974	0.014552503824234009	-0.0014128184411674738	-0.012906181626021862	0.04366394132375717	-0.048196375370025635	-0.01541212573647499	-0.01088897418230772	-0.08674823492765427	-0.01954079046845436	-0.0658840760588646	-0.03335026651620865	-0.04083719477057457	0.051430292427539825	0.0007353596738539636	0.003552207723259926	-0.025316473096609116	0.02375776320695877	-0.026418516412377357	-0.04524364322423935	-0.000350329268258065	-0.015667429193854332	-0.06488385051488876	0.09395264089107513	0.03872013837099075	-0.07940207421779633	-0.07748028635978699	0.013567649759352207	0.057557057589292526	-0.003411839483305812	0.04866975545883179	0.041356705129146576	0.09884000569581985	0.03451292961835861	-0.005256075877696276	0.006025500129908323	-0.004401077050715685	-0.048081573098897934	0.013873073272407055	0.03871089592576027	-0.04656992852687836	-0.008013200014829636	0.013629189692437649	-0.03492414206266403	-0.009441548958420753	0.011217598803341389	0.025463491678237915	-0.006056551355868578	-0.032505497336387634	-0.007946327328681946	0.017305349931120872	-0.07146044820547104	-0.039301637560129166	0.007531687151640654	0.0035824330989271402	0.08283877372741699	-0.05773996189236641	-0.03773624822497368	0.033230267465114594	0.02332657389342785	-0.009227919392287731	-0.07565750181674957	0.02713124454021454	-0.0035984439309686422	-0.06961217522621155	-0.005889587569981813	-0.06308610737323761	-0.0457615964114666	-0.05169086158275604	-0.008413520641624928	-0.047194577753543854	0.08282337337732315	-0.008930988609790802	-0.07689034193754196	-0.054566990584135056	-0.020478902384638786	-0.022835638374090195	-0.05192558839917183	0.009934285655617714	0.008791238069534302	-0.034877631813287735	-0.02764619141817093	0.012769585475325584	-0.040495142340660095	-0.03507588431239128	0.013031636364758015	-0.02204972319304943	-0.08739255368709564	0.073923759162426	0.0006726479041390121	-0.019051048904657364	-0.0015183653449639678	-0.06771555542945862	0.008847545832395554	0.09849540144205093	0.028181031346321106	0.001999686472117901	0.04444248229265213	0.0061614676378667355	0.1033259853720665	0.007040103897452354	-0.013455762527883053	-0.0028183413669466972	-0.021761741489171982	-0.0462428443133831	0.04316189885139465	-0.02609473094344139	-0.009016535244882107	-0.018340520560741425	0.0013034575385972857	0.05615152418613434	0.02410690113902092	-0.04933701083064079	-0.020339470356702805	0.014209127053618431	-0.0074339378625154495	0.020965902134776115	0.011634266003966331	0.0033151975367218256	-0.04987677186727524	-0.06696856766939163	-0.055910855531692505	-0.0001539987133583054	0.057150278240442276	-0.011385845020413399	0.041945479810237885	-0.03857766464352608	-0.05618055537343025	0.018045485019683838	-0.003543828846886754	0.0876641497015953	-0.0678306594491005	0.011638927273452282	0.07220015674829483	0.01696065627038479	-0.09027376770973206	0.02121426910161972	0.049384068697690964	-0.03394610807299614	-0.06911919265985489	0.014352400787174702	0.02142283506691456	-0.058868784457445145	0.022139469161629677	-0.011754833161830902	-0.04464465752243996	-0.002445278689265251	0.014035305939614773	0.02102583274245262	0.008135142736136913	-0.018562469631433487	-0.01616189442574978	-0.02235267125070095	-0.0011107552563771605	0.027516953647136688	-0.004788067191839218	0.024635713547468185	-0.018263356760144234	0.06879696249961853	-0.056429482996463776	-0.02295021526515484	-0.047628603875637054	0.050495851784944534	-0.0053495257161557674	-0.009509633295238018	-0.011570773087441921	-0.07012474536895752	-0.019731776788830757	0.015384335070848465	0.03442384675145149	-0.06287951022386551	-0.008211489766836166	-0.05867982283234596	-0.0689438059926033	-0.07652150839567184	-0.06391727924346924	-0.00033304677344858646	-0.032368507236242294	0.03858008608222008	-0.007306224666535854	-0.004616268444806337	0.0009268260910175741	0.009056451730430126	0.025029536336660385	0.05397377535700798	0.03577905520796776	-0.02920004352927208	0.0567375011742115	-0.00999650452286005	0.04700538143515587	0.003046214347705245	-0.07198937237262726	0.04345904663205147	0.027951423078775406	-0.059430669993162155	0.09473023563623428	-0.041042935103178024	-0.011643165722489357	-0.02407318539917469	-0.0009816810488700867	0.01834430918097496	0.007451928686350584	-0.004727050196379423	-0.07844751328229904	0.07747037708759308	-0.07657071948051453	-0.023296253755688667	0.04305657371878624	-0.043735262006521225	-0.07870306074619293	-0.04363280162215233	-0.01205294206738472	-0.023877769708633423	0.032574571669101715	-0.009267342276871204	0.017057204619050026	0.05341159924864769	-0.031679730862379074	-0.048402175307273865	-0.0044696819968521595	-0.009361217729747295	0.04330277070403099	-0.0012701210798695683	0.0790560245513916	0.04295025020837784	-0.029827872291207314	0.00460449093952775	-0.0012475994881242514	0.028702853247523308	-0.08511306345462799	0.011588924564421177	0.004552604164928198	-0.0330374576151371	-0.006846905220299959	0.011433083564043045	-0.005784238688647747	-0.057129982858896255	-0.011760988272726536	-0.027814410626888275	0.0706426128745079	0.03276892378926277	0.006986313499510288	0.046029943972826004	-0.004095771815627813	0.04005681723356247	-0.0009211767464876175	-0.023697566241025925	-0.05438082665205002	-0.0138133205473423	-0.0502903051674366	0.023792224004864693	-0.007469152566045523	-0.018540406599640846	-0.01983856037259102	-0.03606802970170975	0.0018298804061487317	0.008186564780771732	-0.07684876769781113	0.01149235013872385	0.018300773575901985	-0.04082436114549637	-0.06482383608818054	0.013234037905931473	-0.045571357011795044	0.0544416569173336	0.06920278817415237	0.028580715879797935	0.007599970325827599	-0.005894046742469072	-0.024022309109568596	-0.02757320925593376	0.0027234486769884825	-0.007377550937235355	-0.06860409677028656	0.005908477120101452	0.06546220928430557	-0.0733613669872284	0.012280731461942196	0.024280790239572525	-0.04843030124902725	0.05530440807342529	0.020370347425341606	-0.05415598303079605	0.0875806137919426	0.0027829885948449373	0.0003152406425215304	0.05509983003139496	-0.0947665125131607	0.11071296036243439	-0.055006880313158035	0.06158814579248428	-0.0019336171681061387	-0.011799450032413006	0.10649903863668442	0.0954393669962883	0.015631916001439095	-0.008350119926035404	-0.08384548872709274
+0.03882107883691788	-0.017629671841859818	0.009312845766544342	0.03618047013878822	-0.05526513606309891	-0.0331689678132534	-0.008849957957863808	0.009624093770980835	0.02281593531370163	-0.04330437630414963	-0.022861797362565994	0.0010959181236103177	-0.0038522672839462757	-0.00954345054924488	0.026165936142206192	-0.03682204335927963	-0.0638936311006546	0.027156595140695572	-0.03236536309123039	-0.025887945666909218	0.05538446456193924	0.03611455857753754	-0.05607311800122261	0.03909870237112045	-0.011905991472303867	0.04501226916909218	-0.03151720017194748	-0.0006345598958432674	-0.02551320753991604	0.020237497985363007	0.06985864788293839	0.0058118198066949844	0.04914730787277222	-0.0463845357298851	-0.027349812909960747	0.06038432940840721	-0.027570508420467377	0.11194154620170593	0.006573399528861046	-0.007460541557520628	-0.02909577265381813	-0.04328594729304314	-0.06067236512899399	-0.057679783552885056	0.0136194359511137	-0.011305403895676136	-0.0013667793245986104	-0.016136234626173973	0.012647278606891632	0.024864057078957558	0.09551022946834564	-0.026596929877996445	-0.03753169998526573	-0.0037895627319812775	0.012558232061564922	0.011229369789361954	0.05356908217072487	-0.05501529574394226	-0.042023979127407074	0.026618190109729767	-0.0691637173295021	0.00026305243954993784	0.01577557995915413	0.009170036762952805	-0.022213341668248177	-0.03026452288031578	-0.005468623712658882	0.07173111289739609	0.0034557629842311144	-0.030850093811750412	0.03100205771625042	0.08748266845941544	-0.07825271040201187	-0.056160714477300644	0.06878369301557541	-0.06051940843462944	-0.031483765691518784	0.053016744554042816	-0.010898666456341743	-0.02549014240503311	0.011544905602931976	-0.037790149450302124	0.1271267682313919	-0.08019302785396576	-0.07311074435710907	-0.03685985878109932	-0.0068119303323328495	-0.06567788124084473	0.06806015968322754	0.022695576772093773	0.006893712095916271	0.07790426164865494	-0.05676720291376114	0.06190215423703194	0.026309041306376457	0.013319015502929688	0.022800205275416374	-0.10077622532844543	-0.0769491195678711	-0.007533378433436155	0.009889809414744377	-0.02429882623255253	0.055016353726387024	0.0021758624352514744	0.05467402562499046	0.025708025321364403	-0.02590559795498848	-0.010781950317323208	0.004156339913606644	0.06253185868263245	0.019991770386695862	0.0813450813293457	0.07209617644548416	0.04797753319144249	0.07170171290636063	-0.020326610654592514	0.007472484838217497	-0.02726919576525688	-0.032587140798568726	0.02175328880548477	-0.006644549313932657	0.034549273550510406	-0.02933656983077526	-0.0727682113647461	-0.05781911686062813	0.030602360144257545	-0.09847461432218552	-0.04658593237400055	0.057426512241363525	-0.045057181268930435	0.13367080688476562	0.04305742681026459	-0.03972925990819931	0.0033198457676917315	0.04346267879009247	0.03124052658677101	-0.005944897886365652	0.026613907888531685	-0.015777448192238808	-0.03255763277411461	0.006101745180785656	0.053165361285209656	-0.01465174276381731	-0.03688573092222214	0.03167206794023514	0.028531163930892944	0.04991517961025238	-0.025691216811537743	-0.06764759868383408	0.007296852767467499	-0.07512427866458893	-0.06321414560079575	-0.01580008678138256	-0.0856873169541359	0.018433529883623123	0.02951567806303501	-0.010675959289073944	0.03680300712585449	-0.014135734178125858	-0.00600539892911911	-0.07560231536626816	-0.03941962867975235	-0.029014229774475098	-0.05335838720202446	-0.05713993310928345	-0.026144111528992653	0.0006932938122190535	0.005015041679143906	-0.02984032779932022	-0.030469520017504692	-0.018931547179818153	-0.015523836016654968	0.03330342099070549	0.09546223282814026	0.07652120292186737	0.03648056462407112	-0.019947726279497147	-0.03985165059566498	-0.0020039714872837067	-0.03011646866798401	0.014844291843473911	-0.030017588287591934	0.026869993656873703	-0.039548274129629135	0.004196613561362028	-0.0018868312472477555	0.06226543337106705	0.0027236496098339558	0.03494337946176529	0.010318649001419544	-0.017008835449814796	-0.04599050059914589	0.052657127380371094	0.08554927259683609	-0.02981608547270298	0.016733750700950623	-0.022447848692536354	0.06909430772066116	-0.009945187717676163	-0.06017424538731575	-0.04004298895597458	0.008956109173595905	0.020114228129386902	0.07531370967626572	0.02300097793340683	-0.07605896145105362	0.10446226596832275	0.033443812280893326	0.04119567573070526	-0.08505315333604813	0.011084756813943386	0.003224355401471257	0.0010256425011903048	0.05967016518115997	0.06415372341871262	-0.05897966027259827	-0.011433687061071396	0.006219156086444855	0.04238380864262581	-0.025799434632062912	-0.01682332716882229	0.0008353631710633636	-0.08428233861923218	0.040554601699113846	-0.03131229057908058	-0.08082723617553711	-0.043901313096284866	0.020032236352562904	-0.020503703504800797	-0.029746226966381073	-0.01280792336910963	0.037003446370363235	-0.011936954222619534	-0.03477887064218521	-0.0011282259365543723	0.0012333985650911927	-0.11123264580965042	0.1111861914396286	-0.009171965532004833	-0.0664593055844307	-0.08842780441045761	0.023356538265943527	0.042220838367938995	0.015893490985035896	0.028631895780563354	0.046138349920511246	0.11081754416227341	-0.0030096089467406273	0.0028433643747121096	0.01550429780036211	-0.05415133759379387	-0.05867686867713928	0.006778066046535969	0.015692543238401413	-0.056555941700935364	-0.04992401972413063	-0.00468852836638689	-0.04591505229473114	-0.01969286985695362	0.015744809061288834	0.046912986785173416	-0.010748140513896942	-0.03225765377283096	-0.020863002166152	0.013879207894206047	-0.03699631243944168	-0.012025558389723301	0.04240291193127632	-0.025423947721719742	0.028210528194904327	0.005623975303024054	-0.022632062435150146	0.031444430351257324	-0.015110273845493793	0.047548290342092514	-0.017328066751360893	0.0746079683303833	-0.039838045835494995	-0.07188721746206284	-0.01930815540254116	-0.06870325654745102	-0.04487597942352295	-0.03387802094221115	-0.03918951004743576	-0.04530254378914833	0.09131591022014618	-0.0036750053986907005	-0.061020154505968094	-0.048385731875896454	-0.004544022958725691	-0.02614443004131317	-0.05781743302941322	-0.010764889419078827	-0.010765401646494865	-0.06617304682731628	-0.036208126693964005	0.05502329021692276	-0.06123580038547516	-0.00867218803614378	-0.011343889869749546	-0.021077433601021767	-0.05324321985244751	0.030237337574362755	0.0006456474657170475	-0.04761188477277756	-0.0038234032690525055	-0.08111291378736496	0.04547153413295746	0.09319228678941727	0.06284458190202713	0.055601105093955994	0.042516496032476425	0.06314864754676819	0.11699079722166061	0.0009142258204519749	-0.013524053618311882	-0.004480157978832722	-0.03153708204627037	-0.045246902853250504	0.029368219897150993	-0.01612018793821335	-0.027738535776734352	-0.025306181982159615	0.015557905659079552	0.06881187856197357	-0.009762796573340893	0.0024706318508833647	-0.00955380778759718	0.035916127264499664	0.0065343002788722515	0.02144797518849373	0.003741827327758074	0.017312921583652496	-0.029569150879979134	-0.06367254257202148	-0.035393305122852325	-0.01949118822813034	0.014067241922020912	-0.009761299006640911	0.04267651587724686	-0.032135751098394394	-0.0826830193400383	-0.007213049568235874	-0.002793521387502551	0.11039852350950241	-0.053186871111392975	0.004597657825797796	0.06278704851865768	-0.012941351160407066	-0.1073279082775116	-0.008396190591156483	0.04989897459745407	-0.05241593345999718	-0.05017697438597679	-0.01786551997065544	0.05952155217528343	-0.023873131722211838	-0.02783760242164135	0.012567649595439434	-0.03949418663978577	-0.009249826893210411	0.023915652185678482	0.03181733191013336	0.025265714153647423	0.0036174249835312366	-0.015297288075089455	-0.012313898652791977	0.03130748122930527	0.02819604054093361	-0.03105904348194599	-8.228847582358867e-05	-0.04425825923681259	0.025165503844618797	-0.0477726049721241	-0.04715327173471451	-0.050905633717775345	0.07562094181776047	0.005331519991159439	-0.03561558946967125	-0.02437480352818966	-0.019357046112418175	-0.009711159393191338	0.047242794185876846	0.006737150717526674	-0.03966055065393448	-0.03840591758489609	-0.05181771144270897	-0.060699839144945145	-0.10269497334957123	-0.036892060190439224	-0.000535887957084924	-0.05977337434887886	0.00583530031144619	-0.03354541212320328	-0.011862433515489101	-0.003096969798207283	-0.021648382768034935	0.054860759526491165	0.04027931019663811	0.035712260752916336	-0.03546832501888275	0.034163787961006165	0.009170734323561192	0.06599177420139313	0.039003849029541016	-0.035021644085645676	-0.00036491616629064083	0.01689404994249344	-0.020314203575253487	0.06112142279744148	-0.028668588027358055	-0.0005108824116177857	0.009738778695464134	-0.013727325946092606	0.013982008211314678	-0.0013264070730656385	0.015986284241080284	-0.051888346672058105	0.045547809451818466	-0.09900464117527008	-0.036471299827098846	0.031821925193071365	-0.022838475182652473	-0.08078626543283463	-0.05887220427393913	-0.017975401133298874	-0.025751160457730293	0.022709043696522713	-0.029961204156279564	0.010002411901950836	0.01442510262131691	-0.0361955463886261	-0.05240907520055771	-0.03826438635587692	-0.0018708608113229275	0.05347878113389015	-0.010726259090006351	0.05787993222475052	0.019057847559452057	0.012309306301176548	-0.008263940922915936	-0.0016227730084210634	0.004497691057622433	-0.08828745782375336	-0.005218460690230131	-0.043145205825567245	-0.026340458542108536	0.014154063537716866	-0.02242613397538662	0.032022129744291306	-0.05734611302614212	-0.03156481310725212	-0.06520590931177139	0.09785830229520798	-0.03511267900466919	0.021180439740419388	0.0061963386833667755	-0.03520817309617996	-0.006429397035390139	0.031282100826501846	-0.043272603303194046	-0.05687295272946358	0.01891227252781391	-0.006719985511153936	0.020972302183508873	0.019151458516716957	0.00024428684264421463	-0.021709918975830078	-0.05171419307589531	-0.016378488391637802	0.02201261557638645	-0.0794837698340416	0.019261205568909645	0.03712272644042969	-0.08092191815376282	-0.05370524898171425	0.009718951769173145	-0.02508925274014473	0.05040263757109642	-0.003590523498132825	0.0639483630657196	-0.02733764424920082	-0.00894117821007967	0.008851557038724422	-0.022456295788288116	0.008970847353339195	0.010934541933238506	-0.06083254516124725	-0.031649164855480194	0.05871563032269478	-0.04061546549201012	0.012529044412076473	0.08050142228603363	-0.04563906043767929	0.03121943213045597	0.04601044952869415	-0.03852827101945877	0.042587753385305405	-0.0009651426807977259	-0.02900894358754158	0.07164271920919418	-0.08424127846956253	0.11115213483572006	-0.08622239530086517	0.09422368556261063	0.00454840250313282	-0.014479352161288261	0.0680289939045906	0.06957537680864334	-0.009341229684650898	0.015373371541500092	-0.043901871889829636
+-0.054023806005716324	-0.08369556814432144	-0.02233423851430416	0.03817403316497803	-0.03171123191714287	0.03704051673412323	-0.04995390772819519	-0.01827852800488472	0.00754517363384366	-0.08305690437555313	-0.048438962548971176	-0.0090969642624259	-0.042165812104940414	-0.006984504871070385	0.02486274018883705	0.029503127560019493	-0.06369750946760178	-0.034628018736839294	0.055920299142599106	-0.049151718616485596	-0.011201051995158195	0.05478493869304657	-0.031910136342048645	0.04500049352645874	0.04700123891234398	0.006996572483330965	-0.02911757305264473	0.049833036959171295	0.0024843260180205107	0.08520720154047012	0.024566492065787315	-0.005940956994891167	0.05289611592888832	0.0027520384173840284	0.003254170995205641	0.04211609065532684	-0.025634655728936195	0.09417279064655304	0.008050286211073399	0.02247089520096779	0.08220534026622772	-0.07984039187431335	-0.02446523681282997	-0.05690130963921547	0.004785418510437012	-0.0710194930434227	-0.030465317890048027	-0.036456651985645294	-0.005155111663043499	0.07996995002031326	0.07950890064239502	-0.006323120556771755	0.017364919185638428	-0.038273464888334274	-0.001947062904946506	0.030989279970526695	0.022672466933727264	-0.005804486107081175	0.03863224387168884	0.0434839203953743	0.00619024271145463	-0.05036528408527374	0.053620148450136185	0.014146356843411922	-0.01571664959192276	0.06402591615915298	0.051963623613119125	0.033244334161281586	-0.09484469145536423	-0.06012443080544472	-0.005784991662949324	0.03897795453667641	-0.009984791278839111	1.1477724910946563e-05	-0.008861803449690342	0.008401179686188698	0.017930250614881516	0.024434713646769524	0.046019699424505234	0.017865439876914024	0.030764080584049225	-0.05300822854042053	0.05091807246208191	-0.0036263458896428347	0.061015356332063675	-0.06377588957548141	-0.0038209648337215185	-0.028779111802577972	-0.005314898677170277	-0.026856832206249237	0.09752387553453445	0.004165777005255222	0.014009230770170689	-0.0019121948862448335	0.009044324047863483	-0.016231216490268707	0.0704476535320282	-0.057670414447784424	0.001669169869273901	0.05343448370695114	0.02334250882267952	-0.029836468398571014	0.08469749987125397	0.03413299843668938	0.017274005338549614	0.03255026042461395	-0.018473898991942406	0.05289308354258537	0.04580863192677498	0.027435658499598503	0.001992253353819251	0.05499107018113136	0.05821744352579117	0.014741764403879642	0.021458344534039497	-0.012468255124986172	-0.038519348949193954	0.025491314008831978	0.027380652725696564	0.02363412082195282	0.00036595528945326805	0.03631158173084259	-0.03863674774765968	-0.0032336346339434385	0.014453046955168247	0.06230435520410538	-0.011324439197778702	0.006771868094801903	0.023120632395148277	-0.016050688922405243	0.0528578907251358	-0.018609698861837387	0.012879198417067528	0.015232748351991177	-0.030654577538371086	0.011382116936147213	0.018565678969025612	0.055010054260492325	0.02399868704378605	0.02552650310099125	-0.014417017810046673	-0.023823264986276627	0.02089534141123295	-0.04319965839385986	0.021736735478043556	0.00221127737313509	-0.062237806618213654	0.011467529460787773	-0.06419596821069717	-0.06964527070522308	-0.035235971212387085	0.042047709226608276	0.0065510086715221405	-0.128589928150177	0.0014278817689046264	-0.010731138288974762	0.054650697857141495	0.03605193644762039	-0.006184339057654142	0.09562753885984421	-0.08534882217645645	-0.06033235415816307	0.07566642761230469	-0.06043243780732155	-0.04835107922554016	-0.001956165535375476	0.023689649999141693	-0.042319703847169876	-0.02294835075736046	-0.003930227365344763	-0.015595984645187855	0.033151254057884216	0.10829006880521774	0.016223860904574394	0.07174047827720642	0.004152970388531685	0.03888796642422676	-0.05990181490778923	-0.036930736154317856	-0.0352499894797802	0.004050157498568296	0.03512812405824661	-0.03019731491804123	0.022254593670368195	-0.07972858101129532	0.0015212481375783682	0.005754308775067329	0.04282242804765701	-0.018992625176906586	0.015108405612409115	0.0064233154989778996	-0.05546140298247337	0.06116999313235283	0.04035719484090805	-0.048352181911468506	0.0575854629278183	0.03607126697897911	0.134440079331398	-0.09231401979923248	-0.03903079405426979	-0.04990480840206146	0.01257260236889124	0.04388168454170227	0.0006044837064109743	-0.023675959557294846	-0.0666312426328659	0.061752475798130035	0.051923301070928574	0.017833828926086426	-0.03767731785774231	0.009694811888039112	0.0017667640931904316	-0.007315917406231165	0.08780105412006378	0.059266116470098495	0.0029762887861579657	-0.040442921221256256	-0.013967500999569893	-0.0029259352013468742	-0.0636231005191803	-0.0286334827542305	0.009316680952906609	-0.08167579025030136	-0.027297377586364746	-0.04926792532205582	-0.017117701470851898	0.023213190957903862	-0.06008637323975563	-0.01242020819336176	0.032582201063632965	0.01493541058152914	0.028737748041749	-0.03552190959453583	0.00878784991800785	-0.03272128477692604	0.004678265191614628	-0.012083780951797962	0.0266020055860281	-0.0734277218580246	-0.05117444694042206	-0.10570202767848969	0.023540804162621498	-0.029294081032276154	-0.04800610989332199	0.03495161607861519	0.031070329248905182	0.06866386532783508	0.059214357286691666	-0.011008069850504398	-0.033565253019332886	-0.03156169503927231	-0.019393466413021088	-0.0636574849486351	-0.025767603889107704	0.035353660583496094	0.01184990257024765	-0.0005384605028666556	-0.11969268321990967	-0.025804530829191208	0.06785821914672852	0.024944571778178215	-0.009592422284185886	-0.020742908120155334	0.011897186748683453	0.009233369491994381	-0.001858449773862958	-0.016089895740151405	0.03870309516787529	-0.01767994835972786	0.0009405401651747525	-0.06846828758716583	0.04556705430150032	-0.022226674482226372	0.013296808116137981	-0.004474602174013853	-0.03642326965928078	0.06612672656774521	-0.0515831783413887	-0.04255698248744011	-0.02864849381148815	-0.0022799319121986628	-0.010196805000305176	0.007663701195269823	-0.0010464574443176389	0.01788240484893322	0.017761830240488052	-0.03575194254517555	-0.02352731116116047	0.005074940621852875	-0.03497549518942833	-0.0026023469399660826	-0.013551164418458939	0.06129977107048035	-0.043712422251701355	-0.02492538094520569	0.02900320664048195	0.017095418646931648	-0.0014699511229991913	0.02893134206533432	0.06767907738685608	-0.0038350268732756376	-0.03934604674577713	0.06554363667964935	0.006487854290753603	-0.06715396791696548	0.035000577569007874	-0.01886487752199173	0.03766031935811043	0.04678229242563248	0.0016363359754905105	-0.0328163206577301	0.04981992766261101	0.07332438230514526	0.005144375376403332	-0.055666737258434296	0.01951761543750763	-0.05655905604362488	0.0035068122670054436	-0.025237929075956345	0.05222358927130699	0.036611158400774	-0.004754385910928249	-0.04269704595208168	-0.060576681047677994	0.08088158816099167	-0.017251931130886078	-0.0305746141821146	-0.05022753030061722	0.025062156841158867	0.04823591187596321	0.030696619302034378	-0.02025529555976391	0.009240924380719662	-0.00760887935757637	-0.0356258749961853	-0.01675805263221264	-0.061176300048828125	0.06224226951599121	-0.04991227388381958	0.040769703686237335	-0.006695253308862448	-0.050050780177116394	0.05991094559431076	0.002801264636218548	0.032133352011442184	-0.080474354326725	-0.01224791444838047	0.00314147025346756	-0.038921818137168884	-0.05100740119814873	-0.07360732555389404	-0.05003414303064346	-0.08311188220977783	-0.034185755997896194	-0.022918062284588814	-0.019362933933734894	0.010243460536003113	-0.06771965324878693	-0.02579273097217083	-0.04258446395397186	-0.01055687665939331	0.039725370705127716	0.0003376077802386135	-0.0015752405161038041	-0.01816493645310402	-0.0642315223813057	-0.06355983018875122	-0.0017644853796809912	-0.0650918111205101	-0.06348433345556259	0.06576479226350784	0.023554442450404167	-0.07213907688856125	-0.008383035659790039	-0.0014203984756022692	-0.09715985506772995	-0.003067638026550412	-0.02896158955991268	0.014536401256918907	-0.027817821130156517	-0.06528892368078232	0.019625363871455193	0.026463566347956657	-0.024015849456191063	-0.042712096124887466	-0.10776334255933762	0.009120100177824497	-0.03978194668889046	-0.0168338343501091	-0.03507222607731819	0.07102800160646439	-0.012546922080218792	0.08023694902658463	-0.09033509343862534	0.022305848076939583	-0.06625436246395111	0.019209539517760277	0.047006137669086456	0.0578157901763916	0.04335126280784607	-0.018594788387417793	0.0775001049041748	-0.05955442413687706	-0.02525109238922596	-0.005806675646454096	-0.0198944341391325	0.03649638593196869	-0.05560246855020523	-0.011952174827456474	0.009793141856789589	-0.07586607336997986	-0.013845865614712238	0.01799289882183075	-0.08206764608621597	0.0713360458612442	-0.03727024793624878	-0.010740997269749641	-0.01923871785402298	0.005428970325738192	-0.07349714636802673	0.010500124655663967	-0.04283365607261658	0.03995562344789505	0.001091850339435041	-0.043253183364868164	-0.07200378179550171	0.023017870262265205	0.025943178683519363	-0.03251024708151817	-0.039845120161771774	0.023675652220845222	-0.02744442969560623	0.0018619706388562918	-0.014134314842522144	0.037419725209474564	-0.04991636425256729	0.11302924156188965	0.0020900836680084467	-0.06941987574100494	-0.01208185963332653	-0.009431824088096619	-0.03295678645372391	-0.003896173322573304	-0.023364774882793427	-0.04372046887874603	0.026679202914237976	0.003442687913775444	-0.008374455384910107	0.030054446309804916	0.02103547938168049	-0.02193892002105713	-0.052905019372701645	-0.03657422587275505	0.07793550938367844	0.023362204432487488	0.02087252028286457	0.027294980362057686	-0.08591626584529877	-0.04638892784714699	0.03713421896100044	-0.047438185662031174	-0.0711580291390419	-0.07955802977085114	0.014916675165295601	0.09747442603111267	-0.05196342617273331	0.013024039566516876	-0.10487224161624908	-0.03644869476556778	-0.010571176186203957	-0.01604939065873623	-0.0531267486512661	-0.047179486602544785	0.0549844428896904	-0.04044957458972931	-0.0011533006327226758	0.046141959726810455	-0.030260836705565453	0.054337985813617706	0.055983711034059525	0.005536393728107214	-0.05073898658156395	-0.02389039471745491	0.009143311530351639	-0.015574431978166103	-0.002094363095238805	0.019790731370449066	-0.07074225693941116	-0.08108022063970566	0.056180838495492935	-0.02108093723654747	0.1090114563703537	0.057040780782699585	-0.06293241679668427	0.015244974754750729	0.10425818711519241	0.002130600856617093	-0.024859031662344933	-0.004853145219385624	-0.012184490449726582	0.030842071399092674	0.015847254544496536	0.0517890490591526	-0.03366105630993843	0.10503626614809036	-0.020808925852179527	0.024773748591542244	-0.03184164687991142	0.010954467579722404	0.09566570818424225	0.03139482066035271	-0.06868299096822739
+0.012229369021952152	0.00783365499228239	0.0013614366762340069	0.025560835376381874	-0.07120707631111145	-0.07625434547662735	-0.017848914489150047	0.008038566447794437	0.022167347371578217	-0.021764300763607025	-0.02973269671201706	0.037958547472953796	-0.022788681089878082	0.018427088856697083	0.0009704828844405711	-0.028896501287817955	-0.09293565154075623	-0.05364135652780533	-0.028701376169919968	-0.038239918649196625	0.026164881885051727	0.0002529011690057814	-0.08932303637266159	0.043981507420539856	0.012571363709867	0.004895755555480719	-0.048027973622083664	-0.03164621815085411	-0.0068649002350866795	0.0009868702618405223	0.061375949531793594	-0.028028063476085663	0.056747667491436005	-0.06668982654809952	-0.06326136738061905	0.06300707161426544	0.014318251982331276	0.08022982627153397	0.04371599480509758	-0.013735425658524036	-0.041501276195049286	-0.004850553348660469	-0.04079309105873108	-0.05865729600191116	-0.00939488597214222	-0.03747955337166786	-0.052442412823438644	-0.028590619564056396	0.03794064745306969	0.026017362251877785	0.08593492954969406	-0.023025454953312874	-0.04278862476348877	-0.02290101908147335	-0.03387366235256195	-0.018761903047561646	0.0023067013826221228	-0.044930778443813324	-0.081112340092659	-0.006939592771232128	-0.05397099629044533	0.021649552509188652	7.422635098919272e-05	0.03555484861135483	-0.006922271568328142	-0.009961195290088654	0.013218949548900127	0.07246144860982895	0.029518406838178635	-0.015918612480163574	0.05323394387960434	0.04889524728059769	-0.03819624334573746	0.0007987514254637063	0.01691729947924614	-0.04474719241261482	-0.003431471064686775	0.04606351628899574	0.016161471605300903	0.05760595202445984	0.029955673962831497	-0.01727713830769062	0.08549133688211441	-0.03071950003504753	0.008722010999917984	-0.039198003709316254	0.003959916066378355	-0.030381886288523674	0.04578941687941551	0.014935116283595562	0.04658794030547142	0.0705418661236763	-0.049845144152641296	0.043396592140197754	-0.025856712833046913	0.03193138167262077	0.057354897260665894	-0.10252922773361206	-0.02679472044110298	0.020491763949394226	-0.005362352821975946	-0.01834268495440483	0.042549632489681244	-0.04675198346376419	0.04673246666789055	0.03525255247950554	-0.03544534742832184	0.01339816115796566	0.059235792607069016	-0.002514264080673456	0.10757530480623245	0.06267417222261429	0.07012595236301422	0.09112237393856049	0.07124654948711395	0.010145212523639202	0.02391037717461586	-0.07032331824302673	-0.008319152519106865	0.028198478743433952	-0.017853738740086555	0.046084098517894745	-0.04691662639379501	-0.03078288584947586	-0.018824197351932526	0.004515752196311951	-0.08939842879772186	-0.021310867741703987	0.028767013922333717	0.01865004561841488	0.08051936328411102	0.039374738931655884	0.014494390226900578	0.020935039967298508	0.03052004612982273	0.017872203141450882	0.014530357904732227	0.01562967710196972	0.005338881630450487	-0.043034639209508896	-0.03935589641332626	0.05551658198237419	0.015056280419230461	-0.00581335136666894	0.010848761536180973	0.03360556811094284	-0.0076139867305755615	0.023900924250483513	-0.037351805716753006	-0.028567291796207428	-0.10374615341424942	-0.03488238900899887	0.00513801071792841	-0.0917675569653511	0.058807190507650375	0.07783056050539017	0.06048941612243652	0.05082652345299721	-0.033763691782951355	0.01735137589275837	-0.003799552796408534	-0.04825756326317787	0.050593215972185135	-0.09511645138263702	-0.018961701542139053	-0.007707534823566675	0.0004425967636052519	0.05069969221949577	-0.0203428752720356	-0.004769516177475452	-0.03197365254163742	-0.07282661646604538	0.011735419742763042	0.08321285247802734	0.018087739124894142	-0.023217448964715004	0.011903066188097	-0.07062255591154099	0.00700375996530056	-0.017658766359090805	0.02906300500035286	-0.09690230339765549	0.016430314630270004	-0.06619912385940552	-0.04325833544135094	-0.010790722444653511	-0.02247348241508007	-0.014165571890771389	-0.008997374214231968	0.030932044610381126	-0.10706965625286102	-0.05427044257521629	0.012030263431370258	0.0571705624461174	-0.010537933558225632	0.05732192099094391	0.05829300731420517	0.06371287256479263	0.0552268847823143	-0.0063495575450360775	-0.08733923733234406	-0.033181458711624146	-0.011189546436071396	0.0574246346950531	0.03370597958564758	-0.07219623029232025	0.10438494384288788	0.03642753139138222	0.037801455706357956	-0.0523526594042778	-0.0323413647711277	0.04349054396152496	0.031681086868047714	0.037224702537059784	0.08110351115465164	-0.03929787501692772	-0.015584285371005535	0.029269203543663025	0.0395825132727623	-0.07538943737745285	-0.016399133950471878	0.025693168863654137	-0.006220026407390833	0.024468490853905678	-0.00994860753417015	-0.09823165088891983	-0.055937591940164566	-0.02785758674144745	0.0023867797572165728	-0.05963388457894325	0.008414838463068008	0.050112057477235794	-0.005474563222378492	-0.013279465958476067	-0.01594661921262741	-0.00433671148493886	-0.05805801600217819	0.08605655282735825	-0.03730953112244606	-0.06836289912462234	-0.019597237929701805	0.010740280151367188	0.012066825293004513	0.04484567418694496	0.0217415913939476	0.03479158133268356	0.12207642942667007	0.009804089553654194	-0.03125381097197533	-0.04353635385632515	-0.048230163753032684	-0.0662631243467331	0.028726276010274887	0.044599492102861404	-0.03787969425320625	-0.08456791192293167	-0.013450595550239086	-0.07072591781616211	-0.009233687072992325	-0.007749119773507118	0.05249962583184242	0.037313543260097504	-0.06613912433385849	-0.06664220988750458	0.06221369281411171	0.039318542927503586	-0.031923603266477585	0.00944002065807581	-0.040398623794317245	0.044455189257860184	-0.028577862307429314	-0.023608960211277008	-0.0014674098929390311	-0.011137502267956734	0.011988346464931965	-0.0026098452508449554	0.08493853360414505	-0.03336222469806671	-0.020051179453730583	-0.02744852751493454	-0.0481267012655735	-0.026148928329348564	-0.002966707106679678	0.025005655363202095	-0.008488963358104229	0.06659995764493942	0.002711803885176778	-0.014043813571333885	0.024236714467406273	-0.0008242676849476993	-0.04539237171411514	-0.0631474182009697	0.003659648122265935	0.005700518377125263	-0.07083268463611603	0.01285955123603344	0.024959735572338104	-0.01803508587181568	-0.032528337091207504	0.09397922456264496	-0.037822071462869644	-0.02397073805332184	0.011597116477787495	0.003460030537098646	-0.05904074013233185	-0.020678533241152763	-0.08573482185602188	0.0008787686820141971	0.07598143070936203	-0.003651436185464263	0.045276109129190445	0.0421140193939209	0.06782180070877075	0.062412913888692856	0.004381589591503143	-0.05104536935687065	0.04347259923815727	-0.0533452071249485	-0.10538315027952194	0.06897542625665665	-0.007588054519146681	0.033925626426935196	-0.028684942051768303	0.008624193258583546	0.05101123824715614	-0.016565894708037376	-0.0014951879857107997	-0.009265225380659103	0.012213299982249737	-0.00442885747179389	0.0643388107419014	0.023992342874407768	-0.02960694581270218	0.024987563490867615	-0.06112176179885864	-0.06846939027309418	-0.0987057238817215	0.023998839780688286	0.0352022685110569	0.042332686483860016	0.028413746505975723	-0.07273441553115845	0.0025405138731002808	-0.019891679286956787	0.09813831746578217	-0.1005573570728302	-0.015587566420435905	-0.05893924459815025	0.030568750575184822	-0.1141250878572464	-0.05320286378264427	0.0008695780416019261	-0.045249223709106445	-0.03682372346520424	-0.0043432703241705894	0.050833430141210556	-0.06477756053209305	-0.01691058650612831	0.023066533729434013	-0.0169563926756382	0.017410442233085632	0.02991645410656929	0.004252745304256678	0.05944403260946274	0.03984754532575607	-0.04278232902288437	0.019740251824259758	0.0222234595566988	0.015594362281262875	-0.015223436057567596	0.06267870962619781	-0.047337669879198074	0.07507029920816422	0.006088831927627325	-0.026631293818354607	-0.09133416414260864	0.016949545592069626	0.0014759330078959465	-0.0022496446035802364	-0.01938249170780182	0.015630707144737244	0.02385745570063591	0.03552519157528877	0.024024102836847305	-0.05820462852716446	-0.022569045424461365	-0.05629502236843109	-0.005854132119566202	-0.05180422216653824	0.011186303570866585	0.02897769585251808	-0.08461829274892807	0.034934964030981064	-0.038058314472436905	-0.03163551166653633	0.005646892357617617	-0.0285954587161541	-0.0012890163343399763	0.005544691346585751	0.016578448936343193	-0.012274658307433128	0.020480509847402573	-0.016137143597006798	0.022269560024142265	0.008859367109835148	-0.032904960215091705	0.041593410074710846	0.045089706778526306	-0.03356172516942024	0.021497078239917755	-0.015757223591208458	-0.028965922072529793	-0.013209660537540913	-0.041847046464681625	0.010033732280135155	0.03315119445323944	-0.015926983207464218	-0.01835874654352665	-0.009601151570677757	-0.041214827448129654	-0.05552109703421593	0.039376020431518555	-0.005707253701984882	-0.10130131989717484	-0.026089567691087723	0.015057619661092758	-0.06081494688987732	0.04549609497189522	0.028137221932411194	0.03362978994846344	0.04537609592080116	-0.02932221256196499	-0.009333022870123386	0.01392038632184267	-0.03294267877936363	-0.028166428208351135	-0.03235742822289467	0.04490496590733528	-0.02702023833990097	0.052648477256298065	0.07453085482120514	-0.015652507543563843	0.010093165561556816	-0.029850415885448456	-0.05462103709578514	0.012228736653923988	-0.04207950830459595	0.01581355184316635	0.048330843448638916	0.010105888359248638	-0.05369299277663231	-0.05808083340525627	-0.07534889876842499	0.05377738177776337	-0.018581602722406387	0.04099879786372185	0.01705731824040413	-0.0689205750823021	-0.012028350494801998	0.001536099356599152	0.031459689140319824	-0.10829168558120728	0.029792513698339462	-0.061923786997795105	-0.010644553229212761	-0.019247815012931824	0.05342233553528786	0.0240006223320961	-0.11318979412317276	-0.014843588694930077	0.00963194202631712	-0.03491659462451935	0.013194586150348186	-0.021323801949620247	-0.02988622523844242	-0.05532549321651459	-0.044543202966451645	-0.09603114426136017	0.0710395947098732	0.01817844808101654	0.010993526317179203	0.0021475525572896004	-0.008096516132354736	0.02732783369719982	-0.017377810552716255	-0.01855950802564621	0.021763836964964867	-0.05849824473261833	-0.008513321168720722	0.07385686784982681	0.023195920512080193	0.057615507394075394	0.06909243017435074	-0.019640687853097916	0.034912656992673874	0.025893211364746094	-0.041660137474536896	0.0197727233171463	-0.017958952113986015	-0.04736603423953056	0.061974428594112396	0.02115578204393387	0.03241235390305519	-0.028906265273690224	0.10877040028572083	-0.0019970517605543137	-0.04044704884290695	0.03898998349905014	0.044692639261484146	-0.026725715026259422	0.07668954133987427	-0.001180768827907741
+0.005845316220074892	-0.05040774494409561	-0.005296595860272646	0.012310133315622807	-0.07834335416555405	-0.054319534450769424	0.011025479063391685	0.04437132552266121	-0.007324552629143	-0.04210318252444267	-0.017270322889089584	-0.0014826871920377016	0.005193805787712336	0.019784308969974518	0.01826965995132923	-0.039127167314291	-0.059636086225509644	0.008287293836474419	-0.06309709697961807	-0.036385126411914825	0.04376118630170822	0.027460766956210136	-0.05591469630599022	0.03945162519812584	-0.013152365572750568	-0.007552339695394039	-0.04957307502627373	0.013006494380533695	0.02472759410738945	0.02096732333302498	0.08723824471235275	-0.021701056510210037	0.04785063862800598	-0.05602811276912689	-0.039285678416490555	0.052570562809705734	-0.044370073825120926	0.10740067064762115	0.023378198966383934	-0.017795152962207794	-0.035435911267995834	-0.012775145471096039	-0.002310287905856967	-0.03108314983546734	0.026775436475872993	-0.029357971623539925	0.005071383435279131	-0.030846288427710533	0.01438049878925085	0.03919601067900658	0.08430119603872299	0.02137570269405842	0.020727986469864845	-0.009416399523615837	-0.001297292299568653	0.020020946860313416	0.01735823042690754	-0.0601702481508255	-0.06189586967229843	0.031232956796884537	-0.06819017976522446	0.008592771366238594	0.0042004710994660854	0.022028397768735886	-0.0063696145080029964	-0.025550683960318565	-0.01741660013794899	0.10403124988079071	0.017893915995955467	-0.029690993949770927	0.007814369164407253	0.07924892753362656	-0.06101372092962265	-0.07275332510471344	0.03757863491773605	-0.03719000890851021	0.013262150809168816	0.018050650134682655	-0.03217311203479767	-0.014988579787313938	-0.0188986137509346	-0.029217276722192764	0.11272057145833969	-0.029132749885320663	-0.025372110307216644	-0.0462791807949543	-0.013712598942220211	-0.0686797946691513	0.07084767520427704	0.016260677948594093	0.04526512324810028	0.057485438883304596	-0.04248600825667381	0.04574204981327057	6.33621821179986e-05	0.01030008215457201	0.006214888766407967	-0.13360679149627686	-0.025228917598724365	-0.011631719768047333	0.01736827939748764	-0.0035265106707811356	0.0884854644536972	-0.00043963774805888534	0.044705405831336975	0.06238158419728279	-0.06759501993656158	-0.0024779040832072496	0.023940129205584526	0.045002177357673645	0.019511617720127106	0.14087235927581787	0.07302968204021454	0.0514444001019001	0.03897770866751671	-0.009289969690144062	-0.024609407410025597	-0.027991099283099174	-0.02666684426367283	-0.0008436240023002028	0.011003966443240643	0.033451106399297714	-0.026584573090076447	-0.062432728707790375	-0.04599566385149956	0.07248598337173462	-0.10318169742822647	-0.021269431337714195	0.08778262138366699	-0.020194729790091515	0.10374436527490616	0.06929372251033783	-0.02298392727971077	0.03792613372206688	0.07276810705661774	0.004377696197479963	-0.01728985458612442	0.0015046891057863832	-0.0003463224566075951	-0.04352429881691933	-0.061345718801021576	0.060554370284080505	0.012581279501318932	-0.055497899651527405	-0.004569911397993565	0.060482513159513474	0.028556618839502335	-0.03283485025167465	-0.061598505824804306	0.014345517382025719	-0.06360478699207306	-0.09117724001407623	-0.003033918561413884	-0.07951321452856064	0.025613142177462578	0.049186818301677704	-0.004404083825647831	0.0378907173871994	0.0022137686610221863	-0.015464271418750286	-0.043470948934555054	-0.07036518305540085	-0.012329250574111938	-0.08049043267965317	0.0029372666031122208	-0.009328397922217846	0.010819459334015846	0.0033733039163053036	-0.037384096533060074	-0.05262437462806702	-0.017020709812641144	-0.025435952469706535	0.01111532747745514	0.07579878717660904	0.034610144793987274	0.01826545223593712	0.009221014566719532	-0.02017674408853054	-0.0060390629805624485	-0.04927171766757965	0.011644325219094753	-0.045655764639377594	0.02835206873714924	-0.09009169787168503	-0.021222015842795372	-0.0015580381732434034	0.02083709090948105	-0.04059436917304993	-0.022338880226016045	-0.031540367752313614	-0.024647939950227737	-0.06324056535959244	0.019101059064269066	0.06426475197076797	-0.03303682431578636	0.04336249455809593	-0.016363831236958504	0.07807951420545578	-0.026475101709365845	-0.02877930924296379	-0.07296127825975418	0.04703391343355179	0.02095063589513302	-0.014067194424569607	-0.006931798066943884	-0.07293019443750381	0.07561394572257996	0.008866185322403908	0.035455260425806046	-0.07807404547929764	0.022433890029788017	0.05353495106101036	0.01583106629550457	0.05715519189834595	0.043734095990657806	-0.04049540311098099	-0.02249189279973507	0.009204993955790997	0.03793776407837868	-0.06527671962976456	0.017733294516801834	0.008717923425137997	-0.055733442306518555	0.042132988572120667	-0.03259006515145302	-0.10772569477558136	-0.030319279059767723	-0.004839058965444565	-0.008019688539206982	-0.05524666607379913	-0.00800749659538269	0.027306219562888145	-0.0046387845650315285	-0.013627379201352596	0.003946390468627214	-0.015123610384762287	-0.07437828183174133	0.08268097043037415	-0.010034800507128239	-0.058802418410778046	-0.058357398957014084	-0.020130423828959465	0.010214491747319698	0.007543070707470179	0.01019356120377779	0.02169288694858551	0.10035371035337448	0.03968087211251259	-0.053393375128507614	-0.0012121460167691112	-0.045531392097473145	-0.051764946430921555	-0.01431287545710802	0.026350386440753937	-0.04260849952697754	-0.06610080599784851	0.021503470838069916	-0.08502969145774841	-0.0008234405540861189	-0.021791674196720123	0.05980025231838226	-0.04588010534644127	-0.021009253337979317	-0.003713375423103571	0.04316382482647896	-0.024720603600144386	-0.060042109340429306	0.013251141645014286	-0.01493056956678629	0.04993319883942604	-0.01296977698802948	-0.018611231818795204	0.03704104945063591	-0.005064194090664387	0.03950406238436699	-0.02884913794696331	0.0679582878947258	-0.023120803758502007	-0.09460193663835526	-0.034198544919490814	-0.05993284285068512	-0.07469142973423004	0.002836642088368535	-0.029254009947180748	-0.030269287526607513	0.08600462973117828	0.013116499409079552	-0.042997606098651886	-0.026608262211084366	-0.04510655254125595	-0.016188571229577065	-0.029009191319346428	-0.03110819309949875	-0.01678755134344101	-0.05372622236609459	0.015088589861989021	0.03700270503759384	-0.07802428305149078	-0.021910864859819412	0.03942776471376419	0.003735407954081893	-0.013961310498416424	0.07060183584690094	-0.06020369753241539	0.007817938923835754	-0.011690711602568626	-0.056164536625146866	0.03386019170284271	0.07844545692205429	0.045930102467536926	0.04261219874024391	0.03041449747979641	0.0290504340082407	0.09652803838253021	0.028590451925992966	-0.03320711478590965	-0.008995919488370419	-0.02201533131301403	-0.06560403853654861	0.04383714124560356	-0.04336646944284439	-0.024290695786476135	0.010226570069789886	4.5027270971331745e-05	0.07721880823373795	-0.015206494368612766	-0.021266711875796318	-0.01144065149128437	0.041968315839767456	-0.016862478107213974	0.015104279853403568	-0.0018540937453508377	-0.015851585194468498	0.013180463574826717	-0.05114633962512016	-0.05836126208305359	-0.039101194590330124	0.03871064633131027	-0.03249504044651985	0.06629341095685959	-0.013673372566699982	-0.07602682709693909	0.009469864889979362	0.007985436357557774	0.12000920623540878	-0.06694253534078598	0.01794852502644062	0.014506355859339237	0.038747258484363556	-0.0943504199385643	0.010148588567972183	-0.006485148333013058	-0.012759402394294739	-0.0659923180937767	0.0279373899102211	0.041842736303806305	-0.06825944781303406	-0.041423045098781586	-0.012534113600850105	-0.03317999467253685	-0.005470201373100281	0.011783257126808167	-0.02064354158937931	0.05520020052790642	0.029527638107538223	-0.014822612516582012	0.008872873149812222	0.039410367608070374	0.026410536840558052	-0.05450192466378212	0.03916991502046585	-0.04035654291510582	0.06885816156864166	-0.017455169931054115	-0.06444238871335983	-0.06233885511755943	0.03529604151844978	-0.025342922657728195	-0.010377414524555206	-0.04552400857210159	-0.0040955329313874245	0.009780626744031906	0.02887170948088169	-0.006271703168749809	-0.0789889320731163	0.005584344733506441	-0.010482621379196644	-0.049132417887449265	-0.08214104175567627	-0.06949163228273392	0.02469148486852646	-0.05049598962068558	-0.004742736462503672	-0.02694656141102314	0.015600201673805714	0.0013710149796679616	-0.02599254995584488	0.04924210160970688	0.06117818132042885	0.021978279575705528	-0.013839561492204666	0.05862421542406082	0.028698239475488663	0.038135770708322525	-0.0068769012577831745	-0.03263984993100166	0.0344734825193882	0.038948334753513336	-0.04170018434524536	0.015850787982344627	-0.035148464143276215	0.03673670068383217	-0.01936575584113598	-0.024806197732686996	0.01057613454759121	-0.015870168805122375	-0.011446733959019184	-0.04534723237156868	0.06401915103197098	-0.07488749176263809	-0.03623603656888008	0.04244926571846008	-0.05025336891412735	-0.08358532935380936	-0.014488807879388332	-0.040671207010746	-0.023054642602801323	0.054184943437576294	-0.0038192004431039095	0.03775835782289505	-0.0009820156265050173	-0.03398401662707329	-0.0234829131513834	0.0035870226565748453	-0.014026246033608913	-0.008142933249473572	0.034920286387205124	0.07042016088962555	-0.001164709683507681	0.003874697955325246	0.01583138294517994	-0.046857479959726334	0.019885579124093056	-0.11708332598209381	-0.005382356699556112	-0.005204812623560429	-0.00040197165799327195	0.018488118425011635	0.017566289752721786	0.041071951389312744	-0.06915757805109024	-0.01560754794627428	-0.031943343579769135	0.062273286283016205	-0.02049338072538376	0.030580509454011917	-0.0014872380997985601	-0.043711837381124496	-0.04300427436828613	0.013857267796993256	-0.023120688274502754	-0.07763296365737915	0.019873708486557007	-0.022514482960104942	0.035997021943330765	-0.010680570267140865	0.046174004673957825	-0.030061939731240273	-0.12188974767923355	-0.013417195528745651	0.006581445690244436	-0.02913513593375683	0.007780534215271473	0.037622932344675064	-0.07599503546953201	-0.048940323293209076	-0.003840548451989889	-0.048000287264585495	0.04800441488623619	-0.03171161562204361	0.038306258618831635	-0.05059249699115753	-0.021308813244104385	-0.005622328724712133	-0.04938941076397896	0.029127473011612892	-0.009913221932947636	-0.07430384308099747	-0.04101597145199776	0.06949145346879959	-0.021057236939668655	0.016516536474227905	0.09196054190397263	-0.03606712818145752	0.04515286535024643	0.01750320754945278	-0.017475556582212448	0.011537153273820877	-0.02216162160038948	-0.005118661560118198	0.021690286695957184	-0.08286037296056747	0.07671449333429337	-0.05912632867693901	0.10274230688810349	0.030674904584884644	0.00528015336021781	0.09456823766231537	0.07917681336402893	0.010342435911297798	0.018536489456892014	-0.06884394586086273
+0.008685002103447914	-0.05747591704130173	-0.008816282264888287	0.007374340668320656	-0.08168601989746094	-0.059578631073236465	0.00614955136552453	0.04362526535987854	-0.015821268782019615	-0.04166438803076744	-0.015534763224422932	-0.0036852809134870768	-0.0020291071850806475	0.018372492864727974	0.024366311728954315	-0.04263497143983841	-0.062433648854494095	0.01842031627893448	-0.058652590960264206	-0.03304752707481384	0.04902120679616928	0.029033513739705086	-0.05185506120324135	0.037399206310510635	-0.013038873672485352	0.00130267848726362	-0.05057823657989502	0.014200862497091293	0.032780542969703674	0.021643633022904396	0.08341490477323532	-0.025571634992957115	0.0545017346739769	-0.050243087112903595	-0.03041098080575466	0.04273022338747978	-0.05435346066951752	0.10959938168525696	0.02958313561975956	-0.010985681787133217	-0.03612431883811951	-0.021036647260189056	-0.01426986325532198	-0.027580181136727333	0.029250936582684517	-0.03391135111451149	-0.005390708334743977	-0.031540483236312866	0.0013474059524014592	0.049565136432647705	0.07592689990997314	0.014880350790917873	0.017687251791357994	-0.0038731475360691547	-0.00043606426334008574	0.017262820154428482	0.01450888067483902	-0.05552663654088974	-0.06792135536670685	0.03891678899526596	-0.06256388872861862	0.012286671437323093	0.0012196768075227737	0.027465680614113808	-0.005025703459978104	-0.02986280992627144	-0.004764724988490343	0.09711766242980957	0.007220900617539883	-0.0368182510137558	0.012213354930281639	0.07985388487577438	-0.06554137915372849	-0.06676171720027924	0.027355223894119263	-0.03267844766378403	0.02161356993019581	0.023479679599404335	-0.02092120610177517	-0.017537254840135574	-0.014615785330533981	-0.021701961755752563	0.11357756704092026	-0.028179002925753593	-0.024920044466853142	-0.044226162135601044	-0.012182514183223248	-0.05861079692840576	0.06387462466955185	0.009095420129597187	0.04968127980828285	0.04014540836215019	-0.045682549476623535	0.04166390746831894	0.004538477398455143	0.014990875497460365	0.005461523309350014	-0.1304382085800171	-0.03227199614048004	-0.01173325628042221	0.011254805140197277	-0.007252139039337635	0.09273523092269897	0.013534367084503174	0.04480265453457832	0.05836501345038414	-0.06181749328970909	0.007328034844249487	0.016903124749660492	0.034542202949523926	0.01852012425661087	0.1366134136915207	0.07790844142436981	0.04592689871788025	0.04620473459362984	-0.00905799400061369	-0.027323581278324127	-0.01818671077489853	-0.025848940014839172	0.0026893983595073223	0.01659526117146015	0.0354774072766304	-0.026452837511897087	-0.05479833111166954	-0.053353987634181976	0.07783874869346619	-0.11392999440431595	-0.027701353654265404	0.08636703342199326	-0.02558293007314205	0.10322558879852295	0.0667860358953476	-0.02416088990867138	0.031146101653575897	0.07197899371385574	-0.0075862524099648	-0.009562748484313488	0.0007111658342182636	-0.0027055416721850634	-0.04012775421142578	-0.059714943170547485	0.05805349349975586	0.008989434689283371	-0.05982527881860733	-0.015425210818648338	0.05748548358678818	0.02108435332775116	-0.030481748282909393	-0.07560218870639801	0.003929242491722107	-0.06852991878986359	-0.08283469825983047	-0.012805772013962269	-0.08719107508659363	0.027735047042369843	0.0362340547144413	-0.011043926700949669	0.037656646221876144	-0.003518269397318363	-0.011273924261331558	-0.05214788392186165	-0.0646628737449646	-0.008665781468153	-0.07944337278604507	0.005813170224428177	0.003198449732735753	0.0103923249989748	0.005411909893155098	-0.04162083938717842	-0.04216320440173149	-0.03086126036942005	-0.026010656729340553	0.00999603234231472	0.06151631847023964	0.0332096628844738	0.015993410721421242	0.004130389541387558	-0.020840482786297798	-0.005743827670812607	-0.0496395006775856	0.020803231745958328	-0.04502992331981659	0.03773879259824753	-0.07379018515348434	-0.014806466177105904	-0.011198284104466438	0.014913531951606274	-0.051429737359285355	-0.017253452911973	-0.028986603021621704	-0.020699329674243927	-0.06427663564682007	0.028891654685139656	0.07072142511606216	-0.03697103634476662	0.04432697221636772	-0.023580871522426605	0.08328407257795334	-0.03591416776180267	-0.02863428182899952	-0.05923691391944885	0.04552652686834335	0.020991992205381393	-0.014572834596037865	-0.019098469987511635	-0.07435771822929382	0.0698075070977211	0.01499343290925026	0.03149788826704025	-0.07702555507421494	0.03158678114414215	0.04860427975654602	0.004492925014346838	0.05923120677471161	0.03979504853487015	-0.050561461597681046	-0.022057456895709038	0.01592496782541275	0.029079748317599297	-0.061224550008773804	0.010849990881979465	0.01182591263204813	-0.05763836205005646	0.03606860339641571	-0.041161708533763885	-0.10541649162769318	-0.01332797296345234	-0.0017102910205721855	-0.019895385950803757	-0.06163252890110016	-0.012233477085828781	0.0331764854490757	-0.006249912548810244	-0.02045658975839615	-0.0006540147005580366	-0.006573839578777552	-0.07537928223609924	0.08099161833524704	-0.005664944648742676	-0.05640903115272522	-0.07244967669248581	-0.01716865971684456	0.00538458488881588	0.01640688255429268	0.012101758271455765	0.023128371685743332	0.10337311029434204	0.043327100574970245	-0.0548102892935276	-0.003147316398099065	-0.04365489259362221	-0.03936421871185303	-0.00976434163749218	0.024180943146348	-0.046471256762742996	-0.06084318459033966	0.02200806885957718	-0.07865395396947861	-0.011652213521301746	-0.009419026784598827	0.06858879327774048	-0.05519961938261986	-0.02953975275158882	0.003222464583814144	0.029728760942816734	-0.01693483255803585	-0.04542054235935211	0.016780082136392593	-0.016221506521105766	0.048350561410188675	-0.016637783497571945	-0.020874088630080223	0.044187840074300766	-0.005722763016819954	0.04761654511094093	-0.02644682303071022	0.07223368436098099	-0.02523774467408657	-0.09513556212186813	-0.03800944238901138	-0.06472022831439972	-0.07403617352247238	-0.006732210051268339	-0.030490923672914505	-0.018786951899528503	0.0901748538017273	0.026893720030784607	-0.04293722286820412	-0.028243346139788628	-0.048862431198358536	-0.01125283632427454	-0.03940381482243538	-0.0190004650503397	-0.017390361055731773	-0.055093683302402496	0.018787967041134834	0.03423656150698662	-0.07499628514051437	-0.018154384568333626	0.039812639355659485	0.0018258608179166913	-0.028091086074709892	0.07354598492383957	-0.053851354867219925	0.00028861386817879975	-0.011415865272283554	-0.05877027288079262	0.03516785055398941	0.07708043605089188	0.04839491471648216	0.03011249005794525	0.0345449298620224	0.04803711920976639	0.10403154045343399	0.022632302716374397	-0.0218651182949543	-0.02189195156097412	-0.02505500242114067	-0.060784582048654556	0.04975352808833122	-0.050889480859041214	-0.01988711580634117	0.0034299029503017664	-0.0026713262777775526	0.07452481240034103	-0.02387361414730549	-0.02590840682387352	-0.008774416521191597	0.037035275250673294	-0.007417780347168446	0.013330722227692604	-0.0006730224122293293	-0.0276789590716362	0.025435347110033035	-0.03951428458094597	-0.06227808818221092	-0.04238119721412659	0.042305752635002136	-0.04918065667152405	0.0713837668299675	-0.013459259644150734	-0.07563327252864838	0.018972720950841904	0.00913967378437519	0.11546226590871811	-0.06433866173028946	0.01730753295123577	0.016326861456036568	0.038233790546655655	-0.08592303842306137	0.008714881725609303	-0.0071103936061263084	-0.01271249633282423	-0.06032991036772728	0.025185957551002502	0.04910594969987869	-0.06407712399959564	-0.05240233987569809	-0.009496929123997688	-0.03477342799305916	-0.011487267911434174	0.01359636802226305	-0.013818446546792984	0.05569272115826607	0.020374178886413574	-0.016522051766514778	0.011441410519182682	0.045884862542152405	0.026032093912363052	-0.06264891475439072	0.04233970865607262	-0.03479120507836342	0.07008803635835648	-0.020145881921052933	-0.061191193759441376	-0.052665580064058304	0.042357880622148514	-0.021540027111768723	-0.006977085489779711	-0.05149241164326668	0.0031064937356859446	0.010469076223671436	0.03201466053724289	-0.004752203822135925	-0.06753979623317719	-0.01318142656236887	-0.0043023256585001945	-0.05898333340883255	-0.0866989940404892	-0.06546729058027267	0.02156357280910015	-0.043109506368637085	-0.0011165596079081297	-0.031032638624310493	0.017074644565582275	-0.0016388266813009977	-0.032494645565748215	0.05929652601480484	0.061832938343286514	0.02569163404405117	-0.02004568837583065	0.06088057532906532	0.03037070669233799	0.03998775780200958	-0.009212911128997803	-0.025462696328759193	0.03673850744962692	0.024473246186971664	-0.03794723376631737	0.009539227001369	-0.03989778831601143	0.037658970803022385	-0.02152235619723797	-0.03507497161626816	0.011530166491866112	-0.015680622309446335	-0.0026816052850335836	-0.04745020717382431	0.06667197495698929	-0.08930357545614243	-0.02603459358215332	0.045066799968481064	-0.0462193600833416	-0.07806264609098434	-0.023268496617674828	-0.04436349868774414	-0.013017375022172928	0.04318283498287201	-0.007890602573752403	0.031062431633472443	0.004801295232027769	-0.027981651946902275	-0.024294767528772354	0.0004822666523978114	-0.01501856092363596	-0.003197452751919627	0.03875281289219856	0.06470891088247299	-0.0014699612511321902	0.017982887104153633	0.008255482651293278	-0.03926778584718704	0.004162369295954704	-0.11332601308822632	-0.0029676659032702446	-0.006635264027863741	0.0048927548341453075	0.018631979823112488	0.014942800626158714	0.03956359997391701	-0.06713832914829254	-0.014163484796881676	-0.030702026560902596	0.0648827776312828	-0.014904464595019817	0.0316804014146328	0.0010107035050168633	-0.03791644051671028	-0.04644500091671944	0.0156188840046525	-0.023214180022478104	-0.07954728603363037	0.015019012615084648	-0.015887632966041565	0.05431649833917618	-0.0063135758973658085	0.041404470801353455	-0.03737734258174896	-0.11500997096300125	-0.004081263672560453	0.00921686366200447	-0.025903955101966858	0.008546443656086922	0.03805696964263916	-0.07929056882858276	-0.04778958112001419	0.0037086820229887962	-0.04770519956946373	0.043496910482645035	-0.024075614288449287	0.04662533476948738	-0.05632563680410385	-0.022625241428613663	0.011820600368082523	-0.04893770068883896	0.02872694842517376	-0.0049189189448952675	-0.0761982873082161	-0.03444047272205353	0.0672607272863388	-0.029533255845308304	0.024814574047923088	0.09058983623981476	-0.04570741951465607	0.04852516949176788	0.013753954321146011	-0.011296029202640057	0.010666973888874054	-0.02819894254207611	-0.016771629452705383	0.017421241849660873	-0.08055149763822556	0.07271275669336319	-0.06406192481517792	0.10476639121770859	0.029745325446128845	0.0011225895723327994	0.10237794369459152	0.06389874964952469	0.007676942273974419	0.02241496928036213	-0.07843542098999023
+0.03992699459195137	-0.0031530505511909723	-5.2964638598496094e-05	0.06399698555469513	-0.05536423996090889	-0.03159976750612259	-0.04228418692946434	0.02258848212659359	0.02297123707830906	-0.04334794729948044	-0.01694568432867527	-0.025403384119272232	-0.03584596514701843	-0.017306070774793625	0.043142400681972504	-0.02638922445476055	-0.06515869498252869	0.018062783405184746	-0.04291708394885063	-0.02421141043305397	0.018832946196198463	0.06858762353658676	-0.08393707126379013	0.04809545353055	-0.01751605235040188	0.026412641629576683	-0.04766261577606201	0.022017931565642357	-0.05996719375252724	0.043824680149555206	0.04532090574502945	0.0023186071775853634	0.05558673292398453	-0.03928114473819733	-0.043634116649627686	0.05477379634976387	-0.01582845114171505	0.09256567060947418	0.03665464371442795	-0.038319144397974014	-0.03634246811270714	0.013222580775618553	-0.005723449867218733	-0.07739285379648209	0.017260616645216942	0.006869322620332241	-0.0035962152760475874	-0.04577463120222092	0.03495214879512787	0.03537214919924736	0.1295759230852127	0.007322649005800486	-0.024107884615659714	-0.023494873195886612	-0.0006167099345475435	0.011181583628058434	0.04457133635878563	-0.09350265562534332	-0.04657876864075661	0.017066791653633118	-0.0830342024564743	0.012610258534550667	0.013112270273268223	-0.010500405915081501	-0.022340746596455574	-0.015106571838259697	-0.01680000126361847	0.07976582646369934	0.046910662204027176	-0.012280198745429516	0.022240832448005676	0.09515349566936493	-0.08003441989421844	-0.06685739755630493	0.0726495236158371	-0.05739834904670715	-0.03644506633281708	0.03179587796330452	-0.014368297532200813	0.007965078577399254	0.023978179320693016	-0.0443187952041626	0.10749535262584686	-0.06861508637666702	-0.030974213033914566	-0.026433654129505157	-0.023771392181515694	-0.08795615285634995	0.04959418252110481	0.031753335148096085	0.004747652914375067	0.08100887387990952	-0.012409841641783714	0.04707940295338631	-0.0032548655290156603	0.037045545876026154	0.04117909446358681	-0.09998490661382675	-0.01724322885274887	-0.014891018159687519	-0.013536394573748112	0.015130959451198578	0.04854926839470863	0.0010230400366708636	0.06018209084868431	0.025264248251914978	-0.04071449488401413	-0.01928597316145897	0.033039990812540054	0.06599929928779602	0.03149168938398361	0.051780715584754944	0.08493752032518387	0.05172489583492279	0.03931252285838127	-0.004689883906394243	0.01977706514298916	-0.04398979991674423	-0.03511101379990578	0.02528323419392109	-0.03885398805141449	0.03662474825978279	-0.02987794019281864	-0.056078169494867325	-0.006300821900367737	0.030719056725502014	-0.07021144777536392	-0.04821173846721649	0.03925313055515289	-0.02966674044728279	0.11556938290596008	0.04508466273546219	-0.020791031420230865	0.0032144812867045403	0.04029252752661705	0.04277043789625168	-0.011172369122505188	0.009231649339199066	-0.00028757224208675325	-0.010295643471181393	0.00395833607763052	0.05459541454911232	-0.016331788152456284	-0.04531430825591087	0.054871395230293274	0.015647457912564278	0.02422858588397503	-0.031114671379327774	-0.08062982559204102	0.022845888510346413	-0.08653192222118378	-0.08730968087911606	0.0013298917328938842	-0.0614822655916214	0.009310485795140266	0.04777290299534798	0.03461499139666557	0.038618169724941254	-0.015109979547560215	-0.028878020122647285	-0.03688402473926544	-0.04619350656867027	-0.03419932723045349	-0.07564104348421097	-0.046247292309999466	-0.051134143024683	-0.002983188722282648	0.014551607891917229	-0.030276667326688766	-0.038287725299596786	-0.015541072003543377	0.031692445278167725	0.030117616057395935	0.11539451777935028	0.0815737247467041	0.03349853307008743	0.018696017563343048	-0.030363596975803375	-0.015154746361076832	-0.022424349561333656	0.001982901943847537	-0.0448005236685276	0.01928863301873207	-0.054439976811409	-0.006049967370927334	0.0063385749235749245	0.05803557112812996	0.02440173178911209	0.04874301701784134	0.05156455934047699	-0.029412493109703064	-0.05389970913529396	0.05512900650501251	0.08790580183267593	-0.026560688391327858	0.037341874092817307	0.005629653111100197	0.07136683911085129	-0.007419415283948183	-0.042869068682193756	-0.04995042085647583	0.03234152868390083	0.03173038735985756	0.0553835853934288	0.0396399050951004	-0.06199886277318001	0.09714183956384659	0.029925769194960594	0.00907378550618887	-0.02283080853521824	0.018470901995897293	0.02069457806646824	0.037009697407484055	0.02567889168858528	0.06470237672328949	-0.016684647649526596	-0.015064871869981289	-0.023957090452313423	0.03603668138384819	-0.07095552235841751	0.013424555771052837	-0.015675459057092667	-0.06607913225889206	0.07459386438131332	-0.015261435881257057	-0.09647650271654129	-0.033365122973918915	-0.0007492791628465056	0.020587127655744553	-0.007017693016678095	0.04385615140199661	0.009349371306598186	-0.010740883648395538	-0.02265097014605999	0.006724162492901087	-0.033820152282714844	-0.12055318057537079	0.1139223575592041	-0.008558769710361958	-0.04085474833846092	-0.043503932654857635	0.0027508395723998547	0.02832220122218132	-0.013824090361595154	0.01256078202277422	0.07882124185562134	0.102862149477005	0.04933638498187065	0.015670940279960632	0.01897049881517887	-0.08063392341136932	-0.06548202782869339	0.009196261875331402	0.0051157413981854916	-0.012762465514242649	-0.04609385505318642	-0.0065527078695595264	-0.07094740122556686	0.004960066173225641	-0.023885974660515785	0.011943207122385502	-0.010684465989470482	0.0047248350456357	-0.05448875576257706	0.05508570000529289	-0.021312177181243896	-0.0745774507522583	0.0574747771024704	-0.0305451862514019	0.02581661008298397	-0.02174326404929161	-0.017414847388863564	0.038634080439805984	-0.01329814549535513	0.05476682633161545	0.004135860595852137	0.06141712889075279	-0.029722178354859352	-0.09143419563770294	-0.00459623197093606	-0.06694689393043518	-0.04762175679206848	-0.005738494452089071	-0.03947659954428673	-0.06182793527841568	0.07124387472867966	-0.005252325441688299	-0.029558204114437103	-0.062444597482681274	0.02257586270570755	-0.049913302063941956	-0.06356120109558105	-0.02959175780415535	-0.004246746189892292	-0.06446246802806854	-0.0437932051718235	0.017008807510137558	-0.06842567771673203	-0.024791233241558075	-0.01591014675796032	-0.03739695996046066	-0.039752859622240067	0.03147212415933609	-0.02344387397170067	-0.0247633159160614	0.012820310890674591	-0.05600758269429207	0.047094110399484634	0.08971160650253296	0.03564693406224251	0.05065927281975746	0.06374698132276535	0.03885800018906593	0.09019532799720764	0.03407891467213631	-0.03867945075035095	-0.0229649618268013	-0.015304670669138432	-0.049288198351860046	0.045073501765728	-0.002666278975084424	-0.032608211040496826	0.021391037851572037	0.011271744035184383	0.06572657823562622	0.0025159118231385946	-0.02352583222091198	-0.026423778384923935	0.03889637067914009	0.0021870583295822144	0.021224886178970337	-0.008250124752521515	0.013633660040795803	-0.0019583359826356173	-0.07226632535457611	0.003009629435837269	-0.045016124844551086	0.009665625169873238	0.012587481178343296	0.027069348841905594	-0.0057513355277478695	-0.05294909328222275	-0.014318146742880344	-0.00018551624089013785	0.10866273194551468	-0.07632353901863098	0.025436662137508392	0.05039381608366966	-0.007764042355120182	-0.11163625866174698	-0.04356435313820839	0.041554152965545654	-0.05274127796292305	-0.050782181322574615	0.023820742964744568	0.03078423999249935	-0.06935443729162216	-0.04864870384335518	-0.010675158351659775	-0.04394816234707832	0.0006791707710362971	0.01930348575115204	0.01806637831032276	0.009762430563569069	-0.010036523453891277	-0.021210376173257828	-0.03445781394839287	-0.0001558165531605482	0.01937129534780979	-0.024471169337630272	-0.0030550514347851276	-0.04287661612033844	0.02125975489616394	-0.055877916514873505	-0.06195444241166115	-0.040041178464889526	0.03990617394447327	0.006044489331543446	-0.021515386179089546	0.004041398409754038	-0.02210617996752262	-0.02385551482439041	0.03313426673412323	0.01007662620395422	-0.05581088736653328	-0.015467481687664986	-0.02611139975488186	-0.036387450993061066	-0.05537992715835571	-0.031633973121643066	-0.013732615858316422	-0.06105586141347885	0.01578354835510254	-0.03274288401007652	-0.01666426472365856	0.01410049106925726	-0.015764614567160606	0.03504739701747894	0.04962208867073059	0.022884203121066093	-0.008413603529334068	0.002238751854747534	-0.017124440521001816	0.028953751549124718	0.05055844783782959	-0.060686416923999786	-0.01614682376384735	0.029739543795585632	-0.04698121175169945	0.03209296986460686	-0.027986304834485054	-0.010602938942611217	0.05587606877088547	-0.00814185943454504	0.01526820007711649	-0.00952525157481432	0.0135158970952034	-0.04076668992638588	0.050865285098552704	-0.06193419173359871	-0.03456462174654007	0.006932536605745554	-0.03161406144499779	-0.09787388145923615	-0.014498751610517502	-0.017406074330210686	-0.047723278403282166	0.05256376042962074	-0.017955711111426353	0.01625780388712883	0.010088751092553139	-0.081767238676548	-0.06416826695203781	-0.06014927476644516	0.013155502267181873	0.02777058444917202	0.01843871735036373	0.04464413970708847	-0.026526980102062225	-0.012003714218735695	-0.017739716917276382	-0.006144016049802303	0.0004800860770046711	-0.07254265993833542	-0.019290145486593246	-0.02918701246380806	-0.026587095111608505	0.01893765479326248	0.0031456106808036566	0.0021409832406789064	-0.06160162016749382	-0.025713277980685234	-0.036467090249061584	0.08285373449325562	-0.054870687425136566	-0.019070830196142197	0.03265923634171486	-0.041376352310180664	-0.0009358541574329138	0.04780828207731247	-0.04578477516770363	-0.07037899643182755	-0.004449091386049986	-0.021431326866149902	0.01790367253124714	0.015712393447756767	0.025467079132795334	-0.06125209480524063	-0.05588393285870552	-0.022087542340159416	0.007021196186542511	-0.05773494392633438	-0.025738080963492393	0.014633659273386002	-0.09432000666856766	-0.07607921212911606	-0.014767416752874851	-0.07229964435100555	0.08219312131404877	0.007387855555862188	0.02243521809577942	-0.006870086770504713	-0.001596988644450903	-0.01511910930275917	-0.0398450568318367	-0.004984778817743063	-0.001520586316473782	-0.04732572287321091	-0.060094378888607025	0.07031068205833435	-0.003169369651004672	0.01415415108203888	0.09669892489910126	-0.011214560829102993	0.02993692271411419	0.05628006532788277	-0.010186484083533287	0.024582475423812866	0.00281401420943439	-0.019121361896395683	0.08823493123054504	-0.05097983032464981	0.08461753278970718	-0.05091143772006035	0.1080111712217331	-0.00125116016715765	-0.017155971378087997	0.05077081173658371	0.11062946170568466	0.0019202363910153508	0.014797262847423553	-0.06333677470684052
+0.00732398359104991	-0.03567681461572647	-0.023220591247081757	0.04231588914990425	-0.0837918370962143	-0.042293522506952286	-0.005166375543922186	0.07723577320575714	0.011193612590432167	-0.039779625833034515	-0.04009469226002693	0.016735577955842018	0.013883200474083424	0.022366158664226532	0.004962886683642864	-0.026153916493058205	-0.05243698135018349	-0.004253902472555637	-0.07736836373806	-0.051389481872320175	0.02472776733338833	0.03328365087509155	-0.06899324804544449	0.06283421069383621	-0.0028684651479125023	-0.004493332467973232	-0.04972081631422043	0.0118517791852355	-0.012829913757741451	0.0443059578537941	0.07248682528734207	0.01819089613854885	0.08774916082620621	-0.05200817435979843	-0.05037105455994606	0.0508200041949749	-0.032418593764305115	0.08385831117630005	0.01704331673681736	-0.0076728989370167255	-0.033138569444417953	-0.0002508598263375461	-0.017578735947608948	-0.06096315383911133	0.03610182926058769	0.0057688308879733086	0.020349781960248947	-0.013817568309605122	0.03280403092503548	0.0189196914434433	0.05449493229389191	0.01892080344259739	0.03756194934248924	-0.0025837330613285303	-0.0043515413999557495	0.021399861201643944	0.021778767928481102	-0.03310389071702957	-0.02166166342794895	-0.014062603004276752	-0.084618479013443	-0.010992594063282013	0.0027672674041241407	-0.020870691165328026	-0.019162829965353012	0.01831657625734806	-0.0006120752077549696	0.09406153112649918	0.049959052354097366	-0.01738240383565426	0.02749931439757347	0.043817974627017975	-0.06716123223304749	-0.010809304192662239	0.044531967490911484	-0.011767078191041946	-0.008774183690547943	0.026664743199944496	-0.05167459696531296	0.010620642453432083	0.00946898851543665	-0.0032133387867361307	0.13922137022018433	-0.04357726871967316	-0.048822540789842606	-0.04330052435398102	0.01035490445792675	-0.07478105276823044	0.05217970907688141	-0.03978641703724861	0.0012880410067737103	0.07223228365182877	-0.019692083820700645	0.029299110174179077	0.010386637412011623	0.04657600075006485	0.004261571913957596	-0.1404513269662857	-0.028975507244467735	0.02640853077173233	-0.010686603374779224	0.012495690956711769	0.0774865597486496	-0.045669931918382645	0.045492324978113174	0.06026928871870041	-0.03938858211040497	-0.03368067741394043	0.014601143077015877	0.0004348939983174205	0.034370727837085724	0.11068325489759445	0.02149236388504505	0.0025995781179517508	0.05179181322455406	0.014779320918023586	0.01984342187643051	-0.034153979271650314	-0.03435901924967766	-0.014003169722855091	-0.034438569098711014	0.05553506687283516	-0.017282210290431976	-0.06384437531232834	-0.03366292640566826	0.04165024682879448	-0.09554019570350647	-0.03324571251869202	0.0748760849237442	-0.07402649521827698	0.11756717413663864	0.029507914558053017	-0.024838697165250778	0.032823607325553894	0.05697820708155632	0.002787173492833972	-0.013255448080599308	0.0014506375882774591	0.030930373817682266	-0.06402072310447693	-0.04311591014266014	0.07168702036142349	-0.01212647557258606	-0.04448214918375015	0.07732240110635757	0.089168481528759	-0.006189912557601929	-0.029994426295161247	-0.05495651438832283	0.020708315074443817	-0.061544377356767654	-0.10097800195217133	-0.01668703928589821	-0.04075714573264122	-0.004484805278480053	0.0564555749297142	0.009487208910286427	0.019266026094555855	-0.012931207194924355	-0.047152988612651825	-0.05566442385315895	-0.012627762742340565	-0.00023750943364575505	-0.0615922287106514	-0.004055103752762079	-0.0025443192571401596	-0.022187184542417526	0.0339432992041111	-0.031986676156520844	-0.02701302245259285	-0.006306648254394531	-0.0164248775690794	0.012850240804255009	0.08613718301057816	0.03781284764409065	0.023399094119668007	0.009471234865486622	-0.04903943091630936	-0.011046082712709904	-0.04332227259874344	0.01878134347498417	-0.05839381366968155	0.048696547746658325	-0.05399756506085396	0.01706112176179886	-0.0013799993321299553	0.02114315703511238	-0.030810663476586342	0.02282702550292015	-0.04034561663866043	-0.014703644439578056	-0.05604388564825058	0.01888461783528328	0.07632661610841751	-0.022789523005485535	0.03943789377808571	0.031208138912916183	0.04280581325292587	-0.027939165011048317	0.001895069726742804	-0.07588609308004379	0.05740661546587944	0.04231219366192818	0.03148361295461655	0.03528953716158867	-0.07330062240362167	0.0717020183801651	0.00031657342333346605	0.012095784768462181	-0.04990537464618683	0.05151311308145523	0.06523507088422775	0.001250491593964398	0.08839941024780273	0.0659082755446434	0.02956162765622139	0.003262692131102085	-0.03311331197619438	0.035315267741680145	-0.11124591529369354	0.0013000218896195292	0.004109776578843594	-0.08003858476877213	0.024129536002874374	-0.04498327150940895	-0.0914076492190361	-0.027083445340394974	0.020116962492465973	0.02208511345088482	-0.023272674530744553	-0.014104440808296204	0.014616928994655609	0.011880582198500633	-0.020351557061076164	-0.01981387659907341	0.0059270490892231464	-0.05721494182944298	0.09071556478738785	-0.004871171433478594	-0.07704613357782364	-0.015538466162979603	0.02879890240728855	0.043965499848127365	-0.01815522089600563	0.035953644663095474	0.02004842832684517	0.0981987863779068	0.0029596637468785048	-0.03543872386217117	-0.029688695445656776	-0.036679524928331375	-0.04225765913724899	-0.018502594903111458	0.043420273810625076	0.008673327974975109	-0.06539933383464813	0.0459829680621624	-0.09642181545495987	-0.008575611747801304	0.0011855489574372768	0.0613357238471508	-0.03616325557231903	-0.009706215932965279	-0.060216233134269714	-0.01937689818441868	-0.04311315342783928	-0.030814792960882187	0.03498231992125511	-0.02586275339126587	0.016529439017176628	-0.03702956810593605	-0.0020858673378825188	0.004163643810898066	0.007939347997307777	0.030229439958930016	-0.05187277868390083	0.06006598472595215	-0.006449780892580748	-0.05757284536957741	-0.01346284057945013	-0.018033089116215706	-0.07136372476816177	-0.017569418996572495	-0.023033976554870605	-0.02110966481268406	0.08824024349451065	0.04741921275854111	-0.05331464484333992	-0.08395320922136307	-0.0332815945148468	-0.046411801129579544	0.0006137516465969384	0.007553246337920427	-0.012821468524634838	-0.06025819107890129	-0.005372888874262571	0.016914116218686104	-0.055042218416929245	-0.008139594458043575	0.06732987612485886	0.024836469441652298	-0.06368090957403183	0.05921490862965584	-0.015720892697572708	-0.028076309710741043	-0.004062278661876917	-0.062089793384075165	-0.0049757822416722775	0.06815655529499054	0.048620425164699554	0.019363632425665855	0.02211816795170307	0.04112512618303299	0.07162315398454666	-0.046263206750154495	0.008317152038216591	-0.0021495071705430746	0.000836799677927047	-0.027168162167072296	0.043844401836395264	-0.03020990826189518	-0.01888098195195198	0.016354287043213844	-0.024525457993149757	0.0565342977643013	-0.013267835602164268	-0.024136226624250412	-0.05389183759689331	0.03482992947101593	-0.0014646765775978565	0.05438942834734917	0.01411988865584135	0.02532144822180271	-0.012350370176136494	-0.07297350466251373	-0.030052633956074715	-0.05618256703019142	0.026529856026172638	0.030667569488286972	0.0068664029240608215	0.0034976224415004253	-0.07032983005046844	-0.0004687095351982862	-0.02017766423523426	0.10738526284694672	-0.09845703095197678	0.0001443160290364176	0.04076376184821129	0.023108763620257378	-0.09828182309865952	0.0046965680085122585	0.006474222056567669	-0.04582548141479492	-0.062380917370319366	0.040322087705135345	0.05135543644428253	-0.06553370505571365	-0.02887910231947899	-0.0219371747225523	0.008527571335434914	-0.028272856026887894	0.023058095946907997	0.016065621748566628	0.06290963292121887	0.03430140018463135	-0.003400961635634303	-0.011949948966503143	0.055321868509054184	0.03406317159533501	-0.007731512188911438	0.07191238552331924	-0.004037202801555395	0.05490042641758919	-0.07761790603399277	-0.07342828810214996	-0.05055566132068634	0.015488097444176674	-0.02853677235543728	0.013632661662995815	-0.06142809987068176	-0.047260962426662445	-0.012958675622940063	0.04014543816447258	-0.02522047981619835	-0.08638011664152145	0.02033865824341774	-0.006786350626498461	-0.09523800015449524	-0.07467807084321976	-0.037712305784225464	0.013400969095528126	-0.03747298941016197	0.02554432861506939	-0.05289660394191742	-0.0020022490061819553	0.026224695146083832	0.01781213842332363	0.02250855043530464	0.08608157932758331	0.031000038608908653	0.010736574418842793	0.05457811430096626	0.0015491920057684183	0.049494896084070206	0.007170079275965691	-0.055690947920084	0.021566197276115417	0.02131049521267414	-0.07903598248958588	0.028613954782485962	-0.03826327621936798	-0.06214967370033264	0.007965320721268654	-0.043663039803504944	-0.014567309059202671	0.036668650805950165	0.019091807305812836	-0.0738741010427475	0.06452907621860504	-0.03688287362456322	-0.02009565941989422	0.0357171855866909	-0.004399693105369806	-0.06633111089468002	-0.05664112791419029	-0.05881843343377113	-0.03215520456433296	0.08516743779182434	-0.01919851265847683	0.002478267764672637	0.032952968031167984	-0.042302053421735764	-0.0661957785487175	-0.0033108636271208525	0.011258096434175968	-0.028190679848194122	0.012633288279175758	0.06863866001367569	0.04701709374785423	-0.006414687726646662	-0.010733581148087978	-0.06902174651622772	0.01609000191092491	-0.03001897595822811	0.013767204247415066	0.016221435740590096	-0.024712717160582542	-0.00608045095577836	0.012706941924989223	0.02474639192223549	-0.05416058748960495	-0.017857899889349937	-0.05502382293343544	0.05134804919362068	-0.0010069044074043632	0.026459239423274994	0.006817243993282318	-0.06368780881166458	-0.020326299592852592	0.041719526052474976	-0.04377749189734459	-0.07837279886007309	-0.0013024762738496065	-0.05900529399514198	0.014514526352286339	-0.002817603526636958	0.018127769231796265	-0.00786596443504095	-0.08422262966632843	-0.009444286115467548	-0.00348140811547637	-0.01871197484433651	-0.0027222007047384977	0.05000688135623932	-0.035594768822193146	-0.031610604375600815	-0.05874256789684296	-0.06668400019407272	0.09507304430007935	0.025684382766485214	0.038965605199337006	-0.03436236456036568	-0.035381950438022614	-0.04035833105444908	-0.02688753604888916	-0.03754156455397606	-0.008132451213896275	-0.06562069803476334	-0.010670769959688187	0.07917210459709167	-0.010583913885056973	0.03646731376647949	0.0900372862815857	-0.04278189316391945	0.032506536692380905	0.011784286238253117	0.013928018510341644	0.03488106653094292	0.012103479355573654	-0.0037931285332888365	0.048729877918958664	-0.04729350283741951	0.07829010486602783	-0.03462290018796921	0.08167699724435806	0.015519729815423489	-0.011696014553308487	0.0657711997628212	0.09145918488502502	0.05184502899646759	0.002545849187299609	-0.053404416888952255
+0.046007055789232254	-0.01570994220674038	-0.0008640674059279263	0.0208086296916008	-0.059961505234241486	-0.04935824126005173	0.0009433460072614253	0.05647798627614975	0.003646818222478032	-0.02055685967206955	-0.011741923168301582	-5.244651038083248e-05	-0.0010736158583313227	0.018460778519511223	0.0324229933321476	-0.03264118358492851	-0.08563664555549622	0.002511132974177599	-0.0620277002453804	-0.02920292317867279	0.04649560898542404	0.0015450792852789164	-0.08038228750228882	0.022550832480192184	-0.012275565415620804	0.015425278805196285	-0.05417429283261299	0.01922994665801525	-0.012351762503385544	-0.002667799824848771	0.042880088090896606	-0.02633979357779026	0.06875316053628922	-0.06742703914642334	-0.050610970705747604	0.08437638729810715	-0.055143091827631	0.13346266746520996	0.019248507916927338	-0.0376090444624424	-0.061486903578042984	0.004733774811029434	-0.023555973544716835	-0.06418808549642563	0.042463988065719604	0.024794653058052063	-0.011630536057054996	-0.020089730620384216	0.016737855970859528	0.019484730437397957	0.1021987721323967	-0.02827952429652214	-0.014351652935147285	0.005371020641177893	-0.03117731213569641	0.024245217442512512	0.04367504268884659	-0.030155299231410027	-0.07560254633426666	-0.00029688034555874765	-0.08426035195589066	-0.0025964679662138224	-0.014296818524599075	0.012900887057185173	-0.044059474021196365	-0.04175128787755966	-0.006212655454874039	0.05689128860831261	0.027348052710294724	0.007015509530901909	0.010959667153656483	0.08037576079368591	-0.07515229284763336	-0.09044897556304932	0.04357483610510826	-0.06282395869493484	-0.03349032253026962	0.05078866705298424	-0.027445564046502113	0.02137797884643078	0.0017398437485098839	-0.0010435119038447738	0.1258004903793335	-0.0718211680650711	-0.03878743201494217	-0.016147561371326447	-0.004562989342957735	-0.05907357111573219	0.041938457638025284	0.05889758840203285	-0.003491470357403159	0.06404692679643631	-0.043108582496643066	0.012779158540070057	0.037091974169015884	0.03418416157364845	0.03878500685095787	-0.09097471088171005	-0.04516986384987831	-0.027265826240181923	0.002033050637692213	-0.01065970491617918	0.05589472874999046	0.029279975220561028	0.050939809530973434	0.01968459039926529	-0.02002873085439205	0.01860789582133293	0.014586081728339195	0.053058646619319916	-0.0008487814920954406	0.08433477580547333	0.087553471326828	-3.0779037842876278e-06	0.05672431364655495	-0.035063598304986954	0.024403490126132965	-0.04650871083140373	-0.02808108739554882	-0.0021102498285472393	-0.031501106917858124	0.018489744514226913	-0.04196222499012947	-0.08321212977170944	-0.034218721091747284	0.014972253702580929	-0.08197544515132904	-0.05181289464235306	0.018242156133055687	-0.0525987409055233	0.09917515516281128	0.07720024138689041	-0.02738383412361145	0.010913385078310966	0.06242327392101288	0.023698337376117706	0.050418779253959656	0.06157037243247032	-0.022696293890476227	-0.02627689205110073	0.018964441493153572	0.035805050283670425	-0.018717899918556213	-0.029919201508164406	0.02495601586997509	0.044957567006349564	0.04927594214677811	-0.020103376358747482	-0.1005953773856163	0.02265119180083275	-0.06434650719165802	-0.08874426037073135	-0.05420675873756409	-0.07299569249153137	0.018538223579525948	0.054691363126039505	-0.024002937600016594	0.057527199387550354	-0.0041732932440936565	-0.0015533840050920844	-0.031152937561273575	-0.056958455592393875	-0.010852048173546791	-0.06065526604652405	-0.058509379625320435	-0.04031915217638016	0.0022800294682383537	-0.01752236671745777	-0.024456113576889038	-0.01106623187661171	-0.014945784583687782	-0.01572398655116558	0.015699010342359543	0.05146428942680359	0.08655326813459396	0.0440886914730072	0.011065395548939705	-0.05966419726610184	-0.023460905998945236	-0.0546739436686039	-0.0004156706272624433	-0.04868447780609131	-0.011519539169967175	-0.05464858561754227	0.024457991123199463	-0.00646178750321269	0.05204937234520912	-0.008830602280795574	0.041851576417684555	0.016911759972572327	-0.02005617879331112	-0.038630519062280655	0.042319245636463165	0.05392991006374359	-0.0036569295916706324	0.04409915208816528	-0.02398919127881527	0.041610926389694214	-0.048732955008745193	-0.06386186927556992	-0.037644971162080765	0.05115382745862007	0.015521487221121788	0.03232371062040329	0.017194703221321106	-0.030766364187002182	0.07621324807405472	0.016702814027667046	0.03530900180339813	-0.056710947304964066	0.014334101229906082	0.011237552389502525	0.015168868005275726	-0.013877898454666138	0.07279394567012787	-0.06176226586103439	-0.03744170442223549	0.024675564840435982	0.052801501005887985	-0.017170554026961327	-0.004349227994680405	0.010370618663728237	-0.09581899642944336	0.05283927917480469	-0.04621287062764168	-0.10411912202835083	-0.029865572229027748	0.03149264305830002	-0.06664402782917023	-0.04178519546985626	-0.015807414427399635	0.024717848747968674	-0.02607942931354046	-0.05193975195288658	0.0001110435405280441	-0.01445789821445942	-0.06128449738025665	0.08440428972244263	-0.012727309949696064	-0.04882686957716942	-0.05914445221424103	0.024904189631342888	0.023805875331163406	0.003129545133560896	0.009781445376574993	0.035573262721300125	0.09397654980421066	0.0008828886202536523	-0.022830119356513023	0.04984831064939499	-0.06603801250457764	-0.04411507025361061	0.0024802079424262047	0.028235657140612602	-0.03774284943938255	-0.004378984682261944	-0.014993916265666485	-0.02071608044207096	-0.004216169007122517	0.030706698074936867	0.0475563183426857	-0.01222884003072977	-0.006612577009946108	-0.02436378225684166	0.045792486518621445	-0.0345400907099247	-0.03604589030146599	0.05414701998233795	-0.01293459814041853	0.037291519343853	-0.027000250294804573	-0.027476690709590912	0.023313498124480247	-0.004275290761142969	0.05609338730573654	-0.04410550370812416	0.0802479013800621	-0.03838449716567993	-0.0699068158864975	-0.02829458937048912	-0.10519715398550034	-0.03233421593904495	-0.011608040891587734	-0.07217716425657272	-0.042089495807886124	0.08794912695884705	0.04090198874473572	0.004198381677269936	-0.0701378807425499	-0.06022097170352936	0.005636552348732948	-0.05713144689798355	-0.007760129403322935	0.013694897294044495	-0.06420402973890305	-0.06488938629627228	0.06785302609205246	-0.03408423811197281	-0.01488549169152975	-0.023999420925974846	-0.03450898081064224	-0.04808424040675163	0.028448987752199173	-0.0512564517557621	-0.0024418551474809647	0.018462054431438446	-0.07434331625699997	0.02228953130543232	0.11895336955785751	0.06569249927997589	0.03829294070601463	0.05438058078289032	0.01866675540804863	0.1273549348115921	0.021945547312498093	-0.029508162289857864	-0.020684298127889633	-0.030941318720579147	-0.08321039378643036	0.029221966862678528	-0.034334875643253326	-0.015495256520807743	-0.029014049097895622	-0.011709137819707394	0.04824243113398552	-0.003934172447770834	-0.048147451132535934	-0.01028326153755188	0.020414048805832863	-0.03257265314459801	0.0305583905428648	0.02614551968872547	0.008966061286628246	-0.02050786279141903	-0.05444063991308212	-0.020693505182862282	-0.03278082609176636	0.017940180376172066	-0.03389237821102142	0.017762446776032448	-0.0215667262673378	-0.031429652124643326	0.005154687911272049	0.009686735458672047	0.09969358146190643	-0.061230748891830444	0.015582707710564137	0.05947834625840187	0.022423377260565758	-0.10616325587034225	-0.006646259687840939	0.05638064816594124	-0.03283367305994034	-0.047093410044908524	-0.0077026910148561	0.02986452728509903	-0.04594527184963226	-0.00349225290119648	0.013394315727055073	-0.038793906569480896	-0.012142744846642017	0.032799817621707916	0.032038625329732895	0.03419635817408562	-0.022928718477487564	-0.022510668262839317	0.01593022421002388	0.02363256923854351	0.06877519190311432	-0.059660445898771286	0.009630433283746243	-0.06312800943851471	0.04286209121346474	-0.026670774444937706	-0.055768340826034546	-0.029648831114172935	0.06608595699071884	0.006476724985986948	-0.06191420927643776	-0.01576637104153633	0.009584027342498302	-0.029030626639723778	0.02136416733264923	0.023900456726551056	-0.029139360412955284	-0.05305870622396469	-0.02620612643659115	-0.04803378880023956	-0.11373770982027054	-0.04676537215709686	0.021829450502991676	-0.06594722718000412	0.00868266262114048	-0.022709032520651817	-0.022064819931983948	0.001752103096805513	-0.010148529894649982	0.03417682647705078	0.0680764764547348	0.0521051287651062	-0.026171501725912094	0.03668717294931412	0.014038541354238987	0.019509807229042053	0.027388431131839752	0.0107116112485528	-0.00114136200863868	-0.0027455927338451147	-0.02972814068198204	0.032245222479104996	-0.02562905102968216	0.02418353781104088	-0.007641769014298916	-0.027702970430254936	0.0227950606495142	-0.003278059186413884	0.02512422949075699	-0.017048323526978493	0.026703255251049995	-0.0886666476726532	-0.013183509930968285	0.03715301677584648	-0.028033258393406868	-0.09545250982046127	-0.03829576447606087	0.004365830682218075	-0.019370131194591522	0.019233664497733116	0.0008786209509707987	-0.0008226624922826886	0.008386754430830479	-0.03819300979375839	-0.049179863184690475	-0.0583096407353878	0.005439263302832842	0.05873039737343788	-0.03721797466278076	0.05562400072813034	0.015010382980108261	-0.028314469382166862	0.009057227522134781	-0.0074318586848676205	0.019548770040273666	-0.06540913879871368	-0.03452387452125549	-0.02675527334213257	-0.004142875783145428	0.03818744793534279	-0.03859197348356247	0.021443713456392288	-0.0566682443022728	-0.02865680865943432	-0.04177435487508774	0.062360942363739014	-0.021776316687464714	0.005894250236451626	0.06507149338722229	-0.03167445585131645	0.004247010685503483	-0.016177238896489143	-0.052407655864953995	-0.039515554904937744	0.040183838456869125	-0.021816441789269447	0.04561269283294678	0.043421968817710876	0.006541905924677849	0.01177793089300394	-0.08628645539283752	-0.011145565658807755	0.003148870076984167	-0.08870571106672287	0.020495494827628136	0.004456738010048866	-0.08381691575050354	-0.048495933413505554	0.015761034563183784	-0.00991808157414198	0.08086815476417542	-0.020116379484534264	0.04113679751753807	-0.03684549033641815	-0.02646208181977272	-0.028275616466999054	-0.03161948546767235	0.021606320515275	0.0008008463773876429	-0.04861157760024071	-0.011670445092022419	0.07197058945894241	-0.0363546758890152	0.01921970583498478	0.09409437328577042	-0.03659992292523384	0.031351715326309204	0.027450915426015854	-0.031846921890974045	-0.00467465678229928	-0.014791914261877537	-0.04753765091300011	0.04357420653104782	-0.07508926093578339	0.07962165772914886	-0.09780816733837128	0.08934736251831055	0.03813133016228676	-0.006234799977391958	0.07241812348365784	0.08085037022829056	-0.026790998876094818	0.0009042313322424889	-0.04693680256605148
+0.011780218221247196	-0.049433283507823944	-0.022538628429174423	0.016480274498462677	-0.025918371975421906	-0.05518573522567749	-0.04309991002082825	0.04789012297987938	0.0009005775791592896	-0.041669949889183044	-0.01323638390749693	-0.03976217284798622	-0.03714970499277115	-0.01978249102830887	0.06833713501691818	-0.05301913619041443	-0.03368915989995003	0.05294277146458626	-0.010706937871873379	-0.015852918848395348	0.02149980142712593	0.07237187772989273	-0.03620403632521629	0.042587488889694214	-0.02837689220905304	-0.022865071892738342	-0.05316833779215813	0.061991218477487564	0.005892335902899504	0.1077747717499733	0.059385236352682114	-0.06829190999269485	0.07672614604234695	-0.08075739443302155	0.034955400973558426	0.02229643613100052	-0.038297947496175766	0.07633022218942642	0.09902042150497437	-0.03381534665822983	0.0008585540344938636	-0.011981319636106491	0.025310322642326355	-0.07993491739034653	0.02756459452211857	0.015444175340235233	-0.06868251413106918	-0.07876957952976227	-0.01325371116399765	0.03876245766878128	0.09167500585317612	-0.01728523150086403	-0.02162144146859646	0.004442537669092417	-0.03814294561743736	-0.05304490774869919	0.03965837508440018	-0.05769870802760124	-0.03444718196988106	0.04032805934548378	-0.09352975338697433	0.010123070329427719	-0.0011310507543385029	0.025209272280335426	-0.04770497605204582	-0.05060768872499466	0.05248429626226425	0.0347161740064621	0.01549314334988594	0.00565111730247736	0.0397859588265419	0.049383267760276794	-0.05197303369641304	-0.10483681410551071	-0.0009456573752686381	-0.05468354374170303	0.035847898572683334	0.0013424274511635303	0.01270467322319746	0.04731205850839615	0.026246655732393265	-0.0019450935069471598	0.10952881723642349	-0.019521189853549004	0.022787148132920265	-0.042108260095119476	-0.014281324110925198	-0.03842920437455177	0.03300913795828819	-0.011379699222743511	0.030129004269838333	0.010180567391216755	0.01060367189347744	0.012574344873428345	0.004133069887757301	0.011535529978573322	0.01328901294618845	-0.05270744115114212	-0.014232703484594822	0.0035988909658044577	-0.029481353238224983	0.045842304825782776	0.06547603011131287	0.08414647728204727	0.04168388620018959	0.025031715631484985	-0.02245422825217247	0.05321158468723297	0.10317050665616989	0.0713590756058693	-0.005287583451718092	0.0891665369272232	0.05166701227426529	-0.01916971430182457	0.04081633687019348	-0.00993876252323389	-0.009882689453661442	-0.012628267519176006	0.018546948209404945	0.03622066229581833	0.0026879506185650826	0.018240777775645256	-0.06969070434570312	-0.06431060284376144	0.021173063665628433	0.10595174133777618	-0.09474288672208786	-0.01362962182611227	-0.009954327717423439	0.0014091625344008207	0.08245053887367249	0.040984250605106354	-0.029265038669109344	0.04061412438750267	0.0633322075009346	0.05580432340502739	-0.02320389822125435	0.03506048396229744	0.016757261008024216	-0.029268061742186546	-0.06138712912797928	0.03483244404196739	-0.0007642255513928831	-0.004577011335641146	0.018157323822379112	0.023822622373700142	-0.015075412578880787	-0.03311655670404434	-0.06606389582157135	-0.019406957551836967	-0.06165260449051857	-0.0442359559237957	0.04998590797185898	-0.05042852833867073	0.011003391817212105	-0.026778971776366234	-0.0012338875094428658	-0.015886403620243073	-0.019975464791059494	-0.010743116028606892	-0.07366632670164108	-0.07458849251270294	-0.012852173298597336	-0.039466314017772675	0.005323807708919048	0.0016426360234618187	0.014341609552502632	0.006165217608213425	-0.0567278191447258	-0.031450092792510986	-0.023450113832950592	-0.01662401296198368	0.005788316484540701	0.0827205553650856	0.020433207973837852	0.038492392748594284	0.05683236941695213	-0.04727959260344505	0.01680927909910679	-0.07213051617145538	-0.006254666950553656	-0.0485498271882534	0.028167778626084328	-0.04742388054728508	-0.0036640053149312735	-0.011597703211009502	0.017496639862656593	-0.02372569777071476	0.024518057703971863	-0.005733550991863012	0.007004548329859972	-0.06885375082492828	0.0405755415558815	0.0473732128739357	0.035245735198259354	0.009078550152480602	-0.004950325004756451	0.0744640901684761	-0.03403790667653084	-0.012773764319717884	-0.05918525904417038	0.051780328154563904	0.05695176497101784	-0.009309712797403336	-0.018564550206065178	0.013748280704021454	0.08043964952230453	0.038146838545799255	0.02720005251467228	-0.011823213659226894	0.031822480261325836	0.062181588262319565	-0.04176117479801178	0.013943657279014587	0.03640371561050415	-0.04990638792514801	-0.0037081032060086727	-0.0009711519815027714	0.046361036598682404	-0.0631398931145668	0.029933441430330276	0.020763076841831207	-0.0712537094950676	0.04465063288807869	-0.05445683002471924	-0.10813766717910767	0.0037590893916785717	-0.020378196612000465	-0.024023408070206642	-0.03780459985136986	0.032114576548337936	0.010884879156947136	0.022537613287568092	0.05263696610927582	-0.0044638351537287235	-0.05130331963300705	-0.06531469523906708	0.07548541575670242	-0.0629766657948494	-0.037520457059144974	-0.054779231548309326	-0.015421271324157715	0.0492158979177475	-0.0014390501892194152	0.0263702142983675	0.03378619626164436	0.06637264043092728	0.06515847891569138	-0.03428463637828827	0.023297393694519997	-0.020471220836043358	-0.0662962794303894	-0.02342410758137703	-0.019800353795289993	-0.009974068030714989	-0.06532757729291916	0.06463651359081268	-0.09827930480241776	0.00519712595269084	-0.06063006445765495	0.005587685853242874	-0.04569769650697708	0.02313763089478016	-0.05936453118920326	0.07314829528331757	0.0019368062494322658	-0.032688599079847336	0.09045344591140747	-0.03352930024266243	0.004108883440494537	-0.030373653396964073	-0.01390807144343853	0.07107039541006088	-0.01783798262476921	-0.004472212865948677	-0.03910967335104942	0.08267483860254288	-0.007598232012242079	-0.03967917338013649	-0.05901020020246506	-0.05730787664651871	-0.013868259266018867	0.05567978322505951	-0.02566896378993988	0.0006124912179075181	0.03388265520334244	0.06947828084230423	0.006644922308623791	-0.028898388147354126	-0.06550362706184387	-0.02274801954627037	-0.06709228456020355	-0.03802729398012161	-0.026623792946338654	-0.05719230696558952	-0.004684775602072477	0.06331094354391098	-0.04232688620686531	-0.02905767224729061	0.008369442075490952	0.016406605020165443	-0.057218533009290695	-0.007290369365364313	-0.058880794793367386	-0.037632785737514496	0.028744112700223923	-0.037682484835386276	0.01567780040204525	0.06473680585622787	0.04925383999943733	0.03948485851287842	0.04572669416666031	0.030125221237540245	0.06944765895605087	-0.016541339457035065	-0.017857767641544342	-0.04006236791610718	-0.048031408339738846	-0.02289431355893612	0.017956256866455078	-0.013265453279018402	-0.0064045749604702	0.06082252785563469	-0.015313459560275078	0.042148519307374954	-0.03719192370772362	-0.01507547777146101	-0.030869558453559875	0.06732392311096191	-0.049451861530542374	0.0340087004005909	-0.020999092608690262	0.01829480566084385	0.007308147847652435	-0.020734211429953575	-0.03461316227912903	-0.06964041292667389	0.06160755455493927	-0.07220727950334549	0.014738407917320728	0.01954888366162777	-0.045473042875528336	-0.04529457911849022	0.04278000071644783	0.08864343911409378	-0.05276273563504219	0.008774742484092712	-0.005986848380416632	0.042329151183366776	-0.08634921908378601	0.01357200276106596	-0.031140794977545738	-0.07450926303863525	-0.0015926863998174667	0.03256269916892052	0.02102077379822731	-0.08951369673013687	-0.006797310896217823	-0.019034815952181816	-0.03890013322234154	0.021264079958200455	0.08025065064430237	-0.019890431314706802	0.06850618124008179	0.014946692623198032	-0.0654742643237114	-0.021288899704813957	0.013091502711176872	-0.00551815377548337	-0.018434714525938034	0.023816287517547607	-0.06521400064229965	-0.009846998378634453	-0.044376567006111145	-0.037662383168935776	-0.03191583976149559	0.03050357662141323	-0.008591003715991974	0.023865293711423874	0.00021043985907454044	0.05007919296622276	-0.008840997703373432	-0.004404555074870586	0.03348147124052048	-0.04278431460261345	-0.021558715030550957	-0.008498835377395153	-0.04032367840409279	-0.048977989703416824	-0.02444552257657051	0.028738999739289284	-0.01252425741404295	0.0052585843950510025	-0.04577099531888962	0.050574176013469696	0.0006727786967530847	-0.02119477093219757	0.023900482803583145	0.07513823360204697	0.047743044793605804	0.011934638023376465	0.00679709343239665	0.006836442742496729	0.03869985044002533	0.07069192081689835	-0.03459900617599487	0.001879765884950757	-0.014894033782184124	-0.04168746992945671	-0.04541021212935448	-0.018314287066459656	0.010026847012341022	0.023456666618585587	-0.02228599600493908	0.02699482999742031	-0.03873914107680321	0.0387456975877285	0.013803481124341488	0.051198095083236694	-0.03569205477833748	-0.010805947706103325	-0.05954765900969505	-0.028394896537065506	-0.08443401753902435	-0.050788965076208115	-0.009621283039450645	-0.0263343658298254	0.044951703399419785	-0.07256714254617691	0.017590804025530815	0.017076987773180008	0.004389960318803787	-0.012713014148175716	0.029661739245057106	0.027839861810207367	-0.01158706285059452	0.02787541225552559	0.012488136999309063	-0.051304154098033905	0.017467088997364044	0.0034881785977631807	-0.06984377652406693	0.005958758760243654	-0.04073451831936836	-0.022306030616164207	0.00706894276663661	-0.0076955039985477924	0.007430942729115486	0.027466565370559692	0.04076820984482765	-0.07599186152219772	-0.018936287611722946	-0.05152110382914543	0.08346306532621384	-0.013340483419597149	0.05180206149816513	-0.028430327773094177	-0.07794104516506195	-0.09028856456279755	0.055198054760694504	-0.06243071332573891	-0.10899519920349121	-0.05439721792936325	-0.01943785510957241	0.05804799497127533	-0.0027693319134414196	0.03383682668209076	-0.06627244502305984	-0.10398108512163162	0.04603805020451546	0.030484970659017563	-0.015879571437835693	-0.009026175364851952	0.013045432977378368	-0.030773591250181198	0.008649947121739388	-0.00030565905035473406	-0.07641825079917908	0.04378023371100426	0.042484086006879807	0.0038924089167267084	-0.07554616779088974	-0.0075629595667123795	-0.026039881631731987	-0.06734243780374527	0.037818364799022675	-0.03777915984392166	-0.0334198921918869	-0.01429793145507574	0.06692548096179962	-0.005644975230097771	0.02625049278140068	0.053790368139743805	-0.014959759078919888	0.01850898750126362	0.05799823999404907	0.03422913700342178	-0.012879311107099056	-0.06494473665952682	0.0020772998686879873	0.0620497427880764	-0.051615532487630844	0.066704660654068	-0.04852290824055672	0.1420537233352661	0.041292089968919754	-0.015382373705506325	0.05905589833855629	0.03828122839331627	0.0343719944357872	0.03182797133922577	-0.11060358583927155
+0.06503985077142715	0.021357543766498566	0.01801002025604248	0.0683806836605072	-0.07315951585769653	-0.03975703567266464	-0.019109833985567093	0.016427945345640182	0.0028529614210128784	-0.05298897624015808	-0.02150031365454197	-0.03542647510766983	0.0012378948740661144	0.013105768710374832	0.029104094952344894	-0.04926018416881561	-0.05619414895772934	0.0320567786693573	-0.017794761806726456	-0.03752164542675018	0.02719435840845108	0.009769704192876816	-0.06694971770048141	0.05016665905714035	0.01752641424536705	0.014173300936818123	-0.029903223738074303	0.029000524431467056	0.001438116654753685	0.04108855128288269	0.07942819595336914	-0.02281193993985653	0.0541420616209507	-0.05539954453706741	-0.0519813597202301	0.03428690880537033	-0.026140816509723663	0.11428074538707733	0.028940517455339432	-0.03937852382659912	-0.05484312027692795	-0.012252616696059704	-0.012569062411785126	-0.04135243594646454	0.03376953303813934	0.013107878156006336	-0.023323779925704002	-0.02682476118206978	0.04931369051337242	0.012541064992547035	0.10686381906270981	-0.013533937744796276	-0.03959319740533829	-0.022059697657823563	0.007578584365546703	-0.004711476154625416	0.057250820100307465	-0.06655685603618622	-0.04181976988911629	0.023145535960793495	-0.0891667976975441	-0.021819354966282845	0.017777448520064354	-0.012159816920757294	-0.01837964914739132	-0.032721489667892456	-0.000327136367559433	0.07555463910102844	0.012593118473887444	-0.012187871150672436	0.036633990705013275	0.11009541898965836	-0.07766623049974442	-0.054939743131399155	0.06397540122270584	-0.08832176774740219	-0.012193692848086357	0.03933986276388168	-0.01251851674169302	0.028757378458976746	0.006490732543170452	-0.03855018690228462	0.129438579082489	-0.04655953869223595	-0.05497736111283302	-0.006256613414734602	-0.011896532960236073	-0.0690116211771965	0.06238045543432236	0.03422343358397484	-0.00700647197663784	0.04760034382343292	-0.05176341533660889	0.05231193080544472	0.012364261783659458	0.007992994971573353	0.041004739701747894	-0.0858260914683342	-0.02209443785250187	0.015385439619421959	-0.02337312325835228	-0.005421848502010107	0.0403829887509346	0.000936289899982512	0.03636046126484871	0.03660624846816063	-0.03914421796798706	0.007919235154986382	0.03958011418581009	0.07582353055477142	0.02869805134832859	0.058120641857385635	0.08059942722320557	0.03373261168599129	0.05067051574587822	-0.0024714022874832153	0.02459884248673916	-0.06647209078073502	-0.03631063923239708	0.020190905779600143	0.005942110437899828	0.07662754505872726	-0.03866990655660629	-0.0573197677731514	-0.0388370081782341	0.019154924899339676	-0.07707434147596359	-0.039056237787008286	0.0640743300318718	-0.0024614061694592237	0.13475775718688965	0.054611217230558395	-0.02287420444190502	0.06791950017213821	0.05174512788653374	0.030929284170269966	-0.0045641642063856125	0.01477437186986208	-0.004071471747010946	-0.03501078858971596	0.0022510208655148745	0.04442182183265686	0.0019232981139793992	-0.06703287363052368	0.061148062348365784	0.05584609881043434	0.06012216955423355	-0.04712679237127304	-0.05928363278508186	-0.019235165789723396	-0.0671975165605545	-0.059160616248846054	0.014296057634055614	-0.05001487210392952	-0.01743439771234989	0.013183755800127983	-0.004378579556941986	-0.002982863923534751	-0.019468193873763084	-0.016450637951493263	-0.05921927094459534	-0.02604711800813675	0.0005005868151783943	-0.05620859935879707	-0.056019313633441925	-0.02163119614124298	-0.006736550480127335	0.018866972997784615	-0.06705783307552338	-0.025320930406451225	-0.003362582763656974	0.012972512282431126	0.04576054587960243	0.10422157496213913	0.06523938477039337	0.019865425303578377	0.022328557446599007	-0.03395739197731018	0.015888938680291176	-0.022460419684648514	0.04629926756024361	-0.039336588233709335	0.02685263194143772	-0.049848876893520355	0.027735745534300804	-0.0052977632731199265	0.058088287711143494	-0.010011190548539162	0.059185534715652466	0.032578542828559875	-0.048527877777814865	-0.0326458141207695	0.01885620690882206	0.06957580894231796	-0.030072612687945366	0.044219572097063065	-0.036008626222610474	0.03649316355586052	0.0006465943297371268	-0.03759804368019104	-0.04241817444562912	0.02762759104371071	0.024752411991357803	0.07655502110719681	0.04005764052271843	-0.07187599688768387	0.08045036345720291	0.04279184713959694	0.017846446484327316	-0.055039677768945694	0.007234221324324608	0.011026911437511444	0.022433508187532425	0.054422784596681595	0.07046893984079361	-0.003240135731175542	0.006346229463815689	-0.024297956377267838	0.04844051972031593	-0.0392911434173584	0.005694252904504538	-0.02307361550629139	-0.09509969502687454	0.050259239971637726	-0.08498357981443405	-0.07123696058988571	-0.020802553743124008	0.03333616256713867	-0.002664635656401515	-0.0582006573677063	-0.002806380158290267	0.008402896113693714	0.007326945196837187	-0.015113865956664085	-0.026203710585832596	-0.02372935600578785	-0.10757435858249664	0.13508574664592743	0.021088186651468277	-0.0648086667060852	-0.05687874183058739	0.010260666720569134	0.03215621039271355	-0.022852100431919098	0.03095054067671299	0.05075792968273163	0.09226030111312866	0.024534128606319427	-0.009199858643114567	0.02956985868513584	-0.05067601799964905	-0.0653749406337738	0.007036345545202494	0.013253611512482166	-0.051053259521722794	-0.015363854356110096	0.0221757460385561	-0.053764279931783676	0.011567959561944008	-0.025258980691432953	0.006246762350201607	-0.0035289868246763945	-0.021965300664305687	-0.04660870134830475	0.06038034334778786	-0.03874456509947777	-0.04513410106301308	0.055595044046640396	-0.006305461749434471	0.03767829015851021	0.0030170800164341927	-0.007905900478363037	0.048632871359586716	0.0010470389388501644	0.026026180014014244	-0.013190160505473614	0.08120495826005936	-0.007041957229375839	-0.038332223892211914	0.0005737001192755997	-0.06293941289186478	-0.06603846698999405	-0.02967251092195511	-0.029148651286959648	-0.005610359832644463	0.057809118181467056	-0.0037904167547822	-0.023708434775471687	-0.05456641688942909	0.002635918091982603	-0.02150328829884529	-0.08228083699941635	-0.04083293676376343	0.005345490295439959	-0.05247475951910019	-0.02754690870642662	-0.005317919887602329	-0.03120855800807476	-0.0012877477565780282	-0.02778293751180172	-0.03469308093190193	-0.07711578160524368	0.03197837248444557	-0.044293563812971115	-0.022039424628019333	0.012795678339898586	-0.03778412938117981	0.05105314031243324	0.08957462012767792	0.06820105016231537	0.03271576762199402	0.06150049716234207	0.050062716007232666	0.1206611692905426	-0.0031383640598505735	-0.021635275334119797	-0.009960629045963287	-0.05789371207356453	-0.059590306133031845	0.059377264231443405	-0.013710963539779186	-0.04072617366909981	0.020366370677947998	0.012373468838632107	0.05331062152981758	-0.014757918193936348	-0.04706532135605812	-0.032225217670202255	0.03952847048640251	-0.04454411193728447	0.025963084772229195	-0.0023184027522802353	0.023688513785600662	-0.027025118470191956	-0.05793933570384979	-0.02034704014658928	-0.04307926073670387	0.026582298800349236	-0.007145254872739315	0.026962004601955414	-0.03268624469637871	-0.04260261729359627	-0.03774116560816765	0.013901499100029469	0.09696745872497559	-0.05128715932369232	-0.009767500683665276	0.04377847537398338	0.026566360145807266	-0.09414579719305038	0.004576348699629307	0.027218276634812355	-0.02983325906097889	-0.030433598905801773	0.017230160534381866	0.031227346509695053	-0.08561748266220093	0.017255494371056557	-0.0012551648542284966	-0.03523216024041176	-0.01717735268175602	0.01020287349820137	0.0338953398168087	0.015768464654684067	-0.01344515010714531	-0.04764345660805702	-0.03433628007769585	-0.015796052291989326	0.023744987323880196	-0.011578676290810108	0.027118222787976265	-0.07035073637962341	0.010570628568530083	-0.06269951164722443	-0.03156536445021629	-0.049040138721466064	0.05419135466217995	0.021365216001868248	-0.027780424803495407	-0.0030401393305510283	-0.009817104786634445	-0.013018637895584106	0.0008083084248937666	0.029701871797442436	-0.07288005948066711	-0.027549922466278076	-0.058900367468595505	-0.037012409418821335	-0.06604108959436417	-0.03817683085799217	-0.014820072799921036	-0.0728338360786438	0.018231740221381187	-0.0005963788134977221	-0.015098176896572113	-0.0051547023467719555	-0.0021969389636069536	0.05440271273255348	0.05081966891884804	0.014845087192952633	0.002757010515779257	0.03835009038448334	0.0028260971885174513	0.043487221002578735	0.05853241682052612	-0.05523213744163513	-0.01841863803565502	0.0072898720391094685	-0.0644039511680603	0.0645119845867157	-0.034934114664793015	0.006023092195391655	0.00031484622741118073	0.007556340657174587	0.07197706401348114	-0.02180476300418377	0.024413028731942177	-0.007136893924325705	0.05033206567168236	-0.05121687799692154	-0.035433344542980194	0.02566266991198063	-0.02608666382730007	-0.09286662191152573	-0.049194809049367905	0.007538347505033016	-0.05155425891280174	0.04919975623488426	-0.01904941350221634	0.02471712790429592	0.017951881512999535	-0.04551655054092407	-0.08648674935102463	-0.03761545196175575	-0.004893929231911898	0.07761160284280777	-0.03557824715971947	0.03761855512857437	0.025275634601712227	0.00963425263762474	0.0214069951325655	0.00042033224599435925	-0.0031841143500059843	-0.08235611021518707	-0.01605791039764881	-0.023704731836915016	-0.026135766878724098	0.0034680748358368874	0.0016213457565754652	0.025109868496656418	-0.04672887176275253	-0.02067774534225464	-0.07800907641649246	0.08853127807378769	-0.012897252105176449	0.007842428982257843	0.016892770305275917	-0.049896422773599625	0.01176060177385807	0.027859026566147804	-0.055571433156728745	-0.06668658554553986	-0.002256011590361595	0.006619456224143505	0.012707093730568886	0.011176113970577717	0.016652947291731834	-0.03157096356153488	-0.037709277123212814	0.019921770319342613	0.04140327498316765	-0.06892585754394531	0.014311371371150017	-0.004029741510748863	-0.0625913068652153	-0.07870636135339737	0.007687062490731478	-0.05516502261161804	0.06948211044073105	0.0459759421646595	0.011027966625988483	-0.010407483205199242	-0.014344176277518272	-0.0020195473916828632	-0.06895152479410172	-0.002588049042969942	0.0048004561103880405	0.00017310479597654194	-0.024632062762975693	0.0733390748500824	-0.029363488778471947	0.022957390174269676	0.050582848489284515	-0.043668922036886215	0.015053635463118553	0.048627499490976334	-0.034465353935956955	0.05958882346749306	-0.006462528835982084	-0.021091297268867493	0.10319381207227707	-0.0889420211315155	0.09720460325479507	-0.07087520509958267	0.07235483080148697	0.02373369224369526	-0.014636148698627949	0.06649061292409897	0.07779525220394135	-0.002469911938533187	0.03428560867905617	-0.07500117272138596
+0.046010393649339676	-0.022691015154123306	-0.027555039152503014	0.03446536511182785	-0.08406639844179153	-0.022015850991010666	0.005297920200973749	0.021140379831194878	-0.0024388115853071213	-0.014152545481920242	-0.012108871713280678	0.02305181324481964	0.0008957748068496585	0.008302469737827778	0.02526959963142872	-0.04656899347901344	-0.076052725315094	-0.019908439368009567	-0.0323956124484539	-0.05077299103140831	0.013883913867175579	0.0178688894957304	-0.05965537950396538	0.05581038445234299	-0.0030484015587717295	0.044314462691545486	-0.05411709472537041	-0.008466104045510292	-0.011123193427920341	0.015378137119114399	0.051888685673475266	-0.028648508712649345	0.05031399056315422	-0.03962422162294388	-0.06087076663970947	0.07528044283390045	-0.01741633750498295	0.12746329605579376	0.029505304992198944	-0.027990568429231644	-0.059424664825201035	-0.00559620326384902	-0.03965049982070923	-0.06421422958374023	0.02298198826611042	0.005636002868413925	0.002855964470654726	-0.023932458832859993	0.04482768103480339	0.014142468571662903	0.08414501696825027	0.009255717508494854	-0.03007381036877632	-0.022462163120508194	-0.025493834167718887	0.018032850697636604	0.034004561603069305	-0.042044151574373245	-0.07516493648290634	0.04585977643728256	-0.07658107578754425	0.0004796680004801601	0.0029728307854384184	0.0016253041103482246	-0.010360019281506538	-0.04051718860864639	0.007664143573492765	0.0779200941324234	0.01890416257083416	-0.003366155782714486	0.0282122939825058	0.10210773348808289	-0.05720045417547226	-0.034900035709142685	0.042632363736629486	-0.0735064446926117	-0.0491473414003849	0.04129324480891228	-0.012616212479770184	0.013571768067777157	-0.006942566018551588	-0.026683242991566658	0.12003175169229507	-0.04059775173664093	-0.061639029532670975	-0.0215750802308321	-0.017663640901446342	-0.06646600365638733	0.05077417939901352	0.017043402418494225	0.026161646470427513	0.06481923162937164	-0.07619637995958328	0.043317679315805435	0.032691311091184616	0.024501757696270943	0.058114808052778244	-0.09873954951763153	-0.04691106453537941	-0.020336223766207695	-0.008315995335578918	-0.027320656925439835	0.06091677397489548	-0.007651915308088064	0.05103539302945137	0.00925288163125515	-0.058648787438869476	0.004472372122108936	0.020171284675598145	0.016551025211811066	0.050540741533041	0.08068631589412689	0.07559091597795486	0.031159713864326477	0.08942891657352448	-0.0189971886575222	0.03339669853448868	-0.06999243050813675	-0.02470548450946808	0.0038676150143146515	-0.019618909806013107	0.055199138820171356	-0.011197453364729881	-0.08724799752235413	-0.04770469292998314	0.014806533232331276	-0.1263732761144638	-0.020391928032040596	0.05644191801548004	-0.03747930750250816	0.10773997753858566	0.0726710706949234	-0.015372829511761665	0.031847696751356125	0.012596259824931622	-0.009134499356150627	-0.01325799897313118	0.049882907420396805	0.009088428691029549	0.0008311043493449688	-0.012888161465525627	0.05491892248392105	0.03309096023440361	-0.044504422694444656	0.021397806704044342	0.02298164926469326	0.019650835543870926	-0.014520685188472271	-0.0648740902543068	0.006930125877261162	-0.06141262501478195	-0.0633384957909584	-0.0023767470847815275	-0.05937179923057556	0.0019479815382510424	0.05504939332604408	0.016193794086575508	0.04149641469120979	-0.010336721315979958	-0.034745462238788605	-0.03653956577181816	-0.02091560885310173	-0.010528896935284138	-0.09168397635221481	-0.028790965676307678	-0.01524641364812851	0.005282058380544186	-0.003327852115035057	-0.02681777812540531	0.014938757754862309	-0.016322648152709007	-0.00972718931734562	0.03074229136109352	0.06876194477081299	0.08069124817848206	0.012905526906251907	0.008073708973824978	-0.05410195514559746	0.03166498243808746	-0.04043692350387573	-0.00363304209895432	-0.05567801371216774	0.019836632534861565	-0.045649949461221695	-0.019295936450362206	-0.009133110754191875	0.06240971013903618	-0.00714696804061532	0.016758298501372337	0.00937507301568985	-0.034321852028369904	-0.07087869942188263	0.03612302616238594	0.06716930121183395	-0.04423566535115242	0.017069121822714806	0.014263637363910675	0.06406977027654648	-0.00984529871493578	-0.01611744798719883	-0.061238110065460205	0.031064005568623543	-0.004504297859966755	0.05371106043457985	0.03310905396938324	-0.046480443328619	0.08911925554275513	-0.0032011091243475676	0.025334851816296577	-0.0815763846039772	-0.018629679456353188	0.004867254756391048	0.0036542071029543877	0.027340851724147797	0.09973064810037613	-0.04139241576194763	-0.015533292666077614	0.008000019006431103	0.05598581209778786	-0.04391200840473175	-0.034617844969034195	-0.03960959240794182	-0.08067130297422409	-0.007526872679591179	-0.031136197969317436	-0.10044670850038528	-0.023717930540442467	0.04008621349930763	-0.0004023413348477334	-0.03862636908888817	0.017607320100069046	0.054269127547740936	-0.015435056760907173	-0.0299362912774086	0.0005399486981332302	-0.03179645910859108	-0.07629529386758804	0.08818534016609192	-0.005852328613400459	-0.0883847028017044	-0.04540550336241722	0.010976213961839676	0.04367963969707489	0.0015776485670357943	0.034690044820308685	0.043890003114938736	0.10599114745855331	0.030734756961464882	-0.03373974934220314	0.011852816678583622	-0.03787628188729286	-0.04651283472776413	0.02579762041568756	0.05905012786388397	-0.05052321031689644	-0.04538600519299507	-0.003678633365780115	-0.057253655046224594	0.014310593716800213	0.01572495512664318	0.05308670178055763	-0.027005769312381744	-0.043151937425136566	-0.05062327906489372	0.019851189106702805	-0.018364397808909416	-0.012871112674474716	0.049929000437259674	-0.017805472016334534	0.0405670702457428	-0.003090804908424616	-0.009661324322223663	0.021630821749567986	0.03322029486298561	0.03329196944832802	-0.04278907924890518	0.06348547339439392	0.0005426589632406831	-0.07913205027580261	-0.030404478311538696	-0.0725434347987175	-0.04257351532578468	-0.017595916986465454	-0.044499631971120834	-0.0583377480506897	0.06567195802927017	0.006454231683164835	-0.028587402775883675	-0.06104409694671631	-0.015176499262452126	-0.009496218524873257	-0.07273676246404648	-0.020034566521644592	0.019402066245675087	-0.07320569455623627	-0.03665033355355263	0.01951870508491993	-0.022861042991280556	-0.024913104251027107	0.0014183594612404704	-0.006692150142043829	-0.06866823136806488	0.03801923990249634	-0.01035741250962019	-0.028021201491355896	-0.029839899390935898	-0.09751811623573303	0.03637752681970596	0.11855629086494446	0.07218215614557266	0.056895431131124496	0.02341540716588497	0.046921685338020325	0.11526712775230408	0.019222546368837357	-0.03021516464650631	-0.0032418507616966963	-0.030699675902724266	-0.0754552036523819	0.04946845397353172	-0.027805276215076447	-0.003901936113834381	-0.02709849551320076	0.008242577314376831	0.045838210731744766	0.015081902034580708	-0.03496759012341499	-0.03020094521343708	0.03805416822433472	0.0016314317472279072	0.05312884971499443	-0.0036660409532487392	-0.004344991873949766	-0.004109268542379141	-0.05509624630212784	-0.06497194617986679	-0.04360564798116684	0.03862332925200462	0.010741062462329865	0.07071453332901001	0.021581929177045822	-0.08073876798152924	0.012077861465513706	-0.02189464494585991	0.10321725904941559	-0.09655735641717911	-0.011023137718439102	0.004128980450332165	0.03259868547320366	-0.10401491075754166	-0.014751628041267395	0.033847909420728683	-0.05030452087521553	-0.05083876848220825	0.00831426028162241	0.0449104942381382	-0.03965398296713829	-0.017287107184529305	-0.007216365076601505	-0.04621848091483116	-0.020516766235232353	-0.005157297011464834	0.024910183623433113	0.014309972524642944	-0.01877663843333721	-0.030288441106677055	0.01923445239663124	0.020656194537878036	0.03192325308918953	-0.028575534000992775	0.017170194536447525	-0.023344431072473526	0.04018119350075722	-0.054518911987543106	-0.018183086067438126	-0.05187922716140747	0.08172411471605301	0.0055005247704684734	-0.01086763571947813	-0.021517161279916763	-0.024987973272800446	-0.020030543208122253	0.014193125069141388	0.012524275109171867	-0.033393241465091705	-0.03368271887302399	-0.04392080754041672	-0.0500187911093235	-0.08575651794672012	-0.026795227080583572	0.0141282444819808	-0.03595368191599846	0.027669686824083328	-0.020897110924124718	-0.024157565087080002	0.012658682651817799	-0.006254087667912245	0.0347495898604393	0.05043637007474899	0.05763942003250122	-0.0270315483212471	0.03697488456964493	0.02177816443145275	0.06629450619220734	0.01431613601744175	-0.03782298043370247	0.0286214929074049	0.029225585982203484	-0.06836731731891632	0.04262521117925644	-0.01755952462553978	-0.005009952001273632	-0.005412256810814142	-0.0029013915918767452	0.01271002646535635	-0.01326399389654398	0.013118916191160679	-0.04108629748225212	0.03442447632551193	-0.09628981351852417	-0.018794531002640724	0.053906217217445374	-0.022852493450045586	-0.08775970339775085	-0.05079461634159088	0.018145181238651276	-0.015068617649376392	0.05282004550099373	-0.012164528481662273	0.015780100598931313	0.06875356286764145	-0.017710085958242416	-0.060102347284555435	-0.017637228593230247	-0.02198745124042034	0.04722598195075989	-0.02365938015282154	0.06981823593378067	0.01110418327152729	0.028853289783000946	-0.01304586511105299	0.010970440693199635	0.018203867599368095	-0.07625699788331985	-0.015254728496074677	-0.021008746698498726	-0.04930694401264191	0.02554614096879959	0.011867133900523186	-0.007704375311732292	-0.04543245956301689	-0.0237786415964365	-0.033880725502967834	0.0769391879439354	-0.01682274416089058	0.013502213172614574	0.037233468145132065	-0.016490094363689423	0.016030073165893555	-0.006290890742093325	-0.04610177129507065	-0.08080486208200455	0.022993167862296104	-0.012734738178551197	0.009950809180736542	-0.02217741683125496	0.019131189212203026	0.003894261782988906	-0.08412235230207443	-0.01876254752278328	0.014402464963495731	-0.08593221008777618	0.03288235515356064	0.014026343822479248	-0.07374395430088043	-0.04492856562137604	0.02020816132426262	-0.06904563307762146	0.07448887825012207	0.009198969230055809	0.05723797529935837	0.013004882261157036	-0.008872431702911854	-0.015721749514341354	-0.033259596675634384	-0.0005501153646036983	-0.021503835916519165	-0.03659734129905701	-0.03251800313591957	0.05979533493518829	-0.039699241518974304	0.0376192107796669	0.06479351222515106	-0.03579533472657204	0.06379228085279465	0.05570698156952858	-0.05065155774354935	0.05296911671757698	0.01175572257488966	-0.03565539792180061	0.08048095554113388	-0.04846147075295448	0.09518089145421982	-0.059603411704301834	0.09035065770149231	-0.009519565850496292	-0.030736524611711502	0.08039865642786026	0.08976243436336517	-0.0027551797684282064	0.025495389476418495	-0.04151010140776634
+0.0884537622332573	-0.027008192613720894	-0.010936160571873188	0.06355750560760498	-0.08059199154376984	-0.02369418367743492	-0.010881409049034119	0.05214536935091019	-0.04019586741924286	-0.04609492048621178	-0.06694041937589645	0.025633681565523148	-0.0028407920617610216	-0.004054042976349592	-0.011230002157390118	0.00044431007700040936	-0.036053597927093506	0.007525722961872816	-0.02189617045223713	-0.028164301067590714	0.02819444052875042	-0.040672529488801956	-0.08317368477582932	0.05435778945684433	0.032260045409202576	0.036971721798181534	0.006595424376428127	0.017678488045930862	-0.0018440070562064648	0.0016716632526367903	0.039230696856975555	-0.05229451507329941	-0.027524540200829506	-0.04496604949235916	-0.10689674317836761	0.026108190417289734	-0.04010229930281639	0.1148599237203598	-0.052859216928482056	-0.013644792139530182	-0.05351092293858528	0.044746316969394684	-0.05179278925061226	-0.02792874164879322	0.03876292333006859	-0.011073846369981766	0.03323904424905777	0.023833945393562317	0.010873604565858841	0.029229702427983284	0.07988648116588593	0.019646726548671722	0.0050420998595654964	-0.0363408587872982	-0.01958049274981022	0.06324377655982971	-0.0032779406756162643	-0.018830090761184692	-0.03242838382720947	0.03351651504635811	-0.0570969358086586	-0.014339670538902283	0.033797550946474075	0.017872679978609085	-0.01504229661077261	0.020049309358000755	0.0032246331684291363	0.10074162483215332	0.02617400512099266	-0.06005800887942314	0.00591533025726676	0.07402171939611435	-0.047470204532146454	0.0008121886057779193	0.03226031735539436	-0.05006888508796692	-0.025188827887177467	0.03579585254192352	0.03647252917289734	-0.03469245135784149	0.01236784178763628	-0.012412822805345058	0.11762981116771698	-0.011634337715804577	-0.09415104240179062	-0.002621312625706196	0.0060991994105279446	-0.05224614962935448	0.07143030315637589	0.033338092267513275	-0.010724646039307117	0.07650237530469894	-0.07331106066703796	0.051021214574575424	-0.01794251799583435	-0.003527396358549595	0.025629494339227676	-0.13907507061958313	-0.044509902596473694	-0.01051999256014824	-0.004242458380758762	-0.05494977906346321	0.006570084020495415	-0.026351112872362137	0.0523475743830204	0.04329649731516838	-0.04748280346393585	-0.022142549976706505	0.007582431193441153	-0.006432426627725363	0.05144498869776726	0.062188852578401566	0.05837550014257431	0.10952270030975342	0.11231740564107895	0.011689354665577412	0.02791697531938553	-0.0608094222843647	-0.06252837181091309	-0.014017059467732906	0.009930592030286789	0.06906764209270477	0.02892010658979416	-0.10074397921562195	-0.029207000508904457	-0.010030196979641914	-0.0928030014038086	-0.0029143947176635265	0.07484999299049377	0.0014220398152247071	0.07072218507528305	0.03521875664591789	-0.0073396069929003716	0.002078674267977476	0.013426797464489937	-0.020337754860520363	-0.008813461288809776	0.01728290133178234	0.035164959728717804	-0.009228178299963474	0.03465944528579712	0.023982157930731773	0.027938198298215866	-0.056010738015174866	0.027249975129961967	0.01700255088508129	0.06619351357221603	-0.013543548993766308	-0.029845941811800003	0.017935289070010185	-0.0736553966999054	-0.08432178944349289	-0.007397517096251249	-0.059145815670490265	0.004041730426251888	0.025844288989901543	0.05755256116390228	0.008716948330402374	-0.04127927124500275	0.01464366540312767	-0.015666786581277847	-0.019404133781790733	-0.00018032107618637383	-0.08909939974546432	-0.014938630163669586	0.03146776929497719	-0.03851193189620972	0.019990764558315277	-0.05703778192400932	-0.015195010229945183	-0.003842684207484126	-0.0015984411584213376	0.03622354939579964	0.08568475395441055	0.08992530405521393	0.025294138118624687	0.02227817475795746	-0.012104906141757965	0.0013394023990258574	0.01994173601269722	0.0034519426990300417	-0.025618936866521835	0.042061690241098404	-0.07052698731422424	-0.0025251880288124084	-0.015297421254217625	-0.004742719233036041	-0.023452291265130043	0.04878813028335571	-0.0014298484893515706	-0.04123317077755928	-0.027334490790963173	0.03328241407871246	0.04138540104031563	-0.009090502746403217	0.08555074781179428	0.004329484421759844	0.06157224625349045	-0.006403981242328882	-0.03579897806048393	-0.038856521248817444	-0.009107115678489208	-0.014303799718618393	0.05238476023077965	0.04684102162718773	-0.0927778035402298	0.07002560049295425	0.08494126796722412	0.01694743148982525	-0.0967065617442131	-0.006557524669915438	0.01903662458062172	0.041895970702171326	0.07526402920484543	0.06429670751094818	-0.013433368876576424	-0.05365804210305214	0.05606777220964432	-0.01567784696817398	-0.024336887523531914	-0.04669864475727081	-0.014410935342311859	-0.06657951325178146	0.01832805946469307	-0.034600887447595596	-0.025104613974690437	-0.05540049076080322	0.04575059190392494	0.001649088109843433	-0.05818017199635506	-0.012068632990121841	0.027660414576530457	0.0020247851498425007	-0.11713063716888428	-0.010811456479132175	0.01413238886743784	-0.08358335494995117	0.07740706205368042	0.02743379957973957	-0.08747858554124832	-0.04805677384138107	0.0019818125292658806	0.012605861760675907	0.02280995063483715	0.05113783851265907	0.06120796129107475	0.09906791150569916	0.005409405566751957	-0.028441013768315315	-0.002550768665969372	-0.0632210299372673	0.014723634347319603	0.03404689207673073	0.065440833568573	-0.05688022822141647	-0.03220801055431366	-0.035079602152109146	-0.014215890318155289	-0.0500795878469944	0.025840172544121742	0.04641491547226906	-0.01863943226635456	-0.07825586199760437	-0.019850146025419235	-0.01208497304469347	-0.024998951703310013	-0.0014727000379934907	-0.03041072189807892	-0.008608332835137844	0.09159412980079651	-0.06265421211719513	-0.021534884348511696	0.026960134506225586	-0.0460774190723896	-0.004744266159832478	-0.028190631419420242	0.04479643329977989	-0.06340771913528442	-0.03762199357151985	-0.008341478183865547	-0.07869908213615417	-0.06801829487085342	-0.06277044117450714	-0.0288139246404171	-0.022923940792679787	0.12977848947048187	-0.009181976318359375	-0.03317185118794441	-0.034136246889829636	-0.005585663020610809	-0.018654655665159225	-0.05329267680644989	0.0003485044580884278	0.039180006831884384	-0.0041627828031778336	-0.0031386148184537888	0.00612980080768466	0.0007590306340716779	0.006420322228223085	0.00018773872579913586	0.008414251729846	-0.06781875342130661	0.08093289285898209	-0.006075053941458464	-0.02245670184493065	0.018052686005830765	-0.07707104086875916	0.007314636372029781	0.07438738644123077	0.061199288815259933	0.04615195468068123	0.02931644581258297	0.016667811200022697	0.1250283271074295	0.02453150413930416	-0.04125447943806648	-0.010704949498176575	-0.031685102730989456	-0.06030627340078354	0.06466212868690491	0.013639349490404129	-0.007024380378425121	-0.0386374369263649	0.02695208601653576	0.049860451370477676	0.009282726794481277	-0.023228542879223824	-0.02206839807331562	-0.01856973022222519	0.001632091822102666	0.01329878717660904	0.010598584078252316	-0.00968138873577118	-0.019835127517580986	0.0005541424034163356	-0.056990500539541245	0.010026391595602036	0.02923758514225483	0.005745333153754473	0.0806451290845871	-0.033811215311288834	-0.03729361295700073	0.03990795463323593	-0.010511874221265316	0.0377044714987278	-0.065521240234375	0.018666958436369896	0.061089206486940384	0.045522600412368774	-0.06871777027845383	0.002563952235504985	0.05955588072538376	-0.006145014893263578	-0.0832543596625328	-0.07260514050722122	0.041291482746601105	-0.015223149210214615	-0.03387007862329483	-0.0036492308136075735	-0.02460121549665928	0.017795735970139503	0.0011652196990326047	0.040419578552246094	0.024493679404258728	-0.024006709456443787	0.011321171186864376	0.03040097840130329	-0.01735452190041542	0.06888384371995926	-0.04011785238981247	-2.1042046682850923e-06	0.0039217849262058735	0.08920016884803772	-0.028935028240084648	-0.029457159340381622	-0.026243122294545174	0.017345907166600227	-0.008203222416341305	-0.001435045269317925	-0.04247864708304405	-0.04157596081495285	0.01888270303606987	0.013183120638132095	0.030535316094756126	-0.028436247259378433	-0.019060516729950905	-0.006236230488866568	-0.06747012585401535	-0.08153074234724045	-0.08682961761951447	-0.006919334176927805	-0.04708090052008629	-0.005236595869064331	-0.03658723458647728	-0.05679727718234062	0.04566618800163269	-0.010147018358111382	0.03809052333235741	0.04043170064687729	0.017981750890612602	-0.028865115717053413	0.06739219278097153	-0.011916275136172771	0.04746764153242111	-0.015937399119138718	-0.05654272437095642	0.017169583588838577	0.03767983242869377	-0.0023950545582920313	0.08797575533390045	-0.0019144366960972548	0.021654212847352028	-0.016255512833595276	-0.017835931852459908	0.019423356279730797	0.005631337407976389	-0.017960451543331146	-0.036487776786088943	0.04947515204548836	-0.06001252308487892	-0.03909016400575638	0.08591223508119583	-0.027311913669109344	-0.0728793516755104	-0.036800842732191086	-0.05775468423962593	-0.020120354369282722	0.018290497362613678	0.028145896270871162	0.015119516290724277	0.026620641350746155	-0.05972747877240181	-0.03338999301195145	-0.043117862194776535	-0.06197265535593033	0.06782418489456177	-0.016347963362932205	0.08205050975084305	0.07448256760835648	0.039458610117435455	-0.00873558595776558	0.01263554859906435	-0.008217692375183105	-0.07355145364999771	-0.00760229816660285	0.004517148248851299	-0.020359842106699944	0.0252103079110384	0.018907682970166206	-0.028416674584150314	-0.04101790860295296	-0.025081835687160492	-0.032857172191143036	0.044426146894693375	0.010574288666248322	0.0009128649835474789	0.058590032160282135	-0.04625607281923294	-0.007902679033577442	-0.0253065787255764	0.014007439836859703	-0.06104806438088417	0.05323323979973793	0.0011862210230901837	0.009753354825079441	-0.006756034214049578	0.03138837218284607	0.005820232443511486	-0.06845954060554504	-0.002819126471877098	0.022269969806075096	-0.04751068353652954	0.031959641724824905	0.02569529227912426	-0.10380519926548004	-0.06906836479902267	0.03383304178714752	-0.022633599117398262	0.020190399140119553	-0.010056215338408947	0.0202168021351099	-0.010036446154117584	0.012740232981741428	0.022954316809773445	0.0012808294268324971	0.02950022928416729	-0.03380390629172325	-0.06753039360046387	0.0028267682064324617	0.09308319538831711	-0.052833303809165955	0.019550101831555367	0.03305502235889435	-0.02799786999821663	0.02966708317399025	-0.013677921146154404	-0.028642594814300537	0.0363786406815052	-0.0034736166708171368	-0.006914188619703054	0.0617317296564579	-0.048852842301130295	0.020296825096011162	-0.07264931499958038	0.048571258783340454	-0.017693612724542618	-0.009066532365977764	0.1047147810459137	0.04589250311255455	-0.0028903961647301912	0.04133063927292824	-0.05638730525970459
+0.07321443408727646	0.003151471260935068	-0.030881861224770546	0.012983701191842556	-0.07145331054925919	-0.058264486491680145	0.0035091196186840534	0.031853627413511276	-0.013336120173335075	-0.01652183011174202	-0.02944384329020977	-0.010414010845124722	0.004573664162307978	-0.016617469489574432	0.020903613418340683	-0.03846568986773491	-0.06822393834590912	-0.010519229806959629	-0.043116793036460876	-0.0072390236891806126	0.02073381468653679	0.011774984188377857	-0.11558125913143158	0.0499529130756855	0.005046857055276632	0.03434064984321594	-0.03654146194458008	0.005310984794050455	-0.017028627917170525	0.010992768220603466	0.04819870367646217	-0.016660088673233986	0.04331417754292488	-0.033382952213287354	-0.06506361812353134	0.053916119039058685	-0.03855045512318611	0.13456881046295166	0.024659572169184685	-0.038712695240974426	-0.09404293447732925	0.016849497333168983	-0.021856145933270454	-0.06377170234918594	-0.012871767394244671	0.0014843614771962166	0.008809234015643597	-0.018137041479349136	0.05756432190537453	0.021873779594898224	0.08052580058574677	-0.0026274132542312145	-0.011756100691854954	-0.02115453965961933	0.0019454139983281493	0.033795129507780075	0.03174058347940445	-0.045125603675842285	-0.06911107897758484	0.01944725774228573	-0.08400274813175201	0.020661333575844765	0.007052142638713121	-0.005186574067920446	-0.0034405242186039686	-0.06440766900777817	-0.007108989171683788	0.08498387783765793	0.03262776508927345	0.0006814181106165051	0.00838506780564785	0.07713766396045685	-0.08219720423221588	-0.06076730415225029	0.03190641477704048	-0.07658302038908005	-0.052059393376111984	0.03210480138659477	0.0036466277670115232	-0.012250443920493126	-0.02753293327987194	-0.023301376029849052	0.12470956146717072	-0.03684988245368004	-0.06801504641771317	-0.04775265231728554	-0.028318824246525764	-0.08658833056688309	0.05591682344675064	0.03409542143344879	0.007489398587495089	0.08145702630281448	-0.06406663358211517	0.0395352765917778	0.027778608724474907	0.01242328342050314	0.051108893007040024	-0.11900968104600906	-0.03952397033572197	-0.031444694846868515	-0.028168566524982452	-0.0005653165280818939	0.041964299976825714	-0.006857802625745535	0.043735601007938385	0.004777869675308466	-0.050141673535108566	0.01679486408829689	0.04103652387857437	0.05792359635233879	0.01915881223976612	0.05280803516507149	0.08477024734020233	0.04859130084514618	0.04475266486406326	-0.00456012599170208	0.04639022797346115	-0.043523263186216354	-0.03652985394001007	-0.011022105813026428	-0.03304176777601242	0.030244367197155952	-0.00044924169196747243	-0.07659944891929626	-0.04352116212248802	0.05298667028546333	-0.08751657605171204	-0.05620085075497627	0.03010442480444908	-0.009015871211886406	0.08073825389146805	0.0886072963476181	-0.02290414460003376	0.005516664125025272	0.03920550271868706	-0.008443489670753479	0.005018996074795723	0.05794484168291092	0.00027973458054475486	-0.026387974619865417	0.007557433098554611	0.011790599673986435	-0.005176524166017771	-0.07401598244905472	0.02139929123222828	0.03808609023690224	0.03940803185105324	-0.05940920487046242	-0.05433722957968712	0.028883136808872223	-0.04546710476279259	-0.08259733021259308	0.009051462635397911	-0.04216006025671959	-0.003014578949660063	0.0664350762963295	0.02830173261463642	0.03411360830068588	-0.004584789276123047	-0.01016579195857048	-0.04672607406973839	-0.0454598106443882	-0.010254250839352608	-0.0938684344291687	-0.05787409842014313	-0.04547043517231941	0.017493385821580887	0.015222080051898956	-0.02983867935836315	-0.015833530575037003	0.011209866963326931	0.010793925262987614	0.014686338603496552	0.08766781538724899	0.07769489288330078	0.031367477029561996	0.01192387007176876	-0.023248419165611267	0.0258120559155941	-0.040525954216718674	-0.00373525801114738	-0.039606571197509766	0.011656131595373154	-0.06225349381566048	0.008064753375947475	0.02449941448867321	0.05750502645969391	0.01317361369729042	0.01696603000164032	0.027464112266898155	-0.030750855803489685	-0.06529415398836136	0.004521800205111504	0.0602397657930851	-0.03238122537732124	0.05567030608654022	-0.019596120342612267	0.06906753778457642	-0.010992567986249924	-0.03128543123602867	-0.06600429862737656	0.04159730672836304	-0.013256036676466465	0.05705203488469124	0.05441105738282204	-0.05824801325798035	0.07252954691648483	0.013716381974518299	0.0021915158722549677	-0.06797850131988525	-0.006371359806507826	-0.008941438049077988	0.02174696885049343	0.0258218701928854	0.07994198799133301	-0.02661105990409851	-0.01206185668706894	0.017208212986588478	0.05257571116089821	-0.04231506213545799	-0.013090945780277252	-0.01569415256381035	-0.08606640249490738	0.05275929719209671	-0.02421773225069046	-0.09874477982521057	-0.03083660453557968	0.048662979155778885	-0.006707814987748861	-0.027014056220650673	0.009416305460035801	0.01012260839343071	-0.004128945525735617	-0.0655096098780632	0.013168858364224434	-0.04850463941693306	-0.10446982830762863	0.0984639823436737	0.023862000554800034	-0.10482098907232285	-0.026420490816235542	-0.014145629480481148	0.015982693061232567	-0.0295601487159729	0.026067888364195824	0.04873216524720192	0.10065723955631256	0.016134019941091537	-0.008861592970788479	0.02969146892428398	-0.07168907672166824	-0.05284712091088295	0.01853066496551037	0.04771437495946884	-0.05743569880723953	-0.028978893533349037	0.01495791133493185	-0.027948202565312386	0.004053111653774977	-0.021824834868311882	0.0256931334733963	-0.014033415354788303	-0.025677641853690147	-0.05008118972182274	0.060833629220724106	-0.059903137385845184	-0.05172270908951759	0.053518377244472504	0.003008668776601553	0.061005834490060806	-0.004421620164066553	-0.031078841537237167	0.01962832547724247	0.011143983341753483	0.008861633017659187	-0.0008461311808787286	0.06411994993686676	-0.020365506410598755	-0.08857464045286179	-0.013656582683324814	-0.08417484909296036	-0.04535149037837982	-0.01381361298263073	-0.022923652082681656	-0.060966357588768005	0.0761195570230484	-0.007484682369977236	-0.03867676481604576	-0.060045670717954636	0.0015082249883562326	-0.04589679092168808	-0.06314810365438461	-0.03429615870118141	0.015881022438406944	-0.04415353015065193	-0.07336568832397461	0.046410899609327316	-0.04308110848069191	-0.018097026273608208	-0.01806686446070671	-0.031446076929569244	-0.06811776012182236	0.023364245891571045	-0.03805246204137802	-0.021304944530129433	0.010340075939893723	-0.07398587465286255	0.027724996209144592	0.10072676092386246	0.0686720460653305	0.05672169104218483	0.03526461869478226	0.00034731230698525906	0.11144065856933594	0.01729513145983219	-0.04601334407925606	-0.01475444994866848	-0.03551596403121948	-0.06576809287071228	0.029932726174592972	-0.025660809129476547	-0.010596108622848988	-0.012616140767931938	0.017271561548113823	0.0443207249045372	0.02789279632270336	-0.05597589164972305	-0.021064626052975655	0.003077923785895109	-0.03574643284082413	0.030280956998467445	0.012922583147883415	0.015349755063652992	-0.03318425640463829	-0.05555221065878868	-0.049550898373126984	-0.03666878119111061	-0.00730549544095993	-0.009870906360447407	0.06895265728235245	-0.014070297591388226	-0.025812184438109398	-0.015343091450631618	-0.03764050826430321	0.10342620313167572	-0.06422444432973862	0.008903446607291698	0.030848557129502296	0.043006930500268936	-0.09872308373451233	-0.030526192858815193	0.0749896988272667	-0.03658551350235939	-0.0684792697429657	-0.005091401282697916	0.0007692382205277681	-0.04945937544107437	-0.015163336880505085	0.024692950770258904	-0.04404931142926216	-0.011755350977182388	-0.007785195950418711	0.02371916174888611	-0.002962008584290743	-0.02175210788846016	-0.013448256067931652	0.011778436601161957	-0.007309077773243189	0.05693436414003372	-0.03347760811448097	-0.013068425469100475	-0.034381866455078125	0.03400303050875664	-0.06009311228990555	-0.04135787859559059	-0.0600569061934948	0.044653333723545074	0.02051609940826893	-0.031913984566926956	-0.024967322126030922	-0.003945890814065933	-0.03363579884171486	0.008025149814784527	0.009059824980795383	-0.04145847633481026	-0.019811563193798065	-0.028829822316765785	-0.026630697771906853	-0.065093033015728	-0.028279149904847145	-0.01286667212843895	-0.045131806284189224	0.011115328408777714	-0.007585844956338406	-0.024262648075819016	0.030110305175185204	-0.02586267702281475	0.02452734485268593	0.06060950458049774	0.03456580638885498	0.0008938207756727934	0.0377206951379776	0.008456752635538578	0.03620688244700432	0.03204463794827461	-0.03695374354720116	-0.00638207932934165	0.04196827486157417	-0.06780373305082321	0.0415724515914917	-0.022302377969026566	0.03529844805598259	-0.0006718662334606051	-0.004473258275538683	0.013467246666550636	-0.014044509269297123	0.02696107141673565	-0.02983635663986206	0.02835608460009098	-0.06601064652204514	-0.03381238877773285	0.04585711285471916	-0.05394340306520462	-0.0636405423283577	0.00030247855465859175	-0.00863740686327219	-0.017707429826259613	0.05534939840435982	-0.007614295929670334	0.06371074914932251	0.04008059948682785	-0.08007611334323883	-0.08212757855653763	-0.04980957508087158	0.006910540163516998	0.07119160145521164	-0.036065567284822464	0.047110483050346375	0.03440496698021889	-0.002396950265392661	0.014376980252563953	-0.00427534244954586	0.021746661514043808	-0.0958452969789505	-0.016857337206602097	-0.014365184120833874	-0.017748089507222176	0.05117743834853172	-0.015738580375909805	0.0034988922998309135	-0.03264470025897026	-0.0055034467950463295	-0.030244557186961174	0.06336383521556854	-0.009393776766955853	-0.014842547476291656	0.035108782351017	-0.04523097723722458	0.011620482429862022	-0.015071891248226166	-0.027795884758234024	-0.05083773657679558	0.01992213912308216	-0.013967742212116718	0.010079088620841503	0.022719204425811768	0.048997264355421066	-0.008592960424721241	-0.08536500483751297	-0.02056046947836876	0.008085054345428944	-0.0761818140745163	0.01605493575334549	0.022707100957632065	-0.08123062551021576	-0.06238601729273796	-0.002823764458298683	-0.044563181698322296	0.04523715749382973	-0.02952176332473755	0.03430905193090439	-0.005585171282291412	0.012206187471747398	-0.03572984039783478	-0.02981656789779663	-0.005879134871065617	-0.0173901729285717	-0.038984186947345734	-0.052827607840299606	0.08429364860057831	-0.021925058215856552	0.01697358675301075	0.07036980986595154	-0.005532219540327787	0.023092515766620636	0.029810534790158272	-0.045549578964710236	0.04380328953266144	-0.012361645698547363	-0.03741716966032982	0.07722645252943039	-0.06730055809020996	0.08131612092256546	-0.060938186943531036	0.0971115380525589	-0.01984136737883091	-0.04384226351976395	0.0858316645026207	0.08985882252454758	0.0018192854477092624	0.028048239648342133	-0.034550804644823074
+0.0666869729757309	-0.06705203652381897	0.01747470535337925	0.005147828254848719	-0.04305684566497803	-0.0047402880154550076	-0.016904251649975777	0.023737790063023567	-0.006163561716675758	-0.05948175862431526	-0.004499714355915785	0.028613755479454994	-0.02732120081782341	0.0059724655002355576	0.004418130498379469	-0.040190890431404114	-0.01924072951078415	0.015484211035072803	-0.03234328702092171	-0.036071691662073135	0.037259139120578766	0.027972394600510597	-0.03599994629621506	0.027176078408956528	-0.025193404406309128	-0.0006934195407666266	-0.038838449865579605	0.003333216067403555	-0.0009978062007576227	0.052007272839546204	0.07395730167627335	-0.04875204712152481	0.04155388101935387	-0.03314914554357529	-0.059139642864465714	0.053285930305719376	-0.046207841485738754	0.10339023172855377	0.022655842825770378	0.018999971449375153	-0.03280605003237724	-0.02960456721484661	-0.047912389039993286	-0.01736598275601864	0.017262406647205353	-0.006641009822487831	0.018512660637497902	-0.017449963837862015	0.03295557573437691	0.03152691200375557	0.09368225932121277	0.010180270299315453	0.001949834986589849	0.01382644847035408	0.026808006688952446	0.04150136932730675	0.028778333216905594	-0.038706377148628235	-0.03447087109088898	-0.00043418435961939394	-0.08674909919500351	-0.007003763224929571	-0.004990038927644491	0.024380363523960114	0.018607838079333305	-0.025523461401462555	-0.030823875218629837	0.0912952795624733	0.016735592857003212	-0.04563448950648308	-0.00027588155353441834	0.10927590727806091	-0.06775664538145065	-0.08319424092769623	0.05382406711578369	-0.05255817994475365	-0.004783809185028076	-0.002227021148428321	-0.020727530121803284	-0.017328845337033272	-0.03423497825860977	-0.04750460758805275	0.12140871584415436	-0.03869922459125519	-0.017690155655145645	-0.030962582677602768	-0.011114554479718208	-0.08164216578006744	0.09056972712278366	0.030045216903090477	0.03742515668272972	0.034405700862407684	-0.042435456067323685	0.055709391832351685	-0.02629273757338524	-0.03017304465174675	0.015600142069160938	-0.05389273911714554	-0.02417105808854103	-0.00974782183766365	0.048257119953632355	0.008989494293928146	0.07524304836988449	-0.012608646415174007	0.10255276411771774	0.037345901131629944	-0.052689820528030396	-0.008517436683177948	0.041560228914022446	0.022883882746100426	0.03252169117331505	0.13234440982341766	0.04401085898280144	0.03672170266509056	0.06041295453906059	0.022707227617502213	0.020050961524248123	-0.06390491873025894	0.0071528395637869835	0.0017642740858718753	-0.0014506978914141655	0.04848489165306091	-0.045914553105831146	-0.07212888449430466	-0.020142750814557076	0.06025273725390434	-0.06593619287014008	-0.04316418245434761	0.09217198193073273	-0.018983159214258194	0.08617983758449554	0.07753622531890869	-0.051999568939208984	0.04909881576895714	0.03283064439892769	-0.01246173121035099	-0.06545035541057587	0.028607647866010666	0.03805965557694435	-0.038951072841882706	-0.021323738619685173	0.0793343037366867	-0.005554867908358574	-0.03425899147987366	0.030838122591376305	0.024210887029767036	0.06197335198521614	-0.03084002621471882	-0.04804113134741783	0.041229572147130966	-0.027977442368865013	-0.09488476067781448	0.012374422512948513	-0.060004398226737976	0.016797244548797607	0.0584888719022274	0.015566032379865646	0.02616898901760578	0.016141977161169052	-0.024144811555743217	-0.07298821955919266	-0.08890888839960098	-0.06591004878282547	-0.06276660412549973	-0.010949437506496906	0.019842499867081642	0.031673017889261246	0.024518007412552834	-0.07308691740036011	-0.03408900275826454	0.015831148251891136	-0.04344956576824188	0.01183822751045227	0.09576577693223953	0.08215659856796265	0.06321422010660172	-0.03093693032860756	-0.023983603343367577	0.0013011773116886616	-0.002996232360601425	0.044420026242733	-0.06542575359344482	0.0123244384303689	-0.06608914583921432	0.01233038678765297	0.000889189715962857	0.04094995558261871	0.015799617394804955	-0.003207472153007984	-0.019403215497732162	-0.0021571421530097723	-0.05618078261613846	-0.0023793992586433887	0.0213074441999197	-0.004224539268761873	0.014325817115604877	-0.01950024627149105	0.09929588437080383	-0.009109579026699066	-0.039948225021362305	-0.05938494950532913	0.02043415606021881	0.03804789483547211	0.0023994031362235546	0.03212064132094383	-0.05904232710599899	0.08733811229467392	0.031101178377866745	0.08397939801216125	-0.08060474693775177	-0.0204838365316391	0.0008997399127110839	0.041194941848516464	0.041334159672260284	0.014826368540525436	-0.06584867835044861	-0.01770893856883049	0.012453936040401459	0.05611947178840637	-0.04057438299059868	-0.04542398825287819	-0.008942896500229836	-0.03039347007870674	0.02623605541884899	-0.01602807641029358	-0.08202925324440002	-0.0919208750128746	0.018121831119060516	0.02708320878446102	-0.052530329674482346	-0.001117273815907538	0.02508491650223732	-0.028811724856495857	-0.006003366783261299	0.06034335494041443	-0.07214263826608658	-0.07439085096120834	0.06978379189968109	-0.0063178869895637035	-0.045766204595565796	-0.047117721289396286	-0.02143903821706772	0.06542282551527023	0.019435355439782143	0.03756938874721527	0.029570406302809715	0.06731630116701126	-0.01662786304950714	-0.05420559272170067	-0.009340839460492134	0.0109016764909029	-0.029466252774000168	0.006490865256637335	0.012011643499135971	-0.03575902059674263	-0.08799275010824203	0.01085684448480606	-0.054847802966833115	0.00994699914008379	-0.030292021110653877	0.016969697549939156	-0.02158832736313343	0.004463671240955591	0.004916460253298283	0.014186838641762733	-0.031046634539961815	0.02202128805220127	-0.005472707096487284	-0.045161500573158264	0.03063817322254181	-0.024132877588272095	0.035852979868650436	0.016048984602093697	-0.04659731686115265	-0.02109435945749283	-0.034572504460811615	0.06273405998945236	-0.0530378594994545	-0.07042478024959564	-0.07952240109443665	-0.04889041930437088	-0.03913126885890961	-0.001405608025379479	-0.01887447200715542	-0.02518174797296524	0.08410228043794632	-0.02097487263381481	-0.058011747896671295	-0.00013615674106404185	-0.030824216082692146	-0.04472788795828819	-0.02947663702070713	-0.020955465734004974	-0.009553449228405952	-0.01920531876385212	0.0004825174401048571	0.021080175414681435	-0.06651827692985535	-0.0040146103128790855	-0.004670875612646341	0.032692499458789825	-0.0016788298962637782	0.023558776825666428	0.006453292444348335	-0.019573837518692017	-0.028527816757559776	-0.06381039321422577	0.045853592455387115	0.06128446385264397	0.08198776841163635	0.06283646076917648	0.0018261680379509926	0.03559725359082222	0.10612914711236954	0.04581761360168457	-0.053516171872615814	-0.0046459645964205265	-0.020674873143434525	-0.04670262709259987	0.05086144432425499	0.006439779885113239	0.021413978189229965	-0.010722428560256958	-0.0022042274940758944	0.052605342119932175	0.0013542389497160912	0.009090529754757881	-0.018028371036052704	0.04591500014066696	-0.023329949006438255	0.004091671202331781	-0.013788129203021526	0.022838084027171135	-0.0039916024543344975	-0.029708608984947205	-0.05038449913263321	-0.021454881876707077	0.03288868069648743	-0.027925563976168633	0.06295496225357056	-0.010578696615993977	-0.06024005264043808	-0.007778145372867584	0.019015811383724213	0.078802689909935	-0.025281615555286407	0.017099585384130478	-0.005433803424239159	0.04494825750589371	-0.14022131264209747	0.02138254977762699	-0.007721878122538328	-0.03433402255177498	-0.0813886970281601	0.004668538458645344	0.03998459503054619	-0.020723596215248108	-0.046203989535570145	-0.06247067078948021	-0.029192538931965828	-0.02697407826781273	-0.010153845883905888	0.01053417194634676	0.08524428308010101	0.04211267828941345	-0.033244408667087555	0.018225086852908134	0.006971287075430155	0.024812916293740273	-0.009340541437268257	-0.024699071422219276	-0.05647028982639313	0.022972937673330307	-0.06627297401428223	-0.054421354085206985	-0.07246477901935577	0.0115083958953619	-0.026299243792891502	-0.001886110519990325	-0.018755098804831505	-0.03434614837169647	-0.03445788100361824	-0.037936050444841385	0.04299502447247505	-0.041515931487083435	0.02151631936430931	-0.011838994920253754	-0.028064526617527008	-0.08256159722805023	-0.04679375886917114	0.002503781346604228	-0.014177527278661728	0.0006124670035205781	-0.01264658197760582	-0.014897745102643967	0.0370350256562233	0.00792667269706726	0.011946036480367184	0.06731041520833969	0.006533937994390726	0.007646892219781876	0.06684790551662445	0.05757579952478409	0.027459261938929558	0.03287750110030174	-0.060817886143922806	-0.028866909444332123	0.013917208649218082	-0.044298313558101654	0.036750685423612595	0.008575995452702045	0.013213403522968292	-0.03458915278315544	-0.015819596126675606	0.004638706333935261	-0.04724148288369179	0.018585948273539543	-0.02220231108367443	0.08732828497886658	-0.07558201998472214	-0.051968663930892944	0.02697552554309368	-0.04771672934293747	-0.08512630313634872	-0.03590802848339081	-0.05359791964292526	-0.03874170035123825	0.04743581637740135	-0.026912175118923187	0.0018844364676624537	0.04855235666036606	0.008438090793788433	-0.04558534547686577	0.017604755237698555	-0.023055674508213997	-0.0005503493011929095	0.03910761699080467	0.06623928248882294	0.006890384946018457	0.018571730703115463	0.02608788199722767	-0.041289087384939194	0.03176610544323921	-0.11218871921300888	-0.01162358745932579	-0.013889772817492485	-0.01546624954789877	0.0028508356772363186	0.0007663971045985818	0.012681326828897	-0.05280885845422745	0.00890785176306963	-0.03773077204823494	0.10401133447885513	-0.00982829462736845	0.012792587280273438	-0.01982155442237854	-0.06659329682588577	-0.03929428383708	0.025131840258836746	-0.02938716486096382	-0.07480398565530777	0.03891389071941376	-0.022369029000401497	-0.008075260557234287	-0.0546761080622673	0.03949471190571785	0.00723913824185729	-0.08804114162921906	-0.018249116837978363	0.001226958236657083	-0.05963711440563202	0.04669339582324028	0.027917001396417618	-0.10413509607315063	-0.0829014852643013	0.022201986983418465	-0.07574314624071121	0.007875889539718628	-0.04761875420808792	0.032779425382614136	0.006226655561476946	0.010458802804350853	-0.0391971617937088	-0.014990931376814842	0.04784172773361206	-0.007862299680709839	-0.05517807975411415	-0.026431577280163765	0.05044716224074364	-0.027584651485085487	0.0014593614032492042	0.07758402079343796	0.009416431188583374	0.014908438548445702	0.06350759416818619	-0.036389440298080444	0.03443571552634239	0.002923575695604086	0.028936687856912613	0.04524136707186699	-0.04317762330174446	0.07997190952301025	-0.08352774381637573	0.1263275146484375	0.017417700961232185	0.015220952220261097	0.08785635232925415	0.04467609152197838	-0.00577335711568594	0.025555167347192764	-0.062005311250686646
+0.012194216251373291	-0.04084918648004532	-0.026966679841279984	0.04212747514247894	-0.0765887200832367	-0.023548554629087448	-0.0033950188662856817	0.03244272619485855	0.0026332007255405188	-0.04985114559531212	-0.055237822234630585	0.030699770897626877	0.013082562945783138	0.03318078815937042	0.027532357722520828	-0.032525356858968735	-0.06699865311384201	0.010766992345452309	-0.01359452586621046	-0.051910776644945145	0.022566400468349457	-0.0038276135455816984	-0.048301175236701965	0.09557432681322098	0.04227159917354584	0.03545033559203148	-0.04074275866150856	-0.015111168846487999	-0.0002904845750890672	0.030761564150452614	0.059937819838523865	-0.02380526438355446	0.02785366401076317	-0.043364476412534714	-0.06790774315595627	0.06399522721767426	-0.022185420617461205	0.12260349094867706	0.0015300262020900846	0.010348092764616013	-0.044574689120054245	-0.005233329255133867	-0.07120336592197418	-0.04905316233634949	0.013865252956748009	-0.010343593545258045	0.008796444162726402	-0.003411333542317152	0.01786773093044758	0.025002513080835342	0.06965286284685135	0.017508942633867264	-0.0304217878729105	-0.023926354944705963	-0.03946584463119507	0.0318768136203289	0.016854336485266685	-0.04280434548854828	-0.03867379203438759	0.01294602919369936	-0.08992191404104233	0.008475175127387047	0.006774942856281996	-0.0019072368741035461	-0.008133018389344215	0.0016443009953945875	-0.023795975372195244	0.0796983540058136	0.005460462532937527	-0.029178094118833542	0.02448289841413498	0.07240235805511475	-0.050243183970451355	-0.017039800062775612	0.05053070932626724	-0.029091056436300278	-0.020216703414916992	0.03619494289159775	-0.0154398949816823	-0.010014583356678486	0.025043867528438568	-0.027898378670215607	0.13721799850463867	-0.024788858368992805	-0.04332064092159271	-0.0207595843821764	0.004837516229599714	-0.039501599967479706	0.04501256346702576	0.0024388276506215334	0.02530696615576744	0.08083520829677582	-0.046598516404628754	0.06476609408855438	0.035488344728946686	-0.021381348371505737	0.0062699345871806145	-0.15300244092941284	-0.048846617341041565	0.019981786608695984	0.01881513185799122	-0.016826152801513672	0.07114186882972717	-0.03111160919070244	0.05392030254006386	0.03759021311998367	-0.07729025185108185	-0.010201094672083855	0.021881012246012688	-0.013813302852213383	0.041318248957395554	0.10675657540559769	0.028513336554169655	0.06055714562535286	0.07105764001607895	0.0005821384256705642	0.009586187079548836	-0.039687857031822205	-0.05986887961626053	0.0022174157202243805	0.0010200276738032699	0.059577275067567825	0.0038699309807270765	-0.07922330498695374	-0.03323650732636452	0.008056649938225746	-0.12821903824806213	0.010193814523518085	0.07311753183603287	-0.04354981333017349	0.11402248591184616	0.04132304713129997	-0.011679529212415218	0.026653792709112167	0.03672654554247856	0.010505120269954205	0.0030291324947029352	0.008600311353802681	-0.0013172805774956942	-0.030217016115784645	-0.03300480544567108	0.07122455537319183	0.025703517720103264	-0.029015855863690376	0.06560295075178146	0.03324340656399727	0.012566863559186459	0.0032984106801450253	-0.02721666730940342	-0.023487024009227753	-0.07808735221624374	-0.08000336587429047	0.029538359493017197	-0.05380687117576599	0.016080347821116447	0.04074518755078316	0.05326998606324196	0.05906062200665474	-0.006641804240643978	-0.034715957939624786	-0.05511697754263878	-0.0018850177293643355	-0.024236414581537247	-0.06246063858270645	0.021396880969405174	0.01558959111571312	-0.032029204070568085	0.0073001328855752945	-0.05380920693278313	-0.015619233250617981	-0.009239823557436466	-0.028238445520401	0.023164667189121246	0.08137484639883041	0.05446489900350571	-0.009137389250099659	0.013908149674534798	-0.06788179278373718	-0.030009765177965164	0.00458594411611557	0.022576548159122467	-0.05295340344309807	0.03822057321667671	-0.03453322872519493	-0.020956795662641525	-0.018364261835813522	0.033862847834825516	-0.02280842326581478	-0.0024434535298496485	-0.040747322142124176	-0.03264255449175835	-0.0509420670568943	0.0568251796066761	0.050106972455978394	-0.030365483835339546	0.029582787305116653	0.014734914526343346	0.07608923316001892	0.005905694793909788	0.023557858541607857	-0.058269329369068146	0.020705973729491234	0.010722504928708076	0.05428572744131088	0.04227861762046814	-0.05957121402025223	0.0790095329284668	0.022037554532289505	0.054713174700737	-0.08518163114786148	0.0026404266245663166	0.015080097131431103	-0.013484287075698376	0.08405154198408127	0.08266198635101318	-0.03812476992607117	-0.017473705112934113	0.01644877903163433	0.05383748188614845	-0.07869523018598557	-0.029791271314024925	-0.013231135904788971	-0.06524255871772766	-0.02136952430009842	-0.03199658542871475	-0.07703396677970886	-0.04819219559431076	0.03186507523059845	-0.012292981147766113	-0.038690727204084396	-0.026797592639923096	0.06281102448701859	-0.01846584863960743	-0.027423730120062828	0.011455217376351357	0.005324152763932943	-0.056761015206575394	0.06967479735612869	-0.02150079607963562	-0.08844920992851257	-0.07034684717655182	0.008543401025235653	0.036087196320295334	0.04321146383881569	0.03451050445437431	0.06486129015684128	0.09548909962177277	0.04132765904068947	-0.05280854180455208	0.01630224473774433	-0.014721102081239223	-0.033130474388599396	0.013977188616991043	0.06213567033410072	-0.031206253916025162	-0.06283242255449295	0.00561579829081893	-0.06811872124671936	-0.014318968169391155	0.014524316415190697	0.04054304212331772	-0.031123358756303787	-0.04746465012431145	-0.0240881759673357	-0.016790272668004036	-0.023706408217549324	0.009244092740118504	-0.012363906018435955	-0.03946462646126747	0.07305168360471725	-0.05167384445667267	-0.043204694986343384	-0.006020580418407917	0.01001299824565649	0.01488765049725771	-0.05580776184797287	0.04192563518881798	-0.031599029898643494	-0.06302732974290848	-0.02502126805484295	-0.05672222375869751	-0.08332474529743195	-0.0443689189851284	-0.0156073784455657	-0.030699148774147034	0.09027007967233658	0.004329056944698095	-0.07877445220947266	-0.03138906508684158	-0.04297865927219391	-0.008801049552857876	-0.04856444150209427	0.014979126863181591	-0.013260167092084885	-0.055207397788763046	-0.02755626104772091	0.026155225932598114	-0.02994387038052082	-0.007722232956439257	0.06613814830780029	0.012069234624505043	-0.058589089661836624	0.06608396023511887	0.006888228002935648	-0.02743886224925518	-0.028548739850521088	-0.10713045299053192	0.03127533197402954	0.09152519702911377	0.02460034005343914	0.026935312896966934	0.03425004333257675	0.05198248103260994	0.09137056022882462	-0.003997801337391138	-0.015697186812758446	0.015424528159201145	-0.03218269720673561	-0.05851414427161217	0.06161892041563988	-0.015209387056529522	0.0047820620238780975	-0.028175726532936096	-0.006893534678965807	0.029734062030911446	0.03160843998193741	-0.000235248458920978	-0.03215130418539047	0.034353431314229965	0.002348013687878847	0.025243664160370827	-0.02675633132457733	-0.001607243437319994	-0.0020396821200847626	-0.05384073406457901	-0.07988562434911728	-0.03531977906823158	0.05034200847148895	0.007918905466794968	0.05960250645875931	-0.017846604809165	-0.06980657577514648	0.021693266928195953	-0.02025742083787918	0.10503941774368286	-0.08039134740829468	0.008702917955815792	0.05239792913198471	0.030496209859848022	-0.08219844102859497	0.0038457384798675776	0.025915602222085	-0.059918325394392014	-0.06695135682821274	-0.004039929714053869	0.05583320930600166	-0.04499443992972374	-0.05267120152711868	-0.019018448889255524	-0.027152687311172485	0.01171888504177332	0.01445870939642191	0.02019338868558407	0.05155550315976143	0.019778631627559662	-0.016573801636695862	0.043987371027469635	0.03177051618695259	0.0243961364030838	-0.03166945278644562	0.029898058623075485	-0.01132743339985609	0.09462920576334	-0.040547680109739304	-0.024417825043201447	-0.07876089960336685	0.033206552267074585	-0.03285876289010048	0.003049603197723627	-0.04528772458434105	-0.026791349053382874	-0.011457638815045357	0.022198742255568504	0.035664383322000504	-0.06196698918938637	-0.02677391842007637	-0.05624212324619293	-0.0696621686220169	-0.06701185554265976	-0.04389479383826256	0.007468523923307657	-0.029640503227710724	0.04431227222084999	-0.0620749406516552	-0.020902788266539574	0.019347934052348137	0.0006334920763038099	0.023030303418636322	0.02831428498029709	0.033988550305366516	-0.02659744955599308	0.05628739669919014	0.004356087185442448	0.05113470181822777	-0.005189673975110054	-0.050612255930900574	0.04970855638384819	0.05083639547228813	-0.042555492371320724	0.051450781524181366	-0.04190122336149216	-0.021941006183624268	0.003430645214393735	-0.017586715519428253	-0.00586717901751399	0.009993396699428558	-0.0024016532115638256	-0.07641030102968216	0.036631930619478226	-0.07755438983440399	-0.04373907670378685	0.03388955816626549	-0.05797501280903816	-0.07237721979618073	-0.06083323061466217	-0.036195747554302216	-0.019863879308104515	0.03798743709921837	0.002540244022384286	0.017143819481134415	0.0737142562866211	-0.031140990555286407	-0.04510659724473953	0.01969173364341259	-0.031140107661485672	0.01706153340637684	0.007300528232008219	0.059413712471723557	0.02434528060257435	0.003984578885138035	0.00345939421094954	-0.016201438382267952	0.01634080894291401	-0.05615382269024849	0.013937478885054588	0.0006612641736865044	-0.051280513405799866	0.009805350564420223	0.028656762093305588	0.029887527227401733	-0.06106499582529068	0.00045206042705103755	-0.041032981127500534	0.05432498827576637	0.016303472220897675	0.04070422798395157	0.014511657878756523	-0.019167520105838776	0.014738506637513638	0.024742498993873596	-0.015481592155992985	-0.07122635841369629	0.011754775419831276	-0.053663741797208786	0.031282924115657806	-0.023470981046557426	-0.016910942271351814	0.010487196035683155	-0.07438091188669205	0.0027537387795746326	-0.0108346464112401	-0.050488561391830444	0.004750716034322977	0.02075601927936077	-0.05839065462350845	-0.06222721189260483	0.0005835345946252346	-0.060507066547870636	0.049943096935749054	0.017287123948335648	0.05577492713928223	-0.034232012927532196	-0.015324873849749565	0.003171361982822418	0.004062578547745943	0.029477974399924278	-0.023893512785434723	-0.07106512784957886	0.000946815765928477	0.08646552264690399	-0.02842225693166256	0.010468296706676483	0.07616563141345978	-0.014090979471802711	0.05744005739688873	0.013806067407131195	-0.0481046698987484	0.045626137405633926	0.009074239991605282	-0.023181689903140068	0.05974511429667473	-0.057346656918525696	0.08867914974689484	-0.05337151512503624	0.09248946607112885	0.0003929768572561443	-0.005346084479242563	0.11071357876062393	0.07097883522510529	0.020817115902900696	0.046952929347753525	-0.059009380638599396
+0.002700724871829152	-0.031189506873488426	0.004735927563160658	0.03393308073282242	-0.06280001997947693	-0.05002657324075699	0.0001550720480736345	0.020240267738699913	0.04593099653720856	-0.03836015239357948	-0.007882440462708473	0.012459270656108856	0.01556952390819788	0.04162876307964325	0.03849637880921364	-0.022380873560905457	-0.06399185955524445	0.004888765513896942	-0.04664255306124687	-0.03588392958045006	0.04236473888158798	0.009213023819029331	-0.07034942507743835	0.04840938001871109	-0.03172208368778229	0.01750364899635315	-0.0729951560497284	0.010933086276054382	-0.029561325907707214	0.019833913072943687	0.06182790547609329	-0.017746826633810997	0.07152800261974335	-0.07261865586042404	-0.03454719856381416	0.0326913520693779	-0.037686001509428024	0.1031135842204094	0.020116670057177544	-0.0261272881180048	-0.03160792961716652	-0.005714193917810917	-0.06761568039655685	-0.04603457450866699	0.004314036574214697	-0.026366189122200012	0.010489361360669136	-0.010943560861051083	0.018232695758342743	0.010386507958173752	0.03999081999063492	0.020226066932082176	-0.02207537740468979	-0.03480910509824753	-0.018898585811257362	-0.0033272055443376303	0.051325902342796326	-0.10142944008111954	-0.07406248897314072	0.0429655946791172	-0.07943163067102432	0.01557113416492939	0.019357971847057343	0.013024933636188507	-0.012462220154702663	-0.023023400455713272	0.015320593491196632	0.05475053936243057	0.022664761170744896	-0.030952366068959236	0.04302040860056877	0.06312423199415207	-0.054053351283073425	-0.057788826525211334	0.029895639047026634	-0.016442354768514633	0.011162027716636658	0.037399474531412125	-0.007880236953496933	0.02569470927119255	-0.010293284431099892	-0.04130542650818825	0.10091286152601242	-0.0008163675083778799	-0.02379227988421917	-0.06880240887403488	0.022196359932422638	-0.036909591406583786	0.034091923385858536	-0.025065654888749123	0.005290853325277567	0.07399076223373413	-0.054922886192798615	0.0715905949473381	0.037600960582494736	0.029424279928207397	-0.005449476186186075	-0.14477571845054626	-0.03535260260105133	0.0034861029125750065	-0.0011564262676984072	-0.008614948950707912	0.048640068620443344	0.019565002992749214	0.050151124596595764	0.057577311992645264	-0.051361456513404846	0.0300145260989666	0.04101134091615677	0.04389755055308342	0.03132505714893341	0.08374112844467163	0.032428815960884094	0.0549524687230587	0.017516423016786575	-0.02008405514061451	-0.002165287034586072	-0.017257481813430786	-0.01970156468451023	0.009173578582704067	0.006341395899653435	0.0003800330450758338	-0.02690775692462921	-0.05469900742173195	-0.04828986898064613	0.05482161045074463	-0.12718847393989563	-0.028663307428359985	0.04746434837579727	-0.03696059063076973	0.10644987225532532	0.04399910196661949	-0.003640022361651063	0.00822613388299942	0.060191258788108826	0.04432663321495056	0.00013884849613532424	0.010176303796470165	-0.009110230021178722	-0.049900755286216736	-0.03715205192565918	0.06823525577783585	0.006037758197635412	-0.0574088916182518	0.04425419867038727	0.04624662548303604	-0.011936192400753498	-0.026798700913786888	-0.0899934396147728	-0.006865316070616245	-0.09483761340379715	-0.07762537151575089	0.012898338958621025	-0.08054427802562714	0.007864972576498985	0.03985391557216644	0.035488419234752655	0.041471026837825775	-0.030207229778170586	-0.02395797334611416	-0.06057371199131012	-0.021950216963887215	-0.003200110048055649	-0.061029355973005295	-0.022448088973760605	-0.02920975536108017	0.016134750097990036	0.049121368676424026	-0.02454490028321743	-0.03267157822847366	0.007518400438129902	-0.0032653037924319506	0.021854130551218987	0.05688941478729248	0.021499862894415855	-0.004661760292947292	0.0035223360173404217	-0.05605927109718323	0.014689774252474308	-0.027484524995088577	0.01185187604278326	-0.03346413001418114	0.026575153693556786	-0.055406294763088226	-0.015825072303414345	-0.011040190234780312	0.0007351840613409877	-0.023730836808681488	-0.021660607308149338	-0.053290192037820816	-0.0672559067606926	-0.08509387075901031	0.03495470806956291	0.08328990638256073	-0.031408846378326416	0.03358794003725052	0.011063098907470703	0.05402425676584244	-0.015322914347052574	0.00933304987847805	-0.04343752562999725	0.04656301811337471	0.004692858550697565	0.029314639046788216	-0.031401026993989944	-0.06386976689100266	0.05972854420542717	-7.928894774522632e-05	0.026169758290052414	-0.05870839208364487	0.025268200784921646	0.03558659553527832	-0.023373238742351532	0.04882923513650894	0.07199748605489731	-0.03550766035914421	-0.014099028892815113	0.020091045647859573	0.06855172663927078	-0.08262484520673752	0.02765013463795185	0.011924170888960361	-0.06422042846679688	0.02467995323240757	-0.02508772350847721	-0.10167267918586731	-0.017193663865327835	-0.01625092141330242	-0.033275019377470016	-0.039559148252010345	-0.02375425025820732	0.016240078955888748	0.006113950628787279	-0.03450969606637955	-0.01425604522228241	0.01135925855487585	-0.06768597662448883	0.1066451296210289	-0.010015136562287807	-0.059396371245384216	-0.032673753798007965	-0.009638129733502865	0.04508170485496521	-0.013083528727293015	0.013269787654280663	0.044888366013765335	0.1200956255197525	0.045618969947099686	-0.014170568436384201	-0.014061378315091133	-0.03997139260172844	-0.07931698113679886	0.008509152568876743	0.06019550934433937	-0.03252066299319267	-0.015515529550611973	0.016550149768590927	-0.055799394845962524	-0.013486934825778008	-0.0028324800077825785	0.04662144184112549	-0.0143791688606143	-0.02183816209435463	-0.04064255580306053	0.032609570771455765	0.004818157758563757	-0.030696900561451912	0.024498647078871727	-0.009171769954264164	0.06092260405421257	-0.019429374486207962	-0.0679178386926651	0.033643584698438644	-0.0059442766942083836	0.04851134866476059	-0.02389402501285076	0.048503030091524124	-0.0303714107722044	-0.1039697602391243	-0.032549332827329636	-0.03974098339676857	-0.05923004075884819	-0.010848719626665115	0.0026142816059291363	-0.05108915641903877	0.10637658834457397	0.0046617090702056885	-0.049982789903879166	-0.033603545278310776	-0.01845720410346985	-0.012274300679564476	-0.07585487514734268	0.011841610074043274	-0.02391211688518524	-0.07619639486074448	-0.015283109620213509	0.044677186757326126	-0.08161955326795578	-0.016353612765669823	0.0277448408305645	-0.03224692493677139	-0.06674090772867203	0.06073269993066788	-0.037287600338459015	-0.06342758238315582	0.0031136428005993366	-0.08359244465827942	0.01917661726474762	0.07308405637741089	0.010190622881054878	0.025479691103100777	0.024968400597572327	0.060869865119457245	0.08245600759983063	-0.02925412729382515	-0.012931587174534798	0.0007403655909001827	-0.012233760207891464	-0.06321585923433304	0.03703482821583748	-0.0766318291425705	0.0029103572014719248	0.008690220303833485	-0.024029990658164024	0.07745635509490967	-0.02017485350370407	-0.007819882594048977	0.014560951851308346	0.06630586087703705	0.015171836130321026	0.0562356635928154	0.016132794320583344	-0.018321841955184937	0.00600588321685791	-0.06364213675260544	-0.05754038691520691	-0.031051287427544594	0.021183960139751434	-0.025857865810394287	0.051498301327228546	-0.007833467796444893	-0.09068933129310608	0.011935495771467686	-0.00033478435943834484	0.10535372048616409	-0.09042003750801086	-0.00515448022633791	0.030919179320335388	-0.02998276613652706	-0.10293611139059067	-0.014482196420431137	0.02337714657187462	-0.0397004708647728	-0.046692490577697754	0.03435086831450462	0.04440239816904068	-0.07426617294549942	-0.028284145519137383	0.030795138329267502	-0.0194111131131649	-0.022917931899428368	0.07526460289955139	0.006624117493629456	0.029014142230153084	-0.030311530455946922	0.0029159439727663994	-0.02170211263000965	0.05136660486459732	0.03669141232967377	-0.025378094986081123	0.052566129714250565	-0.01871827431023121	0.09290352463722229	-0.02311842143535614	-0.04151006042957306	-0.07636894285678864	0.06395726650953293	0.00680782413110137	-0.007850357331335545	-0.04016575962305069	-0.006395893637090921	-0.006859756540507078	0.02850992977619171	-0.0017210718942806125	-0.07766091823577881	-0.00319299497641623	-0.021874163299798965	-0.06532518565654755	-0.0577789731323719	-0.039430778473615646	0.017312748357653618	-0.0799621045589447	0.0013384895864874125	-0.04585590213537216	0.0020042818505316973	0.00962437130510807	-0.05062731355428696	-0.0014837507624179125	0.04999176412820816	0.026239970698952675	-0.029428593814373016	0.04975263774394989	-0.011210858821868896	0.041702497750520706	0.017887579277157784	-0.011804629117250443	0.05488770082592964	0.02221631444990635	-0.06213298812508583	0.01942950300872326	-0.055438946932554245	0.01114580873399973	0.039277564734220505	-0.024045029655098915	0.01376102864742279	0.022501468658447266	-0.001986989052966237	-0.06454067677259445	0.057425327599048615	-0.057962339371442795	-0.019098319113254547	0.008004941046237946	-0.042366258800029755	-0.08284538239240646	-0.013004971668124199	-0.01330273412168026	-0.06232014670968056	0.04681580886244774	-0.010621684603393078	0.03479190543293953	0.03018159046769142	-0.06748218089342117	-0.03806581720709801	0.01221949141472578	-0.028236670419573784	-0.010163561441004276	-0.004981104750186205	0.07698547840118408	-0.00033062053262256086	0.020062314346432686	0.010985505767166615	-0.016649840399622917	0.0015003079315647483	-0.07522230595350266	-0.0035867050755769014	-0.017473416402935982	-0.02776597999036312	-0.019244153052568436	-0.022187968716025352	0.05744495987892151	-0.08810960501432419	-0.022342022508382797	-0.036002758890390396	0.08412383496761322	-0.029905321076512337	-0.02082693576812744	0.011261739768087864	-0.024578968062996864	-0.026899095624685287	0.03563887998461723	-0.021458232775330544	-0.08959285914897919	-0.013845033943653107	-0.05574377253651619	0.0512663759291172	0.013831479474902153	0.010896939784288406	-0.025398319587111473	-0.0892503410577774	-0.03456488624215126	-0.005720257759094238	-0.06272570043802261	0.00373639864847064	0.019577983766794205	-0.051881179213523865	-0.050655197352170944	-0.025030124932527542	-0.05250438675284386	0.0698605552315712	0.013517132960259914	0.04390789940953255	-0.03192676976323128	-0.03367111086845398	-0.0223920326679945	-0.04964052140712738	0.026218803599476814	0.00913036148995161	-0.10495404154062271	-0.032498907297849655	0.07352057099342346	-0.03413857892155647	0.04027397185564041	0.07219014316797256	-0.03207022324204445	0.02658243477344513	0.0067909094505012035	-0.031943656504154205	0.015489138662815094	-0.04414179548621178	-0.0340387299656868	0.04440658912062645	-0.04748917371034622	0.074641153216362	-0.0483272485435009	0.09611956775188446	-0.013245784677565098	-0.008936525322496891	0.07527967542409897	0.08137547224760056	0.01728268340229988	-0.0031071044504642487	-0.05758531019091606
+0.0172245055437088	-0.021281909197568893	-0.02061752788722515	0.03770262375473976	-0.0915745422244072	-0.06636278331279755	-0.022169971838593483	0.073765829205513	0.01513090543448925	-0.02622983418405056	-0.05141124874353409	-0.0031783152371644974	0.003721846267580986	0.011944573372602463	0.04291137680411339	-0.017044514417648315	-0.06357279419898987	-0.019882038235664368	-0.05328673496842384	-0.046627677977085114	0.02825157344341278	0.03453925997018814	-0.0745200514793396	0.06342042237520218	0.015826918184757233	0.015227247029542923	-0.07351371645927429	0.01248924620449543	-0.010337715968489647	0.034946564584970474	0.03709692507982254	0.008810343220829964	0.05383025109767914	-0.0801793709397316	-0.046060048043727875	0.05819347873330116	-0.01889689452946186	0.09478340297937393	0.01750119775533676	-0.03362206369638443	-0.03326310217380524	0.017009461298584938	-0.013965407386422157	-0.0673128068447113	0.014322840608656406	-0.01017370168119669	0.00849522277712822	-0.03675171732902527	0.021311530843377113	0.03346838802099228	0.09240183234214783	-0.01287718303501606	-0.0058104610070586205	-0.03833046555519104	-0.02727460116147995	0.017138389870524406	0.011585788801312447	-0.05361632630228996	-0.04446536675095558	-0.0005359898204915226	-0.1010531634092331	0.011933729983866215	-0.02607676573097706	0.0007012579008005559	-0.036639582365751266	-0.0005129182245582342	-0.0009620275814086199	0.09276220947504044	0.04637604206800461	-0.02077968418598175	0.03652879223227501	0.06462787836790085	-0.06919699907302856	-0.040925998240709305	0.03974036127328873	-0.03867129236459732	-0.005844417028129101	0.04014622047543526	-0.017820175737142563	0.015663303434848785	0.03738527372479439	-0.031655509024858475	0.12001754343509674	-0.05402498319745064	-0.03491818532347679	-0.03923358395695686	-0.02467166818678379	-0.05343801528215408	0.04369919374585152	0.0019055521115660667	0.02288995496928692	0.08699580281972885	-0.01249644160270691	0.03200644627213478	0.004327965900301933	0.03847179561853409	0.026247583329677582	-0.15364018082618713	-0.02913549914956093	0.013898313976824284	-0.022326327860355377	0.0016083869850263	0.07111171633005142	-0.014961152337491512	0.04476138949394226	0.04807596281170845	-0.042440250515937805	-0.02939070388674736	0.03308522701263428	0.03774415701627731	0.02748236618936062	0.07777474075555801	0.07742570340633392	0.044747043401002884	0.023799432441592216	-0.013639057986438274	0.017068974673748016	-0.035657357424497604	-0.046721670776605606	0.017642516642808914	-0.0153645696118474	0.04060547426342964	-0.028368208557367325	-0.07591715455055237	-0.006474008783698082	0.03930053859949112	-0.0773458182811737	-0.016969338059425354	0.052996449172496796	-0.04548231512308121	0.11470165103673935	0.03241325542330742	-0.00756502291187644	0.027751803398132324	0.06516142934560776	0.042569391429424286	0.013439586386084557	0.011702383868396282	0.028851468116044998	-0.044078897684812546	-0.050033967941999435	0.047413747757673264	0.005481108091771603	-0.04626065492630005	0.05228932574391365	0.0701645016670227	-0.00038644333835691214	-0.028900552541017532	-0.04527664557099342	0.009767829440534115	-0.08119632303714752	-0.09337473660707474	0.008183252066373825	-0.06663170456886292	0.023220058530569077	0.0551433190703392	0.023687995970249176	0.039507072418928146	-0.016983455047011375	-0.016964992508292198	-0.03842238709330559	-0.06276700645685196	-0.0015637282049283385	-0.0676836222410202	-0.009077810682356358	-0.030235596001148224	-0.007221342530101538	0.005291094537824392	-0.028865965083241463	-0.042678721249103546	-0.017050640657544136	-0.01965237781405449	0.012281914241611958	0.10655248910188675	0.03941284865140915	0.008405040949583054	0.038958966732025146	-0.03379673510789871	-0.018637901172041893	-0.04043314605951309	0.003932121209800243	-0.06356744468212128	0.0306533295661211	-0.06122809275984764	-0.021594060584902763	-0.005872613284736872	0.024698033928871155	-0.015174261294305325	0.024160537868738174	6.42687373328954e-05	-0.019080135971307755	-0.044448837637901306	0.040424808859825134	0.08400765061378479	-0.011976927518844604	0.08674874156713486	0.02136171981692314	0.06443731486797333	-0.02384919300675392	-0.020052270963788033	-0.07078356295824051	0.04842438921332359	0.04140438139438629	0.03264879062771797	0.024881651625037193	-0.059018660336732864	0.10635944455862045	0.033681415021419525	0.020091185346245766	-0.056610096246004105	0.028496522456407547	0.06537744402885437	0.01825372874736786	0.06851174682378769	0.08452210575342178	-0.009590914472937584	-0.00824729073792696	-0.03339892253279686	0.017001444473862648	-0.0766715332865715	0.02383154071867466	0.025452595204114914	-0.07454611361026764	0.0449131615459919	-0.048854682594537735	-0.09798874706029892	-0.04941800981760025	-0.006933931261301041	0.0033645236399024725	-0.01807084120810032	-0.008174114860594273	0.020625632256269455	-0.010325209237635136	-0.029667267575860023	-0.028914785012602806	0.009263455867767334	-0.06044777110219002	0.09449724107980728	-0.02305775135755539	-0.07475469261407852	-0.04876060038805008	0.023347610607743263	0.018017183989286423	0.0021198007743805647	0.012752673588693142	0.05304330587387085	0.11190835386514664	0.036582592874765396	-0.012440089136362076	0.010918384417891502	-0.06433453410863876	-0.05126110464334488	-0.01425013318657875	0.016629653051495552	-0.0379871167242527	-0.057676851749420166	0.017691131681203842	-0.10221733897924423	-0.021671026945114136	-0.03179094195365906	0.04646662250161171	-0.017250286415219307	-0.014472724869847298	-0.052936356514692307	0.0484008714556694	-0.024985797703266144	-0.08231543749570847	0.03290897235274315	-0.036232661455869675	0.03552878275513649	-0.044212501496076584	-0.02027074247598648	0.026712793856859207	0.008670195937156677	0.0059667304158210754	-0.04639105126261711	0.05441892519593239	-0.03440894931554794	-0.059516895562410355	1.1193106729479041e-05	-0.05274629965424538	-0.055187374353408813	-0.009838538244366646	-0.0280289389193058	-0.019423551857471466	0.08955443650484085	0.028179701417684555	-0.03063477948307991	-0.05074406787753105	-0.030167635530233383	-0.03859884291887283	-0.025667987763881683	-0.0018739316146820784	0.017264554277062416	-0.07327894121408463	-0.031339388340711594	0.040397610515356064	-0.06426165997982025	-0.029796553775668144	0.06322070211172104	-0.03143783286213875	-0.05376805365085602	0.04799533635377884	-0.037145089358091354	-0.022341260686516762	0.015158087015151978	-0.06676052510738373	0.008240923285484314	0.09488385915756226	0.0332806222140789	0.025561334565281868	0.06612497568130493	0.015307879075407982	0.06468410044908524	-0.006325961090624332	-0.01951569691300392	0.006887928117066622	-0.010980930179357529	-0.06258101761341095	0.022128092125058174	-0.01886427029967308	-0.018671290948987007	0.0018708317074924707	-0.011485510505735874	0.050783295184373856	0.0032959713134914637	-0.04704924672842026	-0.023854579776525497	0.014492872171103954	-0.012189188972115517	0.04611814022064209	0.029468342661857605	0.003050287254154682	-0.017284438014030457	-0.07560896873474121	-0.03386632725596428	-0.07989779859781265	0.03831639513373375	0.011020385660231113	0.01742127165198326	-0.0009798379614949226	-0.04113144427537918	-0.018937276676297188	-0.0006319346721284091	0.11897547543048859	-0.09721207618713379	0.026180177927017212	0.03371676802635193	0.017097990959882736	-0.104343481361866	-0.013013744726777077	0.01795322075486183	-0.0555068701505661	-0.06166493892669678	0.015174861997365952	0.03534998372197151	-0.07373504340648651	-0.0361042320728302	0.008392926305532455	-0.03008045069873333	0.009283717721700668	0.04417242109775543	-0.0021449378691613674	0.0531744584441185	0.024588806554675102	-0.012798079289495945	0.00489170104265213	0.015640992671251297	0.03314073383808136	-0.020729705691337585	0.043098729103803635	-0.027642078697681427	0.0628322884440422	-0.03770485147833824	-0.06994961202144623	-0.058678001165390015	0.020790643990039825	-0.009980587288737297	-0.0021201777271926403	-0.04771191626787186	-0.0185853224247694	-0.008276484906673431	0.06624995172023773	0.018844489008188248	-0.055241916328668594	-0.006473463959991932	-0.029834508895874023	-0.053506769239902496	-0.080593541264534	-0.06259198486804962	0.030336447060108185	-0.07081810384988785	0.03891719505190849	-0.05181478336453438	0.003313326509669423	-0.004327013622969389	-0.007791308686137199	0.02065582387149334	0.046910662204027176	0.03263154253363609	-0.011929858475923538	0.040540438145399094	-0.014528901316225529	0.05826801806688309	0.007442181464284658	-0.06066051498055458	0.03754222020506859	0.03588389232754707	-0.04513755440711975	0.030657747760415077	-0.03479862958192825	-0.035387419164180756	0.009336795657873154	-0.046188972890377045	0.017269829288125038	0.0481589175760746	0.020355762913823128	-0.06035393103957176	0.04480340704321861	-0.05278230458498001	-0.03029012866318226	0.0223365630954504	-0.009938539005815983	-0.08278942108154297	-0.03377445787191391	-0.04148118197917938	-0.03295894339680672	0.06180369108915329	-0.011837616562843323	0.02136116474866867	0.022658806294202805	-0.061445605009794235	-0.036711666733026505	-0.01860130950808525	0.02680615708231926	0.008771735243499279	0.02394489012658596	0.05421144515275955	0.0024762621615082026	-0.009682429023087025	0.015250002034008503	-0.05219779908657074	0.033021241426467896	-0.040004223585128784	0.005185317248106003	-0.0029210697393864393	-0.010404017753899097	0.02149873413145542	0.021482447162270546	0.03469950705766678	-0.07575228065252304	-0.025687962770462036	-0.050987593829631805	0.0463455393910408	0.010411393828690052	0.02897750958800316	0.027686912566423416	-0.06353110820055008	-0.024535423144698143	0.01145838387310505	-0.012891046702861786	-0.0756160318851471	-0.002757698530331254	-0.05781546235084534	0.02500024251639843	-0.007200880907475948	0.026446806266903877	-0.016135169193148613	-0.0928252562880516	-0.00917865615338087	0.008253121748566628	-0.02594282478094101	-0.004085086286067963	0.036270368844270706	-0.030678074806928635	-0.04992152750492096	-0.05247153341770172	-0.06347782164812088	0.08534131199121475	0.03799210488796234	0.005570146720856428	-0.040383823215961456	-0.013593751937150955	-0.008371053263545036	-0.030443593859672546	-0.016878213733434677	-0.015063348226249218	-0.06546422094106674	-0.025802984833717346	0.09467973560094833	0.014583192765712738	0.01878940872848034	0.0888950377702713	-0.04706249758601189	0.031103042885661125	0.009088762104511261	0.017659563571214676	0.015650805085897446	-0.014089543372392654	-0.03538508713245392	0.06353335082530975	-0.052034586668014526	0.07010389119386673	-0.044520895928144455	0.09633064270019531	0.01791374571621418	-0.05214747041463852	0.071225106716156	0.09290729463100433	0.035101268440485	0.004414577968418598	-0.05667106434702873
+0.048144806176424026	-0.019100923091173172	-0.04245712235569954	0.041414253413677216	-0.07867791503667831	-0.046391811221838	-0.0029581543058156967	0.03275011479854584	-0.003258496057242155	-0.036743275821208954	-0.030806099995970726	-0.0032855463214218616	0.023901253938674927	0.011875293217599392	0.02446836419403553	-0.013881511054933071	-0.042993441224098206	0.04775103181600571	-0.0296819768846035	-0.027781443670392036	0.024185147136449814	-0.004883718676865101	-0.057417478412389755	0.04287285730242729	0.009463201276957989	0.02286001667380333	-0.03425728529691696	-0.004267587326467037	0.0075304233469069	0.024421878159046173	0.03378923237323761	-0.015676649287343025	0.044474709779024124	-0.04919963702559471	-0.08255418390035629	0.051451798528432846	-0.04902966693043709	0.11973759531974792	0.004257795866578817	-0.020505113527178764	-0.10274755954742432	0.007517049554735422	-0.0781988799571991	-0.05181800574064255	0.02556184120476246	0.021964672952890396	0.02221655659377575	0.02113935351371765	0.032546266913414	0.036731332540512085	0.03784154728055	0.009123965166509151	-0.005803869571536779	-0.014365668408572674	-0.017210233956575394	0.02441331185400486	0.07089094817638397	-0.044370368123054504	-0.04753175750374794	0.03237810358405113	-0.0789111778140068	-0.01455352921038866	0.023259608075022697	0.001699063228443265	-0.010275019332766533	-0.04376908391714096	-0.010328381322324276	0.07352631539106369	-0.0022743339650332928	-0.022578299045562744	-0.00383388320915401	0.07292548567056656	-0.04144289344549179	-0.045499611645936966	0.04667845368385315	-0.0715189278125763	-0.046921730041503906	0.0469500757753849	-0.012890709564089775	-0.01247342862188816	-0.02358955144882202	-0.04597220942378044	0.13744989037513733	-0.030849149450659752	-0.08349799364805222	-0.044194359332323074	-0.01346072368323803	-0.040922071784734726	0.025971664115786552	0.005647222511470318	-0.008088517934083939	0.06062119081616402	-0.07147137075662613	0.04511665180325508	0.033986158668994904	0.008241464383900166	0.020296745002269745	-0.12493035942316055	-0.06206992268562317	0.01104728039354086	-0.016940070316195488	-0.029044976457953453	0.0301703792065382	0.00029649256612174213	0.035070233047008514	0.0020570431370288134	-0.05736977234482765	0.0023367018438875675	0.020655309781432152	0.026134449988603592	0.017681460827589035	0.05891092121601105	0.05724237486720085	0.025212183594703674	0.06446027010679245	-0.0015150259714573622	0.022735469043254852	-0.044052280485630035	-0.04173623025417328	-0.009645167738199234	0.009728310629725456	0.01658620871603489	0.01467494759708643	-0.06766017526388168	-0.047471847385168076	0.012906421907246113	-0.11508196592330933	-0.035187382251024246	0.040551088750362396	-0.044371992349624634	0.10751979798078537	0.041931625455617905	-0.045399297028779984	0.013507308438420296	0.03266078606247902	0.022874455899000168	0.012290101498365402	0.05205610394477844	-0.0046969265677034855	-0.04231973737478256	-0.0007725579780526459	0.042975109070539474	-0.015019801445305347	-0.08325338363647461	0.084532231092453	0.05329958721995354	0.05540981516242027	-0.05722620710730553	-0.07961253076791763	0.01015695184469223	-0.06639126688241959	-0.06123073026537895	-0.0017939741956070065	-0.05556250363588333	0.0004754101682920009	0.03093673661351204	0.005612998269498348	0.03387074172496796	-0.01651684194803238	-0.03232097998261452	-0.06452278047800064	0.0016003747005015612	-0.010162286460399628	-0.06897436827421188	-0.04657227173447609	-0.02735828049480915	-0.008433084934949875	0.03170819208025932	-0.06477063149213791	-0.028475841507315636	0.006543747615069151	-0.0030277532059699297	0.06938107311725616	0.0660436823964119	0.07527368515729904	0.004404367879033089	0.0006125581567175686	-0.05391161888837814	0.002609384013339877	-0.013927132822573185	0.022243039682507515	-0.02710963599383831	0.022163838148117065	-0.045689988881349564	0.03991681709885597	-0.0035446544643491507	0.055445458739995956	-0.029745599254965782	0.003136088140308857	-0.029953764751553535	-0.0667920932173729	-0.06104573607444763	0.04635849595069885	0.07691073417663574	-0.035317547619342804	0.0296760406345129	-0.05352392420172691	0.04176652058959007	-0.018955375999212265	-0.005820162128657103	-0.04444089159369469	0.055172935128211975	-0.020095953717827797	0.05788707360625267	0.01366511918604374	-0.08765972405672073	0.06552735716104507	-0.010990653187036514	0.012975476682186127	-0.06263651698827744	0.022938484326004982	0.0201222226023674	-0.025251833721995354	0.05187828838825226	0.07639583200216293	0.014095031656324863	0.0036412696354091167	0.010120905935764313	0.08199822902679443	-0.05527716875076294	-0.0033398251980543137	-0.008228366263210773	-0.09228431433439255	-0.0006947469664737582	-0.03292030096054077	-0.08199550211429596	-0.0360860750079155	0.06148059293627739	-0.02533186413347721	-0.019518354907631874	-0.035365913063287735	0.0088429544121027	0.022855553776025772	-0.052570000290870667	-0.015188680961728096	-0.012494917027652264	-0.07860749959945679	0.08394929766654968	0.039621222764253616	-0.09654679894447327	-0.05102325230836868	-0.022241832688450813	0.03517390415072441	-0.0446745902299881	0.04118359088897705	0.06905543059110641	0.05156034976243973	0.0026824427768588066	0.012630710378289223	0.010361069813370705	-0.03547344356775284	-0.047066789120435715	0.002429431304335594	0.06131473183631897	-0.04407908022403717	-0.008085604757070541	0.018814347684383392	-0.02250532992184162	0.00611493457108736	0.025860104709863663	0.008756608702242374	-0.03814113885164261	-0.05494096130132675	-0.02160690538585186	0.006360143423080444	-0.07390692085027695	-0.004468205384910107	-0.0004205034638289362	-0.01603434607386589	0.06978543847799301	-0.01411628257483244	-0.04010094702243805	0.02210710570216179	-0.002781517570838332	0.022334134206175804	-0.008698969148099422	0.0607539601624012	0.006993155926465988	-0.07794873416423798	0.004784658085554838	-0.048988908529281616	-0.06941163539886475	-0.04308820888400078	-0.016482798382639885	-0.0413832925260067	0.10709389299154282	0.019218934699892998	-0.06210221350193024	-0.08419349789619446	-0.024557296186685562	-0.02067956142127514	-0.07913044840097427	-0.007570027373731136	0.004582719411700964	-0.07356712967157364	-0.05575234070420265	0.023222506046295166	-0.020624244585633278	-0.0005409343866631389	-0.005453595891594887	-0.010378489270806313	-0.08556762337684631	0.04731570556759834	-0.009446056559681892	-0.04482020437717438	-0.0024179916363209486	-0.05287347361445427	0.019312407821416855	0.06671443581581116	0.04049798846244812	0.008820908144116402	0.012435702607035637	0.014701728709042072	0.1055290475487709	-0.022577812895178795	0.004536907654255629	-0.019801752641797066	-0.06471456587314606	-0.03734496608376503	0.04686249420046806	-0.04723554849624634	-0.024057229980826378	-0.011918325908482075	0.008932061493396759	0.0511290617287159	0.016635892912745476	-0.040571533143520355	-0.022060202434659004	0.020708689466118813	-0.010036516934633255	0.04632198065519333	-0.005285533145070076	0.0187616515904665	-0.047040775418281555	-0.06890830397605896	-0.036211613565683365	-0.0005720711196772754	0.007709725294262171	-0.010185377672314644	0.04596439003944397	-0.03991233929991722	-0.04610750824213028	0.005698904395103455	0.0030446345917880535	0.11743080615997314	-0.05636456608772278	-0.014340586960315704	0.0772806704044342	0.03264758735895157	-0.060820627957582474	0.019263168796896935	0.05732176825404167	-0.04819919168949127	-0.057232871651649475	-0.003984558861702681	0.02338126115500927	-0.06933937221765518	-0.0019913366995751858	0.0028303884901106358	-0.027475176379084587	-0.010749271139502525	-0.018818529322743416	0.024825016036629677	0.011281303130090237	-0.03054727241396904	0.001896269735880196	0.02195027284324169	0.013485191389918327	0.038642220199108124	-0.040519390255212784	-0.001999716740101576	-0.005585517268627882	0.04975193366408348	-0.07363435626029968	-0.018091348931193352	-0.048238176852464676	0.04953186586499214	0.00362587021663785	-0.047294531017541885	-0.045892976224422455	-0.033857304602861404	-0.02191103994846344	0.01662810519337654	0.015721343457698822	-0.06075075641274452	-0.04385434463620186	-0.0670037493109703	-0.06566570699214935	-0.09068235754966736	-0.028300898149609566	0.013319795951247215	-0.059927668422460556	0.0026102594565600157	-0.03720065951347351	-0.03221197426319122	0.017356613650918007	-0.00010491342982277274	0.011646241880953312	0.06713851541280746	0.027419015765190125	-0.021881770342588425	0.05615582317113876	-0.013050669804215431	0.05201290547847748	0.03773154318332672	-0.034884724766016006	-0.005767901428043842	0.026501985266804695	-0.07995995879173279	0.0715007483959198	-0.04119817167520523	0.025873389095067978	-0.00759505107998848	0.011772851459681988	0.017766328528523445	-0.010693274438381195	0.039811182767152786	-0.02870248816907406	0.057082440704107285	-0.07633154094219208	-0.037676889449357986	0.038954462856054306	-0.03877781331539154	-0.07651623338460922	-0.04005095735192299	0.009377709589898586	-0.002963973442092538	0.035980578511953354	-0.019818201661109924	0.047640375792980194	0.03937634825706482	-0.060214072465896606	-0.08533738553524017	-0.046412404626607895	-0.038091741502285004	0.060129471123218536	-0.029823170974850655	0.036581531167030334	0.05086962878704071	0.007201236207038164	0.008804356679320335	0.020229240879416466	-0.01973884552717209	-0.08454607427120209	-0.01263557467609644	-0.02110137976706028	-0.04899544641375542	0.011874166317284107	-0.037027448415756226	0.005494742188602686	-0.014619878493249416	0.008614093996584415	-0.049671247601509094	0.09343526512384415	0.01245428528636694	-0.03742663189768791	0.047261793166399	-0.008638918399810791	0.04465189948678017	0.04614326357841492	-0.059295158833265305	-0.0549456924200058	-0.008093374781310558	-0.003249769564718008	0.05373638868331909	0.06300471723079681	0.01137025561183691	-0.01350195799022913	-0.03522834926843643	0.010251945815980434	-0.004468585830181837	-0.08848161995410919	0.0357978530228138	0.0259291660040617	-0.07036865502595901	-0.07059943675994873	-0.002091885544359684	-0.04488997533917427	0.06317292898893356	0.017689062282443047	0.0653047040104866	-0.03485124930739403	-0.0019423221237957478	-0.03461223468184471	-0.03381861373782158	0.02191409468650818	-0.0024572706315666437	-0.04153189808130264	-0.006204982288181782	0.07750015705823898	-0.05553804710507393	0.009971985593438148	0.041217368096113205	-0.0408967062830925	0.035009246319532394	0.021179091185331345	-0.05263879895210266	0.08503679186105728	0.00105155142955482	-0.05088493227958679	0.05740053206682205	-0.08301126956939697	0.09950702637434006	-0.0503811314702034	0.07790222764015198	0.003750374773517251	-0.0060047549195587635	0.09607662260532379	0.06627747416496277	-0.01723134145140648	0.013343779370188713	-0.06820163875818253
+0.06166790798306465	0.011693333275616169	0.052497897297143936	0.07889555394649506	-0.06806899607181549	-0.028999650850892067	-0.028495533391833305	-0.011422603391110897	0.02251611277461052	-0.018572146072983742	-0.011912097223103046	-0.026799174025654793	-0.006368245929479599	0.025210101157426834	0.02576645463705063	-0.045861344784498215	-0.07166305929422379	-0.0021029971539974213	-0.008549454621970654	-0.01812744140625	0.06933361291885376	0.02157784067094326	-0.08088299632072449	0.06336218118667603	-0.010557729750871658	0.05104433000087738	-0.048225823789834976	0.029784323647618294	-0.0038603420834988356	-0.016486147418618202	0.06393541395664215	0.002259714063256979	0.05559417977929115	-0.10660682618618011	-0.029186127707362175	0.02660088986158371	-0.028426339849829674	0.08259152621030807	0.057928264141082764	-0.04941440746188164	-0.05266762897372246	0.03049509972333908	-0.03743444010615349	-0.0724809467792511	0.013651804998517036	0.008531881496310234	-0.002888638759031892	-0.0183426421135664	0.04424780234694481	0.02347703091800213	0.10852309316396713	-0.01428813673555851	-0.05164841562509537	-0.0398511067032814	0.00762005802243948	-0.001225562416948378	0.08594706654548645	-0.0911545604467392	-0.06015894189476967	0.039249807596206665	-0.07582806795835495	0.00033810458262450993	0.008700896054506302	-0.001301608164794743	-0.0406804196536541	-0.022971436381340027	0.008564271032810211	0.06648003309965134	0.017086884006857872	-0.028728503733873367	0.06164881959557533	0.11754018068313599	-0.08193793147802353	-0.0128550473600626	0.04844960942864418	-0.05768295377492905	-0.012885636650025845	0.0716509148478508	-0.021367721259593964	0.02642430178821087	0.029881460592150688	-0.04779680073261261	0.074109748005867	-0.015422963537275791	-0.027009833604097366	-0.04113554581999779	0.03059985488653183	-0.05353430286049843	0.03687383607029915	0.0488971509039402	-0.009000810794532299	0.05577302351593971	-0.0391668863594532	0.0840570405125618	0.026601243764162064	0.04045882076025009	0.019213905557990074	-0.10831437259912491	-0.028190387412905693	0.011610872112214565	0.00489717535674572	-0.010209978558123112	0.035214636474847794	-0.015200220048427582	0.0697450265288353	0.014227138832211494	-0.06623609364032745	0.009079858660697937	0.033920880407094955	0.06052640825510025	0.08710575103759766	0.057551056146621704	0.06835207343101501	0.05915907397866249	0.0368732213973999	-0.011331983841955662	0.02603287249803543	-0.06441758573055267	-0.019780095666646957	0.04101593419909477	0.00733037106692791	0.041193701326847076	-0.03839228302240372	-0.06648484617471695	-0.023087380453944206	0.019173868000507355	-0.061229292303323746	-0.04502038285136223	0.05519219487905502	0.0012465812033042312	0.1315569430589676	0.061767466366291046	-0.02988165244460106	0.019401950761675835	0.036402132362127304	0.06244584918022156	0.0007746188784949481	0.03903907537460327	0.004501155111938715	-0.012115840800106525	-0.0021168412640690804	0.038558244705200195	0.02423687092959881	-0.08411621302366257	0.045667216181755066	0.0321517176926136	0.029602443799376488	-0.014729480259120464	-0.06516596674919128	-0.04037334397435188	-0.06559830158948898	-0.04063204303383827	0.008757228031754494	-0.08834757655858994	0.007272313814610243	0.05461778864264488	0.025080809369683266	0.05455123633146286	-0.0312957763671875	-0.023336950689554214	-0.0521562360227108	-0.046304501593112946	-0.01042885147035122	-0.07465384900569916	-0.04242270067334175	-0.052798181772232056	0.03387027233839035	0.021536096930503845	-0.04631664231419563	-0.01976107433438301	-0.019595174118876457	-0.00901008676737547	0.04231880605220795	0.03056819550693035	0.07733045518398285	-0.00211473205126822	-0.0033176077995449305	-0.04071380943059921	0.03152594342827797	-0.017385663464665413	0.008931273594498634	-0.04995638132095337	0.0011282601626589894	-0.07752649486064911	-0.023507598787546158	0.009307594038546085	0.01729401759803295	-0.01969204470515251	-0.005389743950217962	0.02580961026251316	-0.0704643651843071	-0.030077356845140457	0.05903511494398117	0.07225551456212997	-0.006739452481269836	0.03718552365899086	-0.04148407280445099	0.023544125258922577	0.015087132342159748	-0.03928450122475624	-0.05480989068746567	0.04100075736641884	-0.009161454625427723	0.06934763491153717	0.0011660447344183922	-0.06994960457086563	0.05022682994604111	0.02596302144229412	0.02089783363044262	-0.06091925501823425	-0.006025549955666065	0.006206787656992674	0.012763666920363903	0.025461355224251747	0.0750761330127716	-0.015537980012595654	-0.009848256595432758	0.0005628187791444361	0.05007492005825043	-0.05753891170024872	0.042190950363874435	0.002065646229311824	-0.06207270920276642	0.06123502925038338	-0.05956573411822319	-0.08562503755092621	-0.04901248216629028	0.011742068454623222	0.010238660499453545	-0.035533156245946884	0.026737630367279053	0.010490464977920055	-0.0181215051561594	-0.029254380613565445	-0.014845914207398891	-0.015146015211939812	-0.11049097031354904	0.11327250301837921	0.020785003900527954	-0.03747142478823662	-0.05892249569296837	0.021866247057914734	0.03316837176680565	0.00716905202716589	0.02134980633854866	0.025697197765111923	0.08225328475236893	0.02426164411008358	-0.0012009702622890472	0.012202925980091095	-0.061805810779333115	-0.09186267852783203	0.012815681286156178	0.039055053144693375	-0.03244036063551903	-0.011546247638761997	-0.029462309554219246	-0.03342677280306816	0.012500688433647156	-0.002372774062678218	-0.015458657406270504	0.006605468690395355	0.003431574674323201	-0.0360226184129715	0.06553523242473602	-0.026034492999315262	-0.03082948736846447	0.0445462130010128	0.01861731708049774	0.07324452698230743	-0.0011990888742730021	-0.02382637746632099	0.05424615740776062	0.0019156564958393574	0.04041829705238342	-0.010037598200142384	0.048997070640325546	-0.003169671632349491	-0.09095966070890427	-0.024618815630674362	-0.07218468189239502	-0.030771875753998756	-0.011455265805125237	-0.021883120760321617	-0.027607522904872894	0.08979111164808273	0.0016782002057880163	-0.051745034754276276	-0.052044812589883804	-0.002045574365183711	0.002812156919389963	-0.07775624096393585	-0.038280583918094635	0.007901244796812534	-0.08182450383901596	-0.01574297808110714	0.023158224299550056	-0.057560212910175323	-0.0422821044921875	-0.03449871391057968	-0.05327046662569046	-0.05690564960241318	0.03978174924850464	-0.03141385689377785	-0.05211694538593292	-0.001975425286218524	-0.08185871690511703	0.05797494575381279	0.09504148364067078	0.01086448971182108	0.03326128050684929	0.012154112569987774	0.030519142746925354	0.12156622111797333	-0.009590457193553448	-0.0487486869096756	0.0020297023002058268	-0.06679333746433258	-0.07718895375728607	0.06803667545318604	-0.04093076288700104	0.01568625122308731	-0.01112492848187685	-0.005880509503185749	0.07901905477046967	-0.015814607962965965	-0.01653626188635826	-0.008436361327767372	0.02792069874703884	-0.023294838145375252	-0.004814173560589552	0.0036926560569554567	0.0020193178206682205	-0.008910936303436756	-0.04812011867761612	-0.06148704141378403	-0.0325247161090374	0.011906982399523258	-0.022693803533911705	0.04160613194108009	-0.0016422158805653453	-0.06690315902233124	0.009391793981194496	0.007540345191955566	0.09856108576059341	-0.06311894208192825	0.016019515693187714	0.019487114623188972	0.0035588431637734175	-0.08490679413080215	-0.006992565002292395	0.03649758920073509	-0.06023844704031944	-0.010929598473012447	-0.0047737788408994675	0.01723180152475834	-0.05835516005754471	-0.0036740878131240606	0.012893103063106537	-0.03498067334294319	0.025646861642599106	0.044927164912223816	0.032214853912591934	0.013192807324230671	-0.028681514784693718	-0.012212969362735748	-0.024854442104697227	-0.01132711861282587	0.0004960434744134545	-0.019497910514473915	0.015981819480657578	-0.049510229378938675	0.03133847936987877	-0.025399239733815193	-0.004311972763389349	-0.05748334527015686	0.03829655051231384	0.015139843337237835	-0.010594320483505726	0.01810550130903721	-0.013922919519245625	-0.011890137568116188	0.005813860334455967	0.05800139158964157	-0.058569636195898056	-0.03160485625267029	-0.05382696911692619	-0.03380041569471359	-0.09119414538145065	-0.028431927785277367	0.012616627849638462	-0.08136559277772903	0.006029264070093632	-0.03065584972500801	-0.003217133926227689	0.014694740064442158	-0.03850378096103668	0.01722811348736286	0.034700069576501846	0.021009933203458786	-0.048191137611866	0.009739519096910954	-0.02260490134358406	0.04639504849910736	0.03746413439512253	-0.03016497939825058	-9.890022192848846e-05	0.0605015829205513	-0.014429772272706032	0.01914120279252529	-0.011515620164573193	0.0024821748957037926	0.019617240875959396	-0.011955899186432362	0.02506416290998459	-0.006426233798265457	-0.013139111921191216	-0.013478024862706661	0.043429382145404816	-0.04670011252164841	-0.04321502521634102	0.0055967229418456554	-0.04917977750301361	-0.12505294382572174	-0.028833338990807533	0.0007157349609769881	-0.0410749614238739	0.004651614464819431	0.0010503075318410993	0.03691604360938072	0.040735747665166855	-0.04324088618159294	-0.026703299954533577	-0.004100842867046595	-0.03173045814037323	0.04817083477973938	-0.0287069883197546	0.06517625600099564	0.008862129412591457	0.001963646151125431	0.04830875247716904	0.023892715573310852	-0.017254438251256943	-0.1124362125992775	-0.0191058237105608	-0.033567819744348526	-0.03522316366434097	0.03969033807516098	-0.008449063636362553	-0.002863833913579583	-0.06075350195169449	-0.04887525364756584	-0.03260596841573715	0.09396494179964066	-0.01794763095676899	-0.006306944880634546	0.04665762558579445	-0.03841919079422951	0.008640731684863567	0.029520414769649506	-0.04800225421786308	-0.08143705129623413	-0.003196097444742918	-0.010398655198514462	0.01919568143785	0.014846508391201496	0.0034964310470968485	-0.010901560075581074	-0.06958959996700287	-0.015611746348440647	0.019235774874687195	-0.07824958115816116	0.013853560201823711	-0.011888385750353336	-0.058767035603523254	-0.07789920270442963	0.017435168847441673	-0.06983664631843567	0.049468591809272766	0.03903521969914436	0.036180440336465836	0.02277330681681633	-0.005557594355195761	0.01843571662902832	-0.0578950010240078	-0.007931963540613651	0.020230043679475784	-0.020757216960191727	-0.005574349779635668	0.06282823532819748	-0.017962893471121788	0.020135335624217987	0.08012110739946365	-0.022989902645349503	0.020338168367743492	0.044027991592884064	-0.03928869217634201	0.0013109237188473344	-0.04629892855882645	-0.0564824603497982	0.09490388631820679	-0.05696076527237892	0.1032288521528244	-0.06878861039876938	0.08540365844964981	-0.018477989360690117	-0.015121670439839363	0.06845457851886749	0.07515658438205719	-0.02368760108947754	0.015404474921524525	-0.039421383291482925
+0.04930616915225983	0.009347820654511452	-0.0021885258611291647	0.026871947571635246	-0.05663895234465599	-0.05628003925085068	-0.013039532117545605	0.011938372626900673	0.006371914874762297	-0.04346277937293053	-0.0315178781747818	-0.023652929812669754	-0.01846667006611824	-0.014773618429899216	0.06387045979499817	-0.060448914766311646	-0.06890662759542465	0.008852435275912285	-0.009951679967343807	-0.01326035987585783	0.05699713155627251	0.021408595144748688	-0.05670272186398506	0.08690112084150314	0.022843820974230766	0.021870410069823265	-0.016233408823609352	0.009539203718304634	0.0010175439529120922	0.03464769944548607	0.07158225029706955	-0.02566348761320114	0.027371954172849655	-0.054546765983104706	-0.0036636318545788527	0.04083116725087166	-0.022268464788794518	0.10846813768148422	0.047369543462991714	-0.020354995504021645	-0.040938861668109894	-0.0018995836144313216	-0.06451382488012314	-0.06276101619005203	0.02078811451792717	0.010079087689518929	-0.010590038262307644	-0.031701087951660156	0.049533769488334656	0.0412658154964447	0.09642673283815384	-0.012263336218893528	-0.06978052109479904	-0.03248398378491402	0.013732130639255047	0.016439665108919144	0.05281391739845276	-0.06842154264450073	-0.0397074930369854	0.040397632867097855	-0.08764942735433578	0.0018834765069186687	0.02476908080279827	-0.00894763134419918	0.015506257303059101	-0.05639684200286865	-0.011143934912979603	0.06572834402322769	0.0010108735878020525	-0.028656035661697388	0.02954157255589962	0.12435322999954224	-0.10806623101234436	-0.05700797587633133	0.08955245465040207	-0.07103249430656433	-0.019054284319281578	0.030958550050854683	0.002531308215111494	0.013322346843779087	0.012209262698888779	-0.04170691594481468	0.11721162497997284	-0.05499548465013504	-0.059898678213357925	-0.046676862984895706	-0.007796258665621281	-0.04340476915240288	0.07917531579732895	0.04254746809601784	0.014393164776265621	0.05382683500647545	-0.04756869748234749	0.046834979206323624	-0.009971761144697666	-0.02193649299442768	0.0180191770195961	-0.08419261872768402	-0.04182765260338783	-0.020819278433918953	0.0026127882301807404	-0.022352347150444984	0.04805711284279823	-0.0008817785419523716	0.04660685360431671	0.030487477779388428	-0.06193162500858307	0.018128305673599243	0.04731820896267891	0.06801995635032654	0.03550636023283005	0.07215559482574463	0.0821528211236	0.062357135117053986	0.08065277338027954	-0.005360564682632685	0.008913044817745686	-0.04844757542014122	-0.04222307726740837	0.026546582579612732	-0.004725823178887367	0.054017048329114914	-0.006538516376167536	-0.0634882003068924	-0.014164609834551811	0.05584782361984253	-0.09863831847906113	-0.053327515721321106	0.040011096745729446	-0.010509157553315163	0.12932419776916504	0.04389398917555809	-0.024351004511117935	0.01207495667040348	0.059148430824279785	0.023281358182430267	-0.010956598445773125	0.05594810098409653	-0.023660972714424133	-0.0430142804980278	-0.008349557407200336	0.05689460039138794	0.010347165167331696	-0.04512033239006996	0.05689864978194237	0.03941458836197853	0.04052390530705452	-0.004857451654970646	-0.04496990889310837	-0.023154767230153084	-0.09655777364969254	-0.04908746853470802	0.03789355605840683	-0.07086357474327087	-0.027072498574852943	0.010208841413259506	0.020177138969302177	0.020092127844691277	-0.014334369450807571	-0.02258923649787903	-0.06547441333532333	-0.056427083909511566	-0.038275670260190964	-0.06936365365982056	-0.043122515082359314	-0.019201714545488358	0.013809422962367535	0.015329211950302124	-0.04980026185512543	-0.0447855070233345	-0.006946861743927002	-0.027614492923021317	0.04733254387974739	0.07501131296157837	0.056407228112220764	0.017796341329813004	-0.00035558917443268	-0.0539579875767231	0.00674659525975585	-0.02142775058746338	0.033972736448049545	-0.06354312598705292	0.04435648024082184	-0.041568316519260406	0.005401270929723978	-0.005599658470600843	0.04388526454567909	-0.017615649849176407	-0.003248739754781127	0.009959914721548557	-0.048789508640766144	-0.05392387881875038	0.06312086433172226	0.0739530473947525	-0.027701733633875847	0.0021834929939359426	-0.010668110102415085	0.03578787297010422	0.01142068300396204	-0.03339572250843048	-0.07628597319126129	0.006795193068683147	0.016438903287053108	0.0649767816066742	0.04054781422019005	-0.07669702172279358	0.08878756314516068	0.016963886097073555	0.03783164545893669	-0.052065134048461914	-0.004189903382211924	0.01520785503089428	-0.02932155318558216	0.014973897486925125	0.05588485673069954	-0.026904743164777756	-0.0004102730890735984	-0.01024543959647417	0.06827513128519058	-0.04741766303777695	-0.01408403366804123	0.0072593349032104015	-0.09633681178092957	0.043363142758607864	-0.03407924994826317	-0.07428426295518875	-0.038696371018886566	0.034639015793800354	-0.0013424811186268926	-0.0361606664955616	-0.0162542425096035	0.00734261330217123	0.008722509257495403	-0.01289808563888073	0.02573985420167446	-0.003141364548355341	-0.11333317309617996	0.12474822252988815	-0.01623276062309742	-0.06337074935436249	-0.07214493304491043	0.003497558180242777	0.04597444832324982	-0.0031703589484095573	0.04347560182213783	0.05063868686556816	0.06746667623519897	0.015919523313641548	0.004179156851023436	0.004104635678231716	-0.02077501267194748	-0.04395761713385582	-0.011865551583468914	0.027554892003536224	-0.048317328095436096	-0.06881360709667206	0.01050243154168129	-0.027491455897688866	0.010543891228735447	-0.019576771184802055	0.019871974363923073	-0.03586031496524811	-0.028816426172852516	-0.04029109701514244	0.054060198366642	-0.03322276845574379	-0.021085619926452637	0.07036513090133667	-0.022107712924480438	0.04719410091638565	0.0012074074475094676	-0.013183939270675182	0.0206606462597847	-0.003348807105794549	0.00939912535250187	-0.03459413722157478	0.05340170860290527	-0.013449895195662975	-0.06306817382574081	-0.02186751551926136	-0.07185262441635132	-0.07440386712551117	-0.00393336359411478	-0.027373071759939194	-0.007897933013737202	0.07515691220760345	0.015752378851175308	-0.059169284999370575	-0.05535675212740898	-0.014972817152738571	-0.027539409697055817	-0.06638311594724655	-0.03290771692991257	-0.008450360037386417	-0.07918313890695572	-0.014315186068415642	0.034923769533634186	-0.04679371789097786	-0.017988521605730057	-0.010926004499197006	-0.007968035526573658	-0.05580560117959976	0.011016711592674255	-0.014446943067014217	-0.0345613993704319	-0.024981128051877022	-0.04232402145862579	0.05594997480511665	0.0836695209145546	0.0671781450510025	0.05402180552482605	0.02067655883729458	0.048149000853300095	0.12139581888914108	-0.00011729024845408276	-0.03248760476708412	-0.0060969325713813305	-0.06763791292905807	-0.07999049872159958	0.02981627732515335	-0.02526969648897648	-0.03168995678424835	-0.0030737617053091526	0.015786917880177498	0.05918039008975029	-0.023221293464303017	0.013316838070750237	-0.031006060540676117	0.03794890269637108	-0.0037720249965786934	0.006464477628469467	-0.015761997550725937	0.021538926288485527	-0.01892693154513836	-0.0641109049320221	-0.060274895280599594	-0.061726514250040054	0.047078587114810944	-0.0013915494782850146	0.06907918304204941	-0.01000136323273182	-0.06674282997846603	-0.048242565244436264	0.041277285665273666	0.11727005243301392	-0.035982776433229446	0.014129792340099812	0.05496704950928688	0.018567116931080818	-0.07513314485549927	0.013334346003830433	0.015332816168665886	-0.05055403336882591	-0.024094730615615845	-0.019900109618902206	0.06530439853668213	-0.06333831697702408	-0.021851837635040283	0.006482092197984457	-0.056620024144649506	0.007860985584557056	0.009842798113822937	0.011630087159574032	0.031939003616571426	-0.007267031352967024	-0.022507043555378914	0.003912780899554491	-0.007076622918248177	0.01663355715572834	0.005678264424204826	-0.007339420262724161	-0.043647993355989456	0.02491081692278385	-0.07195134460926056	-0.01755494251847267	-0.07059647887945175	0.06735876947641373	-0.03272687643766403	-0.0183743666857481	0.0016822079196572304	-0.002871995558962226	-0.011620732955634594	0.01737586036324501	0.03404546529054642	-0.05069020390510559	-0.014788150787353516	-0.0455935001373291	-0.03385566920042038	-0.07917074859142303	-0.02005752921104431	-0.023716900497674942	-0.05029163509607315	-0.0055681271478533745	-0.04178515821695328	0.013825612142682076	-0.004534497391432524	-0.007895706221461296	0.030132032930850983	0.026700377464294434	0.03852584585547447	-0.012645040638744831	0.031342506408691406	0.029112737625837326	0.05454830452799797	0.025964977219700813	-0.04992391914129257	-0.007270133588463068	0.029695143923163414	-0.043796174228191376	0.047658178955316544	-0.0324651375412941	0.009231195785105228	-0.024690499529242516	0.017939100041985512	0.014209895394742489	-0.037394095212221146	0.016224289312958717	-0.014809166081249714	0.03501301631331444	-0.06749914586544037	-0.05630934238433838	0.03107152320444584	-0.04376073554158211	-0.07658101618289948	-0.03205706179141998	-0.014666736125946045	-0.026434892788529396	0.023393215611577034	-0.04685329645872116	0.021876968443393707	0.03633597120642662	-0.0305007416754961	-0.04129296913743019	-0.01701413094997406	-0.04768209531903267	0.04554393142461777	-0.012458503246307373	0.05814635008573532	0.0006897908169776201	0.029139477759599686	0.014438038691878319	-0.01660097949206829	0.009070371277630329	-0.10823281854391098	0.012396941892802715	-0.035285841673612595	-0.015303021296858788	0.015137269161641598	-0.006655402947217226	0.041158527135849	-0.020914362743496895	-0.028846951201558113	-0.04193494841456413	0.11495617032051086	-0.023851851001381874	0.023181630298495293	0.023292211815714836	-0.033487092703580856	0.0010850203689187765	0.021853765472769737	-0.056822218000888824	-0.08543018251657486	-0.009986089542508125	-0.01692531257867813	0.011572725139558315	0.007360975258052349	0.006647039204835892	-0.009135429747402668	-0.047093138098716736	0.002048108959570527	0.016128113493323326	-0.041425466537475586	0.029286878183484077	0.004592734854668379	-0.05001302808523178	-0.0512765571475029	-0.006954136770218611	-0.05742834135890007	0.05004969239234924	0.0050849393010139465	0.04546164721250534	0.0016174581833183765	0.0067800008691847324	0.021475741639733315	-0.013666943646967411	0.005120750516653061	0.009635942056775093	-0.04693318530917168	0.0033950137440115213	0.06884579360485077	-0.032431021332740784	0.0026473691686987877	0.06569629907608032	-0.01965961419045925	0.023044487461447716	0.06768102943897247	-0.03678297996520996	0.03676407411694527	-0.02485736273229122	-0.02745893970131874	0.09174386411905289	-0.08032701909542084	0.1056695207953453	-0.07558128982782364	0.08571743965148926	-0.010128285735845566	-0.03663543984293938	0.09454535692930222	0.07130864262580872	-0.008778347633779049	0.050020165741443634	-0.07558242231607437
+0.02219366282224655	-0.034046098589897156	-0.02229766920208931	0.01850266195833683	-0.07905048131942749	-0.03853502497076988	-0.021443869918584824	0.06570754200220108	-0.019494015723466873	-0.07053660601377487	0.010530559346079826	0.0016647815937176347	-0.014723041094839573	0.03077513910830021	0.060160957276821136	-0.051846735179424286	-0.06121203675866127	-0.0003842381411232054	-0.056012433022260666	-0.044335100799798965	0.049252837896347046	0.007049639709293842	-0.05022628605365753	0.051016442477703094	-0.004169894848018885	0.0009083186741918325	-0.035286795347929	0.01639174483716488	0.020390398800373077	0.04225970059633255	0.05127115920186043	-0.04238584265112877	0.03666089475154877	-0.03712519630789757	-0.043400246649980545	0.07694888859987259	-0.057387206703424454	0.13642209768295288	0.04908858239650726	0.014429212547838688	-0.04920864477753639	-0.01379462145268917	-0.05572756379842758	-0.04214794561266899	0.055116698145866394	-0.027822639793157578	0.006706118583679199	-0.03345756605267525	0.026692675426602364	0.044732529670000076	0.057508014142513275	0.013883448205888271	0.00918615609407425	0.0064454153180122375	-0.0149705670773983	0.051505137234926224	0.013714153319597244	-0.04189019277691841	-0.045956630259752274	0.011388955637812614	-0.05820361524820328	-0.02120201103389263	-0.005362687632441521	0.008003751747310162	0.025946352630853653	-0.005892347078770399	-0.005873705726116896	0.07259437441825867	0.006255290471017361	-0.040602900087833405	-0.004464191850274801	0.09009513258934021	-0.08452192693948746	-0.0832461416721344	0.044130150228738785	-0.040893979370594025	-0.008731834590435028	0.002350960625335574	-0.01850776933133602	0.009673899970948696	-0.019301213324069977	-0.00045577099081128836	0.12635351717472076	-0.03348362445831299	-0.02102234773337841	-0.035112909972667694	-0.011928591877222061	-0.041850242763757706	0.0708179771900177	0.016192838549613953	0.036070264875888824	0.046370163559913635	-0.0625542402267456	0.0036543416790664196	0.0028819898143410683	0.0005886113503947854	0.011525669135153294	-0.11039461940526962	-0.025000527501106262	-0.03383147716522217	0.0014938995009288192	-0.018656687811017036	0.08961519598960876	-0.004807961639016867	0.06309188902378082	0.06464622169733047	-0.06342910975217819	0.007731047458946705	-0.002265120390802622	-0.002853386802598834	0.01036736462265253	0.1350371241569519	0.06775902956724167	0.038785409182310104	0.07250300794839859	-0.006903383880853653	-0.004290716722607613	-0.026016730815172195	-0.05284787341952324	-0.03128752112388611	-0.040010783821344376	0.05756714195013046	-0.014422319829463959	-0.04099968820810318	-0.024306895211338997	0.045033738017082214	-0.13299044966697693	-0.03092111647129059	0.07089506834745407	-0.04913908243179321	0.08178380131721497	0.06768520176410675	0.0023107407614588737	0.011820594780147076	0.08595901727676392	-0.015262709930539131	0.0038428993429988623	0.03786836937069893	-0.005073392763733864	-0.029940910637378693	-0.022207025438547134	0.0694366991519928	-0.0034201350063085556	-0.04566683992743492	0.0073112729005515575	0.0771557092666626	0.0025568511337041855	0.0004001874476671219	-0.07889622449874878	0.029931161552667618	-0.07105095684528351	-0.0818680077791214	-0.031245389953255653	-0.08865012973546982	0.01784895732998848	0.05610918626189232	0.03305957093834877	0.03821861743927002	0.015236679464578629	-0.021244114264845848	-0.04506517946720123	-0.06443533301353455	-0.003807189641520381	-0.0668841004371643	-0.019765248522162437	0.000810954486951232	0.006781850475817919	0.0004106691631022841	-0.026187827810645103	-0.020103592425584793	-0.017390387132763863	-0.02070344239473343	0.024683868512511253	0.04190476983785629	0.03623039647936821	0.02324015647172928	-0.009303084574639797	-0.07139088213443756	-0.017522649839520454	-0.029637634754180908	0.03861048072576523	-0.0806003212928772	0.018124191090464592	-0.05067793279886246	-0.018730055540800095	-0.011115627363324165	0.0041770595125854015	-0.006246611475944519	-0.01705964468419552	-0.030597126111388206	-0.034774571657180786	-0.06957511603832245	0.04467734321951866	0.06947480142116547	-0.04665931314229965	0.03622845932841301	0.0010646668961271644	0.052177540957927704	-0.05163230746984482	-0.02595425397157669	-0.07191238552331924	0.03486877307295799	0.033903542906045914	0.010072721168398857	0.015969784930348396	-0.05038004368543625	0.05358610302209854	-0.012019621208310127	0.036169759929180145	-0.06489253044128418	0.023645466193556786	0.029110660776495934	-0.005729798227548599	0.028529677540063858	0.02817707322537899	-0.05245530232787132	-0.032269176095724106	0.012258241884410381	0.045768119394779205	-0.07869308441877365	-0.02404508925974369	0.00636639166623354	-0.07375621795654297	0.021510766819119453	-0.004234989173710346	-0.07888039946556091	-0.016807597130537033	0.03178318589925766	-0.03574639931321144	-0.047651976346969604	-0.036800093948841095	0.04290749877691269	-0.014926728792488575	-0.034133266657590866	0.03470718115568161	-0.006636973470449448	-0.05172974616289139	0.0728432834148407	-0.010175340808928013	-0.05726328492164612	-0.07845820486545563	0.0056434995494782925	0.03493073210120201	-0.0007266185712069273	0.02574225515127182	0.03416731581091881	0.06243276223540306	0.014022143557667732	-0.041831523180007935	-0.011653201654553413	-0.030939484015107155	-0.00046481797471642494	-0.038273852318525314	0.02749701403081417	-0.0023670399095863104	-0.06696293503046036	0.028424853459000587	-0.05001075938344002	-0.01128422748297453	-0.005265925545245409	0.08510318398475647	-0.07117784023284912	-0.01359418872743845	-0.007386506535112858	0.0031396709382534027	-0.012864886783063412	-0.00864423718303442	0.004779060371220112	-0.05288948863744736	0.0378672294318676	-0.06192529946565628	-0.000579534622374922	0.01546450611203909	0.0064119333401322365	0.02397593855857849	-0.04565536230802536	0.052953463047742844	-0.04990890994668007	-0.0815870389342308	-0.060244180262088776	-0.06832119077444077	-0.06603546440601349	-0.006811206229031086	-0.026751434430480003	-0.015863312408328056	0.11787200719118118	0.051330287009477615	-0.03181786462664604	-0.0454835370182991	-0.056043464690446854	-0.0032213267404586077	-0.036149293184280396	0.030463932082057	-0.013090593740344048	-0.06821351498365402	-0.0015162911731749773	0.02659466862678528	-0.04383061081171036	0.008084763772785664	0.027543023228645325	0.00604085111990571	-0.028177805244922638	0.03869276121258736	-0.050653956830501556	-0.015886977314949036	-0.030629338696599007	-0.044175442308187485	0.03955088183283806	0.07748893648386002	0.08034195750951767	0.02314562350511551	0.00925462320446968	0.07417993992567062	0.08960919082164764	0.022078771144151688	-0.004443592857569456	-0.009898633696138859	-0.02234475128352642	-0.07913821935653687	0.06770852953195572	-0.048641279339790344	0.002300855703651905	-0.0247911736369133	-0.024361127987504005	0.08325847238302231	-0.023667143657803535	-0.006491003092378378	-0.0006183445802889764	0.053413063287734985	0.03872797638177872	0.03841577097773552	-0.0018579872557893395	-0.029538357630372047	0.02443162351846695	-0.05170494690537453	-0.06682068854570389	-0.08430729806423187	0.05271908640861511	-0.007111468352377415	0.06111588329076767	0.005582261830568314	-0.07815329730510712	0.01911298930644989	0.04363682121038437	0.09793170541524887	-0.04987027123570442	0.018144017085433006	0.02423517405986786	0.019512221217155457	-0.10211368650197983	-0.009493918158113956	-0.01286599412560463	-0.026382848620414734	-0.08875937759876251	0.02670319378376007	0.06747186928987503	-0.05049997568130493	-0.05888913944363594	-0.03476489707827568	-0.05075981840491295	-0.017964810132980347	0.014849592931568623	-0.02081441506743431	0.06019173935055733	0.016087569296360016	-0.0389377698302269	0.04347073286771774	0.04310853034257889	0.0302947498857975	-0.05164996534585953	0.02696867845952511	-0.02333674021065235	0.09501055628061295	-0.06036490201950073	-0.05684177204966545	-0.055981289595365524	0.058418888598680496	-0.043088190257549286	-0.034620948135852814	-0.08578818291425705	-0.020561980083584785	-0.03708820044994354	0.016751835122704506	-0.00953117199242115	-0.052764758467674255	-0.028657812625169754	-0.003861986333504319	-0.06393416970968246	-0.07955361157655716	-0.04741058498620987	0.02405228093266487	-0.02006651647388935	0.008644656278192997	-0.05073947831988335	-0.017850231379270554	0.014870189130306244	-0.008462286554276943	0.03327593579888344	0.03746334835886955	0.06331979483366013	-0.02719702199101448	0.08188489079475403	0.03619372844696045	0.020831547677516937	-0.007157124578952789	0.0035635028034448624	0.023716295138001442	-0.00017649929213803262	-0.04734514281153679	0.02548026852309704	-0.04816208779811859	0.0247646514326334	-0.045954424887895584	-0.03250887617468834	0.004644031636416912	-0.0022097239270806313	0.0003870836226269603	-0.050191137939691544	0.05980631709098816	-0.09272918105125427	-0.009524824097752571	0.03375299647450447	-0.039597656577825546	-0.07775815576314926	-0.04622418433427811	-0.04713507369160652	-0.008462460711598396	0.03242216631770134	0.004697054624557495	0.006499066017568111	0.03033630922436714	-0.025945644825696945	-0.022373657673597336	-0.013899102807044983	-0.027926843613386154	-0.026754159480333328	0.024590836837887764	0.044700197875499725	0.006414681673049927	0.028112269937992096	0.024503162130713463	-0.018231874331831932	0.01017258781939745	-0.07108001410961151	-0.001912004896439612	0.02279968187212944	-0.003865598002448678	0.0090970229357481	0.010399511083960533	0.03836345672607422	-0.0482138954102993	-0.010001162067055702	-0.030403297394514084	0.07336357235908508	-0.0037990801502019167	0.03139914944767952	0.032487474381923676	-0.018207311630249023	-0.016204021871089935	-0.0014831522712484002	-0.03813774883747101	-0.08278144150972366	0.01274156104773283	-0.03357303515076637	0.026882166042923927	-0.031246548518538475	0.02183656394481659	0.014807746745646	-0.10060109943151474	0.0010525563266128302	-0.03868737444281578	-0.03763381764292717	0.03500999137759209	0.018062131479382515	-0.0562344528734684	-0.05548425763845444	-0.02171296998858452	-0.05533013492822647	0.042026031762361526	-0.049081992357969284	0.044778481125831604	-0.025724416598677635	-0.020589841529726982	-0.021534008905291557	-0.030313754454255104	0.03518417850136757	0.0104863615706563	-0.07492875307798386	-0.014312081038951874	0.0725930780172348	-0.025535330176353455	0.03121223673224449	0.07651755958795547	-0.023420225828886032	0.02131255529820919	0.03872980549931526	0.006962500046938658	0.009543020278215408	-0.0018102070316672325	-0.01113175693899393	0.035823266953229904	-0.03908994421362877	0.07209181785583496	-0.07522716373205185	0.10097682476043701	0.022877726703882217	0.01939868927001953	0.09507147967815399	0.055526088923215866	0.02414577268064022	0.03197800740599632	-0.05383278429508209
+-0.0052253613248467445	-0.05608852952718735	0.0019387784413993359	0.04408285394310951	-0.042936693876981735	-0.03730779141187668	-0.04446540027856827	0.03470800071954727	-0.0049643563106656075	-0.07120448350906372	-0.02484850212931633	-0.03368363529443741	-0.008609794080257416	-0.0030970554798841476	0.021699558943510056	0.005662594921886921	-0.046371083706617355	0.025220336392521858	-0.002673603128641844	-0.033094920217990875	0.017994213849306107	0.06632282584905624	-0.043529558926820755	0.05645693838596344	0.0056577627547085285	-0.02011720836162567	-0.034684713929891586	0.0386817641556263	0.01851169392466545	0.08334147185087204	0.029336588457226753	-0.06697108596563339	0.04602142423391342	-0.06572840362787247	0.001721301581710577	0.0377320721745491	-0.01253840234130621	0.07850629836320877	0.06506162881851196	-0.06406678259372711	0.01354339811950922	-0.0036793614272028208	0.03444499149918556	-0.08775666356086731	-0.0059132627211511135	-0.0024174670688807964	-0.04422620311379433	-0.05487164109945297	-0.0018089791992679238	0.08301583677530289	0.0837249755859375	0.0039407433941960335	-0.0034723819699138403	-0.018078694120049477	-0.012766425497829914	-0.031013110652565956	0.04815911129117012	-0.08013387024402618	-0.019621960818767548	0.04316376522183418	-0.057558823376894	-0.006632970180362463	0.04114188998937607	0.06985960155725479	-0.0337967574596405	-0.028701752424240112	0.03381947800517082	0.07601498067378998	0.03195649012923241	-0.02402709238231182	-0.002192440675571561	0.06152089685201645	-0.016574719920754433	-0.04400645196437836	-0.002574366284534335	-0.037142977118492126	0.006426700856536627	-0.0002505301090423018	0.014738345518708229	-0.00043033636757172644	0.05349745228886604	-0.05300768092274666	0.0954582616686821	-0.0037317457608878613	0.022419696673750877	-0.0278770849108696	-0.0028439387679100037	-0.07602956891059875	0.044650185853242874	-0.018382271751761436	0.05441022664308548	0.05116630718111992	-0.01801077462732792	0.04661724716424942	-0.019120942801237106	0.021232282742857933	0.02515237405896187	-0.11085198819637299	-0.008866298012435436	-0.00642765685915947	-0.02032109722495079	0.007809586822986603	0.04621065780520439	0.06848552823066711	0.007328598294407129	0.003701611887663603	-0.04282129928469658	0.014967226423323154	0.08051363378763199	0.05787161365151405	0.008632916957139969	0.10479390621185303	0.07749368250370026	0.03381888568401337	0.02567034214735031	-0.024139100685715675	-0.04058058559894562	-0.0013296774122864008	0.017335811629891396	0.02760547213256359	0.0318792462348938	0.020714743062853813	-0.04045001044869423	-0.10390540212392807	0.022384822368621826	0.11132640391588211	-0.05427474156022072	0.00981533620506525	0.010825586505234241	0.010902059264481068	0.09389066696166992	0.04043202847242355	-0.05815213918685913	0.052512336522340775	0.03614272549748421	0.047650519758462906	-0.049211304634809494	0.03495554253458977	0.010871660895645618	0.0011607601772993803	-0.0720539316534996	0.043521396815776825	0.03733733668923378	-0.051766738295555115	0.008395595476031303	0.020701047033071518	0.009828999638557434	-0.01599564030766487	-0.061753250658512115	-0.03145000711083412	-0.037660665810108185	-0.05296904966235161	0.04558132588863373	-0.08448762446641922	0.0015154429711401463	0.0246074628084898	-0.003514346433803439	0.014049246907234192	-0.028731420636177063	0.02573024109005928	-0.07210195064544678	-0.07542181015014648	0.017378821969032288	-0.0713711753487587	-0.02719104290008545	-0.00451828446239233	-0.0019245666917413473	-0.005458011757582426	-0.06466357409954071	-0.05282808467745781	-0.00838512647897005	-0.009671294130384922	0.05900868773460388	0.09222375601530075	0.07122942060232162	0.025785263627767563	0.04794757068157196	-0.023372121155261993	0.009744139388203621	-0.03459126129746437	-0.011902870610356331	-0.03433572128415108	0.019782917574048042	-0.07423978298902512	-0.056188665330410004	-0.010021486319601536	0.02341831475496292	-0.005390583071857691	-0.002605319954454899	-0.027302203699946404	-0.012745609506964684	-0.0445975624024868	0.03049832209944725	0.030933525413274765	0.00013086776016280055	0.03336108848452568	-0.021578693762421608	0.09762944281101227	-0.047533873468637466	-0.007215403951704502	-0.07688717544078827	0.056452322751283646	0.03292081132531166	-0.002042451174929738	-0.016973719000816345	-0.012341299094259739	0.07017731666564941	0.053348690271377563	0.020296473056077957	-0.06634635478258133	0.03670301288366318	0.07403501868247986	0.008123029954731464	0.05962303280830383	0.04763079807162285	-0.02829398773610592	-0.0140564339235425	0.0007263539591804147	0.030164668336510658	-0.06664151698350906	0.012442650273442268	0.0097213014960289	-0.07804404199123383	0.03002612106502056	-0.025682155042886734	-0.06136470288038254	-0.027498889714479446	-0.03553769737482071	0.008283491246402264	-0.05079100653529167	0.05045926198363304	0.022051822394132614	0.016689321026206017	0.02977355383336544	-0.02224096842110157	-0.049861107021570206	-0.04945744201540947	0.06927932798862457	-0.04626177251338959	-0.023348040878772736	-0.058978963643312454	-0.014696712605655193	-0.008000779896974564	-0.005631688050925732	0.0049618021585047245	0.04686763137578964	0.0779334008693695	0.07500269263982773	-0.03454330191016197	-0.005703352391719818	-0.060873545706272125	-0.10381175577640533	-0.036824941635131836	0.021380793303251266	-0.00575558515265584	-0.055746302008628845	0.00889640860259533	-0.12008746713399887	-0.01230952050536871	-0.027825428172945976	-0.0021351089235395193	-0.04859581217169762	0.008099676109850407	-0.05213461443781853	0.0461602658033371	-0.00785014871507883	-0.050597257912158966	0.038743339478969574	-0.0022141069639474154	0.031072579324245453	-0.0065289749763906	0.005025526974350214	0.09442512691020966	-0.014528971165418625	0.006718127056956291	-0.005377898458391428	0.07909567654132843	-0.013206757605075836	-0.03752872347831726	-0.040289390832185745	-0.06698253005743027	-0.05054449662566185	0.038268644362688065	-0.015062343329191208	-0.023381583392620087	0.04964155703783035	0.017355389893054962	-0.0190809965133667	0.0045939660631120205	-0.029280202463269234	-0.03544556349515915	-0.07132694125175476	-0.045003075152635574	0.0009768554009497166	-0.04039556160569191	-0.00278351828455925	0.013213090598583221	-0.04875706508755684	-0.04147220402956009	0.018076248466968536	0.010868340730667114	-0.04866955801844597	0.07358196377754211	-0.01579858362674713	-0.016291113570332527	0.03323865309357643	-0.05155176296830177	0.016669226810336113	0.05537565425038338	0.014444521628320217	0.0462246835231781	0.05513179302215576	0.022813918069005013	0.0754665806889534	0.009076658636331558	-0.027301345020532608	-0.03968893364071846	-0.014605757780373096	-0.041595835238695145	0.05923868343234062	0.00040271380566991866	-0.009983090683817863	0.04344482719898224	-0.014154249802231789	0.06999117136001587	0.0008238937007263303	-0.011698552407324314	-0.039854977279901505	0.05100119858980179	-0.05565916746854782	0.041763268411159515	-0.07230066508054733	0.0012913452228531241	-0.014640895649790764	-0.030948858708143234	-0.03317015990614891	-0.024523019790649414	0.051498301327228546	-0.05695652589201927	0.05246592313051224	0.01981407217681408	-0.05487740784883499	-0.0014869620790705085	0.004205092787742615	0.09032030403614044	-0.08027368038892746	0.0006018764688633382	0.008139422163367271	0.024044621735811234	-0.07972278445959091	-0.01513294130563736	-0.042619384825229645	-0.047214724123477936	-0.017808694392442703	-0.007172819226980209	0.0019827571231871843	-0.07601264119148254	-0.03646237030625343	-0.0297987200319767	-0.043540697544813156	0.037356000393629074	0.03413863107562065	0.006155065260827541	0.06206890940666199	0.02571670524775982	-0.05632326006889343	-0.03947296366095543	-0.012413491494953632	-0.02774306945502758	-0.02050809934735298	0.04065604507923126	-0.02187199518084526	-0.005514421500265598	-0.04107652232050896	-0.028771717101335526	-0.045697733759880066	0.016099972650408745	-0.04815886914730072	0.01582358591258526	0.032655227929353714	0.009475120343267918	0.030229780822992325	0.04726531729102135	0.02458852156996727	-0.028969405218958855	-0.03144758939743042	-0.011834262870252132	-0.0009628534317016602	-0.03175721317529678	-0.05014469847083092	0.04241471365094185	-0.028383437544107437	0.009792470373213291	-0.05324307456612587	0.024481462314724922	-0.0077438997104763985	0.00025206632562913	0.04008224233984947	0.04963160306215286	0.022649820894002914	-0.015171494334936142	0.018742568790912628	-0.02147740125656128	0.031321778893470764	0.04683127626776695	-0.05891122296452522	0.010635889135301113	0.03980059176683426	-0.035467393696308136	0.0006455901893787086	-0.039491865783929825	0.01677384227514267	0.01425385382026434	-0.0005750366835854948	0.04345262050628662	-0.07008417695760727	0.0037405521143227816	0.009666983969509602	0.06054720655083656	-0.042179785668849945	-0.05926686152815819	-0.04350174218416214	-0.021735621616244316	-0.10214519500732422	-0.031485579907894135	-0.019496750086545944	-0.011184255592525005	0.04897978901863098	-0.04074938967823982	0.020366521552205086	0.03675694018602371	-0.015386234037578106	-0.004665331915020943	0.04992296174168587	0.028321770951151848	-0.010686255991458893	0.06385979801416397	0.013842756859958172	-0.03745623305439949	-0.003035181201994419	0.004607164300978184	-0.060279954224824905	0.005599634721875191	-0.08041536808013916	-0.035579267889261246	0.03369130194187164	-0.012865654192864895	0.03582250699400902	0.052343886345624924	0.029522646218538284	-0.08534330874681473	-0.04944480210542679	-0.0066660367883741856	0.0671030580997467	-0.005076000466942787	0.03267602622509003	0.015628697350621223	-0.09797362983226776	-0.09953862428665161	0.053988777101039886	-0.03871631994843483	-0.10664238035678864	-0.050740502774715424	0.005808370653539896	0.06064251810312271	-0.023490628227591515	0.07157253473997116	-0.10196232795715332	-0.10369037091732025	0.014098911546170712	0.026992393657565117	-0.040637657046318054	-0.020751530304551125	0.03598292917013168	-0.05497080460190773	-0.008612561039626598	0.018674436956644058	-0.04970337077975273	0.008648755960166454	0.023317458108067513	0.012600753456354141	-0.06601255387067795	0.009964936412870884	-0.01934671960771084	-0.09549789130687714	0.043834272772073746	-0.009719417430460453	-0.046628210693597794	-0.06199333444237709	0.09799693524837494	-0.017446106299757957	0.025194695219397545	0.06852695345878601	-0.03517009690403938	0.05051891878247261	0.046630021184682846	0.004519984591752291	0.012248920276761055	-0.044001709669828415	-0.007953383028507233	0.07269534468650818	-0.05718564614653587	0.06359473615884781	-0.048230625689029694	0.13732153177261353	0.022711770609021187	-0.005037692375481129	0.05400790646672249	0.06278134882450104	0.04665597528219223	0.03933953121304512	-0.10070743411779404
+0.03252687305212021	0.02519146353006363	-0.0033353669568896294	-0.0022032451815903187	-0.10960739105939865	-0.06572362780570984	0.009752399288117886	0.05187203735113144	-0.03229423984885216	-0.05911986529827118	-0.03965000435709953	-0.05067944899201393	-0.0006943768821656704	0.016690775752067566	0.009753541089594364	-0.04812806844711304	-0.07431493699550629	0.01125173270702362	-0.02983906865119934	-0.043924689292907715	0.05998694896697998	-0.011553442105650902	-0.08881097286939621	0.04917460307478905	0.047943904995918274	-0.028199579566717148	-0.03458355367183685	0.00566559424623847	0.007745194248855114	0.020212315022945404	0.05888482183218002	-0.015782378613948822	0.048184752464294434	-0.030314791947603226	-0.03518673777580261	0.021000612527132034	-0.04254275932908058	0.09754230827093124	0.043423909693956375	-0.08556237071752548	-0.05446793511509895	0.0030957800336182117	-0.040545422583818436	-0.02341422624886036	0.02877533994615078	-0.010981692932546139	-0.06461072713136673	-0.03348030894994736	0.0460064634680748	0.040728725492954254	0.061705805361270905	-0.014715772122144699	-0.018195366486907005	-0.023229945451021194	-0.0025385671760886908	0.014016546308994293	0.00867865514010191	-0.03530283644795418	-0.05578817054629326	0.03826604783535004	-0.05902820825576782	0.020758947357535362	0.03756779804825783	0.02089783176779747	0.003909674007445574	-0.04710621014237404	-0.000998049508780241	0.10792865604162216	0.025894800201058388	-0.010359995998442173	-0.01691802218556404	0.07825223356485367	-0.06566283851861954	-0.022559039294719696	0.07917289435863495	-0.04363667592406273	-0.006910555996000767	0.0422927588224411	0.005654217675328255	0.0032181781716644764	-0.003938688896596432	-0.030794234946370125	0.12037953734397888	-0.03992769867181778	-0.04376719519495964	-0.04973805323243141	0.018155692145228386	-0.0623810850083828	0.08030860871076584	0.028936930000782013	0.008158468641340733	0.05039283260703087	-0.05824204534292221	0.05478953197598457	-0.015411310829222202	0.005886574741452932	0.020549342036247253	-0.13769789040088654	-0.05229926109313965	0.001350419595837593	0.014919876120984554	-0.02324051782488823	0.04240432009100914	-0.004179964307695627	-0.0012366482987999916	0.051613785326480865	-0.029419122263789177	0.032506123185157776	0.01594231091439724	0.029718831181526184	0.02433585748076439	0.08362635225057602	0.09756308048963547	0.04636207967996597	0.03010408952832222	-0.0029290709644556046	-0.017283190041780472	-0.01902535744011402	-0.024356357753276825	0.024050021544098854	0.002718805568292737	0.05480102449655533	-0.007594873663038015	-0.033545058220624924	-0.043705061078071594	0.08485490083694458	-0.06835722923278809	-0.02024967037141323	0.05386461317539215	-0.006202945951372385	0.08267228305339813	0.04371163621544838	-0.03419535234570503	0.03101402148604393	0.10064706951379776	0.010095511563122272	0.04911688715219498	0.031129296869039536	-0.06661132723093033	-0.057016801089048386	-0.04224997013807297	0.04422162473201752	-0.008887461386620998	-0.0866200402379036	0.018672356382012367	0.06696514785289764	0.039060868322849274	-0.02089342288672924	-0.055639855563640594	-0.01425536721944809	-0.09018970280885696	-0.09278880804777145	-0.01220704521983862	-0.1024073138833046	0.011306985281407833	0.04475271701812744	-0.015936503186821938	0.024788958951830864	-0.016321402043104172	0.017812024801969528	-0.04474516585469246	-0.05109491944313049	0.002467252081260085	-0.0697287917137146	-0.027155445888638496	-0.016969500109553337	0.007097275927662849	0.05404812842607498	-0.06387395411729813	-0.011421709321439266	0.024645453318953514	0.0010218028910458088	0.026309126988053322	0.060339078307151794	0.010729944333434105	0.0015650071436539292	0.023064499720931053	-0.019723141565918922	-0.030669452622532845	-0.02098444104194641	0.07338413596153259	-0.038168005645275116	0.06714221090078354	-0.05373143404722214	0.020559580996632576	-0.020781945437192917	0.025437723845243454	-0.05811062082648277	0.0013163505354896188	-0.021624496206641197	-0.03774157166481018	-0.0660783126950264	0.0310550257563591	0.044929374009370804	-0.022388456389307976	0.030538858845829964	-0.03418119251728058	0.04527977481484413	-0.02402222901582718	-0.009202007204294205	-0.04222489148378372	0.050130605697631836	0.016781894490122795	0.05983220040798187	0.024475008249282837	-0.09049833565950394	0.034869320690631866	0.0031795790418982506	0.013461877591907978	-0.04608927294611931	0.07058478891849518	0.03182898834347725	0.006414515897631645	0.05420158430933952	0.04441240057349205	-0.005256136413663626	0.01882762648165226	0.00016286715981550515	0.0803714320063591	-0.05839928239583969	0.029602820053696632	0.04887307435274124	-0.09743692725896835	0.04907212033867836	-0.035608600825071335	-0.06176886707544327	-0.0024612369015812874	0.027546683326363564	-0.04398789256811142	-0.061547424644231796	-0.020676473155617714	0.014453222043812275	-0.015491537749767303	-0.030115138739347458	0.004062331281602383	0.043247025460004807	-0.07987872511148453	0.07964406162500381	-0.00046659281360916793	-0.052191734313964844	-0.05445215851068497	0.0015620302874594927	-0.01440387312322855	0.004908960312604904	0.054233502596616745	0.044522613286972046	0.1087479367852211	0.011341662146151066	0.011250652372837067	-0.023299891501665115	-0.07439646869897842	-0.05806040018796921	-0.028940314427018166	0.039869822561740875	-0.045800771564245224	-0.06744205206632614	0.031820882111787796	-0.041555702686309814	-0.01519178505986929	-0.003144147340208292	0.03565663844347	-0.01684371940791607	-0.03222839906811714	-0.03566645830869675	0.039424117654561996	-0.03081457130610943	-0.04745585098862648	0.01702313870191574	-0.000496895459946245	0.0566340908408165	0.0009801579872146249	-0.03058927319943905	0.02910783141851425	0.013642344623804092	0.044307731091976166	-0.01620548963546753	0.06473081558942795	-0.04859231039881706	-0.056554049253463745	-0.044530682265758514	-0.07002358883619308	-0.08659791946411133	-0.021748656406998634	-0.04364408552646637	-0.003381875343620777	0.08883374184370041	0.020391404628753662	-0.05288583040237427	-0.06263695657253265	-0.03150622919201851	-0.044459160417318344	-0.05695153400301933	-0.03289797157049179	-0.017212187871336937	-0.054865628480911255	-0.016349023208022118	0.03322130814194679	-0.03822411224246025	-0.033803947269916534	0.043383318930864334	-0.002704241080209613	-0.0745467096567154	0.06983233988285065	-0.01583223044872284	-0.003044439246878028	0.03270250931382179	-0.049176402390003204	0.005940754432231188	0.08474726229906082	0.04153408110141754	0.007561301346868277	0.04920664057135582	0.06105537340044975	0.10431353002786636	0.01499154046177864	-0.06818970292806625	-0.024827685207128525	-0.06898818165063858	-0.08294776827096939	0.03437747061252594	-0.04056260362267494	-0.03740315139293671	-0.02629145234823227	-0.030754506587982178	0.07756148278713226	-0.034769970923662186	0.0017653024988248944	-0.04921860247850418	0.012256392277777195	-0.055048588663339615	0.022019390016794205	-0.002696244278922677	-0.028555534780025482	0.024330466985702515	-0.04133230075240135	-0.0374823696911335	-0.05453861504793167	0.009866793639957905	-0.019751641899347305	0.025231700390577316	-0.008115587756037712	-0.01653989776968956	0.020833777263760567	-0.014251425862312317	0.08061619848012924	-0.0476258359849453	0.020388241857290268	0.043040454387664795	0.008156835101544857	-0.05885445326566696	-0.015266784466803074	0.0013944467063993216	-0.006466625723987818	-0.0012677378254011273	-0.012114998884499073	0.06295731663703918	-0.05694980174303055	-0.015208998695015907	0.007160242181271315	-0.037519458681344986	-0.018555399030447006	0.002202289644628763	0.016548005864024162	0.05825328081846237	-0.02400066889822483	-0.018050532788038254	0.009028686210513115	-0.006677424535155296	0.010976425372064114	-0.013560541905462742	0.05876181647181511	-0.008347883820533752	0.06800109148025513	-0.037169262766838074	-0.06200598180294037	-0.07199550420045853	0.039380837231874466	-0.0365002378821373	-0.037952668964862823	-0.008115279488265514	0.0351436585187912	0.020217249169945717	0.05131089314818382	0.016264168545603752	-0.04537883773446083	-0.014154809527099133	-0.020441990345716476	-0.02031657285988331	-0.06606069952249527	-0.014127855189144611	-0.013670897111296654	-0.06210356578230858	-0.0028990814462304115	-0.04757225513458252	-0.010062675923109055	0.003157225204631686	-0.01630324497818947	0.02905150130391121	0.04209158569574356	0.03438480570912361	0.006295227445662022	0.04741862788796425	0.015201276168227196	0.014716926962137222	-0.005886461585760117	-0.053338825702667236	0.028568372130393982	0.032518159598112106	-0.02867359295487404	0.05961397662758827	-0.046423137187957764	0.0416608564555645	-0.04427973926067352	0.01941419579088688	0.016396239399909973	-0.06620413810014725	-0.0028825518675148487	-0.004856790415942669	0.05508335307240486	-0.045924313366413116	-0.07454317063093185	0.04296470806002617	-0.03789224475622177	-0.0928172916173935	0.026430495083332062	-0.032453540712594986	-0.0262455977499485	0.036329157650470734	-0.02152303233742714	0.017229868099093437	0.03278582543134689	-0.08393718302249908	-0.06100763753056526	-0.04500827565789223	-0.012799329124391079	0.027663735672831535	0.008356448262929916	0.047465041279792786	0.051397912204265594	-0.026072746142745018	0.021373776718974113	-0.03946952149271965	0.020034579560160637	-0.09126313775777817	-0.00044350724783726037	-0.004881052765995264	-0.006718446034938097	0.059736497700214386	0.011037679389119148	0.039159588515758514	-0.017362244427204132	-0.04295777902007103	-0.04133987054228783	0.04087253287434578	-0.04553885757923126	-0.014061282388865948	0.02460082434117794	-0.026361219584941864	0.0005762844230048358	0.04016807675361633	-0.029744308441877365	-0.05079726129770279	0.008783881552517414	-0.04303188621997833	0.05960836634039879	0.06160921975970268	0.0374760776758194	-0.025216417387127876	-0.059145569801330566	-0.023329701274633408	0.02049454301595688	-0.049989696592092514	0.020149430260062218	-0.007691678125411272	-0.056544605642557144	-0.07312345504760742	-0.03585401177406311	-0.020534414798021317	0.05288611724972725	-0.026185989379882812	0.028912076726555824	-0.04193947836756706	0.009919535368680954	0.03477681055665016	-0.06464293599128723	-0.007498424965888262	0.004957091063261032	-0.0820661336183548	-0.008724956773221493	0.0996217280626297	-0.04662080481648445	0.03450990468263626	0.06804928183555603	-0.050149716436862946	0.030595524236559868	0.011987832374870777	-0.03372301161289215	0.0316365584731102	-0.01328221894800663	-0.03446456789970398	0.03984470292925835	-0.06847701221704483	0.08088483661413193	-0.058655545115470886	0.09791047126054764	0.005025024525821209	-0.02749326266348362	0.09183783084154129	0.04631062597036362	-0.0054873572662472725	0.022036608308553696	-0.09430532902479172
+0.06327813118696213	0.03375604376196861	0.013285582885146141	0.020097998902201653	-0.12282588332891464	-0.049561336636543274	-0.032488077878952026	0.019911272451281548	0.02323225513100624	-0.021152295172214508	-0.023802148178219795	-0.029764449223876	-0.0026445419061928988	-0.010469170287251472	0.025283033028244972	-0.03971606120467186	-0.07347140461206436	-0.03033135086297989	-0.011408197693526745	-0.016215234994888306	0.03933999687433243	0.014443378895521164	-0.06648819893598557	0.07731881737709045	0.02802939899265766	0.043669477105140686	-0.04527876153588295	0.011011508293449879	-0.05339612439274788	0.017250701785087585	0.0515911728143692	0.016214855015277863	0.00890268199145794	-0.02466859295964241	-0.051104843616485596	0.062015652656555176	-0.03236540034413338	0.1138385459780693	0.03216686472296715	-0.03727513924241066	-0.029324360191822052	0.028862018138170242	-0.02453932911157608	-0.013097526505589485	0.00601513497531414	-0.002565623726695776	-0.026006370782852173	-0.009210560470819473	0.022899990901350975	0.010948865674436092	0.10258008539676666	-0.0054073939099907875	-0.04406002163887024	-0.030338337644934654	-0.00026177166728302836	0.04193561151623726	0.03639397770166397	-0.08134639263153076	-0.06084591522812843	0.04424072429537773	-0.06564473360776901	0.020783832296729088	0.021513495594263077	-0.01164475828409195	0.023693088442087173	-0.03301947936415672	-0.01725587248802185	0.08529655635356903	0.01790846884250641	0.0028518494218587875	0.06640709936618805	0.09664779901504517	-0.08196661621332169	-0.04054000973701477	0.06471683830022812	-0.06080983579158783	-0.06032481789588928	0.0789521262049675	0.004342313855886459	-0.008400370366871357	0.024922212585806847	-0.03928288817405701	0.06396631896495819	-0.04361533746123314	-0.06473086029291153	-0.03554680943489075	-0.015175768174231052	-0.06443822383880615	0.06422652304172516	0.04625782370567322	0.00762820802628994	0.08087955415248871	-0.0729040652513504	0.0443372018635273	-0.0029468464199453592	-0.009471798315644264	0.03686714172363281	-0.11808802932500839	-0.03701359033584595	-0.01157289370894432	-0.0036514359526336193	-0.015257107093930244	0.0024930881336331367	-0.012427442707121372	0.045522626489400864	0.0631631538271904	-0.03250463306903839	0.033772338181734085	0.017488446086645126	0.07304766029119492	0.039080362766981125	0.018071182072162628	0.117811419069767	0.0901816114783287	0.049512799829244614	0.003513979958370328	-0.007855006493628025	-0.030352449044585228	-0.06185377761721611	0.004373097792267799	-0.018007058650255203	0.04250616952776909	0.0020600988063961267	-0.05993416905403137	-0.027514036744832993	0.03049696423113346	-0.09654958546161652	-0.000750772887840867	0.04399321600794792	-0.006988667417317629	0.07548896223306656	0.025465909391641617	-0.023886315524578094	0.006825168151408434	0.05918608605861664	0.04947502911090851	0.012388485483825207	0.021808622404932976	-0.03594013303518295	-0.01834075152873993	-0.0024355752393603325	-0.003353710053488612	0.028802406042814255	-0.08804094791412354	0.011001707054674625	0.020483238622546196	0.05816783756017685	0.01178253348916769	-0.06467775255441666	-0.020901333540678024	-0.09439212083816528	-0.07295528054237366	0.010531540960073471	-0.06925057619810104	-0.00717605696991086	0.06629914045333862	0.03395215421915054	0.03213146701455116	-0.019737254828214645	-0.020830413326621056	-0.026780465617775917	-0.011814362369477749	-0.025729317218065262	-0.08908087015151978	-0.018987705931067467	-0.07231908291578293	0.0044750249944627285	0.015138165093958378	-0.03336421400308609	-0.029811955988407135	-0.010298414155840874	-0.003248484106734395	0.02114778757095337	0.09994268417358398	0.07604477554559708	0.009265162982046604	0.03462308645248413	-0.020239640027284622	-0.011686095036566257	-0.028947073966264725	0.0009711209568195045	-0.013513730838894844	0.04733527824282646	-0.060467202216386795	-0.014600375667214394	-0.0028607884887605906	0.05608073249459267	0.007554355077445507	0.014467542991042137	0.010378151200711727	-0.021952031180262566	-0.0674259290099144	0.06135083734989166	0.07180079817771912	-0.02712537720799446	0.03397151082754135	-0.005885445978492498	0.04078134521842003	0.018022984266281128	-0.02072325348854065	-0.03797466307878494	0.0332736074924469	0.006832095794379711	0.09051428735256195	0.05093010514974594	-0.07737671583890915	0.08812473714351654	0.007140387315303087	0.019824549555778503	-0.05592329427599907	0.00782029889523983	0.0297734085470438	0.0052790287882089615	0.030880197882652283	0.08783827722072601	-0.03196847066283226	0.02736232988536358	-0.017306189984083176	0.06605707108974457	-0.057200606912374496	0.02282855659723282	-0.008353378623723984	-0.06325313448905945	0.0503968745470047	-0.02898308075964451	-0.08192581683397293	-0.030202392488718033	0.0395209975540638	-0.012148010544478893	-0.029893908649683	0.0062312176451087	0.022055668756365776	-0.0018515161937102675	-0.028585977852344513	0.011028697714209557	0.012247533537447453	-0.12522795796394348	0.09947976469993591	-0.013856302946805954	-0.05795183777809143	-0.06150699779391289	0.01607782393693924	0.014393358491361141	-0.0013921019854024053	0.045179277658462524	0.07960214465856552	0.09494545310735703	0.04660185053944588	0.0331227108836174	0.015221181325614452	-0.0718238577246666	-0.041633013635873795	0.010357585735619068	0.03341895714402199	-0.05695728957653046	-0.029615983366966248	-0.002564468653872609	-0.0511358343064785	-0.03267049044370651	0.010793520137667656	0.019031545147299767	-0.04133045673370361	-0.047730959951877594	-0.0506550632417202	0.029829055070877075	-0.02616015076637268	-0.06032225117087364	0.035501930862665176	-0.02367538958787918	0.08552277088165283	-0.0035523215774446726	-0.042389266192913055	0.014154959470033646	0.022096997126936913	0.02409694343805313	0.006082707084715366	0.04713142290711403	-0.038272589445114136	-0.08342861384153366	-0.019241660833358765	-0.06772832572460175	-0.06174109876155853	-0.0006328254821710289	-0.040970079600811005	-0.0322100855410099	0.09092233330011368	-0.002633908996358514	-0.05083392187952995	-0.06883417814970016	0.021114440634846687	-0.004058179445564747	-0.04106952250003815	-0.032268721610307693	0.016390133649110794	-0.05918412283062935	-0.05333442986011505	0.038145918399095535	-0.03227575868368149	-0.04673207551240921	0.0033531293738633394	-0.062493227422237396	-0.0548497773706913	-0.0030491980724036694	-0.03908352926373482	-0.023343082517385483	0.01567005179822445	-0.06930335611104965	0.051740799099206924	0.12502941489219666	0.043793920427560806	0.0322461873292923	0.008583289571106434	0.02084510214626789	0.10966408252716064	0.01701425202190876	-0.06997667998075485	0.011418337933719158	-0.06147295609116554	-0.056400056928396225	0.024831576272845268	-0.047945089638233185	-0.03442121669650078	-0.0199715755879879	0.0009572222479619086	0.048264846205711365	0.03312860056757927	-0.01897338218986988	-0.005546277388930321	0.023964595049619675	-0.0033988661598414183	0.024358538910746574	-0.004103143699467182	-0.016299327835440636	-0.003253377741202712	-0.001670590601861477	-0.025768723338842392	-0.05224081873893738	0.035017941147089005	0.017406340688467026	0.04458009824156761	-0.041798435151576996	-0.048772308975458145	0.009854266420006752	-0.010526309721171856	0.09035144746303558	-0.0736120343208313	0.037409767508506775	0.047064993530511856	-0.008665724657475948	-0.051102057099342346	-0.021851733326911926	0.06246957927942276	-0.05289322882890701	-0.02053607627749443	-0.027867600321769714	0.023073792457580566	-0.06600956618785858	-0.049749597907066345	0.032338980585336685	-0.048526279628276825	0.035341545939445496	0.005290137138217688	0.015327868983149529	-0.009805666282773018	-0.04034480080008507	0.004449462052434683	0.007923007942736149	-0.0026678917929530144	0.03696303442120552	-0.035457544028759	-0.0077996449545025826	-0.015058744698762894	0.056080207228660583	-0.03520970791578293	-0.02037208527326584	-0.0600067637860775	0.0661064013838768	-0.007392908446490765	0.01046967227011919	-0.012755078263580799	0.028144964948296547	-0.006086085457354784	0.03588734194636345	0.017003729939460754	-0.06591805070638657	-0.025117915123701096	-0.03271203860640526	-0.01799795776605606	-0.06407465785741806	-0.04123803973197937	0.010481826029717922	-0.0891142413020134	0.005423562601208687	-0.03331301733851433	-0.023924119770526886	-0.03185029327869415	-0.050696637481451035	0.011005852371454239	0.01819043606519699	0.03132560849189758	0.0031154565513134003	0.029845619574189186	0.01888410933315754	0.0307600237429142	0.02670041285455227	-0.06050010398030281	0.03407711163163185	0.05822031944990158	-0.008424397557973862	0.05258550867438316	-0.017188450321555138	0.03297353535890579	0.012685291469097137	0.003464306006208062	0.02073059044778347	-0.032405827194452286	-0.016352247446775436	-0.059239380061626434	0.036510732024908066	-0.053906407207250595	-0.035488709807395935	0.0474935881793499	-0.050740912556648254	-0.08630028367042542	0.01168089359998703	-0.03180263191461563	-0.023595144972205162	-0.0054428717121481895	0.0021853477228432894	0.03363398462533951	0.04190387576818466	-0.0883743017911911	-0.04388420283794403	-0.06592229753732681	-0.005661914125084877	0.06733518838882446	-0.0032765932846814394	0.04888887703418732	-0.003436633385717869	0.0071560381911695	-0.002978007774800062	0.006238260772079229	0.01969520002603531	-0.07866004854440689	0.00192402140237391	-0.05227060243487358	-0.05201034992933273	0.0372166708111763	0.009561615064740181	0.020829182118177414	-0.04708359017968178	-0.042689722031354904	-0.055525731295347214	0.08050066977739334	-0.045400578528642654	0.004049880430102348	0.024957606568932533	-0.015159638598561287	0.01457269862294197	-0.0037798318080604076	-0.02293490245938301	-0.07904750108718872	0.00829285103827715	-0.01373712532222271	0.009950300678610802	0.024064894765615463	0.015597917139530182	-0.017388785257935524	-0.07598762214183807	-0.009161666966974735	0.03980981186032295	-0.05873619019985199	0.010217842645943165	-0.012998433783650398	-0.0775981917977333	-0.07313423603773117	-0.015103902667760849	-0.023293232545256615	0.0484166294336319	-0.015593218617141247	0.03366465866565704	-0.008987851440906525	0.004705745726823807	0.03649139031767845	-0.05755365639925003	-0.014352775178849697	0.008801046758890152	-0.020986095070838928	-0.03910304978489876	0.06069844588637352	-0.007494557648897171	0.008450806140899658	0.07750304788351059	-0.030711229890584946	-0.007304750382900238	0.05127815902233124	-0.06054472550749779	0.025244586169719696	-0.0022172918543219566	-0.06442724168300629	0.07093019038438797	-0.05160941928625107	0.08328720182180405	-0.03487017750740051	0.0889960527420044	-0.05237872153520584	-0.05906001478433609	0.06789274513721466	0.06148280203342438	-0.011776403523981571	0.027377689257264137	-0.05228907987475395
+0.01740347035229206	-0.043630022555589676	0.0026652675587683916	0.010967826470732689	-0.03396134823560715	-0.04324032738804817	0.009388772770762444	0.05277732014656067	-0.017660818994045258	-0.054339613765478134	-0.03923853486776352	0.012726698070764542	-0.010571800172328949	-0.009520761668682098	0.024664629250764847	-0.022346459329128265	-0.08837983757257462	0.010174013674259186	-0.06415281444787979	-0.05509215593338013	-0.009768946096301079	0.03021187335252762	-0.07215999811887741	0.046889789402484894	-0.009709076955914497	0.023605965077877045	-0.04020412638783455	-0.02084248885512352	-0.012155815958976746	0.029159896075725555	0.06947468221187592	0.002723671728745103	0.09460419416427612	-0.04165080934762955	-0.029047049582004547	0.05057278648018837	-0.032790038734674454	0.10685655474662781	-0.015990296378731728	-0.009187530726194382	-0.04384380206465721	-0.014380845241248608	-0.012818684801459312	-0.07100677490234375	0.030153919011354446	-0.007970232516527176	0.013450603000819683	0.005835092160850763	0.005233314353972673	0.02546091191470623	0.07751153409481049	0.026473987847566605	0.002851598197594285	-0.020530998706817627	-0.0011282392079010606	0.041283927857875824	0.0204528346657753	-0.025569096207618713	-0.056719888001680374	0.04841618984937668	-0.11085090786218643	-0.010712831281125546	0.03245174139738083	0.006130230613052845	-0.041358016431331635	-0.02880384773015976	-0.04817007854580879	0.09889437258243561	0.021329089999198914	-0.04453460872173309	0.032280031591653824	0.057765696197748184	-0.04608374089002609	-0.05607602000236511	0.028713267296552658	-0.03487671539187431	0.003401433117687702	0.022346654906868935	-0.006518245674669743	-0.009259065613150597	0.007137069012969732	-0.007527523208409548	0.12856948375701904	-0.037200238555669785	-0.037027761340141296	-0.049492884427309036	-0.002861937740817666	-0.0567423440515995	0.08994697779417038	0.00026437186170369387	0.007357554975897074	0.07425572723150253	-0.012242322787642479	0.015810247510671616	0.023697301745414734	-0.011056257411837578	-0.030030159279704094	-0.09941218793392181	-0.04869717359542847	-0.02047659456729889	0.02704791910946369	0.022565605118870735	0.07161717861890793	0.029429182410240173	0.08061390370130539	0.026187477633357048	-0.02275971695780754	-0.01254594512283802	0.053607337176799774	-0.018464958295226097	0.008798939175903797	0.13121330738067627	0.035144735127687454	0.029538147151470184	0.06984802335500717	0.026808541268110275	0.021668419241905212	-0.03022710420191288	-0.01669749803841114	-0.021545683965086937	-0.018694033846259117	0.018946362659335136	0.003938095178455114	-0.09147088974714279	-0.0364038459956646	0.05746028199791908	-0.09645365923643112	-0.055103693157434464	0.08932822942733765	-0.039661962538957596	0.12067854404449463	0.05273406207561493	-0.01030617207288742	0.010029125027358532	0.03700966015458107	-0.01080921571701765	-0.022947318851947784	0.018552100285887718	0.003337712027132511	-0.038867682218551636	-0.00044257304398342967	0.053398169577121735	-0.046420466154813766	-0.050694070756435394	0.034393008798360825	0.025386175140738487	0.009412684477865696	-0.033571504056453705	-0.0493178591132164	0.034181807190179825	-0.03151350095868111	-0.09379282593727112	-0.007476005237549543	-0.0537702701985836	-0.011631925590336323	0.05380719155073166	0.0010252121137455106	0.017835039645433426	-0.012354806065559387	-0.03275240212678909	-0.07328949868679047	-0.04034087434411049	-0.03671403229236603	-0.09792780876159668	-0.017013126984238625	-0.024993812665343285	-0.015162916854023933	0.06170148402452469	-0.06454553455114365	-0.022539451718330383	-0.010694647207856178	-0.031246835365891457	-0.017611907795071602	0.07119696587324142	0.0638929083943367	0.035265035927295685	0.0028946043457835913	-0.06673046946525574	-0.0005395484040491283	-0.0342065654695034	-0.003636847250163555	-0.009497914463281631	0.05508771166205406	-0.050095900893211365	0.022748976945877075	-0.01917017623782158	0.04259568080306053	-0.0174594447016716	0.014386333525180817	0.0075256736017763615	0.00855858251452446	-0.03390567749738693	0.041604962199926376	0.04703723266720772	0.0017267694929614663	0.02350914105772972	0.012185991741716862	0.07229997962713242	-0.02472854033112526	-0.045385174453258514	-0.060388512909412384	0.012482650578022003	0.04373965412378311	0.02613067626953125	0.015213181264698505	-0.0582563579082489	0.0836014375090599	0.04695397615432739	0.01299295388162136	-0.06713898479938507	0.006084507331252098	-0.0008873188635334373	0.004193563014268875	0.05910515785217285	0.06793970614671707	-0.042869072407484055	-0.021601606160402298	0.03171005845069885	0.047852933406829834	-0.055422719568014145	-0.060618069022893906	0.01251663826406002	-0.07795574516057968	0.01146052684634924	-0.02737116813659668	-0.08823581039905548	-0.023318476974964142	0.05262431129813194	-0.021826529875397682	0.0003390756028238684	-0.0016685628797858953	0.02325635962188244	-0.03819720447063446	-0.019074758514761925	0.009255294688045979	-0.04978608712553978	-0.084454745054245	0.08251489698886871	0.00939583033323288	-0.09755407273769379	-0.06198566406965256	-0.017076140269637108	0.05332368612289429	-0.00013688286708202213	0.050426870584487915	0.025289902463555336	0.08964670449495316	0.013220575638115406	-0.0391022190451622	0.0025850008241832256	-0.003303789533674717	-0.03370780125260353	0.0069021498784422874	0.0465923435986042	-0.053597159683704376	-0.04855591803789139	0.004374923650175333	-0.027061209082603455	-0.006273257080465555	0.02017933875322342	0.05732095614075661	-0.044478341937065125	-0.004767775069922209	-0.003921351861208677	0.007673205807805061	-0.05170971900224686	0.0025986505206674337	0.0532718151807785	0.006344961933791637	0.053925689309835434	-0.048338714987039566	-0.03472679480910301	0.026555750519037247	-0.021804092451930046	0.017349516972899437	-0.07213867455720901	0.07199598103761673	0.0014005560660734773	-0.08324534446001053	-0.00608050636947155	-0.08859682828187943	-0.02764970064163208	-0.0218772292137146	-0.05857916548848152	-0.058898285031318665	0.07684466242790222	0.03042004071176052	-0.07002437114715576	-0.03661518916487694	-0.07103944569826126	-0.038277581334114075	-0.028750764206051826	-0.010655676014721394	-0.021133383736014366	-0.009057409130036831	-0.0008138864068314433	0.058415062725543976	-0.07979337871074677	-0.004704550374299288	0.043421242386102676	0.006512701045721769	-0.05999580770730972	0.06333482265472412	-0.02085793763399124	-0.024614408612251282	-0.0006933578406460583	-0.08782988786697388	0.010154258459806442	0.07391716539859772	0.0620885044336319	0.024335801601409912	0.03920267894864082	0.028255654498934746	0.1118335872888565	0.012444084510207176	-0.009192788042128086	-0.028416238725185394	-0.00795248243957758	-0.04126764088869095	0.02391485497355461	-0.013992762193083763	-0.003756533609703183	-0.018410684540867805	0.006999127566814423	0.05333004146814346	0.011202138848602772	-0.02344246208667755	-5.606305421679281e-05	0.027680860832333565	-0.014631246216595173	0.03003033809363842	0.011722751893103123	0.027992432937026024	-0.011531440541148186	-0.056175246834754944	-0.05673479661345482	-0.0032744307536631823	0.053510211408138275	-0.040453363209962845	0.05275927484035492	-0.03797876834869385	-0.06256343424320221	0.008283798582851887	-0.02491070330142975	0.0989382341504097	-0.06469839066267014	0.02155805379152298	0.05858318880200386	0.056074488908052444	-0.10287350416183472	0.010948597453534603	0.036349304020404816	-0.04206293821334839	-0.07539196312427521	-0.0045463573187589645	0.03778073564171791	-0.051619887351989746	-0.044575970619916916	-0.004547481425106525	-0.04261201620101929	-0.026821071282029152	0.000492956314701587	0.02825200743973255	0.028473101556301117	0.0011391988955438137	-0.017173659056425095	-0.008816029876470566	0.030599532648921013	0.03556377440690994	-0.014151503331959248	0.018024876713752747	-0.03265540674328804	0.023090213537216187	-0.0682191401720047	-0.04105529189109802	-0.017980504781007767	0.06049424037337303	-0.027649903669953346	0.020657282322645187	-0.0023464474361389875	-0.06642743200063705	-0.029770152643322945	-0.0058324518613517284	0.0004844671057071537	-0.04565955325961113	-0.017323650419712067	-0.00401722127571702	-0.08755180984735489	-0.08398637920618057	-0.06712641566991806	-0.010279756039381027	-0.020283736288547516	0.021862702444195747	-0.05730760470032692	0.045771703124046326	0.017748719081282616	0.016757216304540634	0.05104009434580803	0.06113016977906227	0.04453841596841812	-0.028229551389813423	0.022831056267023087	0.003578015137463808	0.04785826802253723	-0.0010562395909801126	-0.04078465327620506	0.012456843629479408	0.0002808432618621737	-0.04337723180651665	0.050246428698301315	-0.017956940457224846	0.003737563733011484	-0.004129217937588692	-0.05283964052796364	-0.02263842150568962	0.014141451567411423	-0.012791892513632774	-0.04301739111542702	0.0432993546128273	-0.054762400686740875	-0.025594113394618034	0.02042311057448387	-0.04573792219161987	-0.07069186121225357	-0.062090903520584106	-0.04301181808114052	-0.023682016879320145	0.06038951873779297	-0.012976763769984245	0.019242702051997185	0.03146671876311302	-0.026050759479403496	-0.06761963665485382	0.0035391473211348057	-0.02233574353158474	0.011745406314730644	-0.0008685227949172258	0.059890370815992355	0.00892837718129158	0.011624936945736408	-0.03441436216235161	-0.04449854791164398	0.023044437170028687	-0.11386832594871521	0.0124115701764822	-0.0031163895037025213	-0.022744670510292053	0.011628974229097366	0.0009339580428786576	-0.004124898463487625	-0.057672396302223206	-0.012126055546104908	-0.0024307004641741514	0.050175897777080536	-0.007823485881090164	0.032308343797922134	-0.0019307538168504834	-0.046502288430929184	-0.03913126885890961	0.0312533974647522	-0.04636303707957268	-0.0751664862036705	0.01827121712267399	-0.04349028691649437	0.040268249809741974	-0.01347597036510706	0.02985904924571514	-0.03755885362625122	-0.09788093715906143	-0.006867602001875639	-0.026959974318742752	-0.05028512328863144	-0.025779949501156807	0.03664020076394081	-0.10500942915678024	-0.02569311484694481	0.012240037322044373	-0.03056999482214451	0.0430249385535717	0.007155721541494131	0.055293191224336624	-0.0468955896794796	-0.01865914650261402	-0.023566601797938347	0.004177187569439411	0.013363735750317574	-0.03010805882513523	-0.08720558881759644	-0.023811375722289085	0.06879279017448425	-0.0814661979675293	0.030773313716053963	0.07006587833166122	-0.05972980707883835	0.05636196210980415	0.005470316857099533	-0.04779799282550812	0.04471831023693085	0.0026326668448746204	-0.021259842440485954	0.018618596717715263	-0.08843893557786942	0.059968095272779465	-0.0671653002500534	0.09573324769735336	0.0039673056453466415	-0.01014700997620821	0.08316177874803543	0.09657405316829681	0.008161784149706364	0.026397332549095154	-0.08626406639814377
+0.020163634791970253	0.0030472504440695047	0.04475552961230278	-0.003531613852828741	-0.07311013340950012	-0.0038198719266802073	0.006805660668760538	-0.01448962464928627	-0.07178009301424026	-0.04864248260855675	0.0536014586687088	-0.05530768632888794	-0.0006741416291333735	-0.024907950311899185	0.03711332008242607	-0.012726978398859501	-0.0648118332028389	0.016361191868782043	-0.044063955545425415	-0.013673589564859867	0.07337062805891037	-0.04347243532538414	-0.07622520625591278	-0.010941320098936558	-0.03470654413104057	0.010074576362967491	-0.03576863184571266	0.00659960275515914	-0.036129847168922424	0.024416416883468628	-0.008052690885961056	-0.024088142439723015	0.010785755701363087	-0.03552623093128204	-0.05624447390437126	0.0057998839765787125	0.06408967822790146	0.08049969375133514	0.04889391362667084	-0.08410313725471497	-0.0628090351819992	0.03795143961906433	-0.028693731874227524	-0.0252726711332798	0.005467682611197233	-0.019943028688430786	0.017262756824493408	-0.018036216497421265	0.05375819280743599	0.03139117360115051	0.021245455369353294	0.020308302715420723	-0.06739635020494461	-0.02521529421210289	0.06989157944917679	0.011459428817033768	0.03960425779223442	-0.01791580393910408	-0.04712391272187233	0.08016090840101242	-0.044931817799806595	0.021854380145668983	-0.03394642099738121	-0.03641790524125099	0.006149953231215477	-0.00896504893898964	0.015693088993430138	0.04840691387653351	0.02711397036910057	-0.04216223582625389	-0.04735787212848663	0.0919317901134491	-0.031526222825050354	-0.05070870369672775	0.051684506237506866	-0.03142767772078514	0.007035496179014444	0.004490968305617571	-0.017042012885212898	-0.010945509187877178	-0.09021146595478058	-0.07198809832334518	0.1572709083557129	-0.04426282271742821	-0.02961410954594612	-0.05414414778351784	-0.022123567759990692	-0.02795061655342579	0.04320555180311203	0.040381643921136856	-0.028958532959222794	0.012138985097408295	-0.08297041058540344	0.08638732880353928	-0.04726863652467728	-0.0043939086608588696	0.03949834033846855	-0.10589318722486496	0.007332721259444952	0.018492206931114197	-0.04271314665675163	-0.06292218714952469	0.0195172056555748	0.005866173654794693	0.014557202346622944	-0.0036355957854539156	-0.04221367835998535	-0.033979546278715134	-0.030430389568209648	0.0003566720988601446	-0.005134889855980873	0.10185481607913971	0.04455481469631195	0.06329642236232758	0.005557629279792309	0.04424991458654404	0.044767990708351135	-0.05296776071190834	0.03262108564376831	0.0070648882538080215	0.00518141221255064	0.019171465188264847	0.028603950515389442	0.00614745169878006	0.040928687900304794	0.05900752544403076	-0.07049047946929932	-0.021019309759140015	0.058170802891254425	-0.026998581364750862	0.069188192486763	0.06726381182670593	-0.0026668966747820377	0.051746394485235214	0.04827980324625969	-0.0026569454930722713	0.0050764307379722595	0.04240870475769043	-0.04216248542070389	-0.0673217922449112	-0.0008107544272206724	0.004935138858854771	-0.02543732523918152	-0.04042849689722061	-0.01486813835799694	0.05951148644089699	0.02546650916337967	-0.07144945859909058	-0.03463999927043915	0.09006227552890778	-0.04621649533510208	-0.10133323818445206	-0.030473195016384125	-0.10375040024518967	0.023196492344141006	0.039047710597515106	0.018298309296369553	0.05548999831080437	-0.027589544653892517	0.03653622046113014	-0.019733550027012825	-0.11975942552089691	-0.06848437339067459	-0.03610136732459068	-0.037772875279188156	0.03347430378198624	0.03725177422165871	-0.006436568219214678	-0.025191208347678185	-0.014438403770327568	0.07008469104766846	0.051106926053762436	0.015058810822665691	0.005045481026172638	-0.008774146437644958	-0.045508529990911484	0.061377786099910736	-0.01634090393781662	-0.01800672709941864	0.004548429045826197	0.09506867080926895	-0.03240964561700821	0.08126109093427658	-0.06454075127840042	-0.03590758517384529	-0.013298183679580688	0.008379771374166012	-0.0007785891066305339	0.006988921668380499	-0.02886759489774704	-0.06582803279161453	0.0005185577902011573	0.009067525155842304	0.03376102074980736	-0.0582263320684433	0.06084180250763893	0.0004834065621253103	0.07448125630617142	-0.07203050702810287	-0.021495850756764412	-0.05883760005235672	0.03203677758574486	0.010147111490368843	0.012159937992691994	-0.02259606309235096	-0.01391628198325634	-0.047278594225645065	0.0256106648594141	-0.0358581468462944	-0.053680289536714554	0.07725955545902252	-0.02667400985956192	0.0862855389714241	0.015232957899570465	-0.00438213674351573	-0.04140068590641022	0.005262577906250954	-0.007127564400434494	0.03962300717830658	0.004316601436585188	0.01592264138162136	-0.005945152137428522	-0.03975421190261841	-0.032375674694776535	-0.02578454092144966	-0.00878468994051218	-0.029559997841715813	0.025081714615225792	-0.009804039262235165	-0.016553031280636787	-0.00858346838504076	0.07691249251365662	-0.11920462548732758	-0.024992426857352257	0.05696101114153862	0.01552469190210104	-0.035088930279016495	0.05671281740069389	0.008919928222894669	-0.045672137290239334	-0.027334226295351982	0.03431426361203194	0.011844288557767868	0.052559465169906616	0.0021975054405629635	0.01728425733745098	0.0602404810488224	0.02538379840552807	-0.00199329387396574	-0.051571205258369446	-0.04480576515197754	-0.045603301376104355	-0.00780459214001894	-0.01774575747549534	-0.013003385625779629	-0.04717668145895004	-0.03820464760065079	-0.010504386387765408	0.03942030668258667	-0.04047587886452675	0.04407520592212677	-0.02080533653497696	0.0023719032760709524	0.05710047855973244	0.00265224976465106	-1.68643455253914e-05	-0.04000793397426605	-0.027036719024181366	-0.0003225416294299066	0.017692115157842636	-0.0028349615167826414	0.02544170245528221	0.01636999472975731	-0.024915507063269615	0.015062759630382061	-0.01861262321472168	0.014712990261614323	-0.051080942153930664	-0.05258231610059738	-0.04495072737336159	-0.08406957238912582	-0.03394739702343941	-0.06491692364215851	-0.023914720863103867	-0.04047702997922897	0.05431940406560898	0.013174299150705338	-0.06888893991708755	-0.019220983609557152	-0.02968222089111805	-0.031642451882362366	-0.046702176332473755	-0.05698108300566673	0.07343093305826187	-0.018989909440279007	0.0232757069170475	0.00646994961425662	-0.05821704864501953	-0.03233610838651657	-0.007162499241530895	-0.018214654177427292	-0.040315836668014526	0.09649690240621567	-0.0004326478228904307	0.007678069174289703	0.01801915466785431	-0.022844675928354263	0.060306958854198456	0.021808629855513573	0.013350729830563068	0.07531865686178207	0.02613699994981289	0.03930258750915527	0.02724556252360344	0.0422297902405262	-0.015511355362832546	-0.049749698489904404	-0.06458288431167603	-0.11909854412078857	0.06778006255626678	-0.02097824215888977	0.03961711749434471	-0.05881573632359505	0.036663223057985306	0.07686017453670502	-0.03462333604693413	0.017263032495975494	-0.016972234472632408	-0.003747564274817705	-0.01458553597331047	0.011999960988759995	-0.04068015143275261	-0.022906886413693428	0.000442283897427842	-0.04685723036527634	-0.04659751430153847	-0.0030096485279500484	-0.009179752320051193	-0.03862548619508743	0.05959576368331909	0.007304114755243063	-0.02178700640797615	-0.014798789285123348	0.03141907975077629	0.03039252944290638	-0.02398828975856304	0.021751543506979942	0.011878246441483498	0.01793643832206726	-0.08783502131700516	-0.03142029792070389	0.031169362366199493	0.0431985966861248	-0.018779926002025604	0.004500077571719885	0.07527539879083633	-0.01403865497559309	0.0005374281900003552	-0.05453566834330559	0.003859285032376647	-0.02728801965713501	0.0402601957321167	-0.035112638026475906	-0.01868666708469391	-0.049198638647794724	-0.009475572034716606	-0.022488495334982872	-0.03531249240040779	-0.04099126160144806	0.033786192536354065	0.050210293382406235	-0.021880481392145157	0.03864527493715286	-0.040949154645204544	-0.08086927980184555	-0.07328687608242035	0.06901267915964127	-0.07626429200172424	-0.03960205242037773	-0.02574671059846878	-0.0007169665768742561	-0.03293490782380104	0.052884574979543686	0.0076880850829184055	0.019591709598898888	-0.0059928931295871735	0.017783673480153084	-0.07687948644161224	-0.06492914259433746	-0.0005960658891126513	-0.005061446689069271	-0.013132932595908642	-0.0009975596331059933	-0.02170083485543728	0.0031520037446171045	0.06247195601463318	0.02036099135875702	0.005727594718337059	0.00683485297486186	0.023501520976424217	-0.020834438502788544	0.07190050929784775	0.01768193207681179	0.025332313030958176	-0.043021876364946365	-0.05426326021552086	0.017580725252628326	-0.0014916331274434924	-0.023828430101275444	0.12509828805923462	-0.04568467661738396	-0.005078968591988087	-0.02974816970527172	0.047047022730112076	-0.009777242317795753	-0.00855192355811596	-0.027116935700178146	0.01773400418460369	0.0955342948436737	-0.029854996129870415	-0.06689722836017609	-0.0018421645509079099	-0.06762060523033142	-0.11733068525791168	0.03124995343387127	0.0072066099382936954	-0.0306685883551836	0.037053026258945465	0.016027498990297318	0.07510567456483841	0.049486760050058365	-0.023397279903292656	-0.010524495504796505	-0.013503171503543854	-0.04093735292553902	0.03769277036190033	0.03303735703229904	0.07826058566570282	0.03218938037753105	-0.019164754077792168	0.033454280346632004	0.04631291329860687	0.008398480713367462	-0.10716810077428818	0.03792787715792656	-0.0010026111267507076	0.057317767292261124	0.026136580854654312	-0.03772728890180588	0.0017744816141203046	-0.02367853745818138	-0.006947149056941271	0.02048330008983612	-0.0009840669808909297	-0.01094500720500946	-0.0583471842110157	0.08593230694532394	-0.015947606414556503	-0.006330556236207485	0.009516497142612934	-0.003012582194060087	-0.004726670682430267	0.0350169762969017	-0.05970850586891174	0.015876535326242447	0.006400726269930601	0.026914173737168312	0.035892996937036514	-0.045601729303598404	-0.07089727371931076	0.001981545938178897	-0.03865869343280792	0.03500362113118172	0.01044455450028181	-0.03461279720067978	-0.12052613496780396	-0.005480020307004452	-0.01212911307811737	-0.05014358460903168	-0.007053666282445192	-0.014510306529700756	-0.00721898814663291	0.0742979422211647	0.0027679812628775835	-0.012758363969624043	0.0984109416604042	0.011764672584831715	-0.11712034046649933	-0.03308594226837158	0.11353221535682678	-0.069837786257267	0.05093885958194733	0.0017931073671206832	0.023441152647137642	0.04452455788850784	0.018006108701229095	0.013788145035505295	0.02651035226881504	-0.031576693058013916	0.008728653192520142	0.004017454572021961	-0.05471304804086685	0.0587216354906559	-0.06160621717572212	0.05772598460316658	-0.0043439706787467	0.004461548291146755	0.10706931352615356	0.07896646857261658	0.07022461295127869	-0.01752111315727234	-0.059800729155540466
+0.05080700293183327	-0.03493307903409004	0.0068503995425999165	0.06596678495407104	-0.06467990577220917	-0.029313843697309494	6.994255818426609e-05	0.020610887557268143	-0.010436663404107094	-0.03405757248401642	-0.015469089150428772	-0.016832593828439713	-0.005497743841260672	0.012563102878630161	0.03670959919691086	-0.02919822558760643	-0.0591052770614624	0.017996780574321747	-0.024855680763721466	-0.036367692053318024	0.034669432789087296	0.010198403149843216	-0.06604944169521332	0.0576089546084404	-0.007348461076617241	0.02884945645928383	-0.051844507455825806	0.027558643370866776	-0.01095687597990036	0.037073396146297455	0.049962807446718216	-0.02711029350757599	0.03473693132400513	-0.07254540920257568	-0.058908767998218536	0.03909997642040253	-0.04243830591440201	0.1177317202091217	0.020928123965859413	-0.034273549914360046	-0.06228704750537872	0.011506184004247189	-0.031990982592105865	-0.049659620970487595	0.061829689890146255	0.023348983377218246	0.01961088925600052	-0.03371240571141243	0.049410779029130936	0.035765714943408966	0.11124377697706223	-0.0031887185759842396	-0.01653238572180271	-0.020413365215063095	-0.023017264902591705	0.007071642205119133	0.05165662616491318	-0.05514328554272652	-0.0657462328672409	0.04989958181977272	-0.09224382042884827	-0.035598479211330414	0.011566616594791412	-0.006167626939713955	-0.039180874824523926	-0.018866317346692085	-0.011869056150317192	0.08638116717338562	0.003590811276808381	-0.025721073150634766	0.02338532544672489	0.11992673575878143	-0.062401559203863144	-0.05931184068322182	0.040976062417030334	-0.06585069745779037	-0.0075218044221401215	0.04159722849726677	-0.010816561058163643	0.016522789373993874	-0.00836267787963152	-0.03393084183335304	0.12530362606048584	-0.042987603694200516	-0.054377246648073196	-0.022149283438920975	-0.010583728551864624	-0.06588423252105713	0.06741675734519958	0.04124649241566658	0.007029528263956308	0.0626692920923233	-0.05063958466053009	0.05230752378702164	0.034146498888731	0.00978696346282959	0.03757072985172272	-0.09820903837680817	-0.032582662999629974	-0.0027892880607396364	-0.012144720181822777	-0.012352773919701576	0.06173304095864296	0.01263184193521738	0.06599177420139313	0.019623536616563797	-0.06481830775737762	0.011312109418213367	0.022549398243427277	0.04357905685901642	0.029065603390336037	0.08201801031827927	0.07807867974042892	0.033253420144319534	0.05926792323589325	-0.023557821288704872	0.015989696606993675	-0.05977878347039223	-0.03270159289240837	0.006998030934482813	0.01164921186864376	0.06080712378025055	-0.01774093508720398	-0.08604159206151962	-0.012107294984161854	0.027145231142640114	-0.10731139779090881	-0.018313316628336906	0.08241729438304901	-0.03393497318029404	0.12538330256938934	0.057924382388591766	-0.019023803994059563	0.054940544068813324	0.04625186696648598	0.019210029393434525	-0.011540371924638748	0.044802404940128326	0.005007468163967133	-0.0013397629372775555	-5.9535959735512733e-05	0.0545739009976387	0.00044206908205524087	-0.06418341398239136	0.03849899023771286	0.050931740552186966	0.04102331027388573	-0.03852299973368645	-0.0610324926674366	0.015333225019276142	-0.06649816036224365	-0.06477400660514832	-0.0032632092479616404	-0.06835848093032837	-0.012314298190176487	0.030568767338991165	0.005645090714097023	0.03350000083446503	-0.011306176893413067	-0.013026118278503418	-0.06511718034744263	-0.05870432406663895	-0.026553530246019363	-0.06551973521709442	-0.04519123211503029	-0.028590865433216095	0.014660854823887348	-0.0030102599412202835	-0.03641097620129585	-0.027782728895545006	-0.005820837803184986	0.009188205003738403	0.03946875408291817	0.0701831504702568	0.081402488052845	0.03802790120244026	0.01466797012835741	-0.039334964007139206	0.027022268623113632	-0.03712303563952446	0.0045600575394928455	-0.030082684010267258	0.012871218845248222	-0.05777173489332199	-0.012298882938921452	-0.013014017604291439	0.04989694058895111	-0.019344234839081764	0.02189798839390278	0.008077929727733135	-0.023467538878321648	-0.059073589742183685	0.049632251262664795	0.07920318841934204	-0.02699430286884308	0.039368946105241776	-0.03322671353816986	0.05205105245113373	-0.039046648889780045	-0.04435930773615837	-0.041687361896038055	0.047545384615659714	0.010019993409514427	0.05231278017163277	0.019473649561405182	-0.04741809144616127	0.08479664474725723	0.026167480275034904	0.022339364513754845	-0.08655926585197449	0.005491163115948439	0.004317395389080048	0.02001337893307209	0.036773115396499634	0.08291321247816086	-0.02533072791993618	-0.026004569604992867	0.0014943411806598306	0.054747313261032104	-0.03539588302373886	0.0016286730533465743	-0.017196692526340485	-0.09083962440490723	0.02469794638454914	-0.05313761159777641	-0.0795004740357399	-0.02218024991452694	0.04953853413462639	-0.00950081180781126	-0.01647259294986725	0.00360541301779449	0.02446705475449562	-0.02199999988079071	-0.023083338513970375	-0.006430197041481733	-0.02192080393433571	-0.09811537712812424	0.09923755377531052	0.0100558428093791	-0.06027648597955704	-0.07146766781806946	0.014356892555952072	0.03975505381822586	-0.003259778255596757	0.04028839245438576	0.029709748923778534	0.08245065063238144	0.03867369145154953	-0.0067759668454527855	0.030700650066137314	-0.045329220592975616	-0.040589358657598495	-0.005004809703677893	0.016527147963643074	-0.045213036239147186	-0.018446730449795723	-0.004492324311286211	-0.054572928696870804	-0.006332408171147108	-0.009936634451150894	0.018838658928871155	-0.021973636001348495	-0.01910851150751114	-0.005257151089608669	0.04605710878968239	-0.03310014307498932	-0.04076685383915901	0.02806004509329796	-0.004656421951949596	0.050905223935842514	-0.03966287150979042	-0.014938604086637497	0.0509367398917675	0.01762363500893116	0.020841000601649284	-0.05386083200573921	0.06284233182668686	-0.009843084029853344	-0.08864418417215347	-0.034171782433986664	-0.08261308819055557	-0.03932495042681694	-0.01010474655777216	-0.04349888488650322	-0.03329967334866524	0.08289460092782974	0.011017285287380219	-0.03495920076966286	-0.07491140812635422	-0.03287158161401749	-0.013624131679534912	-0.06046586111187935	-0.019575875252485275	0.005316560156643391	-0.04731351137161255	-0.030349062755703926	0.020163560286164284	-0.04265604913234711	-0.01757516898214817	-0.030515557155013084	-0.016231944784522057	-0.05710676312446594	0.055758703500032425	-0.039200346916913986	-0.021406186744570732	0.02145092561841011	-0.07637807726860046	0.04764387756586075	0.10862613469362259	0.07923377305269241	0.04535995423793793	0.04862518236041069	0.033478591591119766	0.11636704951524734	0.01574091799557209	-0.031192515045404434	-0.023172063753008842	-0.0275404192507267	-0.06293822079896927	0.04700386896729469	-0.02276897430419922	-0.005551410373300314	-0.003909970633685589	-0.008272439241409302	0.07982891798019409	-0.005040883086621761	-0.04440620169043541	-0.024795079603791237	0.030389120802283287	-0.024252865463495255	0.013781012035906315	0.009448950178921223	0.01827158033847809	-0.011015880852937698	-0.05162905901670456	-0.04966254159808159	-0.030382346361875534	0.04536477103829384	-0.023964721709489822	0.04047677665948868	-0.014646539464592934	-0.042407382279634476	0.014760795049369335	0.01691362075507641	0.09307277947664261	-0.05288558080792427	0.010833489708602428	0.038748349994421005	0.020669132471084595	-0.1107582300901413	0.008525202982127666	0.030226631090044975	-0.04758111387491226	-0.06246460601687431	0.011037229560315609	0.03588514402508736	-0.05078190192580223	-0.009472345001995564	-0.009054169990122318	-0.06117026507854462	-0.006411309819668531	0.021251676604151726	0.03390691056847572	0.030308565124869347	-0.01712125539779663	-0.05309848487377167	-0.016163742169737816	-0.01214143168181181	0.02506108209490776	-0.03855794295668602	0.011671556159853935	-0.030701400712132454	0.0315360464155674	-0.05543360859155655	-0.035070497542619705	-0.03580857440829277	0.06475730240345001	0.0059378426522016525	-0.029446624219417572	-0.02214071713387966	-0.030882127583026886	-0.02825831063091755	0.004494606051594019	0.027786489576101303	-0.05965756997466087	-0.016999874264001846	-0.042987048625946045	-0.05987121909856796	-0.10221157968044281	-0.06161588057875633	0.013727216050028801	-0.05109533667564392	0.021850917488336563	-0.027656977996230125	0.004397314507514238	0.005554840434342623	-0.004105599131435156	0.035673514008522034	0.055783361196517944	0.04293965920805931	-0.02694544568657875	0.036984194070100784	-0.0013833476696163416	0.0503733791410923	0.02737503871321678	-0.04692663624882698	0.010227656923234463	0.016555411741137505	-0.05731026828289032	0.04245096445083618	-0.02767973020672798	0.015547612681984901	-0.01509411446750164	-0.016605287790298462	0.050079233944416046	-0.007610107772052288	-0.0021649471018463373	-0.028469908982515335	0.06197293475270271	-0.07051576673984528	-0.020956996828317642	0.016758304089307785	-0.029817868024110794	-0.10013338178396225	-0.051303911954164505	-0.0038804677315056324	-0.01661248318850994	0.04145907983183861	-0.022041961550712585	0.01713753305375576	0.029360612854361534	-0.03495727851986885	-0.05278397351503372	-0.023029958829283714	-0.00877167284488678	0.05185863748192787	-0.0004365804197732359	0.06854789704084396	0.022045647725462914	-0.001906967838294804	-0.007990006357431412	-0.003103142837062478	0.010534604080021381	-0.09083262830972672	-0.016183624044060707	-0.02421068772673607	-0.011253722943365574	0.014772377908229828	0.012429913505911827	-0.0012518481817096472	-0.06002530828118324	-0.019782841205596924	-0.04308996722102165	0.09016997367143631	-0.0021057662088423967	0.0149850407615304	0.03638777881860733	-0.036218758672475815	-0.009655816480517387	0.004600200802087784	-0.05706218630075455	-0.08359580487012863	-0.0038991952314972878	-0.004209218081086874	0.025113781914114952	-0.0017583353910595179	0.008553986437618732	-0.016127178445458412	-0.06285306066274643	-0.011534628458321095	0.015031724236905575	-0.07554271817207336	0.01071124617010355	0.01742260903120041	-0.08131247013807297	-0.057233210653066635	0.020572541281580925	-0.04827139526605606	0.06178949773311615	0.020222466439008713	0.016597971320152283	-0.0046272873878479	-0.006728336215019226	-0.017432887107133865	-0.04969555884599686	0.018322471529245377	-0.019313843920826912	-0.0436394028365612	-0.020066414028406143	0.06654424965381622	-0.043440576642751694	0.03758233040571213	0.054356638342142105	-0.0507587194442749	0.04286376014351845	0.054686110466718674	-0.030012590810656548	0.03926781937479973	0.0003864451718982309	-0.00910253543406725	0.08123767375946045	-0.08414586633443832	0.10237469524145126	-0.08198917657136917	0.09819842129945755	0.027740048244595528	-0.0056528677232563496	0.08189025521278381	0.08340667933225632	0.004129448905587196	0.0036610581446439028	-0.08264313638210297
+0.07352782785892487	0.01838613860309124	0.0385904386639595	0.07166887819766998	-0.08495904505252838	-0.040025968104600906	-0.03548189997673035	0.0033126575872302055	0.013641060329973698	-0.024167058989405632	-0.009177256375551224	-0.015818173065781593	0.0032580664847046137	0.007371103391051292	0.0010078920749947429	-0.03507969528436661	-0.07834511250257492	-0.022295694798231125	-0.02537720277905464	0.005501972511410713	0.0800669714808464	0.017992276698350906	-0.08628755807876587	0.057628508657217026	0.001177899306640029	0.06931614130735397	-0.010816986672580242	0.014723721891641617	-0.01731104589998722	-0.010558459907770157	0.05458446964621544	0.00546798063442111	0.03027782402932644	-0.07706184685230255	-0.05207933112978935	0.04666261747479439	-0.028352243825793266	0.10091415047645569	0.05306428670883179	-0.041095469146966934	-0.07022522389888763	0.024621937423944473	-0.037274930626153946	-0.06415005773305893	-0.011286364868283272	0.008632983081042767	0.006956759374588728	-0.005812468472868204	0.05513237044215202	0.032739728689193726	0.10353817045688629	-0.012440853752195835	-0.036544278264045715	-0.021796466782689095	0.017989199608564377	0.028659919276833534	0.07790914177894592	-0.06920801848173141	-0.05950314551591873	0.023234637454152107	-0.05843159183859825	-0.010392062366008759	4.840745896217413e-05	-0.0032416859176009893	-0.01695086434483528	-0.012017126195132732	-0.0036020271945744753	0.07000243663787842	0.039163265377283096	-0.03708212450146675	0.041877079755067825	0.11099883168935776	-0.07908258587121964	-0.00909950491040945	0.03502313047647476	-0.05601012706756592	-0.05421970784664154	0.046427104622125626	-0.023979712277650833	0.008467367850244045	0.010485557839274406	-0.05684081092476845	0.1065593883395195	-0.03869038447737694	-0.057110145688056946	-0.034072425216436386	0.013272514566779137	-0.0770115926861763	0.04751218110322952	0.06101050227880478	-0.02169501595199108	0.07322628051042557	-0.06413445621728897	0.07432951778173447	0.010389385744929314	0.03265569359064102	0.04031125828623772	-0.12391500920057297	-0.03586549684405327	-0.0047349645756185055	-0.02189047634601593	-0.015416126698255539	0.03833693265914917	-0.021484874188899994	0.07510006427764893	0.007896799594163895	-0.0624813474714756	-0.005277944263070822	0.010585459880530834	0.04474852979183197	0.07666052132844925	0.04980888217687607	0.08134929090738297	0.07078002393245697	0.052136629819869995	-0.014104645699262619	0.027389459311962128	-0.051718682050704956	-0.038860030472278595	-0.001729870680719614	-0.003540582489222288	0.05041023716330528	-0.025239311158657074	-0.06078953295946121	-0.023597348481416702	0.007366342470049858	-0.07474903762340546	-0.042038943618535995	0.05130559578537941	-0.016346855089068413	0.1135355681180954	0.08090435713529587	-0.043275270611047745	0.004243725910782814	0.03416574373841286	0.03469576686620712	-0.011002673767507076	0.05010800436139107	-0.002170297782868147	0.005216198042035103	0.019411414861679077	0.020106468349695206	0.009141447022557259	-0.09200044721364975	0.0290482509881258	0.04838409274816513	0.04807788133621216	-0.021654266864061356	-0.061136700212955475	-0.007469074800610542	-0.0536189079284668	-0.046692684292793274	-0.005252382252365351	-0.07828906923532486	-0.002923651598393917	0.07886044681072235	0.048611391335725784	0.06297597289085388	-0.03821196034550667	-0.001960691763088107	-0.034140296280384064	-0.0423719584941864	-0.006633685901761055	-0.09141699224710464	-0.05455916002392769	-0.05066646263003349	0.02781795896589756	-0.0029567936435341835	-0.03201810270547867	-0.025813251733779907	-0.012534551322460175	0.001509132911451161	0.0446772575378418	0.0467604324221611	0.09810925275087357	-0.004901218228042126	-0.020830532535910606	-0.050403397530317307	0.026787621900439262	-0.003337801666930318	0.013268410228192806	-0.04082448035478592	-0.0031412679236382246	-0.08016123622655869	-0.02684265375137329	0.030531784519553185	0.024562416598200798	0.008896727114915848	-0.00722167594358325	0.024546826258301735	-0.06660706549882889	-0.02634563483297825	0.032695915549993515	0.06308318674564362	-0.02614968828856945	0.053725775331258774	-0.03606351837515831	0.03200530633330345	0.01143760234117508	-0.04197940230369568	-0.06796290725469589	0.03905515745282173	-0.03822878748178482	0.0882226973772049	0.037740517407655716	-0.06315489113330841	0.04782472550868988	0.02035800740122795	-0.0005586679326370358	-0.08251464366912842	0.003573537804186344	0.003930179867893457	0.03943725302815437	0.032127514481544495	0.06545685976743698	-0.013931254856288433	-0.01649986021220684	0.006236201152205467	0.04666499048471451	-0.05921708419919014	0.0064717247150838375	-0.016544286161661148	-0.059275269508361816	0.05159221589565277	-0.029622869566082954	-0.0541103333234787	-0.06456486135721207	0.03712812066078186	0.03554696962237358	-0.03164634481072426	0.02339598350226879	0.015181226655840874	-0.01905001886188984	-0.048476070165634155	-0.002224974101409316	-0.01831371709704399	-0.12670624256134033	0.10142512619495392	0.025661511346697807	-0.05297677218914032	-0.049560997635126114	0.03229970484972	0.02178458496928215	-0.0029410761781036854	0.028588593006134033	0.04594910517334938	0.06668241322040558	0.022053098306059837	0.009932453744113445	-0.012485804967582226	-0.0828695297241211	-0.07238524407148361	0.012354881502687931	0.03795982897281647	-0.00671949191018939	-0.020311294123530388	-0.02106161043047905	-0.0277547687292099	-0.003152391640469432	0.006105893757194281	0.019486481323838234	-0.011249301955103874	-0.021037615835666656	-0.021826688200235367	0.038957349956035614	-0.04098034277558327	-0.0317898690700531	0.025312408804893494	0.013092474080622196	0.08691289275884628	-0.011859407648444176	-0.008305205963551998	0.0452842153608799	0.003710920223966241	0.04343080148100853	0.03411346301436424	0.06131695955991745	-0.01743670366704464	-0.08899161964654922	-0.02750377357006073	-0.07926721125841141	-0.042638733983039856	-0.025654656812548637	-0.015462053939700127	-0.05093364790081978	0.08902899920940399	-0.007969885133206844	-0.05995531752705574	-0.061253782361745834	0.0331062488257885	-0.012413129210472107	-0.06988707184791565	-0.030570240691304207	0.01590506173670292	-0.0786554291844368	-0.032228607684373856	0.0019551708828657866	-0.05045139789581299	-0.04155633971095085	-0.027253765612840652	-0.05744187906384468	-0.05647486075758934	0.040288668125867844	-0.026553405448794365	-0.050490036606788635	-0.019069910049438477	-0.07609402388334274	0.05539247766137123	0.08224308490753174	0.01432841643691063	0.05325955152511597	-0.006050433032214642	0.01702374964952469	0.09927910566329956	-0.008650514297187328	-0.03546413034200668	-0.005609613843262196	-0.05104770138859749	-0.06900247186422348	0.08650952577590942	-0.03826897591352463	0.016732221469283104	-0.027943607419729233	0.01531419437378645	0.07719158381223679	0.012196524068713188	-0.016917673870921135	-0.016357330605387688	0.014461386017501354	-0.012130684219300747	0.018160495907068253	-0.015399892814457417	0.004461649339646101	-0.01903853751718998	-0.030753714963793755	-0.058610204607248306	-0.022569697350263596	-0.004650191403925419	0.00710167596116662	0.04545952007174492	-0.0011079355608671904	-0.06757114082574844	0.017622532323002815	-0.01159032341092825	0.08750462532043457	-0.061548106372356415	0.01760842464864254	0.03467420116066933	1.4444375665334519e-05	-0.08732395619153976	-0.030384011566638947	0.0605345144867897	-0.036303162574768066	-0.056934654712677	-0.015615951269865036	0.014575546607375145	-0.051277659833431244	-0.013424543663859367	-0.009635240770876408	-0.03291388973593712	0.038624122738838196	0.02044091932475567	0.03989683836698532	-0.011851134710013866	-0.0017810860881581903	-0.030079422518610954	-0.009944380261003971	-0.015316097065806389	0.007251160219311714	-0.03364638611674309	0.00038681612932123244	-0.03750539571046829	0.0451057031750679	-0.0506308488547802	-0.012746823020279408	-0.06497323513031006	0.03295416384935379	0.013561021536588669	-0.035631876438856125	0.004109843634068966	-0.02853267267346382	-0.008685694076120853	0.02797364443540573	0.0259788129478693	-0.051765602082014084	-0.04618766903877258	-0.041651539504528046	-0.0323239304125309	-0.0911102220416069	-0.01567428559064865	0.01162167452275753	-0.07791945338249207	-0.01370326615869999	-0.022672202438116074	-0.02568049542605877	0.01752668246626854	-0.02062869444489479	0.02727949433028698	0.021195773035287857	0.014488309621810913	-0.042307063937187195	0.026186665520071983	-0.015650125220417976	0.03829801455140114	0.019560454413294792	-0.02456078678369522	-0.010642471723258495	0.06492818146944046	-0.03335319831967354	0.04696867987513542	-0.02951672486960888	-0.004085691645741463	0.005823104176670313	0.0018187110545113683	0.006504001561552286	-0.003074745647609234	-0.009898093529045582	-0.02547743171453476	0.039797134697437286	-0.06337878108024597	-0.0328977070748806	0.03510301187634468	-0.04916521534323692	-0.11404558271169662	-0.02411186322569847	-0.016172150149941444	-0.03479225933551788	-0.005232205148786306	0.03659779950976372	0.036400165408849716	0.04514659568667412	-0.06562602519989014	-0.02382580377161503	-0.014235269278287888	-0.030197788029909134	0.046654265373945236	-0.023600496351718903	0.06751994788646698	0.030576925724744797	0.007034975569695234	0.038013044744729996	0.04782336950302124	-0.018749764189124107	-0.0992654487490654	-0.02559530735015869	-0.0018611627165228128	-0.03186063468456268	0.03102053329348564	-0.00012055197294102982	-0.015111061744391918	-0.043597180396318436	-0.03768134117126465	-0.01993318647146225	0.08771231025457382	-0.011451050639152527	-0.008312691934406757	0.06746658682823181	-0.04037502408027649	0.02143682911992073	0.007879597134888172	-0.02840101346373558	-0.0769023671746254	0.015686694532632828	-0.007099652662873268	-0.000916118617169559	0.021614348515868187	0.003400996560230851	-0.001669553341343999	-0.05658969283103943	-0.031222175806760788	0.016138175502419472	-0.06413190066814423	0.029460065066814423	-0.0006777732050977647	-0.06989926844835281	-0.07922650873661041	0.009786815382540226	-0.05328049510717392	0.013328065164387226	-0.001014996087178588	0.038788434118032455	0.03216782212257385	0.017725199460983276	0.007361889351159334	-0.04012410715222359	-0.007176893763244152	0.035867366939783096	-0.012017713859677315	-0.02733905240893364	0.05905017629265785	-0.01333867758512497	0.015897514298558235	0.07823002338409424	-0.007374056149274111	0.023647159337997437	0.06088877096772194	-0.04416274651885033	0.014876672066748142	-0.017519667744636536	-0.06121611222624779	0.09981810301542282	-0.059031855314970016	0.09615437686443329	-0.0664861798286438	0.09296255558729172	-0.024840792641043663	-0.006228546146303415	0.07024761289358139	0.07208652794361115	0.00021605419169645756	0.03915639966726303	-0.024288492277264595
+-0.011461573652923107	-0.03737691417336464	0.00229413784109056	0.03393097594380379	-0.0831635370850563	-0.08299946784973145	-0.009696790017187595	0.03774302825331688	0.011403577402234077	-0.01956804282963276	-0.008367669768631458	0.0035710909869521856	-0.047221697866916656	-0.0028905048966407776	0.019514797255396843	-0.005867960397154093	-0.07461192458868027	-0.010710560716688633	-0.06151442974805832	-0.027011334896087646	0.047922439873218536	0.022533230483531952	-0.10818986594676971	0.022487586364150047	-0.03728891536593437	-0.005354085937142372	-0.07742352038621902	0.03167447820305824	-0.023531626909971237	0.03663939610123634	0.050087932497262955	-0.02248617820441723	0.08300703018903732	-0.061480239033699036	-0.04690251871943474	0.03552386164665222	-0.023263899609446526	0.07870891690254211	0.0529954731464386	-0.012690939009189606	-0.04846145212650299	0.018245846033096313	3.567731982911937e-05	-0.04851357638835907	0.03152371197938919	-0.004675219766795635	0.005082074552774429	-0.05101972818374634	0.05932283401489258	0.05715450271964073	0.10586031526327133	0.026942716911435127	-0.0014873150503262877	-0.04048794507980347	-0.003032016335055232	-0.0014005491975694895	0.008593500591814518	-0.04887789115309715	-0.09457062184810638	0.0277209784835577	-0.10536316782236099	0.01572495698928833	-0.019083593040704727	0.00785223301500082	-0.037719111889600754	0.0011299513280391693	-0.017549993470311165	0.10727649927139282	0.018861887976527214	-0.05807361751794815	0.02737443521618843	0.08529330044984818	-0.06408070772886276	-0.05785344913601875	0.02343038283288479	-0.030532147735357285	0.024398529902100563	0.04058169946074486	-0.027010852470993996	0.020617900416254997	-0.03490801528096199	-0.046367354691028595	0.11974675953388214	-0.048029519617557526	-0.006608898751437664	-0.04960240051150322	-0.016048967838287354	-0.05681919679045677	0.05225350335240364	0.03416882082819939	0.025373132899403572	0.04788987711071968	-0.02874520607292652	0.029655873775482178	-0.01420997828245163	0.057729534804821014	0.05274864286184311	-0.1070004403591156	0.0012505664490163326	0.006011417601257563	-0.014964393340051174	0.010237295180559158	0.0827241837978363	0.0012890220386907458	0.07175766676664352	0.02239813655614853	-0.036678388714790344	0.007499585393816233	0.0540039986371994	0.029859595000743866	0.05921158939599991	0.0907539576292038	0.08996900171041489	0.057348769158124924	0.03064468875527382	0.003347894409671426	0.011175153777003288	-0.040773600339889526	0.0011427134741097689	0.016368944197893143	-0.007742391899228096	0.03028932958841324	-0.027683373540639877	-0.029545454308390617	0.0026101511903107166	0.05130410194396973	-0.09134022146463394	-0.026910966262221336	0.060941606760025024	-0.04258212819695473	0.09607939422130585	0.062304358929395676	0.013279701583087444	0.020948000252246857	0.07756387442350388	0.011904987506568432	-0.004733240231871605	0.016841303557157516	0.012840603478252888	-0.025580206885933876	-0.025392858311533928	0.05076908692717552	-0.025888966396450996	-0.040032338351011276	0.015040295198559761	0.047651153057813644	-0.01496349461376667	-0.016671212390065193	-0.0702967718243599	0.042271438986063004	-0.07248494029045105	-0.07239644974470139	-0.0021169253159314394	-0.09142167121171951	0.017727654427289963	0.0714416652917862	0.041404347866773605	0.049211274832487106	-0.024017062038183212	0.004482873249799013	-0.02524164877831936	-0.08480555564165115	-0.025713616982102394	-0.073966845870018	-0.014049886725842953	-0.009585353545844555	0.0359455905854702	0.020114600658416748	-0.025208646431565285	-0.030856283381581306	-0.02427085116505623	-0.0002041868428932503	0.0036404079291969538	0.05784968286752701	0.030717993155121803	0.00406741164624691	0.015068945474922657	-0.054148126393556595	0.012790119275450706	-0.06456974148750305	0.011689187958836555	-0.05952819809317589	0.0411117784678936	-0.04978509247303009	-0.04733326658606529	-0.0032580771949142218	-0.007703769952058792	-0.029813721776008606	-0.002909017028287053	0.02996603585779667	-0.031897153705358505	-0.05452294647693634	0.017637697979807854	0.0916990339756012	-0.023237504065036774	0.058136992156505585	0.017762724310159683	0.09019321948289871	-0.04026766121387482	-0.039014436304569244	-0.08100248128175735	0.03334665298461914	0.01481872983276844	-0.004704456776380539	0.007773610297590494	-0.06697346270084381	0.07379889488220215	0.02821383997797966	-0.010489949025213718	-0.05635129660367966	0.031575169414281845	0.04238416254520416	0.07167331874370575	0.011615739203989506	0.059673044830560684	-0.041325125843286514	-0.03840775787830353	0.0039023272693157196	0.041823990643024445	-0.07369977235794067	0.030450958758592606	0.01101172249764204	-0.03819667920470238	0.02486962266266346	-0.035473257303237915	-0.10365524888038635	-0.009044528007507324	-0.015947746112942696	0.004520880989730358	0.00044049086864106357	0.023393290117383003	0.01602153107523918	-0.04413415491580963	-0.005556243937462568	0.004432464949786663	-0.009715139865875244	-0.06218639761209488	0.08850041776895523	-0.025130053982138634	-0.041132356971502304	-0.04165750741958618	0.014919448643922806	0.03687653690576553	0.01855183206498623	0.004907542373985052	0.025683756917715073	0.10620220005512238	0.07206558436155319	-0.017639951780438423	-0.03944264352321625	-0.07253296673297882	-0.05109594762325287	0.016966212540864944	0.012018225155770779	-0.03447747975587845	-0.03818446770310402	0.0008205214398913085	-0.09491187334060669	0.018775837495923042	-0.018291352316737175	0.04428581893444061	-0.009746643714606762	-0.018130939453840256	0.006954689975827932	0.0634443461894989	0.010429616086184978	-0.08783861994743347	0.026761073619127274	-0.020249029621481895	0.0333046056330204	-0.06302513927221298	-0.010661878623068333	0.025761928409337997	0.008106574416160583	0.050018858164548874	-0.046860214322805405	0.05365223437547684	-0.02020375430583954	-0.11068777740001678	-0.03192748874425888	-0.08484254032373428	-0.04445214569568634	0.02676915004849434	-0.02014913037419319	-0.03139548376202583	0.06715259701013565	0.03411996737122536	-0.01973666250705719	-0.043643735349178314	-0.021052837371826172	-0.03174438327550888	-0.034273695200681686	-0.021345308050513268	-0.007611189037561417	-0.06359819322824478	0.008111389353871346	0.0307893268764019	-0.08273901790380478	-0.01852353662252426	0.050457343459129333	-0.048734329640865326	-0.02579931914806366	0.05081557855010033	-0.03378232941031456	-0.016175396740436554	0.019379369914531708	-0.057171788066625595	0.030056945979595184	0.09843070805072784	0.015970535576343536	0.045260149985551834	0.06134141609072685	0.0258374884724617	0.04359126463532448	0.02644522674381733	-0.04219791665673256	-0.010785073041915894	-0.018703974783420563	-0.06240785866975784	0.03315712884068489	-0.040756307542324066	0.014382023364305496	-0.005142484791576862	-0.004485632758587599	0.07973878085613251	-0.024260366335511208	-0.028385162353515625	-0.018680285662412643	0.022191695868968964	0.015264385379850864	0.0062467074021697044	0.03382789343595505	-0.02140664868056774	0.03764277696609497	-0.06205157935619354	-0.02836945652961731	-0.06762119382619858	0.03744871914386749	-0.02828919142484665	0.031719572842121124	0.013324190862476826	-0.05181126669049263	0.02001224271953106	0.015389191918075085	0.0909196063876152	-0.0759686604142189	0.040078289806842804	-0.009073717519640923	0.027901457622647285	-0.11937659233808517	-0.02770593948662281	0.015796581283211708	-0.016344977542757988	-0.0480254590511322	0.05693965032696724	0.018198633566498756	-0.07002177834510803	-0.020522108301520348	-0.011011178605258465	-0.047228988260030746	-0.021363640204072	0.043186213821172714	-0.0011759608751162887	0.026344984769821167	0.021933402866125107	-0.0357871875166893	-0.03200194984674454	0.013752027414739132	0.006302061025053263	-0.03418244794011116	0.04352783039212227	-0.035450782626867294	0.04841530695557594	-0.023757724091410637	-0.07627090811729431	-0.05945810303092003	0.04444143921136856	-0.000682527432218194	-0.015145399607717991	-0.017620176076889038	-0.024759231135249138	0.0013146995333954692	0.03477661684155464	0.008799994364380836	-0.053867071866989136	0.00774870440363884	0.006600585300475359	-0.0655302032828331	-0.09201716631650925	-0.03625024855136871	0.00773069029673934	-0.042736854404211044	0.028503388166427612	-0.04496312513947487	0.029338141903281212	0.00454249931499362	-0.02362089790403843	0.01653233915567398	0.06186273694038391	0.02806917019188404	0.02882990427315235	-0.003332676598802209	-0.010251489467918873	0.028000377118587494	-0.012481487356126308	-0.04126126319169998	0.03650428354740143	0.02755839191377163	-0.05952540412545204	-0.004578493069857359	-0.04841601476073265	-0.02045428194105625	-0.0003401131252758205	-0.042091015726327896	-0.004594133701175451	0.02966013364493847	-0.003952082246541977	-0.03419184684753418	0.04423099756240845	-0.04574179649353027	0.0024400674737989902	0.0404161736369133	-0.0027584810741245747	-0.10398375242948532	-0.016553359106183052	-0.032806914299726486	-0.03558585047721863	0.06717189401388168	-0.007003646343946457	0.030448464676737785	0.010542000643908978	-0.05455884337425232	-0.014902574010193348	-0.002234086161479354	-0.0017712009139358997	-0.014055210165679455	0.033662501722574234	0.08213933557271957	-0.03215688839554787	0.0017940016696229577	0.013988858088850975	-0.012746698223054409	0.03144096955657005	-0.07908368855714798	-0.00924934633076191	-0.010861909948289394	0.034556906670331955	-0.002385232364758849	0.021801427006721497	0.008061309345066547	-0.06881558150053024	-0.023434046655893326	-0.01230494026094675	0.07965178787708282	-0.026454126462340355	-0.0008221471798606217	0.04135824367403984	-0.06189711391925812	-0.03485107421875	0.009479735046625137	-0.0018004170851781964	-0.09636850655078888	0.005675276275724173	-0.0802384689450264	0.03653091937303543	0.011300391517579556	0.04482278600335121	-0.02641916275024414	-0.09645534306764603	-0.029676195234060287	-0.001963484100997448	-0.017696162685751915	-0.008940014988183975	0.016794268041849136	-0.059584345668554306	-0.045376069843769073	-0.04042784869670868	-0.07353031635284424	0.0960765853524208	0.016624361276626587	0.003668650519102812	-0.019609857350587845	-0.012314965948462486	0.013266156427562237	-0.024837955832481384	0.006959099322557449	0.004741044249385595	-0.08570903539657593	-0.039781730622053146	0.06725428998470306	-0.0018211494898423553	0.0798187106847763	0.0769873857498169	-0.023262379691004753	0.06430013477802277	0.05392613634467125	0.0031803303863853216	0.009048116393387318	-0.008141493424773216	-0.021396391093730927	0.018369346857070923	-0.02775518223643303	0.058741819113492966	-0.037740737199783325	0.11256913095712662	0.01840886101126671	-0.02951267920434475	0.06581679731607437	0.09332434833049774	0.012154712341725826	0.009391886182129383	-0.05699476972222328
+0.03302904590964317	0.0229178536683321	0.023339811712503433	0.07663576304912567	-0.08100968599319458	-0.03428065404295921	0.0030152860563248396	0.005598404444754124	0.015739895403385162	-0.034829169511795044	0.012922253459692001	-0.0326455794274807	0.0240183025598526	0.021254904568195343	0.02661236748099327	-0.04300645738840103	-0.06699556112289429	0.003613570937886834	-0.013946874998509884	-0.053888238966464996	0.018985189497470856	-0.0031774051021784544	-0.06990958750247955	0.04846002906560898	0.006541665643453598	0.006732604932039976	-0.0576603002846241	0.00045971942017786205	-0.008004505187273026	0.011920806020498276	0.0665433257818222	0.00492647523060441	0.04279617592692375	-0.06824921816587448	-0.04643787071108818	0.02459436096251011	-0.0031090471893548965	0.0938989594578743	0.017461759969592094	-0.06034693121910095	-0.04599247872829437	0.01776111125946045	-0.026462161913514137	-0.04485756903886795	0.0022646060679107904	-0.016051139682531357	-0.005106281489133835	-0.012519990094006062	0.05378599464893341	0.003974542021751404	0.08204800635576248	0.0026916973292827606	-0.03402598202228546	-0.05402487516403198	-0.005631758365780115	-0.007188544608652592	0.05119149014353752	-0.08979129791259766	-0.08285513520240784	0.05781622231006622	-0.08555402606725693	0.011689803563058376	0.015620820224285126	-0.0015973327681422234	-0.0047589559108018875	-0.04362417384982109	-0.0076619163155555725	0.07660029083490372	0.021840324625372887	-0.027787206694483757	0.049388136714696884	0.12254678457975388	-0.08021863549947739	-0.04118357598781586	0.07374555617570877	-0.07246425747871399	-0.00564350001513958	0.053402818739414215	-0.007231653667986393	0.029204566031694412	0.00012157550372648984	-0.06144821271300316	0.08022654056549072	-0.016338059678673744	-0.01812722533941269	-0.028133930638432503	0.016880031675100327	-0.04855518788099289	0.06412249803543091	0.022421224042773247	0.008257187902927399	0.06442081183195114	-0.0750073492527008	0.07182999700307846	0.030959466472268105	0.01918298564851284	0.03696981444954872	-0.1320471167564392	-0.03211342915892601	0.03874831274151802	-0.008671337738633156	-0.019803812727332115	0.03462325409054756	-0.006043279077857733	0.03117794543504715	0.04743589833378792	-0.06982091069221497	0.018342837691307068	0.05153591185808182	0.06660225987434387	0.07818645983934402	0.07345423847436905	0.06912394613027573	0.05898907780647278	-0.0036107017658650875	0.006152593996375799	0.01540975272655487	-0.04674890264868736	-0.027419043704867363	0.05673794075846672	0.015946440398693085	0.04477698728442192	-0.02509172074496746	-0.05654614046216011	-0.03838202729821205	0.03527560830116272	-0.06937691569328308	-0.02453831024467945	0.06824146211147308	-0.001851608045399189	0.12716391682624817	0.040105000138282776	0.013804718852043152	0.05774872004985809	0.059459615498781204	0.06648306548595428	-0.007331295404583216	0.008127957582473755	-0.009028310887515545	-0.021282581612467766	-0.039287421852350235	0.05856585502624512	0.024427730590105057	-0.07991664111614227	0.03574144095182419	0.06365492194890976	0.01538238674402237	-0.01205260120332241	-0.0410921685397625	-0.030174151062965393	-0.06548987329006195	-0.06155812740325928	0.021315930411219597	-0.07799314707517624	0.011351612396538258	0.05923443287611008	0.02230668254196644	0.04902006685733795	-0.017680006101727486	-0.018061814829707146	-0.04415798559784889	-0.02700600028038025	-0.0035682939924299717	-0.06684199720621109	-0.04228823632001877	-0.04476312920451164	0.011246060021221638	0.03929685056209564	-0.05659372732043266	-0.016118792816996574	0.008274460211396217	0.008298033848404884	0.04354707524180412	0.06999031454324722	0.03794737905263901	-0.0006741425604559481	0.025540051981806755	-0.03076261840760708	0.011019348166882992	-0.02561374008655548	0.028732426464557648	-0.036356788128614426	0.03566930443048477	-0.057451214641332626	-0.01420859806239605	0.005466090049594641	0.04588308185338974	-0.014058082364499569	-0.00805407389998436	0.003059966955333948	-0.08554092049598694	-0.05010112002491951	0.04339273273944855	0.05479028820991516	-0.030976787209510803	0.04734766483306885	-0.02382304146885872	0.03647252172231674	0.020911097526550293	-0.011395080015063286	-0.04683345556259155	0.02323741838335991	0.015724964439868927	0.050804320722818375	0.00984478835016489	-0.07294384390115738	0.04937693476676941	0.012748942710459232	0.016623318195343018	-0.06369972229003906	-0.015958966687321663	0.006153727415949106	0.020497160032391548	0.043457645922899246	0.07795275002717972	0.006854200270026922	0.021758878603577614	-0.010337668471038342	0.07317332178354263	-0.058841582387685776	0.05354302003979683	-0.0067533026449382305	-0.07073068618774414	0.03768095001578331	-0.05125143378973007	-0.08099488168954849	-0.028144853189587593	0.0060476865619421005	-0.011694096028804779	-0.04441579431295395	0.0020089654717594385	0.03944960981607437	-0.015394880436360836	-0.02848494052886963	-0.025645677000284195	0.0077031757682561874	-0.09060288965702057	0.12367518991231918	0.019479485228657722	-0.05719657614827156	-0.05721314623951912	-0.0031488845124840736	0.004538028500974178	-0.01649797149002552	0.03839400038123131	0.04156338423490524	0.10341022908687592	0.0396997407078743	0.00870192889124155	0.009598645381629467	-0.05464077368378639	-0.09245076030492783	0.018700793385505676	0.049399297684431076	-0.06394055485725403	-0.015481297858059406	-0.0007691630162298679	-0.0684710219502449	0.0194063913077116	-0.023456266149878502	0.00179356150329113	0.004413834307342768	-0.009043194353580475	-0.045630499720573425	0.06568951159715652	-0.032705482095479965	-0.0732756108045578	0.030183183029294014	0.008597965352237225	0.049635570496320724	0.006251049228012562	-0.01935681328177452	0.04604062810540199	0.017773911356925964	0.01785494014620781	-0.01934681460261345	0.03401467576622963	-0.021030589938163757	-0.08823435753583908	-0.00504881888628006	-0.05565762147307396	-0.045182403177022934	-0.02133450098335743	-0.007986526004970074	-0.03895413875579834	0.07156793773174286	-0.0072948443703353405	-0.04721839353442192	-0.04750122129917145	-0.01378664281219244	-0.019576545804739	-0.09480615705251694	-0.036075226962566376	0.02044443041086197	-0.06072371453046799	-0.016704831272363663	0.012677988968789577	-0.052894044667482376	-0.02704530768096447	0.0052376179955899715	-0.041954752057790756	-0.0530536063015461	0.05844022333621979	-0.042137738317251205	-0.02414631098508835	0.0024858834221959114	-0.06699060648679733	0.05240519717335701	0.08508952707052231	0.024743137881159782	0.02559308335185051	0.04692695289850235	0.04793018847703934	0.09562066197395325	0.03115243837237358	-0.044596247375011444	0.027868177741765976	-0.05164071172475815	-0.08597911149263382	0.034434933215379715	-0.03210729733109474	-0.01463543064892292	0.004328777547925711	-0.0030253706499934196	0.08557035028934479	-0.010657832957804203	-0.0254344679415226	0.006870492827147245	0.0648813247680664	-0.02867056615650654	0.03142283111810684	0.023947011679410934	-0.008587967604398727	0.007468853145837784	-0.0642046257853508	-0.029799818992614746	-0.04494703188538551	0.03305064141750336	-0.015873391181230545	0.0613650418817997	0.0023907136637717485	-0.06855686753988266	-0.009653426706790924	0.0017945232102647424	0.11412055045366287	-0.08571886271238327	-0.0013816269347444177	0.022058486938476562	0.007740586064755917	-0.10206626355648041	-0.01812145672738552	0.021528780460357666	-0.038544557988643646	-0.014011313207447529	0.026013154536485672	0.02783132903277874	-0.06761223822832108	0.0011981892166659236	0.02536780759692192	-0.04013975337147713	-0.0039305598475039005	0.02629273384809494	-0.013805302791297436	0.0015268252464011312	-0.04343470185995102	-0.010998492129147053	-0.03672851249575615	-0.014470868743956089	0.0014339699409902096	0.002175753004848957	0.04463980719447136	-0.043406352400779724	0.04646511748433113	-0.019026799127459526	-0.025065356865525246	-0.0845041498541832	0.06361336261034012	0.004620345775038004	-0.01448887586593628	-0.013624346815049648	-0.015515755861997604	-0.005022414028644562	0.01143574994057417	0.02694931998848915	-0.07480104267597198	-0.011648896150290966	-0.06273240596055984	-0.017138155177235603	-0.043504394590854645	-0.04297221824526787	0.004144812934100628	-0.07808955758810043	0.009156649000942707	-0.02815988101065159	-0.0011390737490728498	-0.015805430710315704	-0.029747765511274338	0.02338341809809208	0.040230512619018555	0.014992699958384037	-0.030321408063173294	0.04748867452144623	-0.005909173283725977	0.04988580569624901	0.03125493600964546	-0.058484774082899094	0.03250749781727791	0.05514388531446457	-0.04143170267343521	0.03944598510861397	-0.022669553756713867	0.013081841170787811	-0.002949874382466078	-0.003051642095670104	0.04071897640824318	-0.017365921288728714	-0.022165663540363312	-0.014431177638471127	0.04821423068642616	-0.03323746100068092	-0.03415924310684204	0.01283160038292408	-0.0527254194021225	-0.08568861335515976	-0.0035616527311503887	0.01548116933554411	-0.05105825141072273	0.04173578694462776	-0.005137890577316284	0.05307779461145401	0.02857312373816967	-0.05802730843424797	-0.0497199222445488	-0.006618783809244633	-0.027700627222657204	0.030169859528541565	-0.029413865879178047	0.041939038783311844	-0.016679203137755394	-0.009962833486497402	0.02899421937763691	-0.0002795912732835859	0.02363261766731739	-0.09849352389574051	-0.0027837390080094337	-0.041849229484796524	-0.03290485218167305	0.029367836192250252	-0.0008694088901393116	0.022145820781588554	-0.06503254175186157	-0.04914378002285957	-0.029955023899674416	0.06473053246736526	-0.017581487074494362	-0.008607217110693455	0.02201918140053749	-0.013029644265770912	0.010999929159879684	0.02546285279095173	-0.015993667766451836	-0.04909282177686691	-0.0034451447427272797	-0.026413382962346077	0.0197062436491251	0.006295138504356146	0.04680453985929489	-0.03416161239147186	-0.07202143967151642	-0.03599129989743233	0.03304853290319443	-0.09792204946279526	-0.002062659477815032	-0.009697581641376019	-0.057068999856710434	-0.07909003645181656	-0.008703503757715225	-0.0586719773709774	0.0734400674700737	0.03613727167248726	0.019993416965007782	0.004964506719261408	-0.008680080063641071	-0.0020386686082929373	-0.08627277612686157	0.000886287132743746	0.0050407289527356625	-0.04011255502700806	-0.04372997209429741	0.07613128423690796	-0.02020213194191456	0.03570500388741493	0.04626450687646866	-0.04326387867331505	0.04491107165813446	0.017954090610146523	-0.053892090916633606	0.04244483262300491	-0.021343160420656204	-0.03851763531565666	0.07521060854196548	-0.07872825115919113	0.10851116478443146	-0.05378686264157295	0.07664526998996735	-0.013623662292957306	-0.033161938190460205	0.07615695148706436	0.10806506127119064	-0.015578379854559898	0.0004488153208512813	-0.04768188297748566
+0.02550019882619381	-0.011690123938024044	0.041779182851314545	-0.0038763596676290035	-0.0797497108578682	-0.049431752413511276	0.029599634930491447	0.0012809516629204154	0.02172447368502617	-0.02819235809147358	-0.0412297397851944	-0.006283348426222801	0.008194481022655964	0.023742545396089554	0.045587874948978424	-0.04962466284632683	-0.027248745784163475	0.02195429429411888	-0.045664187520742416	-0.012589683756232262	0.039439622312784195	-0.006497764028608799	-0.05122948810458183	0.0362643226981163	-0.00918770581483841	-0.0029147425666451454	-0.07425332814455032	0.034905094653367996	-0.006656206678599119	0.012317201122641563	0.06175230070948601	-0.02294226735830307	0.028108004480600357	-0.06380971521139145	-0.03081415593624115	0.011587384156882763	-0.04476756229996681	0.07950985431671143	0.00784511398524046	-0.02618357725441456	-0.012787440791726112	0.008677216246724129	-0.030630024150013924	-0.015498210676014423	0.028246436268091202	0.008894482627511024	0.022097041830420494	-0.008687215857207775	0.02464333549141884	-0.0018339065136387944	0.0942147970199585	0.03391307592391968	-0.04267753288149834	-0.04266243800520897	0.017014697194099426	-0.01629454270005226	0.025573285296559334	-0.06363602727651596	-0.07066106796264648	0.07121814042329788	-0.11034242063760757	0.013657189905643463	0.014049881137907505	-0.017682015895843506	-0.023017259314656258	-0.04248955845832825	-0.018113374710083008	0.09504467993974686	-0.0035568876191973686	-0.028123971074819565	0.04928067699074745	0.10935234278440475	-0.07645133882761002	-0.08524274080991745	0.07393193244934082	-0.05955072492361069	0.04187047481536865	0.047710396349430084	-0.027577469125390053	-0.0036384148988872766	-0.05375422537326813	-0.06316729635000229	0.10229901224374771	-0.05303817242383957	-0.0700221061706543	-0.06347491592168808	-0.0013284365413710475	-0.06829230487346649	0.052138712257146835	0.048665307462215424	0.019536178559064865	0.04117020592093468	-0.03795689716935158	0.054904669523239136	-0.003885352984070778	-0.011583833955228329	0.007043700199574232	-0.09472113847732544	-0.024097487330436707	-0.0005934418877586722	0.01595827378332615	-0.010294835083186626	0.03939991816878319	0.008458550088107586	0.03883393108844757	0.0694512128829956	-0.030903378501534462	0.0559968501329422	0.02807447873055935	0.0933462455868721	0.013498412445187569	0.05571509897708893	0.07393012195825577	0.08062591403722763	0.029157619923353195	-0.0017529820324853063	-0.001726991729810834	-0.055168259888887405	-0.018139967694878578	0.0024846612941473722	0.04183170199394226	0.05246036872267723	-0.004179009236395359	-0.07006720453500748	-0.015080790035426617	0.05830278992652893	-0.10254132002592087	-0.01060077641159296	0.05678243935108185	-0.014408718794584274	0.09752876311540604	0.046818289905786514	-0.0262954942882061	0.045203980058431625	0.05452525243163109	0.0251317135989666	-0.009992615319788456	0.006762731354683638	-0.013050606474280357	-0.07499004155397415	-0.019803907722234726	0.028504865244030952	0.020352104678750038	-0.04773181676864624	0.03634274750947952	0.04219059273600578	0.05544532835483551	-0.057155217975378036	-0.06626597046852112	-0.007614112924784422	-0.08443383127450943	-0.09578501433134079	0.013858244754374027	-0.05032544955611229	-0.0024626641534268856	-0.006967826746404171	0.011449386365711689	0.003916820511221886	-0.02255239151418209	0.0015487252967432141	-0.044295597821474075	-0.05009537935256958	-0.061330560594797134	-0.04825417697429657	0.01241077296435833	0.008935037069022655	0.04108026251196861	0.01641838252544403	-0.07016276568174362	-0.05441710352897644	0.007794358301907778	0.01405774150043726	0.020305197685956955	0.05359688028693199	0.028376324102282524	-0.014790583401918411	0.04166746139526367	-0.026173779740929604	0.0014154925011098385	-0.029615700244903564	0.022909818217158318	-0.009425334632396698	0.049511395394802094	-0.07285072654485703	0.0007356491405516863	-0.018838252872228622	0.013119676150381565	-0.0760236456990242	0.007956402376294136	-0.025156108662486076	-0.021300287917256355	-0.06014537811279297	0.02013104222714901	0.07082514464855194	-0.03246789425611496	0.016619905829429626	-0.016417182981967926	0.032662782818078995	-0.02322484366595745	-0.04834557697176933	-0.0411975234746933	0.02929355390369892	0.010591846890747547	0.022747550159692764	-0.01923387683928013	-0.10256867110729218	0.05381901562213898	0.01427373941987753	0.02070748433470726	-0.04340497776865959	0.026520827785134315	0.0259777270257473	0.00322753400541842	0.013481172733008862	0.07232517749071121	-0.04732653871178627	0.010843348689377308	-0.010374993085861206	0.057813432067632675	-0.02785036712884903	0.04611743241548538	-0.004783664830029011	-0.0767788216471672	-0.004616546910256147	-0.07767169922590256	-0.06902044266462326	-0.005878965370357037	0.023938937112689018	-0.013844595290720463	-0.035228971391916275	-0.020272081717848778	-0.001387189025990665	-0.011863416992127895	-0.0012152681592851877	0.015868768095970154	0.02474883943796158	-0.09911161661148071	0.0870918408036232	0.001354038598947227	-0.04235909506678581	-0.046015143394470215	-0.027111612260341644	0.0492057241499424	0.008806559257209301	0.04034978896379471	0.022463759407401085	0.11514320224523544	0.03515824303030968	-0.015355230309069157	-0.008720504119992256	-0.005301783327013254	-0.0385536327958107	0.010513094253838062	0.008278409019112587	-0.06601101905107498	-0.01466379128396511	0.026467422023415565	-0.06269612163305283	-0.020344391465187073	-0.010240421630442142	0.01423577219247818	-0.00524634076282382	-0.028263788670301437	-0.014357184059917927	0.05342930555343628	-0.006651686504483223	-0.071875661611557	0.031793493777513504	-0.004122861661016941	0.07372379302978516	-0.00647406280040741	-0.03182308003306389	0.029405565932393074	-0.017675243318080902	0.03190917521715164	-0.0719243660569191	0.0363665334880352	-0.019276674836874008	-0.06886950880289078	-0.028266141191124916	-0.05402780696749687	-0.07290444523096085	-0.001495412434451282	-0.02848537638783455	-0.03150051459670067	0.06001848727464676	-0.005231399089097977	-0.03881571441888809	-0.06482041627168655	-0.04020314663648605	-0.013815523125231266	-0.032180264592170715	-0.03973560035228729	-0.030583282932639122	-0.03229755163192749	0.00873999111354351	0.03923511132597923	-0.07232356816530228	-0.038947321474552155	-0.007039508316665888	-0.01161711011081934	-0.070294089615345	0.04335162043571472	-0.051688045263290405	0.002211038488894701	0.005823400802910328	-0.04464062675833702	0.03269803524017334	0.09830253571271896	0.05102848634123802	0.0430026613175869	0.027593517675995827	0.002988398540765047	0.11337097734212875	0.010252514854073524	-0.06748221814632416	-0.019457843154668808	-0.05788828060030937	-0.08398433029651642	0.012159667909145355	-0.04347889497876167	-0.009971809573471546	0.009115761145949364	0.014473349787294865	0.05026139318943024	-0.03653498366475105	-0.028426796197891235	-0.011151488870382309	0.024339444935321808	-0.024196716025471687	-0.029926855117082596	0.013229165226221085	0.027813443914055824	-0.00570891797542572	-0.03966455161571503	-0.05684386193752289	-0.0025886890944093466	0.04898804426193237	-0.0297333225607872	0.06449999660253525	-0.06560498476028442	-0.0563722662627697	-0.030273975804448128	0.029744505882263184	0.11100194603204727	-0.032637834548950195	0.03628414869308472	0.036012958735227585	0.019036969169974327	-0.08934871852397919	0.04241378232836723	0.024250561371445656	-0.006869801785796881	-0.014978214167058468	0.014410744421184063	0.05208635330200195	-0.0646548867225647	0.0017646660562604666	0.018140969797968864	-0.003653737483546138	-0.00019644061103463173	0.0441601499915123	-0.010710473172366619	0.034147702157497406	-0.04789289832115173	0.007960999384522438	-0.0324820801615715	0.010360750369727612	0.04132479429244995	-0.009402759373188019	0.021384689956903458	-0.04697592929005623	0.0641767829656601	-0.014048843644559383	-0.04210875928401947	-0.04574177414178848	0.05762869492173195	-0.00885725487023592	-0.0010701263090595603	-0.004244564566761255	0.0046159373596310616	0.006235147826373577	-0.003460332052782178	0.01676456443965435	-0.0656157061457634	0.040142908692359924	-0.03244609385728836	-0.05400702729821205	-0.08029378205537796	-0.062074169516563416	-0.012034596875309944	-0.051372162997722626	-0.012586994096636772	0.012066327966749668	0.014442085288465023	-0.013345055282115936	-0.031415462493896484	0.0015936099225655198	0.060614295303821564	0.04050349444150925	0.010674149729311466	0.05205228179693222	0.025487162172794342	0.048862360417842865	-0.021098030731081963	-0.08152248710393906	0.047843944281339645	0.018948987126350403	-0.04262975975871086	0.04723423719406128	-0.04861610382795334	0.03082466684281826	0.002739271381869912	0.019457828253507614	0.021309880539774895	-0.026930296793580055	-0.02126207761466503	-0.030040282756090164	0.0980818122625351	-0.07270853221416473	-0.04467214271426201	0.0437624454498291	-0.06149657815694809	-0.08581611514091492	0.021483013406395912	-0.03946605697274208	-0.05734080821275711	0.02667774073779583	-0.049266502261161804	0.03584874048829079	0.007745004259049892	-0.039145827293395996	-0.022888462990522385	-0.014722345396876335	-0.03434673696756363	0.046828754246234894	0.007064053323119879	0.09683673828840256	0.019113125279545784	-0.0030275245662778616	0.013742843642830849	-0.019963525235652924	0.04867001995444298	-0.12175840884447098	0.013816269114613533	-0.06248657405376434	0.02254560776054859	-0.03274748474359512	-0.011013721115887165	0.05828390270471573	-0.0878991186618805	-0.0203985758125782	-0.05259143188595772	0.06809266656637192	-0.029760103672742844	-0.0024425361771136522	0.020227832719683647	-0.03364510089159012	-0.03262527287006378	0.0008700768812559545	-0.04481841251254082	-0.08571530878543854	0.0017538241809234023	-0.05268662050366402	0.03862117975950241	0.02813306264579296	0.0075888242572546005	-0.006318495608866215	-0.06118547543883324	-0.005945260636508465	0.04153161123394966	-0.03171023353934288	0.021155348047614098	0.034232039004564285	-0.04865795001387596	-0.053214576095342636	0.014880496077239513	-0.0265166275203228	0.053693242371082306	0.027798201888799667	0.021952616050839424	-0.016771508380770683	-0.006364395841956139	0.011968882754445076	-0.027422389015555382	0.046923115849494934	-0.0040449644438922405	-0.08866047114133835	-0.0018552480032667518	0.0843784287571907	-0.07091088593006134	0.032543428242206573	0.04630599543452263	-0.04149550944566727	0.022620264440774918	0.02989618293941021	-0.054212767630815506	0.030387679114937782	-0.03886563703417778	0.019547726958990097	0.0224261824041605	-0.1065661758184433	0.08546575158834457	-0.06403400748968124	0.08377042412757874	0.01358509436249733	-0.026422224938869476	0.09508459270000458	0.07990459352731705	0.0002953641815111041	-0.021310405805706978	-0.0968942791223526
+0.05888843908905983	-0.0044569228775799274	-0.023066001012921333	-0.004693448543548584	-0.08980879187583923	-0.0403420589864254	-0.019833723083138466	0.04749958589673042	-0.036318544298410416	-0.044961895793676376	-0.02623213455080986	-0.028034670278429985	0.005633185151964426	0.013558746315538883	0.06297950446605682	-0.06920583546161652	-0.08238670229911804	-0.00534621300175786	-0.04308857023715973	-0.0015557145234197378	0.0630810484290123	-0.008528484962880611	-0.07765325903892517	0.03784043714404106	0.022276954725384712	0.036548785865306854	-0.02445785515010357	0.019955281168222427	0.0027905378956347704	0.00970371812582016	0.04259791225194931	-0.03668689727783203	-0.002559355925768614	-0.049710050225257874	-0.04813099652528763	0.055877622216939926	-0.04314553737640381	0.11914147436618805	0.04984242096543312	-0.07135557383298874	-0.06547047197818756	0.01786576397716999	-0.07129166275262833	-0.07410997152328491	0.02489616721868515	-0.021060165017843246	-0.02321324311196804	-0.05515041947364807	0.05823449417948723	0.013644172810018063	0.052330970764160156	-0.029895564541220665	-0.0434940867125988	-0.018255002796649933	-0.03268745169043541	0.023756831884384155	0.012549404986202717	-0.04573502019047737	-0.05187937617301941	0.018644990399479866	-0.05791636183857918	0.022825811058282852	-0.0024851833004504442	0.02920614369213581	0.028669260442256927	-0.05749042332172394	0.017826369032263756	0.039647944271564484	0.01800611801445484	0.008312125690281391	-0.03184981644153595	0.10063108056783676	-0.07359888404607773	-0.07740956544876099	0.059226345270872116	-0.06081415340304375	-0.029239622876048088	-0.005028193350881338	0.007343951612710953	-0.005273592192679644	-0.0025676696095615625	-0.024917302653193474	0.14831151068210602	-0.04526900500059128	-0.03467565029859543	-0.050160497426986694	-0.014134172350168228	-0.05009760707616806	0.05882924050092697	0.056497808545827866	0.03139505535364151	0.05998637527227402	-0.05775579437613487	0.04119941219687462	-0.0005445416900329292	0.013462846167385578	0.0523257739841938	-0.1249827966094017	-0.03453048691153526	-0.0466100238263607	0.002689681714400649	-0.04861724004149437	0.05945703387260437	0.01953294686973095	0.006623131688684225	0.022254176437854767	-0.04743609577417374	0.030508605763316154	0.009924574755132198	0.058297451585531235	0.0014249860541895032	0.07982524484395981	0.09726724028587341	0.06515628844499588	0.03267427906394005	-0.0466645322740078	-0.02514035254716873	-0.039873186498880386	-0.02222427912056446	0.025594666600227356	-0.017793525010347366	0.013400604017078876	-0.022595614194869995	-0.058188021183013916	-0.018676582723855972	0.049646444618701935	-0.09052901715040207	-0.018475936725735664	-0.0013819605810567737	-0.03748663142323494	0.0827791839838028	0.060418251901865005	-0.0412263497710228	0.02735884115099907	0.07248295098543167	0.029470179229974747	0.06082097068428993	0.07437259703874588	-0.06028919667005539	-0.040124718099832535	-0.023936882615089417	0.04942825809121132	0.0016403524205088615	-0.035568125545978546	0.02278911881148815	0.04194576293230057	0.03685620054602623	-0.028693046420812607	-0.06048636883497238	0.027775848284363747	-0.10529866814613342	-0.05710947513580322	0.010290398262441158	-0.07977941632270813	0.029084831476211548	0.046940386295318604	0.03354118391871452	0.022013211622834206	-0.004215820226818323	0.006480715237557888	-0.03670148551464081	-0.0864252895116806	-0.009345638565719128	-0.07232248038053513	-0.04582086578011513	-0.028477462008595467	0.027930893003940582	-0.018077120184898376	-0.0229366272687912	-0.0307046826928854	0.03024156205356121	0.0023894126061350107	0.025076674297451973	0.06085687130689621	0.028300544247031212	0.0037497212179005146	0.0007839265163056552	-0.039035044610500336	-0.02962549775838852	0.003848677035421133	0.03994870185852051	-0.06451202929019928	0.006027062423527241	-0.05732171982526779	-0.0040130699053406715	-0.00892119575291872	0.01807975210249424	-0.014477844350039959	0.00705915130674839	-0.03031212091445923	-0.0549149215221405	-0.07474158704280853	0.01894562877714634	0.04064098745584488	-0.0322069451212883	0.057437412440776825	-0.019140494987368584	0.043768443167209625	-0.0200017299503088	-0.018144160509109497	-0.06491810083389282	0.05192498490214348	-0.038004226982593536	0.06486966460943222	0.024625305086374283	-0.05103054270148277	0.050224099308252335	-0.014885668642818928	0.03429873660206795	-0.05170934647321701	0.03580819070339203	0.015581537038087845	0.008150510489940643	0.005501689855009317	0.05309208482503891	-0.04228578135371208	-0.02392168715596199	0.033839151263237	0.0809626579284668	-0.04012623801827431	0.017106633633375168	0.03545353189110756	-0.10350708663463593	0.05540689453482628	0.0018803097773343325	-0.06710465252399445	-0.043758850544691086	0.010650893673300743	-0.03483152016997337	-0.06711059808731079	-0.005891169421374798	0.03425721824169159	-0.005600516684353352	-0.04495532810688019	0.031953830271959305	0.004921414889395237	-0.06848250329494476	0.08014275878667831	-0.02450312301516533	-0.07149053364992142	-0.05342276021838188	0.006035082042217255	-0.0020994809456169605	0.002241102047264576	0.016847429797053337	0.05092320218682289	0.08972612768411636	-0.0005507678142748773	0.0014439517399296165	0.03090033121407032	-0.07474217563867569	-0.06222832202911377	-0.026658203452825546	0.0304621160030365	-0.01959797367453575	-0.06289336830377579	0.018052589148283005	-0.022921063005924225	0.0018094489350914955	-0.036615774035453796	0.04657469317317009	-0.010328030213713646	-0.015999607741832733	-0.05777466669678688	0.08346197009086609	-0.023293927311897278	-0.044881634414196014	0.025818685069680214	-0.02825106307864189	0.04292664676904678	0.007543159183114767	-0.017232023179531097	0.028667394071817398	-0.006523451767861843	0.035968292504549026	-0.014057627879083157	0.05710155889391899	-0.0709102600812912	-0.05596359446644783	-0.035305868834257126	-0.07267039269208908	-0.08158189058303833	-0.016042856499552727	-0.021230995655059814	-0.03907763212919235	0.09711667895317078	0.011062567122280598	-0.01427332405000925	-0.034692153334617615	-0.021870022639632225	-0.016618378460407257	-0.07138391584157944	-0.024312064051628113	0.014173188246786594	-0.09051232784986496	-0.06022178381681442	0.05489848181605339	-0.03564903512597084	-0.0507960319519043	0.00601950054988265	-0.016265830025076866	-0.06553516536951065	0.034267716109752655	-0.03177514299750328	-0.009407945908606052	0.004028939176350832	-0.06470129638910294	0.04485875740647316	0.07509755343198776	0.06791729480028152	0.042939845472574234	0.029154110699892044	0.018358083441853523	0.09646537899971008	0.009274636395275593	-0.0362907350063324	-0.017357181757688522	-0.07035578042268753	-0.11683004349470139	0.05153089389204979	-0.025060800835490227	-0.02257435768842697	-0.030010513961315155	-0.021579178050160408	0.06261762231588364	-0.04096793010830879	0.003568398766219616	-0.032290972769260406	0.009833935648202896	-0.038900282233953476	0.0501389242708683	-0.011366666294634342	0.0034051949623972178	-0.02015521191060543	-0.042653802782297134	-0.05552307143807411	-0.07659078389406204	0.018205370754003525	-0.013576668687164783	0.02669447287917137	0.00643555074930191	-0.01673334650695324	-0.009664276614785194	0.015708453953266144	0.09754883497953415	-0.020693859085440636	0.033089529722929	0.02579638548195362	0.012486623600125313	-0.08108174800872803	-0.036152780055999756	-0.005242523737251759	-0.0175608042627573	-0.03342340141534805	-0.014030033722519875	0.052219461649656296	-0.0572516992688179	-0.032973963767290115	0.021143553778529167	-0.02666538767516613	0.003679984947666526	0.01960557885468006	0.006662564817816019	0.053313616663217545	-0.012390650808811188	-0.039111457765102386	0.06053338572382927	-0.017602993175387383	0.040867045521736145	-0.02042662724852562	0.03119625337421894	-0.024127250537276268	0.07948794960975647	-0.03204992786049843	-0.06983214616775513	-0.09077531099319458	0.05862048268318176	0.002841467969119549	-0.052884358912706375	-0.03461030498147011	0.03224698826670647	-0.02218121849000454	0.05222848430275917	0.010072324424982071	-0.01942502148449421	-0.03244946151971817	-0.030939634889364243	-0.01935351826250553	-0.06696967780590057	0.00528918020427227	-0.0025829884689301252	-0.055777955800294876	0.0013173933839425445	-0.042065542191267014	-0.02146824635565281	0.044959064573049545	-0.03500683233141899	0.010040262714028358	0.009777351282536983	0.044752512127161026	-0.01703677326440811	0.05744641274213791	0.011743896640837193	0.034641824662685394	0.008488801307976246	-0.006642427295446396	0.002136929426342249	0.03695878013968468	-0.05073250085115433	0.05254136770963669	-0.032213788479566574	0.039785873144865036	-0.006726242136210203	0.02876146510243416	0.03877727687358856	-0.05240190029144287	0.035247690975666046	-0.007156273350119591	0.01795324496924877	-0.07750309258699417	-0.06456361711025238	0.031797830015420914	-0.039449892938137054	-0.09327232837677002	0.027701254934072495	-0.007572282571345568	-0.02742200344800949	0.024311866611242294	-0.010927699506282806	0.012436113320291042	0.04879293963313103	-0.06857649981975555	-0.02885359898209572	-0.04131997749209404	-0.013701366260647774	0.046105798333883286	-0.007460401859134436	0.05526455491781235	0.03105316311120987	-0.010604687966406345	0.050250619649887085	-0.00560831418260932	0.007127008400857449	-0.06582460552453995	-0.01872823014855385	-0.00989637803286314	-0.0068024201318621635	0.05701453611254692	-0.010197759605944157	0.06002388894557953	-0.015240118838846684	-0.025489160791039467	-0.025899719446897507	0.06802322715520859	0.006429692730307579	0.0037749854382127523	0.0496809184551239	-0.044561538845300674	0.011646202765405178	-0.017218507826328278	-0.033047184348106384	-0.045610178261995316	0.014959882013499737	-0.04435546696186066	0.0451992005109787	0.036325037479400635	0.03870385140180588	0.0273390281945467	-0.06886336207389832	-0.0169865433126688	-0.009660325013101101	-0.05397499352693558	0.05539235472679138	0.01047385111451149	-0.0138448067009449	-0.07070140540599823	-0.03682371601462364	-0.058839350938797	0.039156049489974976	-0.04923605918884277	0.02939423732459545	-0.008248522877693176	0.02942134439945221	0.0034581718500703573	-0.035250186920166016	0.018935365602374077	0.0019122147932648659	-0.07672221958637238	-0.014952694065868855	0.10948465764522552	-0.02378932014107704	0.026781806722283363	0.07579369097948074	0.010677914135158062	0.005295419134199619	0.01872626505792141	-0.027287505567073822	-0.008378743194043636	-0.015458430163562298	-0.027567129582166672	0.07286109030246735	-0.047005508095026016	0.054031793028116226	-0.09723222255706787	0.09571525454521179	-0.0067746941931545734	-0.04195127636194229	0.08253753930330276	0.04442250728607178	0.002879832172766328	0.0462508462369442	-0.03282841667532921
+0.024479323998093605	0.0003247964777983725	-0.006420562509447336	0.058079589158296585	-0.07597880065441132	-0.029166074469685555	-0.006482925731688738	0.04040597751736641	-0.005923648364841938	-0.034969523549079895	0.013117426075041294	-0.035344526171684265	0.0018895522225648165	0.014917703345417976	0.03212588280439377	-0.046617425978183746	-0.09142923355102539	-0.003019272582605481	-0.03605286777019501	-0.05845150351524353	0.035352081060409546	0.005442366935312748	-0.06530961394309998	0.048855893313884735	-0.004828921984881163	0.01237068697810173	-0.03890705108642578	0.006659323815256357	-0.0069982255809009075	0.007694595959037542	0.05931772664189339	-0.006538392044603825	0.0312647707760334	-0.049050141125917435	-0.05531792342662811	0.06551223993301392	-0.01744886301457882	0.1133948415517807	0.011606813408434391	-0.05599534511566162	-0.040687400847673416	0.015185214579105377	-0.022527800872921944	-0.04157625883817673	0.04593342915177345	-0.0033562006428837776	-0.01315181516110897	-0.010044206865131855	0.027055401355028152	0.01305039506405592	0.1036287322640419	0.0031568973790854216	-0.016977710649371147	-0.04601538926362991	-0.026598013937473297	0.02961812913417816	0.027443982660770416	-0.06393183767795563	-0.0579155795276165	0.052707489579916	-0.0708535835146904	-0.0039045445155352354	0.016398975625634193	-0.010119696147739887	-0.011336993426084518	-0.03672114759683609	-0.019870255142450333	0.09293342381715775	0.011012502014636993	-0.008272361010313034	0.022455330938100815	0.10617894679307938	-0.06720959395170212	-0.061383266001939774	0.09816817194223404	-0.0723516121506691	-0.02443101815879345	0.04999189078807831	-0.022361943498253822	0.015448135323822498	0.008557277731597424	-0.015922408550977707	0.10473296046257019	-0.02434542216360569	-0.038808826357126236	-0.029832564294338226	0.028702476993203163	-0.04791286960244179	0.0773007795214653	0.03809642046689987	0.0008210181258618832	0.07384674996137619	-0.06806261837482452	0.05558066815137863	0.0124886529520154	0.00909251905977726	0.03723583370447159	-0.12148548662662506	-0.04381629452109337	0.005291538778692484	0.022032730281352997	-0.025257471948862076	0.052026551216840744	-0.003071751445531845	0.02158023789525032	0.04673447832465172	-0.06196971610188484	0.007190377451479435	0.026263270527124405	0.03871334344148636	0.05814654380083084	0.10446229577064514	0.07465452700853348	0.05380379036068916	0.05506134405732155	0.0013088530395179987	0.005704554263502359	-0.044307779520750046	-0.03614383935928345	0.03393099829554558	0.0008991824579425156	0.04444827139377594	-0.02531331032514572	-0.063285693526268	-0.03626911714673042	0.018992342054843903	-0.09625827521085739	0.003575090551748872	0.08076687157154083	-0.017209883779287338	0.11037112772464752	0.03389408811926842	0.003725942224264145	0.042484745383262634	0.06650573760271072	0.05050559714436531	0.017699137330055237	0.03225310891866684	-0.034530144184827805	-0.0008064938010647893	-0.021543163806200027	0.05915563553571701	0.018028756603598595	-0.06047236546874046	0.04105527698993683	0.0573137141764164	0.026944464072585106	0.010232385247945786	-0.046824902296066284	-0.008128688670694828	-0.08193899691104889	-0.06989981979131699	-0.01028237771242857	-0.09976399689912796	0.015336384065449238	0.041687797755002975	0.0102553591132164	0.04562985897064209	-0.010000653564929962	-0.031049339100718498	-0.03250101953744888	-0.04893850162625313	-0.015477806329727173	-0.07487981021404266	-0.022794626653194427	-0.05058097466826439	-0.002370957052335143	0.015718718990683556	-0.054417431354522705	-0.012712888419628143	0.025193745270371437	-0.002042253501713276	0.03475607931613922	0.06984590739011765	0.053093649446964264	0.023411596193909645	0.03279251977801323	-0.03810242563486099	-0.028389660641551018	-0.024526868015527725	0.026199856773018837	-0.03330029547214508	0.026163531467318535	-0.07416007667779922	-0.008334388025105	0.0009244601824320853	0.03908621519804001	-0.020756393671035767	-0.003182553919032216	-0.010058199986815453	-0.04742982238531113	-0.0598435141146183	0.07490900903940201	0.044116005301475525	-0.006729745306074619	0.029038986191153526	-0.01701110042631626	0.05117492005228996	-0.018659312278032303	-0.02204316109418869	-0.06094873696565628	0.02646862156689167	0.024169884622097015	0.03849836811423302	0.014271987602114677	-0.060701239854097366	0.07829438894987106	-0.0015880195423960686	0.037078212946653366	-0.06032386049628258	0.01488411147147417	0.014521983452141285	0.0072479541413486	0.04093187674880028	0.07375745475292206	-0.0029322076588869095	-0.015121610835194588	0.009140072390437126	0.0761965960264206	-0.06860676407814026	0.035901084542274475	0.003927140962332487	-0.08417972922325134	0.037243958562612534	-0.024747004732489586	-0.08303630352020264	-0.04240889847278595	0.02528524398803711	-0.024280346930027008	-0.030942728742957115	-0.015833934769034386	0.04440881684422493	-0.02997696027159691	-0.029227005317807198	-0.004043512046337128	0.013699800707399845	-0.0930827409029007	0.0915885716676712	-0.00681192334741354	-0.05970655754208565	-0.07445981353521347	0.010482462123036385	0.014347709715366364	-0.011529343202710152	0.05521787330508232	0.05585191026329994	0.0867626890540123	0.034747444093227386	0.01639517769217491	0.02196054905653	-0.06711310893297195	-0.05421804264187813	-0.01791856810450554	0.028400443494319916	-0.03515728935599327	-0.04837615042924881	-0.00725052785128355	-0.05491194874048233	0.015609968453645706	-0.005949780810624361	0.023087939247488976	-0.030629649758338928	-0.022109173238277435	-0.03805556893348694	0.04033605381846428	-0.05088186636567116	-0.03765394166111946	0.016072247177362442	-0.008738012053072453	0.04420682415366173	-0.013269311748445034	-0.011461202055215836	0.01974359340965748	0.01352037861943245	0.037289153784513474	-0.04778013378381729	0.04938630759716034	-0.02881794236600399	-0.08674748986959457	-0.014554738067090511	-0.0690365806221962	-0.0515567772090435	-0.008469464257359505	-0.06500304490327835	-0.036159589886665344	0.1024584174156189	0.01865798607468605	-0.03630515933036804	-0.06449977308511734	-0.0503191314637661	0.0053520300425589085	-0.05870259180665016	-0.02894643321633339	0.01662042923271656	-0.073160819709301	-0.017641663551330566	0.04395541548728943	-0.034864746034145355	-0.019328344613313675	0.013622159138321877	-0.012880273163318634	-0.03196166455745697	0.049365390092134476	-0.04398222267627716	-0.009009440429508686	-0.002724296413362026	-0.06099652126431465	0.04276585578918457	0.09548015892505646	0.05541345849633217	0.029795175418257713	0.0345296673476696	0.04084701091051102	0.09939707815647125	0.046961959451436996	-0.0483756884932518	0.024017514660954475	-0.06369474530220032	-0.06721614301204681	0.036401815712451935	-0.025120900943875313	-0.020760394632816315	-0.022674424573779106	-0.02584247849881649	0.09330599755048752	-0.021644197404384613	-0.00285571301355958	-0.0005882629193365574	0.0642133355140686	-0.018594687804579735	0.03690525144338608	0.007971230894327164	-0.007519345264881849	0.01986146904528141	-0.050793685019016266	-0.03822232782840729	-0.05573529750108719	0.04142585024237633	-0.0210654865950346	0.036816634237766266	0.007362800184637308	-0.05703068524599075	0.024794960394501686	0.019126836210489273	0.11296620965003967	-0.07627518475055695	-0.00565557973459363	0.0491882860660553	0.0224720798432827	-0.08897628635168076	-0.009937964379787445	-0.006147210020571947	-0.05517847090959549	-0.012871499173343182	0.0018817804520949721	0.04155878350138664	-0.06379467993974686	-0.020027099177241325	-0.006021536886692047	-0.0471147857606411	0.013342776335775852	0.021073967218399048	-0.019110694527626038	0.02189648151397705	-0.04524900019168854	-0.030845142900943756	0.006364327389746904	0.008042044937610626	0.012367365881800652	-0.05508566275238991	0.04999496787786484	-0.03094867616891861	0.04711905121803284	-0.029517538845539093	-0.05587083846330643	-0.0725710391998291	0.059045515954494476	-0.01792384497821331	-0.03074929676949978	-0.036751117557287216	-0.016616778448224068	-0.009151449427008629	0.018317388370633125	0.023920180276036263	-0.06710534542798996	-0.03591623902320862	-0.05172545090317726	-0.04489448666572571	-0.07819081842899323	-0.059800390154123306	0.024929432198405266	-0.0653320848941803	0.00930296815931797	-0.07013385742902756	-0.012670203112065792	-0.012950033880770206	-0.010355775244534016	0.03414097800850868	0.057716771960258484	0.043754804879426956	-0.03141666576266289	0.06293340027332306	0.013146872632205486	0.04105339199304581	0.02029714174568653	-0.027409415692090988	0.03334486857056618	0.035783551633358	-0.010547955520451069	0.05983611196279526	-0.01079113595187664	0.03646136075258255	-0.0076829842291772366	0.0010555338812991977	0.02499210648238659	-0.03564455360174179	-0.025887513533234596	-0.012687092646956444	0.029567936435341835	-0.04868117347359657	-0.029775964096188545	0.014395699836313725	-0.04881441593170166	-0.11373905092477798	-0.028859132900834084	0.00012461052392609417	-0.023483561351895332	0.021217549219727516	-0.0024843099527060986	0.013782437890768051	0.02666541375219822	-0.05379784107208252	-0.04361867904663086	-0.036932844668626785	-0.03435707837343216	0.017529303207993507	-0.010771792382001877	0.05286747217178345	-0.005563884973526001	-0.0064544761553406715	0.006252884864807129	-0.0015127458609640598	0.012284340336918831	-0.08457803726196289	-0.015104198828339577	-0.02986346185207367	-0.051234688609838486	0.04680192843079567	0.011877436190843582	0.004466910380870104	-0.04177538678050041	-0.04193762689828873	-0.036758262664079666	0.07754676043987274	-0.03252173960208893	0.014660588465631008	0.01937841810286045	-0.016553068533539772	0.01667109876871109	0.022670159116387367	-0.025174560025334358	-0.05176880583167076	0.026827875524759293	-0.029776310548186302	0.03496439382433891	0.00434068450704217	0.03639109805226326	-0.012433165684342384	-0.1067982092499733	-0.027778128162026405	0.011334484443068504	-0.08633489906787872	0.014823595061898232	-0.01497676782310009	-0.07735814899206161	-0.049249641597270966	0.009617150761187077	-0.0353132039308548	0.08275635540485382	-0.01351897418498993	0.029574738815426826	-0.012913395650684834	-0.024198660627007484	-0.0042508505284786224	-0.06754017621278763	0.0027524202596396208	-0.02137639932334423	-0.054520439356565475	-0.03419937193393707	0.06472693383693695	-0.03092036582529545	0.0264706052839756	0.07715485244989395	-0.039062272757291794	0.040854956954717636	0.030246790498495102	-0.04282161593437195	0.01828712970018387	0.0020319283939898014	-0.03950626775622368	0.04982827603816986	-0.0590168721973896	0.10146879404783249	-0.08615847676992416	0.0931519865989685	-0.001821403973735869	-0.015028649009764194	0.06958793848752975	0.08550238609313965	-0.014109820127487183	0.027325468137860298	-0.053883664309978485
+0.03946946561336517	0.03075443208217621	0.02959243580698967	0.03232797980308533	-0.11044922471046448	0.0015441077994182706	-0.03427981585264206	0.009638860821723938	0.01691192202270031	-0.07210739701986313	0.010801027528941631	-0.017361275851726532	-0.017544928938150406	-0.0013074890011921525	0.02583462931215763	-0.02465756982564926	-0.07376730442047119	-0.04302018880844116	-0.03913947939872742	-0.0476151667535305	0.05564609169960022	-0.03357455134391785	-0.05333022400736809	0.07020760327577591	-0.002365293214097619	0.04666735604405403	-0.02125079184770584	0.040684644132852554	-0.05870373174548149	0.04890847206115723	0.05661793425679207	-0.023302298039197922	0.019309069961309433	-0.045963186770677567	-0.061988722532987595	0.08439541608095169	0.03101399354636669	0.11582096666097641	0.056057751178741455	-0.0523742213845253	-0.0117379454895854	0.003998736850917339	-0.036757539957761765	-0.04225895181298256	0.04077273607254028	-0.038776859641075134	-0.018993133679032326	-0.01634441502392292	0.06801963597536087	0.009158073924481869	0.06682997196912766	0.028019417077302933	-0.06081240624189377	-0.031178882345557213	0.012378097511827946	0.04766833409667015	0.04380892589688301	-0.07057397812604904	-0.025171691551804543	0.053330760449171066	-0.051995497196912766	-0.009948252700269222	0.0044763581827282906	-0.010938250459730625	0.033962611109018326	0.008000635541975498	0.018957164138555527	0.0688706785440445	0.030110886320471764	-0.017452280968427658	0.020907549187541008	0.10516046732664108	-0.05673487111926079	-0.04034262150526047	0.053682539612054825	-0.05187287554144859	-0.05015752092003822	0.02395826205611229	-0.03045189566910267	0.034921661019325256	-0.026798801496624947	-0.0600261352956295	0.12936735153198242	-0.02754531055688858	-0.029790261760354042	-0.04992574080824852	-0.01664230227470398	-0.05288483574986458	0.07184459269046783	0.027542173862457275	-0.004965487401932478	0.060439109802246094	-0.08228440582752228	0.05266231298446655	-0.023903680965304375	0.011266801506280899	0.04989573732018471	-0.11002202332019806	0.012417256832122803	-0.005765595007687807	-0.023361725732684135	-0.050905171781778336	-0.0047510224394500256	-0.00460507906973362	0.040799450129270554	0.06814505159854889	-0.03648756816983223	0.012079481966793537	0.021704627200961113	0.03767484799027443	0.04645920917391777	0.07521306723356247	0.07912599295377731	0.08096905052661896	0.06716767698526382	-0.002762302290648222	0.0003982100752182305	-0.08773774653673172	-0.01411120779812336	-0.013200750574469566	-0.018643394112586975	0.05593494698405266	-0.009764712303876877	-0.037047360092401505	-0.0027622815687209368	0.0102928401902318	-0.11534493416547775	0.004947981797158718	0.03683452680706978	-0.03263857588171959	0.0736372321844101	0.053911395370960236	-0.006333112251013517	0.033407215029001236	0.03695003688335419	0.03409436717629433	-0.007034861948341131	0.02389248088002205	-0.03088122420012951	-0.03522060066461563	0.011080270633101463	0.007141674403101206	0.01512827631086111	-0.047750916332006454	0.01691472716629505	0.02375747449696064	0.0357954278588295	-0.0023909241426736116	-0.06921660155057907	0.019225407391786575	-0.09857923537492752	-0.05740424245595932	0.021312536671757698	-0.09286028146743774	-0.01543418038636446	0.07725416868925095	0.050270047038793564	0.02332756482064724	-0.010810739360749722	-0.007662213407456875	-0.002092315349727869	-0.04196398705244064	-0.006941612809896469	-0.07659631222486496	-0.039656493812799454	-0.02899441309273243	0.025972187519073486	-0.00025399160222150385	-0.016995174810290337	-0.019138315692543983	0.026354005560278893	0.0222457442432642	0.024970658123493195	0.06752695143222809	0.0670977532863617	-0.008970649912953377	0.024326086044311523	-0.06192823499441147	-0.0007375752902589738	-0.017095090821385384	0.029198933392763138	-0.034332770854234695	0.02456636168062687	-0.0716148093342781	-0.0485227033495903	-0.029474768787622452	-0.003406794276088476	0.0382583886384964	0.020753052085638046	-0.0067895944230258465	-0.06956201046705246	-0.06261134147644043	0.010281901806592941	0.03583453968167305	-0.06336822360754013	0.03167514130473137	0.05333695560693741	0.052709970623254776	-0.02270505577325821	-0.012802896089851856	-0.07905831933021545	0.03141338378190994	0.008324971422553062	0.0777149647474289	0.05201401934027672	-0.05047356337308884	0.045604635030031204	-0.012193286791443825	-0.008163667283952236	-0.056314144283533096	0.03379364684224129	0.025520607829093933	0.055756453424692154	0.017975810915231705	0.06538930535316467	-0.029625559225678444	-0.011493934318423271	-0.008933058008551598	0.06794856488704681	-0.07015106827020645	-0.006915662903338671	-0.043375227600336075	-0.07573030143976212	0.002188272774219513	-0.04163843393325806	-0.04929567128419876	-0.03066343069076538	0.021687477827072144	-0.007417816668748856	-0.057194784283638	0.013492811471223831	0.03908354043960571	-0.0366971455514431	-0.008900677785277367	0.043348416686058044	2.004877205763478e-05	-0.06703272461891174	0.1128832995891571	-0.049334920942783356	-0.0652213990688324	-0.03377062454819679	0.05267758667469025	0.0641462579369545	-0.00906720943748951	0.04260844737291336	0.05818415433168411	0.10280103981494904	0.04489845782518387	-0.014024302363395691	-0.034914225339889526	-0.06891913712024689	-0.06866442412137985	0.005534282419830561	0.03576977178454399	-0.00032054667826741934	-0.057797085493803024	-0.006301705725491047	-0.05602925643324852	0.019958924502134323	0.007282931357622147	0.07467573881149292	-0.030559727922081947	-0.021464332938194275	-0.04959257319569588	0.027896227315068245	-0.0034750080667436123	-0.0502324141561985	0.03350415453314781	-0.03754618391394615	0.03561192750930786	-0.011877498589456081	0.011242163367569447	0.011774792335927486	0.010332971811294556	0.04186569154262543	-0.023327190428972244	0.04667001590132713	-0.03531850501894951	-0.057521626353263855	-0.05713916942477226	-0.07845761626958847	-0.07365584373474121	-0.004864179529249668	-0.01872362196445465	-0.043693386018276215	0.05926844850182533	0.008421354927122593	-0.010977253317832947	-0.042363233864307404	0.003693357342854142	-0.0031555453315377235	-0.051979001611471176	-0.04328195005655289	0.02543627843260765	-0.06913252174854279	-0.0019058333709836006	0.01079457439482212	-0.02168550342321396	-0.027132924646139145	0.0032906883861869574	-0.04249481111764908	-0.0704851746559143	0.023605111986398697	-0.03204263746738434	-0.02649841643869877	0.00616530142724514	-0.05438363924622536	0.061061449348926544	0.10902039706707001	0.049816492944955826	0.07266610860824585	-0.012390609830617905	0.045333728194236755	0.06288953870534897	-0.010255470871925354	-0.007588975131511688	0.0012333655031397939	-0.037996161729097366	-0.10475480556488037	0.06084497645497322	-0.03084859624505043	-0.02314711920917034	-0.03145407885313034	0.011268199421465397	0.0664423257112503	-0.0062162005342543125	0.0074787442572414875	-0.018537776544690132	0.05020313709974289	0.010251520201563835	0.07985220104455948	-0.03760962188243866	-0.007660512812435627	0.0023702739272266626	-0.02564259245991707	-0.03680923208594322	-0.060476139187812805	0.052502501755952835	0.03932308033108711	0.031816281378269196	0.000973811314906925	-0.09538295865058899	-0.0012192606227472425	0.022377552464604378	0.0634416863322258	-0.09170465916395187	0.03182879090309143	0.020312558859586716	-0.013634112663567066	-0.10036096721887589	-0.04599962756037712	0.017310962080955505	-0.012314734980463982	-0.03982265666127205	0.010024279356002808	0.06663940846920013	-0.05810723081231117	-0.02343592420220375	-0.015571522526443005	-0.014991823583841324	-0.016958585008978844	0.04476628080010414	0.009128117002546787	-0.026640303432941437	-0.035656169056892395	-0.02845592424273491	-0.014187462627887726	-0.015436760149896145	0.025557497516274452	0.002524321200326085	0.056434109807014465	-0.017503082752227783	0.05272730439901352	-0.05028871074318886	-0.03597142919898033	-0.0786866545677185	0.10241445899009705	-0.024561578407883644	-0.0016695403028279543	-0.02123616263270378	-0.016725001856684685	-0.004355855286121368	0.03910032659769058	0.0023503813426941633	-0.04456141218543053	-0.02470238320529461	-0.0028629833832383156	-0.04377380758523941	-0.06465287506580353	-0.020751886069774628	0.012680823914706707	-0.059762705117464066	0.011447131633758545	-0.03317396342754364	-0.03698156774044037	-0.0017588938353583217	0.00019697648531291634	0.011112702079117298	0.009324310347437859	0.03224603086709976	0.016935428604483604	0.048696212470531464	0.007419740781188011	0.02281074784696102	-0.006669323891401291	-0.04364370182156563	0.03001120314002037	0.0026881436351686716	-0.04633159190416336	0.10325225442647934	-0.028505543246865273	-0.008791348896920681	-0.0034884123597294092	0.02634686790406704	0.03883703052997589	-0.0291365385055542	-0.013192989863455296	-0.033854346722364426	0.0438401997089386	-0.04610467329621315	-0.013825315982103348	0.036592382937669754	-0.023854637518525124	-0.12345632910728455	-0.011203906498849392	-0.0034300051629543304	-0.05578688532114029	0.04773098975419998	0.01587413065135479	0.01281383354216814	0.04874233156442642	-0.04697063937783241	-0.03295350819826126	-0.032928455621004105	-0.03715695068240166	0.020028064027428627	0.002152304397895932	0.09191802889108658	0.007793057709932327	0.01483538281172514	0.020137974992394447	0.03754035383462906	0.0065253060311079025	-0.06559191644191742	-0.018258804455399513	1.6382393368985504e-05	-0.031294625252485275	0.006661243736743927	0.015401593409478664	0.0450211837887764	-0.03513539582490921	-0.056411344558000565	-0.045263100415468216	0.08029886335134506	-0.03800678253173828	-0.00027507584309205413	0.04485183581709862	-0.034514039754867554	0.01479033101350069	-0.004183280747383833	-0.03577548637986183	-0.08529376983642578	-0.007970087230205536	-0.03632284328341484	0.00023918025544844568	-0.02726322039961815	0.017584286630153656	0.01308459136635065	-0.062190569937229156	-0.030182447284460068	0.017277566716074944	-0.05426798015832901	0.03534481301903725	-0.015538986772298813	-0.034819595515728	-0.06855303794145584	-0.019083399325609207	-0.03757135570049286	0.03893475979566574	-0.023813728243112564	0.022141510620713234	0.01580490544438362	0.009842216968536377	0.006176428869366646	-0.0701788067817688	0.036607526242733	0.02336764894425869	-0.04823929816484451	-0.043184276670217514	0.06685933470726013	-0.012921108864247799	0.07721807807683945	0.04098900035023689	-0.007007332053035498	0.025919781997799873	0.0888705626130104	-0.060735736042261124	0.007972624152898788	-0.0007190977339632809	-0.02503150887787342	0.06546173244714737	-0.016190554946660995	0.05942145735025406	-0.050850335508584976	0.09366053342819214	-0.04374901205301285	-0.01636907272040844	0.0591423436999321	0.07867461442947388	0.055061448365449905	0.05015195906162262	-0.047502268105745316
+0.06533224880695343	-0.036479681730270386	-0.029626429080963135	0.06596184521913528	-0.05070963129401207	0.0025627254508435726	0.011801428161561489	-0.007984867319464684	0.01351251732558012	-0.05604591593146324	-0.004076128825545311	-0.025363754481077194	0.021514199674129486	0.009710917249321938	-0.004722935613244772	-0.04787763953208923	-0.038010213524103165	0.0570029653608799	-0.021878086030483246	-0.0161807369440794	-0.0018488019704818726	-0.041807789355516434	-0.07330835610628128	0.021596742793917656	-0.031607646495103836	0.026257580146193504	-0.032294176518917084	0.014547094702720642	-0.018818723037838936	0.037079960107803345	0.06969287991523743	-0.008880776353180408	0.017708392813801765	-0.033638108521699905	-0.0965399295091629	0.02886810339987278	-0.03698727861046791	0.10995757579803467	-0.01826697215437889	-0.02694270946085453	-0.0944974347949028	0.00651384424418211	-0.07960128039121628	-0.06036289781332016	0.05119730159640312	0.009304753504693508	0.025883441790938377	0.014480745419859886	0.05580156296491623	-0.022644219920039177	0.06211930513381958	0.00982371810823679	-0.011700359173119068	0.004972638096660376	-0.0361943319439888	0.013980056159198284	0.03532392904162407	-0.030269436538219452	-0.02606615424156189	0.06303801387548447	-0.08077434450387955	-0.026235144585371017	0.038660939782857895	0.0025895664002746344	-0.021573776379227638	-0.02808127924799919	-0.0029158787801861763	0.07266676425933838	-0.019717220216989517	-0.013523259200155735	-0.025850685313344002	0.07752852886915207	-0.029171355068683624	-0.0451851561665535	0.057584695518016815	-0.10155358910560608	-0.04126907140016556	0.03080500103533268	-0.009956016205251217	-0.009427128359675407	-0.03530871495604515	-0.014767972752451897	0.15856407582759857	0.004485554061830044	-0.05766015127301216	-0.027554882690310478	0.023198863491415977	-0.06692170351743698	0.06088773533701897	0.03064369410276413	-0.004863232374191284	0.06668121367692947	-0.05196182802319527	0.07517901062965393	0.05242284759879112	0.019856847822666168	0.05010973662137985	-0.0787588357925415	-0.04362275078892708	0.006528706289827824	0.018457021564245224	-0.02462761290371418	0.024141056463122368	-0.004488387610763311	0.046676382422447205	0.003703728085383773	-0.06274284422397614	0.015066834166646004	0.02743561565876007	0.025161828845739365	0.05642424523830414	0.09625937789678574	0.010479219257831573	0.02220422774553299	0.06391988694667816	0.02529238536953926	0.03904048353433609	-0.07851044833660126	-0.005022508557885885	0.032145652920007706	0.0073399292305111885	0.03821714222431183	-0.002707075560465455	-0.0770546942949295	-0.04207179695367813	-0.016908397898077965	-0.10482241213321686	-0.0245402529835701	0.03922078385949135	-0.026350917294621468	0.08802098035812378	0.06354083865880966	-0.031214449554681778	0.048482466489076614	0.021081652492284775	0.01866253651678562	-0.0147390803322196	0.018898891285061836	-0.0032964313868433237	-0.004125585779547691	0.014161995612084866	0.06405433267354965	-0.02668481320142746	-0.02927297167479992	0.09264146536588669	0.012260676361620426	0.041735678911209106	-0.029339918866753578	-0.05783606693148613	0.025131870061159134	-0.039367903023958206	-0.04192899167537689	0.025752585381269455	-0.02727278135716915	-0.003072619903832674	0.025914475321769714	0.02224624715745449	0.011161376722157001	-0.005965416319668293	-0.06168792024254799	-0.04493795707821846	0.011317692697048187	-0.028497183695435524	-0.07982778549194336	-0.03378548100590706	-0.01710459403693676	-0.00018109445227310061	0.03142668306827545	-0.05958443135023117	0.015020779334008694	0.04858741909265518	0.0358620323240757	0.052812326699495316	0.060601212084293365	0.07683899253606796	0.03734353929758072	-0.00784552562981844	-0.04541186988353729	0.016590604558587074	-0.023998945951461792	0.02971869334578514	0.010415829718112946	-0.008787940256297588	-0.051383472979068756	0.034127067774534225	0.03474665433168411	0.04856857657432556	-0.015364320948719978	0.023316457867622375	-0.02175994962453842	-0.05226374417543411	-0.08160330355167389	0.023518098518252373	0.026011653244495392	-0.007005298975855112	-0.0030776432249695063	-0.039947960525751114	0.06790994852781296	0.004166556987911463	-0.018499670550227165	-0.029198043048381805	0.028601793572306633	-0.02065204083919525	0.08031466603279114	0.041673727333545685	-0.06344813853502274	0.056595392525196075	-0.0002330801507923752	0.05466098338365555	-0.0490129180252552	-0.0030398783273994923	-0.03577064722776413	0.003481984604150057	0.04627086594700813	0.08410528302192688	0.011582473292946815	-0.02031124010682106	0.06758725643157959	0.09544423967599869	-0.043137408792972565	0.010271153412759304	-0.041270818561315536	-0.09916198998689651	-0.01972154900431633	-0.037447113543748856	-0.064118392765522	-0.026109199970960617	0.08449123054742813	-0.031995758414268494	-0.024522796273231506	0.012210874818265438	0.02025742642581463	-0.02150091715157032	-0.041671764105558395	0.010197608731687069	-0.023654425516724586	-0.10821781307458878	0.0711185485124588	0.023630863055586815	-0.10107765346765518	-0.016261577606201172	0.0025659676175564528	0.05080198124051094	-0.02522283047437668	0.056745849549770355	0.04349871724843979	0.06548334658145905	0.007611383218318224	-0.015880916267633438	0.035349290817976	-0.0383191742002964	-0.05937713012099266	0.03908400237560272	0.050609346479177475	-0.033754076808691025	-0.015211496502161026	0.0027854389045387506	-0.04248366877436638	0.040895964950323105	0.006792982574552298	-0.023229623213410378	-0.011790384538471699	-0.011806459166109562	-0.03750418499112129	0.026458129286766052	-0.06892893463373184	0.02774633839726448	0.017629550769925117	-0.006532719824463129	0.020840417593717575	-0.01123689766973257	-0.023135092109441757	0.02282506413757801	0.010081593878567219	0.02908833883702755	-0.03469401225447655	0.05730452761054039	0.0018730924930423498	-0.08988966792821884	-0.028203628957271576	-0.07185350358486176	-0.022252239286899567	-0.02664882130920887	-0.006773686036467552	-0.06956887245178223	0.07285696268081665	-0.005205022171139717	-0.03709139674901962	-0.08618009090423584	-0.040792956948280334	-0.016721075400710106	-0.07322200387716293	-0.04182935878634453	-0.008529068902134895	-0.03509720042347908	-0.04295003414154053	0.04411676153540611	-0.009233785793185234	0.005431237164884806	-0.04071081057190895	0.026175539940595627	-0.06752697378396988	0.04005765914916992	0.0011648612562566996	-0.04333430901169777	0.023817449808120728	-0.08645755052566528	0.032240960747003555	0.09283172339200974	0.07356004416942596	0.02154497243463993	0.012972231954336166	0.034847136586904526	0.09239623695611954	-0.0009363002027384937	-0.043022990226745605	-0.01945257931947708	-0.05396820232272148	-0.01526427362114191	0.05614599585533142	-0.025674771517515182	-0.0018657665932551026	0.006131164729595184	0.002112895017489791	0.052882034331560135	-0.023362068459391594	-0.014171630144119263	-0.023758312687277794	0.0398440808057785	-0.036995213478803635	0.0313708670437336	-0.005169058684259653	0.056428465992212296	-0.0340731255710125	-0.045967139303684235	-0.050690263509750366	0.02070525474846363	0.0003610221028793603	-3.6974986869608983e-05	0.026553723961114883	-0.001576158800162375	-0.05388857051730156	0.01140756905078888	-0.04693394526839256	0.09268863499164581	-0.05770965665578842	-0.03115653805434704	0.05193089321255684	0.042307235300540924	-0.09066617488861084	0.02763969451189041	0.028479037806391716	-0.053437501192092896	-0.04329884424805641	0.0020874261390417814	0.014423629269003868	-0.027570074424147606	0.010491250082850456	-0.018006185069680214	-0.0008721070480532944	-0.03552095219492912	-0.0005047558806836605	0.028392205014824867	0.008915194310247898	-0.040834665298461914	-0.041049834340810776	0.007354391273111105	-0.01893627643585205	0.027605880051851273	-0.03691661357879639	0.010073492303490639	-0.010412863455712795	0.009754742495715618	-0.0388089194893837	-0.030345173552632332	-0.05479898303747177	0.06418795138597488	0.028371747583150864	-0.03248550742864609	-0.02172621712088585	-0.04938114434480667	-0.05183755233883858	-0.04356709495186806	0.022754430770874023	-0.07279506325721741	-0.023871079087257385	-0.07570698112249374	-0.05226323753595352	-0.07199142873287201	-0.0167221762239933	-0.00805719941854477	-0.013462135568261147	0.010447971522808075	-0.03160129487514496	-0.03268216922879219	0.06243593618273735	0.007290326990187168	0.014888036996126175	0.09610237926244736	-0.004536256194114685	-0.0071027930825948715	0.0456966906785965	-0.009660983458161354	0.05274851247668266	0.06271784007549286	-0.04822010174393654	-0.02408275380730629	0.023537229746580124	-0.06899549812078476	0.06031770631670952	0.015565876848995686	0.02813068963587284	0.020754942670464516	0.005544587038457394	0.013186175376176834	-0.03648576885461807	0.010591287165880203	-0.0036618199665099382	0.030088074505329132	-0.038899388164281845	-0.029170449823141098	0.017986536026000977	-0.049241047352552414	-0.07247992604970932	-0.048425186425447464	0.03467843309044838	-0.03855990990996361	0.04107195511460304	-0.04534858092665672	0.02517537772655487	0.05627742409706116	-0.0594281442463398	-0.08388146013021469	-0.020923186093568802	-0.035066280514001846	0.07010563462972641	-0.04849040508270264	0.05599471181631088	0.054106105118989944	-0.00375087046995759	-0.013054431416094303	0.0244823656976223	0.003451654687523842	-0.059831131249666214	-0.01528874970972538	-0.012147173285484314	-0.06564145535230637	0.010429710149765015	-8.012139005586505e-05	-0.010656897909939289	-0.027525966987013817	-0.009029852226376534	-0.04478132724761963	0.08580893278121948	-0.0007046970422379673	-0.009656520560383797	-0.007323544938117266	-0.03108314611017704	0.05657872557640076	0.040565505623817444	-0.04718974977731705	-0.04185288026928902	0.01077783852815628	-0.02971062995493412	0.021858667954802513	0.024816172197461128	0.023436112329363823	-0.012338335625827312	-0.055302005261182785	0.0023798812180757523	0.00019150557636749	-0.13178551197052002	0.007723227143287659	0.02163238450884819	-0.06463470309972763	-0.041319698095321655	0.03399103134870529	-0.06998225301504135	0.08289749920368195	0.008352094329893589	0.07243809849023819	0.0053526186384260654	-0.018532495945692062	-0.04472798481583595	-0.04850823059678078	0.01720581203699112	-0.056277867406606674	-0.030045902356505394	-0.005746450740844011	0.0626741573214531	-0.07626960426568985	0.043615538626909256	0.030176613479852676	0.015069027431309223	0.05197475850582123	0.046043429523706436	-0.09190486371517181	0.06296372413635254	0.03517447039484978	-0.008151366375386715	0.05914941802620888	-0.08009067922830582	0.09095655381679535	-0.08490019291639328	0.09821818023920059	-0.008751251734793186	0.006532380823045969	0.05875280871987343	0.0891609936952591	-0.016086187213659286	0.03654791787266731	-0.030057422816753387
+0.022382454946637154	-0.005490126088261604	0.004425330087542534	0.012217861600220203	-0.08774487674236298	-0.027623796835541725	-0.02755415067076683	0.04499297961592674	0.023160895332694054	-0.05264835059642792	-0.03252158686518669	-0.005503067746758461	-0.018253425136208534	0.009154374711215496	0.023429280146956444	-0.03841736167669296	-0.06360892206430435	-0.019397815689444542	-0.04542791470885277	-0.060897018760442734	0.033580098301172256	0.026246292516589165	-0.05804652348160744	0.07235901802778244	0.007888277061283588	0.017714446410536766	-0.0607181116938591	0.02933713234961033	-0.043345753103494644	0.05484151467680931	0.08216915279626846	-0.005733879748731852	0.07141394168138504	-0.0608992762863636	-0.04496842622756958	0.054151780903339386	-0.025467799976468086	0.08108169585466385	0.039902493357658386	-0.042163826525211334	-0.029920559376478195	0.004240759182721376	-0.00809508003294468	-0.03992825374007225	0.04489637911319733	-0.011095773428678513	-0.017543883994221687	-0.02633015438914299	0.03415929153561592	0.012881353497505188	0.09794401377439499	0.010753102600574493	-0.02328159287571907	-0.034866370260715485	0.00910276174545288	0.03865784779191017	0.02052515186369419	-0.07787762582302094	-0.04132254049181938	0.026134463027119637	-0.09533468633890152	0.022901762276887894	0.004835754632949829	0.000249191332841292	-0.00752646429464221	-0.009770924225449562	-0.018631529062986374	0.09332257509231567	0.037209220230579376	-0.008077790029346943	0.03982892259955406	0.0812329649925232	-0.06233636289834976	-0.04688809812068939	0.055045925080776215	-0.026906078681349754	0.002167661441490054	0.02984737791121006	-0.02548076957464218	0.02922419086098671	0.014254006557166576	-0.03681774437427521	0.11670610308647156	-0.034085750579833984	-0.013248774223029613	-0.05958414077758789	-0.022592181339859962	-0.06949383020401001	0.07819521427154541	0.0033975106198340654	0.013584182597696781	0.066648930311203	-0.00029363055364228785	0.04890107735991478	-0.010553247295320034	0.022761929780244827	-0.004185544326901436	-0.10708324611186981	0.012738161720335484	0.005486772395670414	0.020976923406124115	0.011928074061870575	0.049905210733413696	-0.003376850625500083	0.059023451060056686	0.0810701921582222	-0.04353457689285278	0.00035630937782116234	0.054162513464689255	0.03639249503612518	0.03602218255400658	0.09212994575500488	0.07661666721105576	0.04947194457054138	0.041175372898578644	0.011702236719429493	-0.0041059451177716255	-0.05808408930897713	-0.013988038524985313	0.01510279905050993	-0.01410482358187437	0.034374434500932693	-0.02082231640815735	-0.05927150323987007	-0.014095050282776356	0.049506235867738724	-0.08628014475107193	-0.021878210827708244	0.06698859483003616	-0.03352193906903267	0.10465048253536224	0.048017486929893494	-0.027659591287374496	0.033854465931653976	0.04947401583194733	0.041640061885118484	-0.002013188786804676	-0.0042785159312188625	-0.002209531841799617	-0.061511095613241196	-0.04075150936841965	0.04357665777206421	-0.011929675005376339	-0.06830047816038132	0.05552419275045395	0.017425332218408585	0.025781946256756783	-0.020803045481443405	-0.07137980312108994	0.0072580487467348576	-0.09929897636175156	-0.09341658651828766	0.03060692548751831	-0.049698490649461746	-0.0027875045780092478	0.05633010342717171	0.021805454045534134	0.007272430695593357	-0.0038252153899520636	-0.05318306386470795	-0.028666319325566292	-0.04476679861545563	-0.030474886298179626	-0.08768738806247711	0.016816753894090652	-0.046828676015138626	0.025126781314611435	0.047021277248859406	-0.062059227377176285	-0.038603637367486954	-0.0011945138685405254	-0.005138033535331488	-0.00394296133890748	0.09868043661117554	0.046998023986816406	0.014459526166319847	0.026007136330008507	-0.03119780495762825	-0.021201707422733307	-0.0268833190202713	0.02481873705983162	-0.04404836148023605	0.035878509283065796	-0.0679367259144783	-0.005635015666484833	-0.0293147973716259	0.020249824970960617	-0.006893017794936895	0.023096119984984398	0.005357017740607262	-0.02677042782306671	-0.06933072209358215	0.03568832576274872	0.049878232181072235	-0.01664678007364273	0.03359115123748779	0.045160405337810516	0.06942299753427505	-0.010207941755652428	-0.01761387288570404	-0.06023157760500908	0.04639584198594093	0.05228712409734726	0.03821852430701256	0.03549278900027275	-0.0711880773305893	0.09282301366329193	0.0007640466210432351	0.025691034272313118	-0.02959156595170498	0.043538279831409454	0.05613281950354576	0.02406698279082775	0.04088350385427475	0.07124526798725128	-0.021327324211597443	-0.007088310085237026	-0.025965681299567223	0.06139659881591797	-0.10568394511938095	0.0075326417572796345	0.0007959234644658864	-0.049434464424848557	0.050348665565252304	-0.049646370112895966	-0.10912733525037766	-0.03933968022465706	0.0018343115225434303	0.0061533087864518166	-0.045945003628730774	0.020896829664707184	0.006217137444764376	-0.022743456065654755	0.01935560815036297	0.022892765700817108	-0.020154301077127457	-0.09214171767234802	0.09759485721588135	-0.03833677992224693	-0.05591975152492523	-0.032886773347854614	0.01397587452083826	0.056524306535720825	-0.008735998533666134	0.03683090955018997	0.06174638494849205	0.10338957607746124	0.043954722583293915	-0.02368532307446003	0.004426221828907728	-0.05075483024120331	-0.06200810894370079	0.003559019649401307	0.026782037690281868	-0.02392786182463169	-0.07645998150110245	0.017708580940961838	-0.07484845072031021	0.014781166799366474	-0.02488008327782154	0.029283883050084114	-0.028187770396471024	0.010453112423419952	-0.05529606342315674	0.04958682134747505	-0.015357818454504013	-0.055953800678253174	0.0560329370200634	-0.03313799574971199	0.039700668305158615	-0.02160884439945221	-0.02214221842586994	0.01811246946454048	-0.005478797480463982	0.03500112146139145	-0.039982736110687256	0.07711414247751236	-0.00907299853861332	-0.08052915334701538	-0.04037334397435188	-0.04732438176870346	-0.0718228667974472	0.008021212182939053	-0.04629746824502945	-0.019343459978699684	0.06847018003463745	0.01568610779941082	-0.036509040743112564	-0.05615251511335373	-0.02523493953049183	-0.01881701685488224	-0.029936175793409348	-0.05347127094864845	-0.01370667852461338	-0.06667464226484299	0.0006687517161481082	0.03599809855222702	-0.07283221185207367	-0.029729800298810005	0.042032867670059204	-0.017839152365922928	-0.045420512557029724	0.029885118827223778	-0.04398411884903908	-0.01384809147566557	0.02359963394701481	-0.07023321092128754	0.0375913567841053	0.09640073031187057	0.042653847485780716	0.02119886688888073	0.02208082191646099	0.037380002439022064	0.08848466724157333	-0.004007589537650347	-0.03130210563540459	-0.016185736283659935	-0.028883080929517746	-0.06333113461732864	0.03723711520433426	-0.02786118909716606	-0.03374125808477402	0.006169111933559179	-0.02107403241097927	0.048566702753305435	0.0009253238094970584	-0.006479570642113686	-0.027269063517451286	0.041359663009643555	-0.02292742021381855	0.049897778779268265	0.003784716594964266	0.003162068547680974	0.021577833220362663	-0.045959390699863434	-0.03858301043510437	-0.06555265933275223	0.05401380732655525	0.003306356258690357	0.01616988144814968	-0.005519811529666185	-0.057288140058517456	-0.006836199667304754	0.005882450379431248	0.10049596428871155	-0.07827837765216827	0.04600975289940834	0.0262799970805645	0.01600540056824684	-0.10260337591171265	-0.0028945293743163347	-0.0014214020920917392	-0.04175443574786186	-0.044272903352975845	0.02774074301123619	0.04912225902080536	-0.08042094111442566	-0.059055496007204056	-0.0066376784816384315	-0.028037885203957558	-0.017148246988654137	0.024670355021953583	0.011631539091467857	0.0366954430937767	0.0030249517876654863	-0.010538478381931782	-0.007923326455056667	-0.0026971413753926754	0.02645721286535263	-0.0032824710942804813	0.03662226349115372	-0.03269897401332855	0.04560835286974907	-0.046855855733156204	-0.05482823774218559	-0.05056295916438103	0.043659843504428864	-0.012959802523255348	0.028043271973729134	-0.002226415788754821	-0.010901681147515774	-0.015654081478714943	0.0214869175106287	0.022069286555051804	-0.08241622895002365	0.006672672927379608	-0.0026610959321260452	-0.051147136837244034	-0.07802800834178925	-0.04355012625455856	-0.0022348833736032248	-0.07028644531965256	0.030473075807094574	-0.044486477971076965	0.019469497725367546	0.011993277817964554	-0.006526210345327854	0.01680927723646164	0.06298258155584335	0.019245048984885216	0.019224809482693672	0.02206192910671234	0.013400579802691936	0.028059782460331917	0.01662597991526127	-0.05839795619249344	0.02033313922584057	0.026147376745939255	-0.04073915630578995	0.0403439886868	-0.014807971194386482	-0.006122478749603033	0.029040660709142685	-0.023862583562731743	0.018869783729314804	-0.014945138245821	0.024232009425759315	-0.03728632628917694	0.05760698392987251	-0.030803469941020012	-0.035000771284103394	0.019143760204315186	-0.03248172253370285	-0.10796863585710526	-0.005368869286030531	-0.0357285775244236	-0.06509657204151154	0.07616850733757019	-0.007927932776510715	0.005741175264120102	0.03090507537126541	-0.057905085384845734	-0.06280511617660522	-0.018539108335971832	-0.003896075999364257	0.002786214929074049	0.0312621146440506	0.0749613419175148	-0.009934053756296635	-0.001032728236168623	0.004678697790950537	-0.05272074043750763	0.016262037679553032	-0.07388563454151154	0.004418882541358471	-0.012354527600109577	-0.044076189398765564	0.01776493340730667	0.023083776235580444	0.03986632451415062	-0.05025557801127434	-0.02582560107111931	-0.050556834787130356	0.0661652460694313	-0.04526170715689659	0.004802792798727751	-0.010358495637774467	-0.05893757939338684	-0.012763208709657192	0.0410061813890934	-0.05283777415752411	-0.09727973490953445	-0.02148810401558876	-0.04924499988555908	0.03826097771525383	-0.01948508247733116	0.013627104461193085	-0.024503257125616074	-0.09132158011198044	-0.008787941187620163	0.00044401895138435066	-0.02343887835741043	-0.0033095625694841146	0.007538010366261005	-0.07327999919652939	-0.06945861130952835	-0.03637707605957985	-0.07330837845802307	0.08368970453739166	0.0030171882826834917	0.019953599199652672	-0.026628782972693443	-0.022014440968632698	0.010006153956055641	-0.06321833282709122	0.0012131578987464309	-0.020550571382045746	-0.046033598482608795	-0.03214544802904129	0.06779926270246506	-0.0015276006888598204	0.03907923400402069	0.0931258350610733	-0.021198922768235207	0.024013372138142586	0.04908718913793564	-0.03202710673213005	0.0018198717152699828	-0.008815285749733448	-0.028701700270175934	0.0522351898252964	-0.03921936824917793	0.06225243955850601	-0.051780372858047485	0.11481370776891708	-0.003389671677723527	-0.03102291375398636	0.07059802114963531	0.08069455623626709	0.02103632688522339	0.03343430906534195	-0.08990401774644852
+-0.012414969503879547	-0.053787630051374435	-0.02894447185099125	0.00208616117015481	-0.10235167294740677	-0.041637733578681946	-0.008783390745520592	0.033985793590545654	0.007899255491793156	-0.04914580285549164	-0.04653019458055496	0.0047602602280676365	0.00391701515763998	-0.012053145095705986	0.028941873461008072	-0.021616555750370026	-0.0678916722536087	-0.010002519004046917	-0.038796134293079376	-0.024864230304956436	0.022852858528494835	0.015782024711370468	-0.06256311386823654	0.06169625371694565	0.010738811455667019	0.029158923774957657	-0.058732178062200546	0.0040521337650716305	-0.027411693707108498	0.06494899839162827	0.048908036202192307	-0.003950159065425396	0.032154861837625504	-0.04889754205942154	-0.05522124469280243	0.04445064812898636	-0.02804097905755043	0.1037859320640564	0.02746935747563839	-0.02881641499698162	-0.031121518462896347	-0.003126932308077812	-0.04718994349241257	-0.06010652706027031	0.012978523038327694	-0.026812592521309853	0.0037078086752444506	-0.027401624247431755	0.021762188524007797	0.04215405881404877	0.07391932606697083	0.02964545413851738	-0.01733010821044445	-0.022620875388383865	-0.026999466121196747	0.032337039709091187	0.0009729615412652493	-0.035275302827358246	-0.05391013249754906	0.05751661956310272	-0.0777239054441452	0.01922389678657055	0.002692479407414794	0.022624829784035683	-0.016314685344696045	0.00040254052146337926	-0.019238470122218132	0.0840686708688736	0.0036828278098255396	-0.029862048104405403	-0.002260122448205948	0.07688252627849579	-0.04412829875946045	-0.039751507341861725	0.022123878821730614	-0.021481139585375786	-0.025386298075318336	0.009631884284317493	0.00041967802098952234	-0.017735039815306664	-0.00043079923489131033	-0.057094987481832504	0.13965997099876404	-0.05090895667672157	-0.022949667647480965	-0.07088290899991989	-0.03132413327693939	-0.053912483155727386	0.05602865666151047	0.009918981231749058	0.04878601059317589	0.06361047923564911	-0.016283372417092323	0.05208737403154373	0.002775650005787611	0.009082495234906673	0.02904346212744713	-0.14680089056491852	-0.02096625231206417	-0.0032932746689766645	0.007269399240612984	-0.022309329360723495	0.07212488353252411	0.012278810143470764	0.05966901406645775	0.04361782595515251	-0.0667150467634201	0.006351865362375975	0.029666779562830925	0.011307952925562859	0.023923130705952644	0.11484064906835556	0.05970226228237152	0.06081247702240944	0.05125868320465088	0.008699259720742702	-0.0186051893979311	-0.02640790306031704	-0.02197837270796299	0.005453481338918209	0.004967379383742809	0.0254278015345335	0.005448059178888798	-0.06814680993556976	0.011512222699820995	0.058201804757118225	-0.10989812761545181	6.045786358299665e-05	0.033818166702985764	-0.05665283650159836	0.08695252984762192	0.049701932817697525	-0.018354713916778564	0.010759255848824978	0.04111390560865402	0.006876657716929913	-0.006430888548493385	0.017033899202942848	-0.006384788081049919	-0.031712546944618225	-0.055173609405756	0.04307593032717705	-0.004638157319277525	-0.03256700932979584	0.025388803333044052	0.022258995100855827	0.009758561849594116	-0.01316027157008648	-0.07251741737127304	0.024640226736664772	-0.08870438486337662	-0.07706137746572495	0.03254276141524315	-0.0758008062839508	0.02611444890499115	0.0549759715795517	0.049672048538923264	0.049788083881139755	-0.004514634609222412	-0.0036182941403239965	-0.022153910249471664	-0.054986827075481415	-0.02655426785349846	-0.09732988476753235	0.01616981439292431	-0.006277823820710182	0.015806332230567932	-0.004302107263356447	-0.029922952875494957	-0.03478629142045975	-0.0034823762252926826	0.003950712736696005	0.0303769763559103	0.07053958624601364	0.0322636142373085	-0.010668416507542133	0.02363944984972477	-0.05765267834067345	-0.02452455461025238	-0.035604171454906464	0.009146489202976227	-0.03213924914598465	0.04129417985677719	-0.050738271325826645	-0.037041548639535904	-0.0033185817301273346	0.02371879294514656	-0.013020438142120838	-0.02020895481109619	-0.03658609464764595	-0.021133223548531532	-0.07863761484622955	0.04699425399303436	0.047717127948999405	-0.03287571296095848	0.03420369699597359	0.04621683806180954	0.08975884318351746	-0.025783060118556023	-0.020121734589338303	-0.06303723901510239	0.03655679151415825	0.008139492012560368	0.020394090563058853	0.010949474759399891	-0.05914687365293503	0.07602649182081223	0.004927781410515308	0.021698089316487312	-0.0660681501030922	0.03664303943514824	0.05096476897597313	0.01326060388237238	0.04610666632652283	0.08716359734535217	-0.04777943715453148	-0.012417567893862724	0.020873915404081345	0.05788771063089371	-0.08151561766862869	-0.008120909333229065	0.009713343344628811	-0.07288835197687149	-0.022225596010684967	-0.010694533586502075	-0.09014362096786499	-0.02495887130498886	0.01761123724281788	-0.008327425457537174	-0.016071848571300507	0.012372691184282303	0.04007530212402344	-0.03707655519247055	-0.006347430404275656	0.014293462038040161	0.020727531984448433	-0.06823451071977615	0.05512389540672302	-0.05434512719511986	-0.08631248027086258	-0.04778742045164108	0.012360279448330402	0.011065810918807983	0.025932161137461662	0.03107399307191372	0.06345909088850021	0.1027769148349762	0.06379251927137375	-0.034753408282995224	-0.024643421173095703	-0.04867706075310707	-0.044517360627651215	0.01091313548386097	0.040012139827013016	-0.017157617956399918	-0.0671611800789833	0.008710198104381561	-0.10236933827400208	-0.010214913636446	0.012110930867493153	0.06268112361431122	-0.056890614330768585	-0.02760167047381401	-0.005082306917756796	0.02281823568046093	-0.007638023234903812	-0.046836428344249725	0.018410712480545044	-0.03992501646280289	0.051996324211359024	-0.04362965375185013	-0.02288113534450531	0.016343051567673683	0.01075144112110138	0.03215702995657921	-0.032521165907382965	0.062325261533260345	-0.032026827335357666	-0.09125759452581406	-0.0509013794362545	-0.07437248528003693	-0.06828795373439789	-0.001640776521526277	-0.02019060030579567	-0.054667335003614426	0.07478082180023193	0.01948297955095768	-0.058861978352069855	-0.05899117514491081	-0.05304679274559021	-0.02906845323741436	-0.025759655982255936	-0.015181504189968109	-0.00968175008893013	-0.06424114108085632	-0.007416925858706236	0.04882919043302536	-0.07074154168367386	-0.030817942693829536	0.08260493725538254	0.010263729840517044	-0.0527278408408165	0.0758589431643486	-0.016419334337115288	-0.017698774114251137	0.007675500586628914	-0.08764024078845978	0.02410050295293331	0.08704822510480881	0.017446499317884445	0.039061326533555984	0.022991685196757317	0.029687179252505302	0.05256262049078941	-0.0008470993489027023	-0.04117455706000328	-0.04395775496959686	-0.0210314579308033	-0.07754982262849808	0.03981269523501396	-0.03576882928609848	-0.0017869037110358477	-0.032749373465776443	0.006941427942365408	0.044820692390203476	0.0037491912953555584	-0.0020703314803540707	-0.03923877328634262	0.018397962674498558	0.008177682757377625	0.03964848443865776	-0.022393008694052696	-0.0017495969077572227	0.005134665872901678	-0.046689897775650024	-0.06422345340251923	-0.035750862210989	0.04783684015274048	0.005277595017105341	0.05696843937039375	0.00048674424760974944	-0.07807306200265884	0.023559357970952988	-0.018291354179382324	0.10525187104940414	-0.09687048941850662	0.04569080471992493	0.027156565338373184	0.02041352353990078	-0.08111295104026794	-0.014806356281042099	0.017597587779164314	-0.04514956474304199	-0.07422133535146713	-6.249790749279782e-05	0.06128827482461929	-0.03781478852033615	-0.0749664455652237	-0.01937197707593441	-0.02838812582194805	0.004013973753899336	0.0235595703125	-0.008065316826105118	0.03318368270993233	0.020007150247693062	-0.020299261435866356	0.01902037486433983	0.012476710602641106	0.004649341572076082	-0.026118170469999313	0.03627774491906166	0.006395104341208935	0.07237298041582108	-0.023783618584275246	-0.0357670895755291	-0.07933991402387619	0.046307165175676346	-0.045881424099206924	0.0033614966087043285	-0.029186144471168518	-0.019135208800435066	-0.002660586265847087	0.06003083288669586	-0.0016177932266145945	-0.048770371824502945	-0.024605639278888702	-0.00329141435213387	-0.06261703372001648	-0.07851415872573853	-0.03138720616698265	0.02853291667997837	-0.03758645057678223	0.017874741926789284	-0.06392169743776321	0.016270950436592102	0.00963857863098383	-0.003740989835932851	0.01874467357993126	0.035165730863809586	0.027498843148350716	-0.013386988081037998	0.04439089074730873	0.005665430333465338	0.04914553463459015	-0.041857168078422546	-0.06882748007774353	0.05562638118863106	0.05132533237338066	-0.058910906314849854	0.029819391667842865	-0.04432842135429382	-0.008833149448037148	0.004765328951179981	-0.024696234613656998	-0.023487186059355736	0.004539228975772858	-0.010401902720332146	-0.045063912868499756	0.05283666029572487	-0.07804328948259354	-0.03827819600701332	0.024978896602988243	-0.03794524818658829	-0.06988483667373657	-0.009326165542006493	-0.04985389858484268	-0.02410924807190895	0.0437302328646183	-0.012081216089427471	0.020445264875888824	0.04717991501092911	-0.061653438955545425	-0.004089495167136192	0.010298681445419788	-0.012409777380526066	-0.003058995120227337	0.05642656609416008	0.07526182383298874	-0.009664038196206093	-0.0012681146617978811	-0.014540063217282295	-0.02366762049496174	0.03483128920197487	-0.07071588933467865	0.008282030001282692	0.007311290130019188	-0.0071481335908174515	0.0029054542537778616	0.033964645117521286	0.03668482229113579	-0.06988094002008438	-0.023368321359157562	-0.0036773926112800837	0.04344227537512779	-0.009417424909770489	0.018643472343683243	0.030035993084311485	-0.04394984990358353	-0.03132937476038933	0.0207280945032835	-0.025668852031230927	-0.09187783300876617	-0.0036497453693300486	-0.06552702188491821	0.06204855814576149	0.005737903527915478	0.014271561987698078	-0.027105143293738365	-0.08679910004138947	-0.030534574761986732	-0.010650185868144035	-0.03675629198551178	-0.013037102296948433	0.051531001925468445	-0.059130359441041946	-0.03998217731714249	-0.02260163240134716	-0.06085019186139107	0.051439542323350906	0.002366713248193264	0.04963936284184456	-0.05673058331012726	0.008610210381448269	0.01118595153093338	0.0009967605583369732	0.03674444928765297	-0.027167344465851784	-0.10391375422477722	-0.023882439360022545	0.08911878615617752	-0.03591747209429741	0.05179081857204437	0.07440678030252457	-0.01675877906382084	0.0696251317858696	0.04470039904117584	-0.03127576410770416	0.00268258317373693	-0.0009555575670674443	-0.025267014279961586	0.025602078065276146	-0.06315084546804428	0.0634383112192154	-0.041407931596040726	0.12424127012491226	0.0015353362541645765	-0.023754969239234924	0.08429855108261108	0.09495726227760315	0.04761350527405739	0.02997877635061741	-0.07899541407823563
+0.014002226293087006	-0.04577925428748131	-0.014569098129868507	0.042247019708156586	-0.06277886778116226	-0.02285018377006054	-0.026006974279880524	0.01842617802321911	-0.014219092205166817	-0.036621805280447006	-0.06006805598735809	-0.00464880745857954	0.016916576772928238	0.009319799952208996	0.03912578523159027	-0.03105875849723816	-0.08668111264705658	0.019443953409790993	-0.0073477537371218204	0.006622628308832645	0.02568967640399933	0.0296874288469553	-0.0743853896856308	0.05409197136759758	0.03345004469156265	0.058627787977457047	-0.04107825830578804	-0.00031969102565199137	-0.010770262219011784	0.04656291753053665	0.029754364863038063	-0.0064254687167704105	0.03291233256459236	-0.08760880678892136	-0.033734966069459915	0.041632551699876785	-0.024954134598374367	0.10253670066595078	0.03788120672106743	-0.04545389115810394	-0.07086717337369919	0.02277073822915554	-0.06455839425325394	-0.11894319206476212	-0.009202432818710804	-0.01312115229666233	0.004305452108383179	-0.025936158373951912	0.026538189500570297	0.04470284283161163	0.07670539617538452	-0.028288917616009712	-0.044858235865831375	-0.011257102712988853	-0.04849802702665329	0.0037558868061751127	0.03045303001999855	-0.011565779335796833	-0.0562516450881958	0.031016774475574493	-0.06244393065571785	0.015900803729891777	0.00804151501506567	0.020473921671509743	-0.05222282186150551	-0.00987250916659832	0.013812643475830555	0.040678758174180984	0.020923929288983345	-0.01114911213517189	0.0022743050940334797	0.08559936285018921	-0.0450437068939209	-0.026419784873723984	-0.0037960584741085768	-0.030649617314338684	-0.023533817380666733	0.016000349074602127	0.02430211380124092	-0.012304815463721752	0.04883229732513428	-0.047881029546260834	0.16004538536071777	-0.05138683691620827	-0.01498226448893547	-0.05077962204813957	-0.006867355667054653	-0.04554241523146629	0.024467917159199715	0.03875605762004852	0.03959260880947113	0.06890273839235306	-0.005959310103207827	0.05860816314816475	0.05105690658092499	0.01403171569108963	0.04073423147201538	-0.1492990106344223	-0.05520005151629448	0.003946521319448948	0.006903147324919701	-0.027307897806167603	0.08018554002046585	0.022499850019812584	0.055206045508384705	-0.006676388438791037	-0.059628572314977646	0.005842460785061121	0.03549238294363022	0.016212038695812225	0.019281508401036263	0.09038577228784561	0.056621670722961426	0.03498915955424309	0.028459247201681137	-0.02452687919139862	0.008806527592241764	-0.017246726900339127	-0.006811478640884161	0.0342375822365284	-0.00403783330693841	0.0009995730360969901	-0.0024368646554648876	-0.08938523381948471	0.007860035635530949	0.040750473737716675	-0.09132789075374603	-0.01462919358164072	-0.012612270191311836	-0.04859447479248047	0.1268855780363083	0.05505573749542236	-0.04667837172746658	-0.0018742723623290658	0.02155088260769844	0.03374132886528969	0.025431150570511818	0.07090926915407181	-0.00499925808981061	-0.008863336406648159	-0.03250076249241829	0.044214826077222824	-0.010276570916175842	-0.026089122518897057	0.044556550681591034	0.027870412915945053	0.013184953480958939	-0.032495710998773575	-0.05866442620754242	0.0018014766974374652	-0.06312354654073715	-0.05724746361374855	0.023832082748413086	-0.062183767557144165	0.028018567711114883	0.04757312685251236	0.05842726305127144	0.072751984000206	-0.02544831857085228	0.008869191631674767	-0.06009746342897415	-0.05791812017560005	-0.021205097436904907	-0.08379566669464111	-0.014115422032773495	-0.02222898229956627	0.004441718105226755	-0.03642239421606064	-0.02818930335342884	-0.023360159248113632	-0.01234518177807331	-0.005465264897793531	0.03779013827443123	0.05275397375226021	0.06054392084479332	-0.024929560720920563	0.014007648453116417	-0.07771457731723785	-0.021768679842352867	-0.006761983502656221	-0.01334065105766058	-0.033880241215229034	0.00417416961863637	-0.035907480865716934	-0.029254697263240814	0.010785243473947048	0.052482955157756805	-0.004494049586355686	-0.009710067883133888	-0.019546587020158768	-0.03290000930428505	-0.044847406446933746	0.0622711181640625	0.05596812814474106	-0.008456183597445488	0.05632154643535614	-0.010155622847378254	0.07703331857919693	-0.007771753240376711	-0.002751255640760064	-0.05822243168950081	0.04755260795354843	-0.03969119116663933	0.06943230330944061	0.017062529921531677	-0.031123552471399307	0.05590923875570297	0.03282218798995018	0.015524069778621197	-0.08070328086614609	0.003189418464899063	-0.008972945623099804	0.0037930116523057222	0.039802707731723785	0.08462334424257278	-0.03995300084352493	-0.019494138658046722	0.05314091220498085	0.06514458358287811	-0.04206680506467819	-0.016388894990086555	0.01924620382487774	-0.09001307189464569	-0.0024257514160126448	-0.012716315686702728	-0.06116257607936859	-0.04659011587500572	0.011384062469005585	-0.017633721232414246	-0.010655544698238373	0.04761151969432831	0.05659424886107445	-0.034605298191308975	-0.024299928918480873	0.002143018413335085	-0.0011165242176502943	-0.0710049495100975	0.044475752860307693	-0.039357032626867294	-0.082792729139328	-0.07344599813222885	0.016232870519161224	0.0027148292865604162	0.04321529343724251	0.024930376559495926	0.05138062313199043	0.07487151771783829	0.03587399423122406	-0.02118728496134281	0.025872595608234406	-0.054639674723148346	-0.08427947014570236	-0.0064286901615560055	0.04940151795744896	-0.014597591012716293	-0.04248511791229248	-0.008890350349247456	-0.05853605642914772	-0.0019545138347893953	0.009598308242857456	0.018057066947221756	-0.019526707008481026	0.009420211426913738	-0.02030688151717186	0.042140550911426544	-0.018029218539595604	-0.01802424155175686	0.03537970408797264	-0.005768929608166218	0.0746578797698021	-0.03388024866580963	-0.042541272938251495	0.03233026713132858	0.008929722011089325	0.014782646670937538	-0.02101484127342701	0.06800082325935364	-0.033298324793577194	-0.08118628710508347	-0.04486715421080589	-0.07839998602867126	-0.042648252099752426	-0.03127529099583626	-0.005794860422611237	-0.0652436837553978	0.05534329637885094	0.011974571272730827	-0.06763678789138794	-0.05350162088871002	-0.05437008664011955	-0.02978450618684292	-0.07770976424217224	-0.021334774792194366	-0.004717533010989428	-0.08676833659410477	-0.05777658894658089	0.049590058624744415	-0.045320186764001846	-0.05720587074756622	0.042563240975141525	0.0019457004964351654	-0.07272803038358688	0.08054471015930176	0.013461064547300339	-0.03242383897304535	0.007068904582411051	-0.1269415318965912	0.044112078845500946	0.05738820880651474	0.014006768353283405	0.04357549175620079	0.05213302746415138	0.014238331466913223	0.08195033669471741	-0.018422922119498253	-0.021375078707933426	-0.06202784925699234	-0.04377683624625206	-0.10274246335029602	0.06642309576272964	-0.003936741966754198	0.022469570860266685	-0.037078384310007095	0.013193434104323387	0.03376384079456329	0.02597535029053688	-0.0045758942142128944	-0.04810042679309845	0.007999055087566376	-0.029069172218441963	0.03713236004114151	-0.03772880509495735	0.019808020442724228	-0.043070316314697266	-0.05377453193068504	-0.07637223601341248	-0.015093601308763027	0.025348646566271782	-0.01754670962691307	0.03621715307235718	0.014205258339643478	-0.02793865092098713	0.007888010703027248	-0.036070019006729126	0.10257930308580399	-0.0562482550740242	0.02194356732070446	0.03945644572377205	0.025351569056510925	-0.09176121652126312	-0.027545608580112457	0.03677917644381523	-0.06516382098197937	-0.060524679720401764	-0.034275420010089874	0.029010677710175514	-0.020035888999700546	-0.0425674244761467	0.001871652202680707	-0.025673439726233482	0.027728097513318062	0.04113280400633812	0.02062102220952511	0.03902685269713402	0.016822755336761475	-0.04242199659347534	0.03204353153705597	-0.02247898280620575	-0.011884220875799656	-0.004668327979743481	-0.002387531567364931	-0.005667895544320345	0.04825873300433159	-0.019930025562644005	-0.01255018636584282	-0.07479364424943924	0.024644162505865097	-0.015559269115328789	-0.0128428740426898	0.0051021878607571125	0.0007874614675529301	-0.024949070066213608	0.05366905778646469	0.020476574078202248	-0.013121903873980045	-0.061369869858026505	-0.04862974211573601	-0.05244183912873268	-0.08899465203285217	0.010225184261798859	0.019073806703090668	-0.03497815132141113	0.021850327029824257	-0.07847472280263901	0.016544301062822342	0.047891635447740555	-0.005478403065353632	0.014322851784527302	0.0252224188297987	0.043320003896951675	-0.06366932392120361	0.022317569702863693	-0.03527675196528435	0.05288514494895935	-0.001226309803314507	-0.047354113310575485	0.013213491067290306	0.05675811693072319	-0.04124753177165985	0.01672273874282837	-0.044186968356370926	-0.03336493670940399	0.029436912387609482	-0.01381287258118391	-0.012864302843809128	-0.0009835732635110617	0.010530557483434677	-0.0171021968126297	0.03029640205204487	-0.0837232917547226	-0.054294854402542114	-0.011356777511537075	-0.04528075456619263	-0.06995505839586258	-0.013846849091351032	-0.01390691939741373	-0.012871025130152702	0.03090268187224865	-0.02164473943412304	0.030825460329651833	0.07974694669246674	-0.060241684317588806	-0.01573064737021923	0.017849735915660858	-0.007262304425239563	0.04318871721625328	0.014716805890202522	0.04759223759174347	0.0014404631219804287	-0.034435827285051346	0.009152795188128948	-0.02530004270374775	0.005702822934836149	-0.05464766547083855	-0.011903975158929825	0.01523634698241949	-0.025912435725331306	0.048055216670036316	0.014870893210172653	0.015444762073457241	-0.0376189649105072	-0.015728190541267395	-6.169631433294853e-06	0.03685315325856209	0.04869978502392769	0.011025871150195599	0.056157346814870834	-0.051346007734537125	-0.006506448145955801	0.02906092256307602	-0.045915015041828156	-0.07176412642002106	-0.020368793979287148	-0.03992500901222229	0.06854134798049927	0.03408188372850418	0.002264506882056594	-0.012196744792163372	-0.05961650609970093	-0.02084706723690033	-0.02650650590658188	-0.0658990740776062	-0.0056880465708673	0.03933211788535118	-0.0460173524916172	-0.05663168430328369	-0.006089712027460337	-0.08189032226800919	0.02186659723520279	0.03949820250272751	0.05600471794605255	-0.027401959523558617	0.03699347376823425	0.005048518069088459	0.008118799887597561	0.04504260793328285	-0.01973409578204155	-0.07782937586307526	0.00045635062269866467	0.11820680648088455	-0.040258582681417465	0.04191998392343521	0.05956527590751648	0.004657292738556862	0.0640416219830513	0.021872976794838905	-0.02437690645456314	0.006927683018147945	-0.021566249430179596	-0.04526223614811897	0.07731698453426361	-0.0716872587800026	0.0845826044678688	-0.06714046001434326	0.12348180264234543	-0.003983431030064821	-0.022898169234395027	0.08393549174070358	0.07606367766857147	0.038716088980436325	0.04108887165784836	-0.05313882231712341
+-0.014548031613230705	-0.0389380007982254	-0.02724343352019787	0.016424745321273804	-0.08019264787435532	-0.027343548834323883	-0.03764432296156883	0.0589863546192646	-0.004615176003426313	-0.07161125540733337	-0.056507546454668045	0.005732166115194559	0.02476421929895878	0.016899626702070236	0.0094869090244174	-0.038083650171756744	-0.05257836729288101	-0.017955733463168144	0.00044158758828416467	-0.0487218014895916	0.02185095101594925	0.008292973972856998	-0.07583139091730118	0.08923455327749252	0.06160959228873253	-0.023085571825504303	-0.040261123329401016	0.043012235313653946	0.0024816985242068768	0.08696399629116058	0.04362460598349571	-0.054089680314064026	0.031828902661800385	-0.063051737844944	-0.0673905611038208	0.022571949288249016	-0.07104312628507614	0.09433615207672119	0.03809918835759163	-0.02994108572602272	-0.04267393797636032	0.042723651975393295	0.00401119003072381	-0.04442259296774864	0.02371535263955593	0.022661814466118813	-0.03416987881064415	-0.04776022955775261	0.015322321094572544	0.032303351908922195	0.08049549907445908	0.01975826360285282	0.0016793739050626755	-0.01667424663901329	-0.030610136687755585	0.040445782244205475	0.0071461396291852	-0.03716795891523361	-0.032826293259859085	0.004860973451286554	-0.06306284666061401	0.025304757058620453	0.009610109962522984	0.010116164572536945	-0.006827345117926598	0.03980071842670441	-0.029639920219779015	0.06264360249042511	0.004326065070927143	0.002159483963623643	-0.006665822584182024	0.06582925468683243	-0.04214626923203468	-0.02196129411458969	0.03546985983848572	0.006816723383963108	-0.00453186733648181	0.008875962346792221	-0.005293909925967455	0.02490517497062683	0.030962662771344185	-0.0287796538323164	0.1285683512687683	-0.02477223053574562	0.03301955386996269	-0.05520571395754814	-0.02162603661417961	-0.05540936067700386	0.04727661609649658	0.012991753406822681	0.055342897772789	0.04797294735908508	0.03351437300443649	0.042610589414834976	-0.014489351771771908	-0.007132040802389383	0.016914136707782745	-0.1295005977153778	0.03531014174222946	0.026294516399502754	0.011210320517420769	-0.006402898579835892	0.07722926884889603	-0.00024684055824764073	0.03477117419242859	0.07387896627187729	-0.08528711646795273	0.023447800427675247	0.04897946119308472	0.01920740120112896	0.012048576958477497	0.12274503707885742	0.03529180958867073	0.04551830142736435	0.03236079588532448	0.01833057962357998	-0.028642861172556877	-0.0038383002392947674	-0.020146755501627922	0.010298607870936394	-0.012969428673386574	0.05270495265722275	-0.013259073719382286	-0.0808144360780716	0.05350988358259201	0.06328567117452621	-0.05540809780359268	0.00828497763723135	0.02650153823196888	-0.020737046375870705	0.05536697804927826	0.05152207985520363	-0.005500456318259239	0.051968272775411606	0.037913087755441666	0.01773158833384514	0.022947542369365692	0.016719525679945946	-0.008997038938105106	-0.04991436377167702	-0.0756128579378128	0.046148110181093216	0.010230978950858116	-0.04120075702667236	0.05624806880950928	0.02223958633840084	0.0017995652742683887	0.0035332555416971445	-0.05769777670502663	-0.016310755163431168	-0.08441238105297089	-0.09976826608181	0.028737351298332214	-0.014514871872961521	0.027262277901172638	0.037941835820674896	0.05651605501770973	0.03379010781645775	0.00878817681223154	-0.001678707543760538	0.0026869457215070724	-0.0488746352493763	-0.011828433722257614	-0.07660671323537827	0.05050140619277954	0.010516036301851273	0.023824624717235565	-0.006832896266132593	-0.06416958570480347	-0.017649896442890167	-0.0009538205922581255	0.013470870442688465	0.0389222651720047	0.05269858241081238	0.021755553781986237	-0.008207850158214569	0.08501549065113068	-0.048533931374549866	-0.06886604428291321	-0.028216229751706123	0.04980075731873512	-0.07365420460700989	0.010329454205930233	-0.05031931772828102	-0.02761327661573887	0.007259036879986525	-0.000331283692503348	-0.02293218858540058	-0.010588675737380981	-0.03411248326301575	-0.03590846434235573	-0.058533791452646255	0.05262313783168793	-0.0014917792286723852	-0.01120629720389843	0.04821931943297386	0.06210927292704582	0.07057312875986099	-0.024281885474920273	0.002586446702480316	-0.05512404814362526	0.05438290908932686	0.033887483179569244	-0.009887163527309895	0.020580865442752838	-0.027605364099144936	0.03368525207042694	-0.004850978963077068	0.05471397936344147	-0.026361435651779175	0.06025507301092148	0.07097741961479187	0.0072382185608148575	0.04355192184448242	0.07955637574195862	-0.025262832641601562	-0.03521418198943138	0.004572328180074692	0.0402495302259922	-0.1177147850394249	-0.0011382036609575152	0.008197452872991562	-0.062260475009679794	0.0052132317796349525	-0.031515467911958694	-0.09754983335733414	-0.029381439089775085	0.004619637504220009	-0.0008371665608137846	-0.05342062562704086	0.00938290823251009	0.02917967177927494	-0.021561287343502045	-0.008217794820666313	0.021410109475255013	0.016992002725601196	-0.04047785699367523	0.03720958158373833	-0.06494216620922089	-0.03370376303792	-0.01511876005679369	0.02013583481311798	-0.02256200462579727	0.0240373145788908	0.00793501827865839	0.07081132382154465	0.06579139828681946	0.07010376453399658	-0.0716707780957222	0.0054231309331953526	-0.042520228773355484	-0.017775794491171837	-0.013272833079099655	0.040955785661935806	0.03389379382133484	-0.043984830379486084	0.014479496516287327	-0.09527028352022171	0.00215069018304348	-0.013204138725996017	-0.004563711117953062	-0.048612579703330994	-0.003678726265206933	-0.02877259999513626	0.025720668956637383	0.0029039655346423388	-0.05114022642374039	0.015518469735980034	-0.05256527662277222	0.06142060086131096	-0.06184319406747818	-0.01407402753829956	0.01186745148152113	0.004279931075870991	0.021801603958010674	-0.03023221716284752	0.08278784900903702	-0.035803426057100296	-0.05845262482762337	-0.06965412199497223	-0.0329873152077198	-0.08056514710187912	-0.0018484550528228283	-0.03461622819304466	-0.0008456401992589235	0.06952561438083649	0.021262040361762047	-0.04454071447253227	-0.05742473155260086	-0.07532310485839844	-0.01512108463793993	-0.03254874795675278	-0.027529438957571983	-0.01334443874657154	-0.030945109203457832	-0.01545962505042553	0.035677067935466766	-0.033293467015028	-0.011981572955846786	0.09365712851285934	0.04916985332965851	-0.02404225617647171	0.0879901722073555	-0.05263752117753029	0.003335546003654599	0.03770247846841812	-0.052787259221076965	0.016399741172790527	0.07115431874990463	-0.002576813567429781	-0.0041163647547364235	0.035297684371471405	0.04431251809000969	0.07322196662425995	0.008865583688020706	-0.06535247713327408	-0.04383838549256325	-0.03211335092782974	-0.08334412425756454	0.07851563394069672	-0.010508794337511063	-0.005183614324778318	-0.010447634384036064	-0.04805572330951691	0.02015751041471958	0.0042456998489797115	0.006593146361410618	-0.07152139395475388	0.020905176177620888	-0.04201393947005272	0.025502175092697144	-0.03664306178689003	0.0018887531477957964	0.03317231312394142	-0.033340029418468475	-0.05534489080309868	-0.08342405408620834	0.038042102009058	0.0013721074210479856	0.05514969676733017	0.011133727617561817	-0.009373859502375126	0.025857599452137947	-0.004146340768784285	0.08059295266866684	-0.08558272570371628	0.037851229310035706	0.022873984649777412	0.04320918023586273	-0.05944439396262169	-0.007849172689020634	-0.01566205732524395	-0.0412457212805748	-0.04938296973705292	-0.02296484261751175	0.034509774297475815	-0.043753694742918015	-0.07772164046764374	-0.037983283400535583	-0.018474016338586807	0.016026752069592476	0.018625684082508087	-0.014035079628229141	0.08622289448976517	0.03405231237411499	-0.022185856476426125	0.03332776948809624	-0.017444821074604988	-0.007262531202286482	-0.015922555699944496	0.04680394008755684	-0.012182952836155891	0.07622580975294113	-0.013835962861776352	-0.02264115773141384	-0.0818476602435112	-0.009901892393827438	-0.08915724605321884	-0.003232389222830534	-0.019750623032450676	0.018740499392151833	-0.021547943353652954	0.019187480211257935	0.02257419377565384	-0.09555009007453918	-0.003406521398574114	0.007615603040903807	-0.04964671656489372	-0.02413610741496086	-0.04215380549430847	0.027956180274486542	-0.03216950222849846	0.03589046001434326	-0.06900691986083984	0.023125456646084785	0.048531074076890945	0.04192253574728966	0.012528356164693832	0.05489461123943329	0.018729956820607185	0.006023370195180178	0.07751113176345825	0.012156462296843529	-0.011317330412566662	-0.019221952185034752	-0.05778321251273155	0.03055630251765251	0.07016819715499878	-0.041324589401483536	0.003584312740713358	-0.04227476194500923	0.010311255231499672	0.016831588000059128	-0.032875411212444305	0.009763556532561779	-0.028110306710004807	0.0005515955272130668	-0.001506194588728249	0.04533425346016884	-0.010393714532256126	-0.07211119681596756	0.012573418207466602	-0.05319202318787575	-0.07099197804927826	0.007990820333361626	-0.06675583869218826	-0.04421176388859749	0.03550134226679802	0.01922568678855896	0.01543625071644783	0.04688486456871033	-0.05085223540663719	-0.024088628590106964	0.03940270468592644	-0.004144645296037197	-0.02063657157123089	0.07050157338380814	0.04599063843488693	0.010984712280333042	-0.03342706337571144	0.03140750527381897	-0.07209423929452896	0.03441479429602623	-0.05114342272281647	0.011564753018319607	0.04216088354587555	-0.023661378771066666	0.019408060237765312	0.035170573741197586	0.04018368571996689	-0.0709674209356308	-0.017442850396037102	-0.010370571166276932	-0.0008342970977537334	-0.007229935843497515	0.025801915675401688	0.043848272413015366	-0.06924290955066681	-0.036631837487220764	0.020373200997710228	-0.030935104936361313	-0.09344888478517532	-0.021209735423326492	-0.0762772485613823	0.0851542130112648	-0.016054289415478706	-0.006931908428668976	-0.0268329456448555	-0.11040737479925156	-0.017272600904107094	-0.025694001466035843	-0.015738101676106453	-0.019555004313588142	0.008381322026252747	-0.06672940403223038	-0.06256337463855743	-0.01522008329629898	-0.07967565208673477	0.06095558777451515	0.0032701194286346436	0.013889589346945286	-0.06375624239444733	-0.01131060067564249	0.0007555437623523176	-0.021009407937526703	0.030656199902296066	-0.050656553357839584	-0.06488344073295593	-0.02337249554693699	0.12731267511844635	0.004825713112950325	0.03360386937856674	0.102850042283535	0.010383104905486107	0.048697929829359055	0.02491348423063755	-0.005220921244472265	-0.03908287361264229	-0.02374703250825405	-0.007517253514379263	0.02961686998605728	-0.03273370862007141	0.045623768121004105	-0.05498270317912102	0.11087559908628464	0.04052114859223366	0.014080286957323551	0.08042951673269272	0.07665150612592697	0.05098909139633179	0.04190817102789879	-0.10199347883462906
+0.05923936516046524	0.04083222150802612	0.003450424410402775	0.028467660769820213	-0.07019445300102234	-0.06053052097558975	-0.012901097536087036	0.015687141567468643	0.05307697132229805	-0.04248695820569992	0.025410015136003494	-0.038969285786151886	-0.023595917969942093	0.0023865015245974064	0.031784139573574066	-0.031366001814603806	-0.06452660262584686	-0.0023612005170434713	-0.013167258352041245	-0.03359365090727806	0.011389953084290028	0.0050214240327477455	-0.06674126535654068	0.03229011595249176	-0.0056990510784089565	-0.020374003797769547	-0.05254779011011124	-0.012315028347074986	-0.0460396334528923	0.060492485761642456	0.045612234622240067	-0.009588375687599182	0.07367124408483505	-0.02859857678413391	-0.03768985718488693	0.06947695463895798	-0.004642480053007603	0.13494154810905457	0.07205363363027573	-0.0248162429779768	-0.05551140382885933	-0.024702822789549828	-0.04863360524177551	-0.018683891743421555	0.0122782988473773	0.011754577979445457	-0.0631791353225708	-0.03710038959980011	0.059324830770492554	0.03283493220806122	0.11224599927663803	-0.03090118244290352	-0.08324205875396729	-0.0033486471511423588	-0.035673804581165314	0.011142674833536148	0.05616060271859169	-0.07981002330780029	-0.07332579046487808	0.006806318182498217	-0.08437992632389069	-0.0033467186149209738	-0.006750260014086962	-0.014475250616669655	0.0012847597245126963	-0.048932500183582306	-0.009352250024676323	0.053812406957149506	0.03264313563704491	-0.005175686441361904	0.03713071718811989	0.10123679041862488	-0.09461219608783722	-0.09262630343437195	0.06685630977153778	-0.08486625552177429	-0.04600336402654648	0.047654032707214355	-0.013462281785905361	0.06423750519752502	-0.01682935282588005	-0.04359804838895798	0.09681452065706253	-0.0773007869720459	-0.0035794011782854795	-0.01391240768134594	-0.00636453740298748	-0.050957001745700836	0.05372077226638794	0.05291462317109108	-0.016089100390672684	0.023458683863282204	-0.06107475608587265	0.019670279696583748	0.004088647197932005	-0.020226627588272095	0.05411248654127121	-0.06590216606855392	-0.04853035509586334	0.011411396786570549	-0.011836174875497818	0.01798475719988346	0.030919887125492096	0.008641829714179039	0.06372804194688797	0.04150286689400673	-0.028808804228901863	0.03605649620294571	0.054372724145650864	0.052884235978126526	0.02718966081738472	0.02985488250851631	0.0861632227897644	0.0050056688487529755	0.03424753248691559	0.005270396824926138	0.04977642372250557	-0.0511021725833416	-0.012734674848616123	0.01807324029505253	-0.0334324948489666	0.05314686521887779	-0.060740865767002106	-0.015901656821370125	-0.025961952283978462	0.0068702613934874535	-0.08426306396722794	-0.05160465091466904	0.013488282449543476	-0.0011169839417561889	0.07787608355283737	0.04682954028248787	-0.010332834906876087	0.019578974694013596	0.07957568764686584	0.030488373711705208	0.002104439539834857	0.02258298546075821	-0.016155308112502098	-0.012445073574781418	0.023521555587649345	0.05896533653140068	-0.028524940833449364	-0.034830689430236816	0.04065367579460144	0.06228802353143692	0.04724143445491791	0.004143525846302509	-0.08666069805622101	-0.0023556489031761885	-0.06877156347036362	-0.07276232540607452	0.005344086792320013	-0.07927875220775604	0.0012182726059108973	0.06403879821300507	0.01042875461280346	0.06929483264684677	0.013977592810988426	-0.015816619619727135	-0.03704794496297836	-0.03473888337612152	-0.029470257461071014	-0.05006770044565201	-0.05976342409849167	-0.056657664477825165	0.022457489743828773	0.025336023420095444	-0.0235234834253788	-0.0014376492472365499	0.005493967328220606	0.007339769508689642	0.02886362187564373	0.09469077736139297	0.0736151933670044	0.030630916357040405	0.008172301575541496	-0.08077304810285568	-0.001711956923827529	-0.04746919870376587	0.05781794339418411	-0.05582793802022934	0.004910157062113285	-0.025443250313401222	0.03043139912188053	0.007248013745993376	0.07326173782348633	0.03853185474872589	0.009800390340387821	0.03998260945081711	-0.05225463584065437	-0.058289606124162674	0.038181617856025696	0.05865361914038658	-0.0042425720021128654	-0.00029610245837830007	-0.01418678555637598	0.051277726888656616	0.01917325332760811	-0.02227616310119629	-0.03611598163843155	0.019937589764595032	0.053338538855314255	0.07136566191911697	0.06661152839660645	-0.023967986926436424	0.08681213110685349	0.004049439914524555	0.0390266589820385	-0.02034398540854454	-0.01862012967467308	-0.023572014644742012	0.009803001768887043	-0.019665170460939407	0.060180820524692535	-0.043427664786577225	0.02018311247229576	-0.03273104503750801	0.10609564930200577	-0.020381104201078415	0.019858604297041893	-0.007487467490136623	-0.06809353828430176	0.04660850763320923	-0.04258265718817711	-0.09824375808238983	-0.020260531455278397	0.02480350062251091	-0.054143667221069336	-0.018902840092778206	-0.017197681590914726	0.0143832266330719	-0.026196492835879326	-0.011904505081474781	0.012673553079366684	-0.0069130053743720055	-0.07689840346574783	0.09982471168041229	-0.025845184922218323	-0.03334551677107811	-0.027309468016028404	0.016618410125374794	0.04687722399830818	-0.024729754775762558	0.0325932577252388	0.07098011672496796	0.0635404959321022	0.029225118458271027	0.018077442422509193	0.008318164385855198	-0.0462518036365509	-0.050723519176244736	0.0075591676868498325	0.010875919833779335	-0.05671323090791702	-0.03782377392053604	0.021387772634625435	-0.05501285940408707	0.006495846435427666	-0.001752435346134007	0.017791301012039185	0.0006260167574509978	-0.012143376283347607	-0.03808602690696716	0.054699480533599854	-0.006738159339874983	-0.038636863231658936	0.039197854697704315	-0.054689809679985046	0.006581415887922049	-0.020394781604409218	-0.0061365156434476376	0.009039731696248055	0.025151394307613373	0.009959451854228973	0.006684786640107632	0.056606438010931015	-0.0546041801571846	-0.0727403536438942	-0.05775913968682289	-0.08390645682811737	-0.01528729684650898	0.01078018732368946	-0.0192814189940691	-0.023903362452983856	0.031501784920692444	0.0202342476695776	-0.006534859538078308	-0.06372027099132538	-0.01409302931278944	-0.05572119727730751	-0.08678222447633743	-0.017690135166049004	-0.00977142434567213	-0.06072283908724785	-0.0678628534078598	0.010414082556962967	-0.01326893549412489	0.011679299175739288	-0.016875341534614563	-0.0589660219848156	-0.04068562388420105	-0.035924267023801804	-0.040955089032649994	-0.0557212196290493	-0.0016512173460796475	-0.035300642251968384	0.04395030066370964	0.11223884671926498	0.06447338312864304	0.044747572392225266	0.04159758985042572	0.0638287290930748	0.05527462810277939	0.026443561539053917	-0.06202254816889763	0.014599906280636787	-0.03504611924290657	-0.06874466687440872	0.0027347607538104057	-0.03195735067129135	-0.018679644912481308	-0.0014137027319520712	-0.007123361807316542	0.042426180094480515	0.01968410611152649	-0.03299751132726669	-0.0021361755207180977	0.07349628955125809	-0.02570459246635437	0.04683639481663704	0.0033425705041736364	-0.004718668293207884	0.018499938771128654	-0.05867723375558853	0.022228453308343887	-0.0799298956990242	0.006361723877489567	0.01973763480782509	0.016894593834877014	-0.006895160768181086	-0.04127644747495651	-0.03480996936559677	0.01847437582910061	0.08789847046136856	-0.06702768057584763	-0.015428779646754265	0.024232657626271248	-0.0216074138879776	-0.1133696585893631	-0.03396115079522133	0.04666540399193764	-0.04948835074901581	-0.027211710810661316	0.021643539890646935	0.026332061737775803	-0.05591274052858353	0.009019963443279266	-0.022093862295150757	-0.06327733397483826	-0.03348350524902344	0.02816871739923954	0.00773779209703207	0.01555850263684988	-0.02272169478237629	-0.05320411175489426	-0.0005407786229625344	0.008258883841335773	0.021930407732725143	-0.0071353851817548275	-0.017309412360191345	-0.06474607437849045	0.012614094652235508	-0.05006040632724762	-0.03883841261267662	-0.09033150225877762	0.06973908841609955	-0.0018007750622928143	-0.06556625664234161	-0.015176596119999886	0.019756430760025978	-0.03436317667365074	-0.017403002828359604	0.036168891936540604	-0.0505778044462204	-0.03318716958165169	-0.059375304728746414	0.018948519602417946	-0.0696704238653183	-0.013251109048724174	0.016914060339331627	-0.07056904584169388	-0.012049435637891293	-0.02359086461365223	-0.04165388643741608	-0.04870373010635376	0.008350792340934277	-0.001913161133415997	0.034080248326063156	0.015471680089831352	0.02207230217754841	0.029395274817943573	0.018873941153287888	0.016070399433374405	0.06664803624153137	-0.04738076776266098	-0.017907442525029182	-0.028199577704072	-0.06023378297686577	0.024886921048164368	-0.040924567729234695	-0.003060184419155121	-0.0302675049751997	0.0022041485644876957	0.03216129541397095	-0.0215491633862257	0.014347991906106472	-0.009651358239352703	0.03229932859539986	-0.051483456045389175	-0.007896463386714458	0.01777138188481331	-0.030422264710068703	-0.08048500120639801	-0.023963071405887604	0.03136208653450012	-0.051765188574790955	0.027215775102376938	-0.03168866038322449	0.027055134996771812	0.024562301114201546	-0.07029452174901962	-0.09032940864562988	-0.060366373509168625	-0.0051222993060946465	0.03596953675150871	-0.02651241049170494	0.0149633027613163	-0.03200146183371544	-0.0020042755641043186	0.005848746746778488	0.005021592136472464	0.036190759390592575	-0.046102214604616165	-0.034992754459381104	-0.04070640727877617	-0.016792060807347298	-0.004670493304729462	-0.01853753998875618	0.02905372530221939	-0.03803161904215813	-0.019127123057842255	-0.07261499762535095	0.10187580436468124	-0.05718199163675308	-0.01727290265262127	0.017565883696079254	0.0030081290751695633	0.02016073279082775	0.04568374156951904	-0.0289112888276577	-0.05452200397849083	0.02034577541053295	-0.02593647502362728	-0.017022164538502693	0.029116569086909294	0.018252957612276077	-0.013054369948804379	-0.03199917823076248	-0.03885524719953537	0.03523321449756622	-0.09822247922420502	0.019197113811969757	-0.044995274394750595	-0.07390750199556351	-0.07629861682653427	-0.01837931014597416	-0.048188019543886185	0.08013756573200226	-0.012755917385220528	0.023289693519473076	0.006659926846623421	-0.00796505343168974	-0.02826252579689026	-0.050790850073099136	0.027788477018475533	0.019223423674702644	-0.013217004016041756	-0.03459390252828598	0.049153465777635574	0.011737911030650139	0.015531902201473713	0.06076965853571892	-0.03580016270279884	0.021626697853207588	0.09121359139680862	-0.05280621722340584	0.03186243399977684	0.0023503804113715887	-0.06415002793073654	0.0870278924703598	-0.04669341444969177	0.11460498720407486	-0.04274804890155792	0.1260940581560135	0.020258674398064613	-0.01802346482872963	0.05761760100722313	0.07279235124588013	-0.027395188808441162	0.010531877167522907	-0.05109884962439537
+0.035705097019672394	-0.014264406636357307	0.04485584422945976	0.07584460824728012	-0.042579248547554016	-0.022098736837506294	-0.026977550238370895	0.0035038411151617765	0.03057858906686306	-0.026212044060230255	-0.015453064814209938	0.004895959515124559	-0.05129852518439293	0.006964665837585926	-0.017544208094477654	0.006478935945779085	-0.05429937317967415	0.006077457685023546	-0.027516141533851624	-0.044945716857910156	0.00873157661408186	0.033727943897247314	-0.08606751263141632	0.055187124758958817	-0.033623505383729935	-0.004829308018088341	-0.060672398656606674	0.01648278348147869	-0.06568272411823273	0.032162442803382874	0.03258199244737625	-0.04026849567890167	0.08461851626634598	-0.06497026979923248	-0.045703183859586716	0.040189653635025024	0.02729298360645771	0.07934324443340302	0.05058155581355095	-0.05908556655049324	-0.020681098103523254	0.03107191063463688	0.015839189291000366	-0.07630198448896408	-0.012851535342633724	0.004755476955324411	-0.01703486405313015	-0.0278747770935297	0.03638013079762459	0.05637155473232269	0.12012997269630432	0.03312985226511955	-0.021987788379192352	-0.01876037009060383	-0.012264049611985683	-0.01833553984761238	0.04336104169487953	-0.07982517033815384	-0.06737973541021347	0.0034704229328781366	-0.09716764837503433	0.017839575186371803	0.01104623731225729	0.029199901968240738	-0.04846994951367378	-0.016714531928300858	0.00900799036026001	0.10026111453771591	0.09342440962791443	-0.018522368744015694	0.034917376935482025	0.0844128429889679	-0.055780719965696335	-0.01440173014998436	0.01262625865638256	-0.040264278650283813	-0.0260989461094141	0.03367670252919197	-0.01125727966427803	0.029529284685850143	0.028046077117323875	-0.049070775508880615	0.08618384599685669	-0.02308511734008789	0.008923782035708427	0.014305523596704006	0.01798928529024124	-0.10119848698377609	0.03312312811613083	0.011985658667981625	-0.0012779940152540803	0.0691252276301384	-0.028506755828857422	0.06600842624902725	-0.005028058309108019	0.059503406286239624	0.04766001179814339	-0.10177268087863922	0.00264159357175231	0.0029642321169376373	-0.011656377464532852	0.03396199643611908	0.0042169950902462006	-0.0015926089836284518	0.07399018108844757	0.004913903772830963	-0.04323059320449829	-0.010565780103206635	0.07227057963609695	0.015521585941314697	0.08977702260017395	0.0678112655878067	0.05032267048954964	0.03725693002343178	0.028385555371642113	0.017572704702615738	0.06107969582080841	-0.07030840963125229	0.015652846544981003	0.030578022822737694	-0.01746254973113537	0.04504535347223282	-0.04169103130698204	-0.08206522464752197	0.010883199982345104	0.046138450503349304	-0.03939457982778549	-0.026778848841786385	0.01689189299941063	-0.0039289346896111965	0.08681580424308777	0.0745190978050232	-0.020213497802615166	0.015482314862310886	0.021902430802583694	0.019134854897856712	-0.0677342638373375	-0.0034297264646738768	0.03840671107172966	0.006825147196650505	-0.01916024461388588	0.06388477981090546	0.02124389074742794	-0.025982333347201347	0.01944713108241558	0.007300623692572117	0.017110243439674377	-0.000575198617298156	-0.06664960086345673	0.001247487380169332	-0.041688352823257446	-0.09803956001996994	0.017688611522316933	-0.07428555935621262	0.0037125663366168737	0.10165825486183167	0.02917257696390152	0.07544352859258652	-0.02319173328578472	-0.012133208103477955	-0.02502310276031494	-0.0359417200088501	-0.015843145549297333	-0.06803654879331589	-0.043062467128038406	-0.0029361210763454437	-0.014185820706188679	0.03047686070203781	-0.04398336634039879	-0.0039730374701321125	-0.010861490853130817	-0.004350369796156883	0.008313491940498352	0.09286980330944061	0.09792067855596542	0.038082271814346313	0.015538533218204975	-0.027649367228150368	0.0214215200394392	-0.028625788167119026	-0.01468251645565033	-0.06352762132883072	0.024846989661455154	-0.06418579816818237	-0.030919326469302177	-0.009948057122528553	0.024982355535030365	0.03701053559780121	0.04344690963625908	0.039684735238552094	-0.029849287122488022	-0.027632612735033035	0.007637709844857454	0.036235783249139786	0.0029589212499558926	0.03142140060663223	0.016683606430888176	0.08879624307155609	-0.003327553626149893	-0.003112130332738161	-0.056659452617168427	0.02908151224255562	0.02928083762526512	0.027576105669140816	0.0436830036342144	-0.01625378243625164	0.06339766830205917	0.07123301178216934	0.008025381714105606	-0.05765927582979202	-0.012553112581372261	0.021537743508815765	0.08221055567264557	0.014837820082902908	0.06013054400682449	-0.03850546106696129	-0.008093170821666718	0.004911560565233231	0.027327297255396843	-0.05028378590941429	0.00011350300337653607	-0.027549423277378082	-0.030467620119452477	0.041433218866586685	-0.03633330762386322	-0.07967185229063034	-0.044302888214588165	-0.02169189043343067	0.023533815518021584	-0.04243527725338936	0.08521881699562073	0.031429536640644073	-0.029537258669734	-0.00993913784623146	0.0051645031198859215	-0.06215168535709381	-0.05421324446797371	0.0916142538189888	-0.01754310354590416	-0.014442522078752518	-0.0051242271438241005	0.007378011010587215	0.02619355544447899	0.036667291074991226	0.0009514419361948967	0.03934607282280922	0.13460524380207062	0.06383642554283142	-0.045997221022844315	-0.014340067282319069	-0.07211719453334808	-0.11718428134918213	0.06135983392596245	0.058669764548540115	-0.019483085721731186	-0.04824168235063553	-0.04096028208732605	-0.09023640304803848	0.0011160837020725012	-0.0006355479126796126	0.01090641226619482	0.022035419940948486	0.028480593115091324	-0.053954895585775375	0.028280196711421013	0.014595241285860538	-0.05135072395205498	0.0325443334877491	0.009817918762564659	0.037355951964855194	-0.029383983463048935	0.0029924267437309027	0.05457942932844162	-0.01806338131427765	0.02231578342616558	-0.012573174200952053	0.044955600053071976	-0.02354838326573372	-0.05537799373269081	-0.057626161724328995	-0.10135100036859512	-0.02692519687116146	0.017217032611370087	-0.010873852297663689	-0.06102216988801956	0.0420878529548645	0.006424413062632084	-0.012364232912659645	-0.0143626993522048	-0.0019303516019135714	-0.0643082857131958	-0.0758199393749237	-0.03168761730194092	0.018087994307279587	-0.028174009174108505	-0.028975849971175194	-0.012310908176004887	-0.046264663338661194	-0.028258860111236572	-0.012711145915091038	-0.02903471328318119	-0.04103396460413933	0.051901914179325104	0.023127226158976555	-0.006250773090869188	0.01888592168688774	-0.10543030500411987	0.014241831377148628	0.09952717274427414	0.005528406705707312	0.08789742738008499	0.06987863779067993	0.025695672258734703	0.0839153528213501	0.04930218681693077	-0.06597500294446945	-0.011024112813174725	0.02270513027906418	-0.06835999339818954	0.05806942656636238	0.01773601397871971	0.033777475357055664	0.027154671028256416	0.01798447221517563	0.04468623921275139	0.032310958951711655	-0.02553137019276619	-0.0408533550798893	0.047911807894706726	-0.04274311289191246	0.012458176352083683	-0.028045836836099625	-0.00810585543513298	0.01751943863928318	-0.05063031241297722	-0.004554774146527052	-0.006353253033012152	0.017038360238075256	-0.007563358172774315	0.038079869002103806	0.028587019070982933	-0.06320008635520935	0.00809473730623722	-0.03848208859562874	0.0614900104701519	-0.1172543615102768	0.010903572663664818	7.962435483932495e-05	0.025582363829016685	-0.13990619778633118	-0.05404796823859215	0.04006496071815491	-0.042524032294750214	-0.03512014076113701	0.022049861028790474	0.009751024655997753	-0.044998280704021454	-0.01763044483959675	-0.03422228991985321	-0.022692283615469933	0.00248695258051157	0.04785395786166191	0.04532833397388458	0.030424004420638084	-0.0007824926287867129	-0.028753746300935745	-0.0596720390021801	-0.008238107897341251	0.025488805025815964	0.006004720460623503	0.016720246523618698	-0.032431792467832565	0.020846275612711906	-0.027801159769296646	-0.03474235534667969	-0.03968307003378868	0.007934574037790298	-0.01627308875322342	0.017058489844202995	0.060746658593416214	-0.008422743529081345	0.010708145797252655	0.011393805034458637	0.05803899094462395	-0.015965623781085014	0.003471490228548646	0.001141246291808784	0.002218292560428381	-0.04093746468424797	-0.03141823783516884	-0.012510452419519424	-0.02964046411216259	0.028708962723612785	-0.011319387704133987	-0.036220528185367584	0.017216064035892487	-0.01295564416795969	0.01020003855228424	0.06151290610432625	-0.0018684865208342671	0.006399008445441723	-0.03074432723224163	-0.0272750835865736	0.012260796502232552	0.04814859852194786	-0.09085483849048615	0.0010923061054199934	0.04944877699017525	-0.04195627570152283	0.0027506763581186533	-0.01559237390756607	-0.04372746869921684	0.022642269730567932	-0.0019620582461357117	-0.0016021692426875234	-0.014665533788502216	-0.01687147654592991	-0.021580779924988747	0.055771518498659134	-0.03594547137618065	-0.031943678855895996	-0.0037972950376570225	-0.027555329725146294	-0.11103584617376328	-0.023183181881904602	-0.006229650229215622	-0.05255736783146858	0.07148102670907974	-0.013710380531847477	0.0167709868401289	0.04431841894984245	-0.04123079404234886	-0.03432120010256767	0.01311960443854332	0.008764520287513733	0.0010924164671450853	0.00014013805775903165	0.057183053344488144	-0.032548848539590836	-0.029561862349510193	-0.00841466337442398	-0.02165307104587555	0.030111847445368767	-0.06461101770401001	-0.04624130576848984	0.004169314168393612	-0.011020125821232796	0.036321353167295456	0.033182293176651	-0.023354416713118553	-0.10286662727594376	-0.033417731523513794	-0.0011114651570096612	0.04711703956127167	-0.05026598647236824	-0.01320033147931099	0.03796263411641121	-0.05849212408065796	-0.037324193865060806	0.03564785420894623	-0.0018313913606107235	-0.07533685863018036	0.005123187322169542	-0.031161803752183914	-0.004301241599023342	-0.020943714305758476	0.05260290578007698	-0.04311104863882065	-0.078855961561203	-0.0356719046831131	0.04007842764258385	-0.07783742249011993	-0.0407029427587986	-0.0004693811060860753	-0.10111906379461288	-0.05955655500292778	0.02311570756137371	-0.07020558416843414	0.04851300269365311	0.022652151063084602	0.01094839721918106	0.009257654659450054	0.007432105485349894	-0.03107062354683876	-0.05583682283759117	0.03080790303647518	0.003728691954165697	-0.048986051231622696	-0.036020781844854355	0.06888023018836975	0.004268578253686428	0.03931375965476036	0.07511107623577118	0.0003015674592461437	0.0759008377790451	0.04599134996533394	-0.028865378350019455	0.016808733344078064	-0.02684463933110237	-0.008457743562757969	0.07954825460910797	-0.020083745941519737	0.06805159151554108	-0.01846112310886383	0.13182777166366577	-0.001864752033725381	-0.02150901034474373	0.06284968554973602	0.1129055991768837	0.003778972662985325	0.0089345658197999	-0.0589175708591938
+0.0173697117716074	-0.013546195812523365	-0.02555275894701481	0.00869770161807537	-0.06994669139385223	-0.0735756903886795	-0.037653639912605286	0.042823318392038345	0.039857782423496246	-0.06338609009981155	-0.04081205651164055	-0.024796107783913612	-0.017664318904280663	0.02087104320526123	0.029205402359366417	-0.020980047062039375	-0.04719337075948715	0.006020018830895424	-0.02815834991633892	-0.031991079449653625	0.008573425933718681	0.03634851798415184	-0.03932403028011322	0.09691005200147629	0.00534812593832612	-0.04516631364822388	-0.06301124393939972	-0.005306633189320564	-0.0131046948954463	0.05506091192364693	0.017554059624671936	-0.010728909634053707	0.05445762723684311	-0.05868836119771004	-0.003178860293701291	0.03158903121948242	-0.011114596389234066	0.07374054938554764	0.06582978367805481	-0.04125445708632469	-0.027332976460456848	0.021226568147540092	-0.062026992440223694	-0.07879464328289032	0.0054727112874388695	-0.00943543016910553	-0.03565158694982529	-0.015264884568750858	0.0043376716785132885	0.0817234069108963	0.02134176716208458	-0.009874639101326466	-0.019756320863962173	0.002635719021782279	-0.00511584896594286	0.011772055178880692	0.031196758151054382	-0.09338968992233276	0.005537206307053566	0.01310223899781704	-0.0730368122458458	0.008831456303596497	0.05092386156320572	0.05592135339975357	0.022243017330765724	-0.035261549055576324	0.025310158729553223	0.08145802468061447	0.05398920178413391	-0.02254263497889042	-0.00169146410189569	0.030679138377308846	-0.050167471170425415	0.008949670009315014	0.02935853786766529	-0.038744594901800156	-0.01714727282524109	0.015283317305147648	0.025462230667471886	0.015920499339699745	0.06784047186374664	-0.01104282308369875	0.07670456171035767	0.002262119436636567	-0.0060258712619543076	-0.04632182419300079	0.0073095750994980335	-0.0684954896569252	0.03583747521042824	-0.07093854248523712	0.044976770877838135	0.07689529657363892	0.0022341194562613964	0.017146393656730652	-0.049747832119464874	0.04042968526482582	-0.02337794564664364	-0.1222442239522934	-0.010905550792813301	-0.01047523133456707	-0.00941033847630024	0.01831705868244171	3.6251825804356486e-05	-0.007107364013791084	0.019355935975909233	0.04385912045836449	-0.06752268224954605	-0.00186066678725183	0.03818114846944809	-0.012067819945514202	0.05098478123545647	0.08510378003120422	0.0536009781062603	0.04919089749455452	0.04948992282152176	0.03397113457322121	0.013617217540740967	-0.012707522138953209	-0.03132528439164162	0.0051847100257873535	-0.03164360299706459	0.036975208669900894	0.012468114495277405	-0.0724245235323906	0.024109991267323494	0.09529951959848404	-0.07370251417160034	-0.049551211297512054	-0.012281220406293869	-0.0033497700933367014	0.055196184664964676	0.003556214738637209	-0.049294449388980865	-0.004492699168622494	0.06041482463479042	0.021080417558550835	-0.04812268167734146	0.008931657299399376	0.022414235398173332	-0.05675281956791878	-0.08039452880620956	0.08320222049951553	0.017254775390028954	-0.05400433391332626	0.061694782227277756	0.06151111423969269	-0.022825093939900398	0.03412730246782303	-0.0832367092370987	-0.02842913754284382	-0.08440141379833221	-0.0729266032576561	0.025241872295737267	-0.07688040286302567	-0.005780126433819532	0.06097831204533577	-0.00036102041485719383	0.026256375014781952	-0.031118154525756836	-0.056321509182453156	-0.05659083276987076	-0.015627464279532433	0.025152653455734253	-0.09109067171812057	-0.02593797631561756	-0.01875484175980091	-0.017968490719795227	0.07874515652656555	-0.06098966673016548	-0.03368361294269562	-0.01693061739206314	-0.05485890060663223	0.05735313892364502	0.08119364827871323	0.03982534259557724	0.018561847507953644	0.015387486666440964	-0.023606877774000168	-0.011837371625006199	-0.02669088914990425	0.03814924135804176	-0.08045247942209244	0.05720198154449463	-0.07003194838762283	-0.00537861417979002	-0.02307024411857128	-0.004184845834970474	-0.018982890993356705	-0.013568449765443802	-0.05122268944978714	-0.047756943851709366	-0.0663139596581459	0.0672118067741394	0.06402656435966492	-0.009072018787264824	0.015606564469635487	0.01729028858244419	0.05040203779935837	0.00044247013283893466	0.015875015407800674	-0.07980430871248245	0.050158724188804626	0.06204129010438919	0.03386659547686577	0.03464905172586441	-0.05958493798971176	0.07773023098707199	0.010741837322711945	0.056625958532094955	-0.00478471489623189	0.05009292811155319	0.09975601732730865	-0.062109850347042084	0.0539972260594368	0.048871949315071106	0.017106477171182632	0.03713162615895271	-0.03378501534461975	0.03376884013414383	-0.11164965480566025	0.011139006353914738	0.05896677076816559	-0.05441972240805626	0.03534998372197151	-0.0020279278978705406	-0.07100166380405426	-0.023433517664670944	0.01576923206448555	-0.012921634130179882	-0.038326870650053024	0.006480212789028883	-0.0022743106819689274	0.030130552127957344	-0.0025124552194029093	-0.007519470527768135	0.0017288712551817298	-0.06284315139055252	0.08211936801671982	-0.01783597283065319	-0.040498036891222	-0.010805711150169373	-0.00455484027042985	0.007657227572053671	-0.015578413382172585	0.008175034075975418	0.04686820134520531	0.06905345618724823	0.015007334761321545	-0.013629075139760971	-0.03593099117279053	-0.03398580849170685	-0.07141279429197311	-0.030591806396842003	0.07352140545845032	0.004027397837489843	-0.10650578886270523	0.0027453030925244093	-0.08429215103387833	-0.0476771742105484	-0.0031102073844522238	0.019623897969722748	-0.05720140039920807	0.006663303356617689	-0.09801303595304489	-0.00873484741896391	-0.01298347394913435	-0.013132849708199501	0.03384717181324959	-0.03587447106838226	0.012705069035291672	-0.01656683161854744	-0.022886889055371284	0.04427681118249893	0.001505173509940505	-0.017810914665460587	-0.006666441913694143	0.05808381736278534	-0.005999201908707619	-0.014313596300780773	-0.03607215732336044	-0.045419979840517044	-0.0479138121008873	0.008524646982550621	0.011452073231339455	0.006541519425809383	0.10668069869279861	0.07064612209796906	-0.043625328689813614	-0.044475506991147995	-0.02985350787639618	-0.0830499604344368	-0.030972862616181374	0.0108017697930336	0.0019045488443225622	-0.055289968848228455	-0.006849302910268307	0.010955095291137695	-0.04591045901179314	-0.030395561829209328	0.04419836774468422	0.012901067733764648	-0.04381249472498894	0.02579343691468239	0.0227484330534935	-0.049409233033657074	0.016357148066163063	-0.043973226100206375	-0.0308736190199852	0.05782923474907875	0.025186022743582726	-0.004287606570869684	0.010847286321222782	0.06804440170526505	0.07457120716571808	-0.023097099736332893	-0.027410838752985	-0.022198500111699104	-0.011769795790314674	-0.04241397604346275	0.05233687162399292	-0.044844113290309906	-0.0013832100667059422	0.052007947117090225	-0.019768008962273598	0.06310835480690002	-0.014664808288216591	0.00032631552312523127	-0.04165687412023544	0.021114155650138855	-0.0161814633756876	0.057214733213186264	-0.041977282613515854	-0.011160239577293396	-0.0021511621307581663	-0.0719151645898819	-0.04721326380968094	-0.06761227548122406	0.04162624850869179	0.024789217859506607	0.043233469128608704	0.03113723360002041	-0.07459571957588196	-0.01824551448225975	-0.016865752637386322	0.11946774274110794	-0.10352466255426407	-0.013206071220338345	0.03874407336115837	0.012941459193825722	-0.046538691967725754	-0.0034084038343280554	-0.0388641431927681	-0.07566830515861511	-0.02638707123696804	-0.024575166404247284	0.054231319576501846	-0.07877811789512634	-0.07631175965070724	-0.00949501059949398	-0.014854373410344124	0.01765046827495098	0.01982509158551693	0.007268812507390976	0.1001698225736618	0.017769696190953255	4.171222826698795e-05	0.002702361438423395	0.028112970292568207	0.011408357881009579	0.010692819021642208	0.03546396642923355	0.03701833635568619	0.04910612478852272	-0.06285308301448822	-0.036762308329343796	-0.03377038985490799	0.00464641721919179	-0.08957070857286453	0.033593710511922836	-0.009139268659055233	0.004998614080250263	0.001046665944159031	0.058569539338350296	0.025042477995157242	-0.05254285782575607	-0.00577223114669323	-0.011678038164973259	0.001573139918036759	-0.040248677134513855	-0.020037639886140823	0.030347831547260284	-0.03850424662232399	0.020464202389121056	-0.06709636747837067	-0.006795413792133331	0.010930930264294147	0.004396962001919746	-0.01883258856832981	0.04891548305749893	0.004974107723683119	-0.00953024160116911	0.020509228110313416	-0.019483907148241997	0.03648119792342186	0.05812269076704979	-0.08502218872308731	-0.0028538869228214025	0.040964629501104355	-0.04488084837794304	0.018190205097198486	-0.042472489178180695	-0.003919722978025675	-0.017195794731378555	-0.02177279256284237	-0.013547106646001339	-0.018121974542737007	0.021962318569421768	-0.03318825736641884	0.053799934685230255	-0.02410227246582508	-0.08484891802072525	-0.018692560493946075	-0.03143993392586708	-0.07321552187204361	-0.01304557267576456	-0.028724364936351776	-0.008392475545406342	0.046381063759326935	-0.06404919922351837	0.02190452814102173	0.05433078110218048	-0.04870593175292015	-0.0249578058719635	0.003407539799809456	-0.004112273454666138	-0.044032227247953415	0.033288225531578064	-0.001079494715668261	-0.007787267677485943	0.02467322163283825	0.004093209281563759	-0.08873210102319717	0.003715133760124445	-0.03698905557394028	0.0013212112244218588	0.04235009104013443	-0.03761109709739685	0.02927023358643055	0.029603775590658188	0.03329978138208389	-0.05358634516596794	-0.04170103371143341	-0.033058278262615204	0.062479157000780106	-0.031046297401189804	0.019897809252142906	0.0029217295814305544	-0.06772665679454803	-0.03369256854057312	0.06780491769313812	-0.034289080649614334	-0.1055060625076294	-0.06021541357040405	-0.040921371430158615	0.008860521018505096	-0.013244520872831345	0.06037254258990288	-0.049786556512117386	-0.08879878371953964	0.023692863062024117	-0.004651423543691635	-0.024602452293038368	-0.016786759719252586	0.027666594833135605	-0.01663948781788349	-0.02263939194381237	-0.05925935506820679	-0.07694967836141586	0.0528014712035656	0.01371732447296381	0.05333435535430908	-0.0369727686047554	-0.009523057378828526	-0.006818233523517847	-0.06335383653640747	-0.012162875384092331	0.0015517942374572158	-0.06670050323009491	0.01257530227303505	0.09553447365760803	-0.008795006200671196	-0.006852678954601288	0.07904796302318573	-0.038973890244960785	0.028532084077596664	0.0171185452491045	0.0016352543607354164	0.029604779556393623	-0.04753759875893593	-0.04955122247338295	0.06913922727108002	-0.021383481100201607	0.05476541072130203	-0.010253993794322014	0.13571499288082123	-0.009495851583778858	-0.056954603642225266	0.08064158260822296	0.06899958848953247	0.014261382631957531	0.02659306675195694	-0.0839984267950058
+0.034295454621315	-0.02876030094921589	0.0022199449595063925	0.013059740886092186	-0.11187174171209335	-0.03821970522403717	-0.02185484766960144	0.05423777922987938	-0.029301557689905167	-0.03885960206389427	0.00657276064157486	-0.019523898139595985	-0.014047889038920403	0.008336481638252735	0.035828765481710434	-0.03854867443442345	-0.07550007104873657	0.014073002152144909	-0.05181502550840378	-0.03509395569562912	0.03520360589027405	0.03644890338182449	-0.04464614391326904	0.018760941922664642	-0.005879384931176901	0.0022717800457030535	-0.05021638050675392	-0.0022697639651596546	-0.008643429726362228	0.0234625656157732	0.05993207171559334	0.010982140898704529	0.05902386084198952	-0.028777338564395905	-0.056947290897369385	0.06674826890230179	-0.0437510684132576	0.12111205607652664	0.03904854506254196	-0.02886883355677128	-0.04362291842699051	0.0033304505050182343	-0.03537382185459137	-0.030107246711850166	0.03889237344264984	-0.024072840809822083	0.002777819288894534	-0.010349373333156109	0.012299465015530586	0.04607810080051422	0.06817594170570374	-0.004305154085159302	0.0005935616791248322	-0.008552527986466885	0.010309483855962753	0.047842297703027725	0.028523631393909454	-0.04968162626028061	-0.06284839659929276	0.02246754616498947	-0.07391288876533508	0.014378618448972702	0.010567683726549149	0.02186834067106247	0.028386909514665604	-0.04012977331876755	-0.015302952378988266	0.08128803223371506	0.0128440847620368	-0.02829909883439541	0.020209891721606255	0.10933750122785568	-0.07631070911884308	-0.07209361344575882	0.047186266630887985	-0.041427116841077805	-0.015413409098982811	0.035984937101602554	-0.013601560145616531	-0.03779090568423271	0.014162648469209671	-0.024699406698346138	0.10539473593235016	-0.05165170878171921	-0.022326461970806122	-0.029031723737716675	-0.015796467661857605	-0.06358909606933594	0.06997539848089218	0.019581176340579987	0.027812393382191658	0.04393099620938301	-0.073692686855793	0.018796171993017197	-0.005503426305949688	0.008021696470677853	0.00786516722291708	-0.1175576001405716	-0.04290290176868439	-0.015009945258498192	0.034094516187906265	-0.017361698672175407	0.05916385352611542	0.005401922389864922	0.05011789873242378	0.05589376017451286	-0.033379632979631424	-0.005236340221017599	0.003957719076424837	0.028063267469406128	0.012467996217310429	0.1095319390296936	0.11020876467227936	0.03761421889066696	0.035504214465618134	0.019595345482230186	-0.018680069595575333	-0.016249453648924828	-0.011411605402827263	0.017945125699043274	-0.022976024076342583	-0.0012059526052325964	0.0005090117338113487	-0.05595490708947182	-0.05174165219068527	0.05979591980576515	-0.07957678288221359	-0.03581933677196503	0.06766045093536377	-0.059726715087890625	0.11945241689682007	0.030184289440512657	-0.05360138416290283	0.024558551609516144	0.07706452161073685	0.024606993421912193	0.0028318059630692005	0.03283620625734329	-0.020112350583076477	-0.04492109641432762	-0.03742018714547157	0.06493206322193146	-0.008624051697552204	-0.08027493953704834	0.005175350699573755	0.0633460134267807	0.0546744130551815	-0.008286367170512676	-0.09555406868457794	0.032493822276592255	-0.06777984648942947	-0.0897066593170166	-0.03507501631975174	-0.10625653713941574	0.022852148860692978	0.08986976742744446	-0.013886244036257267	0.03571876510977745	-0.0026925220154225826	-0.032933298498392105	-0.06347958743572235	-0.05302172526717186	-0.030244937166571617	-0.08734113723039627	-0.03387967124581337	-0.04700439050793648	-0.004909433890134096	0.018347006291151047	-0.05787942185997963	-0.04437298700213432	-0.02218582294881344	-0.03473541513085365	0.022747762501239777	0.07980592548847198	0.06035567447543144	0.03613806143403053	-0.004160703159868717	-0.030723195523023605	-0.027605511248111725	-0.02586282044649124	0.03417482599616051	-0.04885472357273102	0.06663740426301956	-0.05041348189115524	0.01497951615601778	-0.02483857050538063	0.06875153630971909	-0.0016339143039658666	0.006119314581155777	-0.025945333763957024	-0.016994070261716843	-0.055017728358507156	0.055868931114673615	0.08395969867706299	-0.013741773553192616	0.048436764627695084	-0.04226534813642502	0.06601565331220627	-0.030277039855718613	-0.021252136677503586	-0.05552598461508751	0.04776598885655403	0.031557388603687286	0.024377549067139626	0.008989120833575726	-0.08222757279872894	0.07817134261131287	0.010004106909036636	0.034303683787584305	-0.07637057453393936	0.0285861324518919	0.04017603397369385	0.011660303920507431	0.03639012947678566	0.02788378670811653	-0.03993178531527519	0.019798636436462402	-0.004816603846848011	0.07153818011283875	-0.04646240174770355	0.011811895295977592	0.03380020335316658	-0.05996883288025856	0.053977400064468384	-0.01551678404211998	-0.09337079524993896	-0.04016343131661415	0.01488006766885519	-0.03361302614212036	-0.04151392728090286	-0.007329990155994892	0.043903548270463943	-0.015367323532700539	-0.017922986298799515	0.0010596539359539747	-0.013836964964866638	-0.0686667338013649	0.08153648674488068	-0.004300463479012251	-0.049359433352947235	-0.08787926286458969	-0.008477949537336826	0.0031410623341798782	0.0008971427450887859	0.024005502462387085	0.04225045442581177	0.05946189910173416	1.0135323464055546e-05	-0.002529778517782688	0.006118368357419968	-0.05387987196445465	-0.04887818172574043	-0.025135958567261696	0.03929155319929123	-0.06089663505554199	-0.05338577553629875	0.00018075427215080708	-0.05501451715826988	-0.001561230979859829	0.012335529550909996	0.0437474325299263	-0.06295713782310486	-0.005547079257667065	-0.004787871148437262	0.004690160974860191	-0.02939631976187229	-0.024477502331137657	0.006550941616296768	-0.014788176864385605	0.04545962065458298	-0.002590057672932744	-0.004836686886847019	0.02501138485968113	-0.014525292441248894	0.02152707800269127	-0.014722203835844994	0.05286944657564163	-0.045377377420663834	-0.09136059135198593	-0.021999234333634377	-0.062254808843135834	-0.068573959171772	-0.015343219973146915	-0.05338820815086365	-0.018453679978847504	0.11892025917768478	0.043579280376434326	-0.05807379260659218	-0.04480478912591934	-0.043740350753068924	-0.00937101524323225	-0.04116123914718628	-0.023234350606799126	0.020613979548215866	-0.08182653784751892	-0.028141863644123077	0.03193800151348114	-0.06096665933728218	-0.02644267864525318	0.01883498765528202	-0.025840522721409798	-0.028703128919005394	0.03617892041802406	-0.026911459863185883	0.004528309218585491	-0.003959043882787228	-0.06855045258998871	0.06416334211826324	0.06572329998016357	0.069528728723526	0.02552633546292782	0.022273028269410133	0.032668158411979675	0.11499318480491638	0.02822350151836872	-0.02418883517384529	-0.018780948594212532	-0.05129697173833847	-0.06765705347061157	0.048278044909238815	-0.04017837718129158	-0.028501953929662704	-0.01787225529551506	-0.008822597563266754	0.08922155946493149	-0.0024117426946759224	-0.005824278574436903	-0.00620845053344965	0.048358254134655	-0.005567849148064852	0.031172286719083786	-0.0020903125405311584	-0.023135431110858917	-0.0018226158572360873	-0.035904593765735626	-0.012996720150113106	-0.04098131135106087	0.038224343210458755	-0.036328669637441635	0.041296783834695816	-0.025857549160718918	-0.049805980175733566	0.011898357421159744	0.02256239764392376	0.10760410875082016	-0.044292889535427094	0.03061136230826378	0.03942776098847389	0.02841140516102314	-0.07563907653093338	0.0019168887520208955	0.010332799516618252	-0.034163955599069595	-0.04092306271195412	0.00357659999281168	0.03929826617240906	-0.06937535852193832	-0.06359510868787766	-0.005991086829453707	-0.03869068995118141	-0.011054562404751778	-0.0242807324975729	-0.0032598664984107018	0.04686666652560234	-0.0011442519025877118	0.0037120773922652006	0.025086557492613792	0.019220931455492973	0.021932654082775116	-0.029689446091651917	0.003879401134327054	-0.024149883538484573	0.04803534597158432	-0.05158267170190811	-0.05876358970999718	-0.05674850940704346	0.053863175213336945	-0.03210880234837532	-0.00019711510685738176	-0.03967722877860069	-0.0005007698782719672	-0.02467265911400318	0.036055393517017365	-0.001930236117914319	-0.047164689749479294	-0.030296236276626587	-0.0019981248769909143	-0.04203043133020401	-0.09893189370632172	-0.04637724161148071	0.016227837651968002	-0.0659538209438324	0.0010093663586303592	-0.06710708886384964	0.006790714804083109	0.0006690602167509496	-0.03828322887420654	0.02372630126774311	0.055261239409446716	0.03301812708377838	-0.019654428586363792	0.050954967737197876	0.03290504962205887	0.03754455968737602	0.028300194069743156	-0.03735899180173874	0.002010843250900507	0.030141334980726242	-0.020443474873900414	0.03434671834111214	-0.027187291532754898	0.015123543329536915	-0.017872938886284828	-0.0143224336206913	0.009570692665874958	-0.033280931413173676	0.015432317741215229	-0.03219105675816536	0.06460846215486526	-0.08207424730062485	-0.04188098385930061	0.038051966577768326	-0.032356929033994675	-0.08559627085924149	-0.01204539556056261	-0.04958101734519005	-0.0022465246729552746	0.037610676139593124	-0.012541311793029308	0.021609025076031685	0.030523143708705902	-0.0505441352725029	-0.04507100209593773	-0.05048223212361336	-0.019158698618412018	0.024141469970345497	0.018681129440665245	0.034540675580501556	-0.014672007411718369	-0.008545510470867157	-0.0027275087777525187	-0.037539418786764145	0.008847876451909542	-0.1049482598900795	0.004269537050276995	-0.04011010006070137	-0.012701615691184998	0.05419137701392174	-0.024831973016262054	0.02542562410235405	-0.02619095705449581	-0.01658802665770054	-0.02521839179098606	0.08271916955709457	-0.01336321048438549	-0.0027216782327741385	0.029549993574619293	-0.025800608098506927	-0.011302080005407333	0.03331318497657776	-0.043978363275527954	-0.055969465523958206	0.021151790395379066	-0.010895339772105217	0.04386678338050842	0.022715061902999878	0.05465953052043915	-0.011348037049174309	-0.08829183131456375	-0.004850748460739851	0.0016285170568153262	-0.06362607330083847	0.030154507607221603	0.023455047979950905	-0.09414102882146835	-0.09556808322668076	-0.019030572846531868	-0.03858304023742676	0.048732392489910126	-0.02565987966954708	0.061291925609111786	-0.04254366084933281	-0.00472473818808794	0.011556961573660374	-0.055893246084451675	0.023438557982444763	0.02008039318025112	-0.05727507546544075	-0.02487199753522873	0.08513694256544113	-0.03193042799830437	0.01396634429693222	0.08458715677261353	-0.04766547307372093	0.004044879227876663	0.009418157860636711	-0.009885765612125397	0.024985553696751595	0.004802086856216192	-0.05217674374580383	0.029145264998078346	-0.06529778242111206	0.08203613758087158	-0.05101775750517845	0.09016022831201553	-0.010076195001602173	-0.030943239107728004	0.09662403911352158	0.05713317170739174	-0.015507413074374199	0.008360006846487522	-0.05813770368695259
+0.04586847499012947	0.008794116787612438	0.005602643359452486	0.07263460755348206	-0.10417933762073517	-0.03421296924352646	-0.0035671971272677183	0.0223979651927948	-0.0078115155920386314	-0.04677613824605942	-0.0266717616468668	-0.043592777103185654	-0.028840746730566025	0.016021206974983215	0.02761799842119217	-0.03155142441391945	-0.04982567951083183	0.023574644699692726	-0.006502051837742329	-0.049655430018901825	0.03745456412434578	0.0007596475770696998	-0.06766937673091888	0.06361036747694016	0.016585543751716614	-0.013388723134994507	-0.049001313745975494	0.02848074398934841	-0.018360957503318787	0.046817123889923096	0.05149925500154495	-0.011800755746662617	0.027813231572508812	-0.06859316676855087	-0.052997708320617676	0.0386810228228569	-0.049710363149642944	0.11469264328479767	0.03331306576728821	-0.04760017991065979	-0.05521661043167114	0.028694046661257744	-0.05554219335317612	-0.03826631233096123	0.07000141590833664	0.002354860305786133	-0.02094016969203949	-0.03488224744796753	0.05409296602010727	0.04448363184928894	0.09989026188850403	-0.01237475872039795	-0.023473089560866356	-0.026949333027005196	-0.02868053689599037	0.01697923056781292	0.04698743671178818	-0.05903036519885063	-0.03823373094201088	0.06254258006811142	-0.06543983519077301	-0.038497183471918106	0.06219581887125969	0.004916562233120203	-0.01522782351821661	-0.0032824042718857527	0.0014587178593501449	0.09124181419610977	-0.001207025721669197	-0.02440030314028263	0.005935727618634701	0.11097510904073715	-0.053257573395967484	-0.03762068226933479	0.06600715965032578	-0.06391916424036026	-0.02326936647295952	0.06808284670114517	0.03019607998430729	0.031189071014523506	0.014916252344846725	-0.046766363084316254	0.08732043206691742	-0.02630588412284851	-0.03816607594490051	-0.026990653946995735	0.00126513687428087	-0.0441882461309433	0.07247442752122879	0.03290213644504547	-0.005308761727064848	0.05677401274442673	-0.06314504891633987	0.046884313225746155	0.004902830813080072	-0.0076713720336556435	0.025638345628976822	-0.11190871894359589	-0.031648267060518265	0.014107891358435154	0.020890917629003525	-0.03165620565414429	0.021607784554362297	-0.011748027987778187	0.06822050362825394	0.05884949490427971	-0.059398628771305084	0.013065030798316002	0.027083514258265495	0.030416056513786316	0.03788289055228233	0.07097265869379044	0.10208573937416077	0.03931037709116936	0.05861080810427666	-0.010749812237918377	-0.00525340111926198	-0.06102607399225235	-0.051980726420879364	0.014952837489545345	-0.009377524256706238	0.056220922619104385	-0.012163401581346989	-0.046980369836091995	-0.017454635351896286	0.017371613532304764	-0.09053511917591095	-0.012071795761585236	0.07323610037565231	0.009345542639493942	0.10163356363773346	0.016300629824399948	-0.005070930812507868	0.0235562976449728	0.0810069739818573	0.04234867915511131	0.017601238563656807	0.04252023994922638	-0.00957566685974598	-0.007403717841953039	0.005933796986937523	0.06017642468214035	0.016707459464669228	-0.07658557593822479	0.029223738238215446	0.05599266290664673	0.036875952035188675	-0.0006165635422803462	-0.08137469738721848	0.006630791816860437	-0.09966124594211578	-0.0664440467953682	-0.006828953977674246	-0.10761389136314392	-0.0027127463836222887	0.03958766907453537	0.010991447605192661	0.029597122222185135	-0.034748777747154236	-0.017498396337032318	-0.045351140201091766	-0.03230736032128334	0.00030094647081568837	-0.07159445434808731	-0.050768934190273285	-0.0449921153485775	-0.0014375902246683836	0.03354273736476898	-0.0377618633210659	-0.02682904526591301	-0.013949510641396046	-0.008368921466171741	0.06867098808288574	0.0751211941242218	0.04674677178263664	0.0263788141310215	0.02426053024828434	-0.04318924620747566	0.0119865071028471	-0.04371805489063263	0.018262995406985283	-0.04022859409451485	0.05291164666414261	-0.056020285934209824	-0.009912560693919659	-0.018331820145249367	0.05041415989398956	-0.031540147960186005	0.0007531261071562767	0.002871360396966338	-0.05638054385781288	-0.060349125415086746	0.08084414154291153	0.09423598647117615	-0.009496364742517471	0.032060183584690094	-0.03846321627497673	0.02940504252910614	0.0003211821604054421	-0.03841625899076462	-0.04739055410027504	0.05186690390110016	0.023713983595371246	0.06278736144304276	0.03164535388350487	-0.09415880590677261	0.0755423977971077	0.023496046662330627	0.02836473472416401	-0.06625039130449295	0.007421114481985569	0.030337903648614883	-2.833424514392391e-05	0.03623398393392563	0.06384233385324478	0.005875172559171915	0.004309631418436766	-0.006176488008350134	0.06080489605665207	-0.03493552654981613	0.03388523310422897	0.02783740870654583	-0.08342897146940231	0.05043035373091698	-0.033992208540439606	-0.08284910768270493	-0.014147271402180195	0.03568011149764061	-0.03722899779677391	-0.01823231764137745	-0.0009180224733427167	0.017651384696364403	-0.019937029108405113	-0.03617892414331436	-0.018576614558696747	0.01340548973530531	-0.09079539775848389	0.10208173841238022	-0.005373206455260515	-0.05581298843026161	-0.06749666482210159	0.0010499792406335473	0.01887771487236023	-0.003614612855017185	0.054101623594760895	0.04977976158261299	0.06781448423862457	0.024241328239440918	0.02773052267730236	-0.011441824026405811	-0.05745017156004906	-0.033710986375808716	-0.02253543585538864	0.039637815207242966	-0.06120613217353821	-0.03299468755722046	-0.018687674775719643	-0.044914912432432175	-0.012710579670965672	0.01511143147945404	0.0210989098995924	-0.02453162707388401	-0.02653610333800316	-0.007403901778161526	0.043841637670993805	-0.01728329248726368	-0.039362583309412	0.018707245588302612	-0.012393342331051826	0.05679881200194359	-0.03135174885392189	-0.04507448151707649	0.031981997191905975	0.02570422738790512	0.0155108030885458	-0.02813166379928589	0.048732053488492966	-0.030873551964759827	-0.09234457463026047	-0.0350465290248394	-0.06260325759649277	-0.04559049755334854	-0.017937658354640007	-0.03795190155506134	-0.012388445436954498	0.11034678667783737	0.015948612242937088	-0.04109597206115723	-0.08179011195898056	-0.020847337320446968	-0.01910400576889515	-0.06796301156282425	-0.01397435087710619	-0.010803287848830223	-0.062359023839235306	-0.010771437548100948	0.015230496414005756	-0.022528139874339104	-0.016641998663544655	-0.013464909046888351	-0.012378928251564503	-0.0455668643116951	0.048532724380493164	-0.03785186633467674	-0.04236343130469322	0.03502136468887329	-0.04627051204442978	0.03300371393561363	0.10960401594638824	0.052443262189626694	0.0163775272667408	0.026322899386286736	0.056920237839221954	0.1036415547132492	0.01834774576127529	-0.06689233332872391	-0.0227266326546669	-0.05844947695732117	-0.0780826136469841	0.03468906506896019	-0.0513036884367466	-0.029378656297922134	-0.004866810981184244	-0.014555058442056179	0.0979333147406578	-0.016039550304412842	-0.02416745200753212	-0.03291962295770645	0.037029992789030075	0.00163640221580863	0.014815669506788254	0.01364617794752121	-0.028990648686885834	-0.00321591435931623	-0.04805636405944824	-0.029944004490971565	-0.042336367070674896	0.041158441454172134	-0.003295361762866378	0.03195379301905632	-0.018149422481656075	-0.049376748502254486	0.027753662317991257	0.02061774581670761	0.07286139577627182	-0.05836113914847374	0.0053427680395543575	0.04240214079618454	-0.006334435660392046	-0.07038882374763489	0.011205863207578659	0.028065314516425133	-0.048856694251298904	-0.043031007051467896	-0.02004934474825859	0.049290403723716736	-0.06296761333942413	-0.0007527528214268386	-0.006184089463204145	-0.07383529841899872	-0.011825009249150753	0.022478463128209114	0.012833312153816223	0.033967092633247375	-0.025345267727971077	-0.016156421974301338	0.0031574296299368143	-0.006740881595760584	-0.006194967310875654	-0.025144627317786217	0.0025751639623194933	-0.007085397839546204	0.05949084088206291	-0.018629806116223335	-0.01894116960465908	-0.05453328415751457	0.055572569370269775	-0.01688113436102867	-0.03491704538464546	-0.018382644280791283	-0.0023342410568147898	0.001691104844212532	0.0074123190715909	0.027477554976940155	-0.06704011559486389	-0.014596191234886646	-0.032400112599134445	-0.03854374587535858	-0.09966855496168137	-0.031306102871894836	0.025867827236652374	-0.0831514447927475	-0.007698028814047575	-0.053074344992637634	-0.018021054565906525	-0.009012822061777115	-0.03156327083706856	0.006679396145045757	0.038996439427137375	0.03570430353283882	-0.031164471060037613	0.02760303020477295	-0.014081069268286228	0.04113411158323288	0.035638827830553055	-0.05103995278477669	0.01740933023393154	0.024005794897675514	-0.02205085940659046	0.047184474766254425	-0.03440694883465767	0.02854977734386921	-0.02899692766368389	-0.00357834598980844	0.030042381957173347	-0.022670285776257515	-0.027854187414050102	-0.024801762774586678	0.05263347551226616	-0.04639006406068802	-0.03754362463951111	0.020014332607388496	-0.01729506626725197	-0.10599441081285477	-0.017255369573831558	-0.00742940790951252	-0.012383519671857357	0.02268015593290329	-0.019989406690001488	0.028852563351392746	0.010213382542133331	-0.08006700873374939	-0.037823259830474854	-0.06643491983413696	-0.035751715302467346	0.03982582688331604	-0.012541511096060276	0.0558730885386467	0.018887417390942574	0.004795611370354891	-0.0103274742141366	-0.0054364935494959354	0.006796922069042921	-0.08921627700328827	-0.005989608354866505	-0.028203576803207397	-0.0319245383143425	0.03893822059035301	0.003532824572175741	-0.01034594140946865	-0.022462226450443268	-0.05724845454096794	-0.04838176816701889	0.08892473578453064	-0.04250088334083557	-0.017633069306612015	0.041221048682928085	-0.004063835367560387	0.016866805031895638	0.041182391345500946	-0.05696845054626465	-0.07196616381406784	0.005035410635173321	0.0037636307533830404	0.025906674563884735	0.038963280618190765	0.022395677864551544	-0.03648878261446953	-0.056596290320158005	-0.020453432574868202	0.0008875227067619562	-0.09310247004032135	0.01314388494938612	-0.010888276621699333	-0.0795414000749588	-0.0752975270152092	-0.006716355215758085	-0.0474301278591156	0.08236735314130783	0.017157448455691338	0.03901253268122673	-0.015909725800156593	-0.013789406046271324	0.017412694171071053	-0.050905030220746994	0.009134365245699883	-0.00866133626550436	-0.050138335675001144	0.0015972262481227517	0.0766611397266388	-0.04001397639513016	0.02560345083475113	0.044179707765579224	-0.05738677829504013	0.013109730556607246	0.05427210032939911	-0.019509539008140564	0.046802178025245667	-0.0011603472521528602	-0.03599855676293373	0.07625861465930939	-0.06061989814043045	0.11250628530979156	-0.03796316310763359	0.07847237586975098	-0.0025697979144752026	0.0023406976833939552	0.08467276394367218	0.06993528455495834	-0.024126354604959488	-0.006409660913050175	-0.08083418011665344
+0.012635655701160431	-0.049873825162649155	-0.02821008861064911	0.04612592235207558	-0.05917513370513916	-0.028345072641968727	0.014294101856648922	0.07246478646993637	-0.0013715128879994154	-0.05054832249879837	-0.00011441125388955697	0.004453704692423344	0.04358256980776787	0.020417196676135063	0.012756885029375553	-0.026167139410972595	-0.06812908500432968	0.023198453709483147	-0.04312599077820778	-0.047894030809402466	0.018341494724154472	0.021506760269403458	-0.07394121587276459	0.021247558295726776	-0.009949027560651302	-0.006992862559854984	-0.046279676258563995	-0.009185154922306538	0.010071944445371628	0.034122712910175323	0.056211914867162704	-0.030055565759539604	0.04875001683831215	-0.051084134727716446	-0.046378858387470245	0.060808394104242325	-0.04151898995041847	0.11512526869773865	-0.0033638933673501015	-0.019954916089773178	-0.07637855410575867	0.01687612198293209	-0.009011715650558472	-0.05430560186505318	0.02664474956691265	0.012613537721335888	0.025056367740035057	-0.0045130993239581585	0.02014734223484993	0.016747400164604187	0.06644488871097565	0.020316245034337044	0.012239756062626839	0.0062158419750630856	-0.005222395062446594	0.03330490365624428	0.04526128992438316	-0.04160643368959427	-0.061634767800569534	0.024207666516304016	-0.05649328604340553	0.002452258951961994	0.023886019363999367	0.00017466871940996498	-0.02577311173081398	-0.009470334276556969	-0.01820393279194832	0.06988596171140671	0.04689296334981918	-0.024116480723023415	-0.019869809970259666	0.07021944969892502	-0.05272483453154564	-0.07593227177858353	0.057395271956920624	-0.033098526298999786	-0.015480897389352322	0.011624041944742203	-0.034906595945358276	-0.01637079194188118	-0.0073384628631174564	-0.01891852542757988	0.14228928089141846	-0.04074538126587868	-0.0520084910094738	-0.020348789170384407	-0.0006031832890585065	-0.06828165799379349	0.07529648393392563	0.015569116920232773	0.01492016389966011	0.07420632988214493	-0.04497001692652702	0.044888488948345184	0.023396478965878487	0.02537284418940544	0.003863316960632801	-0.1370542049407959	-0.05742863565683365	-0.01249331422150135	0.014822004362940788	-0.04120634123682976	0.09169251471757889	-0.0015628895489498973	0.027393091470003128	0.03395870700478554	-0.058956630527973175	-0.035345274955034256	0.008670350536704063	0.029710449278354645	-0.012200361117720604	0.1490197479724884	0.03832096606492996	0.012813550420105457	0.04440837725996971	-0.015266794711351395	0.0066355024464428425	-0.011102824471890926	-0.033992428332567215	0.0049353293143212795	-0.03328482061624527	0.011094304732978344	0.00942963920533657	-0.09403384476900101	-0.05015239864587784	0.03901335969567299	-0.11576171964406967	-0.03817395493388176	0.06635144352912903	-0.03481614962220192	0.1190028190612793	0.06125672534108162	-0.017715446650981903	0.025014901533722878	0.05673232302069664	0.018984396010637283	-0.006211970001459122	0.036657754331827164	0.01054031029343605	-0.029346873983740807	-0.02750215120613575	0.08410242199897766	0.003477763384580612	-0.043491750955581665	0.046250224113464355	0.06243690848350525	0.04100288078188896	-0.01806946098804474	-0.0711764544248581	0.04826362058520317	-0.0256230216473341	-0.1229533925652504	-0.028730804100632668	-0.03854651376605034	-0.0038593255449086428	0.04194007068872452	-0.007890895940363407	0.05124518647789955	0.0019000143511220813	-0.04248407110571861	-0.04021189361810684	-0.04573387652635574	-0.04836718365550041	-0.06673427671194077	-0.006056616082787514	-0.009203328751027584	-0.022158153355121613	-0.006507918704301119	-0.0272507481276989	-0.014230792410671711	0.016181521117687225	0.00037160070496611297	0.03837008774280548	0.07247573882341385	0.05750284343957901	0.011615750379860401	0.020482832565903664	-0.047068651765584946	-0.0222601480782032	-0.043068692088127136	-0.0069387443363666534	-0.03740498051047325	0.02237953245639801	-0.07676470279693604	-0.00550424586981535	0.016242144629359245	0.08214715868234634	-0.029294613748788834	-0.004947154317051172	-0.05361045151948929	-0.021120881661772728	-0.035654954612255096	0.05017746612429619	0.060695771127939224	-0.03419109061360359	0.02031725086271763	-0.0067966594360768795	0.05981070175766945	-0.033272866159677505	-0.016422709450125694	-0.05402796342968941	0.06593969464302063	0.025960173457860947	0.003531399415805936	0.008233511820435524	-0.03869040310382843	0.06016510725021362	-0.002840418601408601	0.029537184163928032	-0.07163135707378387	0.015500384382903576	0.011186233721673489	0.005612950772047043	0.045812319964170456	0.050658002495765686	-0.028551308438181877	-0.031508348882198334	0.018962284550070763	0.05907832086086273	-0.0404357947409153	-0.01021536160260439	-0.025145752355456352	-0.07993493974208832	0.01981962099671364	-0.013113327324390411	-0.0943564623594284	-0.03430604189634323	0.007880947552621365	-0.026426222175359726	-0.04033370316028595	0.009180997498333454	0.04637816548347473	-0.011780119501054287	-0.04469684883952141	0.0013215284561738372	-0.002329685492441058	-0.05625646561384201	0.07891017943620682	-0.010237284936010838	-0.0596369206905365	-0.04629286006093025	-0.004515846725553274	0.029830625280737877	0.0035896131303161383	-0.0016659139655530453	0.04718863591551781	0.06860293447971344	0.018040750175714493	-0.04258917644619942	0.02587883360683918	-0.04723546653985977	-0.05014391988515854	-0.016549088060855865	0.07068460434675217	-0.03981148824095726	-0.034466374665498734	-0.008091463707387447	-0.05127423629164696	0.035587236285209656	-0.004933129530400038	0.036429498344659805	-0.057622406631708145	0.011273879557847977	-0.024851597845554352	-0.0018466461915522814	-0.03101896494626999	-0.023760488256812096	0.026204856112599373	-0.030869048088788986	0.03884634003043175	-0.004818249028176069	-0.030550001189112663	0.02071215957403183	0.011837010271847248	0.04712669178843498	-0.039895184338092804	0.059515077620744705	-0.024112099781632423	-0.10620124638080597	-0.014381051994860172	-0.06372580677270889	-0.06610006839036942	-0.034886062145233154	-0.06223597750067711	-0.07656961679458618	0.0831366777420044	0.008023079484701157	-0.04875916987657547	-0.06703371554613113	-0.04011855274438858	-0.002168439095839858	-0.054234132170677185	-0.04111054539680481	0.013754389248788357	-0.05933753401041031	-0.025597888976335526	0.030432000756263733	-0.06950356066226959	-0.011453702114522457	0.01518428884446621	0.018887903541326523	-0.01975005306303501	0.10362722724676132	-0.032356519252061844	0.004780052695423365	-0.011848327703773975	-0.06674833595752716	0.04978334903717041	0.06255113333463669	0.055061399936676025	0.03705689311027527	0.041211776435375214	0.04392319545149803	0.11378173530101776	0.03728915750980377	-0.0157118272036314	-0.016379225999116898	-0.025940844789147377	-0.06011670082807541	0.03751125559210777	-0.040631189942359924	-0.02789885550737381	0.004037618637084961	-0.0006911555537953973	0.05273538827896118	0.02303382009267807	-0.0005391263985075057	-0.020719299092888832	0.0468793660402298	0.0002923529827967286	0.04761473089456558	-0.013020467944443226	-0.01211431436240673	-0.017517268657684326	-0.06964102387428284	-0.03625047579407692	-0.004844107199460268	0.023646079003810883	-0.0085218520835042	0.05604962259531021	0.015581849031150341	-0.05334964394569397	0.006976129021495581	-0.011507917195558548	0.10252822935581207	-0.08164163678884506	-0.01335849054157734	0.05954530090093613	0.02117484249174595	-0.09496276825666428	0.008773674257099628	0.0278178621083498	-0.011435105465352535	-0.06139663606882095	-0.0014344395603984594	0.03819328919053078	-0.023241940885782242	-0.020543018355965614	-0.022165080532431602	-0.03846447542309761	-0.03363792970776558	0.014601796865463257	-0.0027353446930646896	0.052479736506938934	0.008014161139726639	0.009733670391142368	0.01861867494881153	0.03787964954972267	0.020601410418748856	-0.01293210033327341	0.008726225234568119	-0.02142231911420822	0.05427725985646248	-0.042697805911302567	-0.07574629783630371	-0.04187702015042305	0.05990882217884064	-0.04719574376940727	-0.02385863848030567	-0.03878690302371979	-0.016896171495318413	-0.0206054225564003	0.03508913144469261	-0.014039048925042152	-0.04876362532377243	0.000304916116874665	-0.026700671762228012	-0.08303869515657425	-0.10080278664827347	-0.06328511238098145	0.031148210167884827	-0.04656717926263809	-0.014650404453277588	-0.05405985563993454	0.010898340493440628	0.03866603597998619	0.015765951946377754	0.047925740480422974	0.07070593535900116	0.04007979854941368	-0.03387240692973137	0.052980922162532806	0.021879374980926514	0.061503563076257706	0.003751900279894471	-0.0169359240680933	0.01262047328054905	0.03368567302823067	-0.04480127990245819	0.036883942782878876	-0.03360112011432648	0.018845949321985245	0.00561334565281868	-0.019387738779187202	-0.011812293902039528	-0.014346028678119183	0.0014294221764430404	-0.039087481796741486	0.06856799870729446	-0.050237156450748444	-0.041620414704084396	0.04446372389793396	-0.04057667404413223	-0.07531453669071198	-0.02520379237830639	-0.0009398265974596143	-0.023732822388410568	0.07536952942609787	-0.0028068572282791138	0.026697974652051926	0.03644397482275963	-0.038134705275297165	-0.06249683350324631	-0.0018098748987540603	-0.02660158835351467	0.03449614718556404	0.014976759441196918	0.05641637742519379	0.02047591842710972	-0.008550688624382019	-0.022573722526431084	-0.051659900695085526	0.016845818608999252	-0.08992620557546616	0.02371538244187832	0.00017735564324539155	-0.03305647149682045	0.02017790824174881	-0.029104700312018394	0.01562059111893177	-0.04882382974028587	0.0016259971307590604	-0.01660960167646408	0.04053430259227753	-0.01696816459298134	-0.021506307646632195	0.02690078131854534	-0.023856116458773613	-0.014530833810567856	0.03559489920735359	-0.05287513881921768	-0.04410761594772339	0.034599557518959045	-0.01385706290602684	0.018388846889138222	0.0011333499569445848	0.02971881628036499	-0.01596040092408657	-0.10711228102445602	-0.039268624037504196	-0.02424132637679577	-0.06565526127815247	0.024188920855522156	0.029964325949549675	-0.08815242350101471	-0.05116982385516167	-0.020099369809031487	-0.041060272604227066	0.06159847602248192	-0.0237668976187706	0.04829636588692665	-0.04187500849366188	-0.0019640366081148386	-0.03402215242385864	-0.037705712020397186	0.048723917454481125	-0.05022702366113663	-0.06362823396921158	-0.056290607899427414	0.09134235978126526	-0.054098740220069885	-0.0009107627556659281	0.08117637783288956	-0.023954520002007484	0.06721027940511703	0.00693032331764698	-0.0023485138081014156	0.03557942062616348	-0.003347937483340502	-0.017811788246035576	0.04808638617396355	-0.09203041344881058	0.08726014941930771	-0.07418377697467804	0.06866694241762161	0.030117187649011612	0.01913050375878811	0.09345946460962296	0.1083955317735672	0.013856525532901287	-0.014143596403300762	-0.063724085688591
+-0.03369642794132233	-0.054533302783966064	-0.019351428374648094	0.015181019902229309	-0.029400018975138664	-0.015720995143055916	-0.05722527205944061	-0.009208654053509235	0.05708420276641846	-0.042588189244270325	0.0012571642873808742	-0.010725592263042927	-0.0306900292634964	0.0064063300378620625	0.0763191506266594	0.03370462357997894	0.021853968501091003	-0.016755236312747	0.012920192442834377	-0.0010756116826087236	0.031005917116999626	0.030860604718327522	0.01223248615860939	0.11111986637115479	0.005919291637837887	-0.004504410549998283	-0.05026844143867493	0.09465865790843964	0.010142849758267403	0.10104275494813919	-0.004526839125901461	-0.02828478254377842	-0.01104043424129486	-0.024297267198562622	0.048910051584243774	0.04867202788591385	-0.028251945972442627	0.07687021046876907	0.024539418518543243	0.04772122949361801	0.07345221936702728	-0.061329685151576996	-0.010553641244769096	-0.048413194715976715	-0.007045811973512173	-0.061544593423604965	0.009971896186470985	-0.03832055255770683	-0.023978598415851593	0.11046120524406433	0.07226832211017609	-0.012443015351891518	0.012967453338205814	-0.010317378677427769	0.02727261744439602	0.004265943076461554	-0.021796206012368202	-0.037772949784994125	0.0663658082485199	0.0560673326253891	0.0024757757782936096	-0.03393033146858215	0.01403989177197218	0.03126908466219902	0.018936576321721077	0.020706184208393097	0.05158751830458641	0.03328816220164299	-0.035813406109809875	-0.015146158635616302	0.009462617337703705	0.03420219197869301	-0.06103964522480965	-0.035846907645463943	-0.013671699911355972	-0.0044233244843780994	0.017399761825799942	0.019030043855309486	0.057534072548151016	0.022512590512633324	0.038541391491889954	-0.03234608471393585	0.00927012413740158	0.002084150677546859	0.03889569640159607	-0.06537868827581406	-0.018474532291293144	0.0019366082269698381	0.009445746429264545	-0.054478783160448074	0.08372180908918381	0.02963981404900551	0.009262030944228172	0.0028803786262869835	-0.014310223050415516	-0.00887774582952261	0.04389927536249161	-0.0509706512093544	0.05651010945439339	0.008295625448226929	-0.029593126848340034	-0.004586323164403439	0.020104290917515755	0.025260094553232193	0.034042537212371826	0.0735880434513092	-0.04991575703024864	0.04039471969008446	0.056677836924791336	0.07285018265247345	-0.02380840666592121	0.03677854686975479	0.06331370025873184	0.022564854472875595	0.023069916293025017	-0.016633503139019012	-0.015219433233141899	0.0022370237857103348	-0.06401005387306213	-0.006958432029932737	0.019450264051556587	0.07406139373779297	-0.04740667715668678	-0.04741036146879196	0.058097586035728455	0.07818698137998581	-0.0662706270813942	0.007241785991936922	-0.00983063131570816	0.022290591150522232	0.0005419472581706941	-0.003900957526639104	0.03699363395571709	-0.016743892803788185	0.011151234619319439	0.006077886559069157	-0.04243855178356171	0.02297828160226345	0.06901076436042786	0.0039267935790121555	-0.005965817719697952	-0.02901245653629303	0.08189229667186737	-0.01700437441468239	-0.027268854901194572	0.032345566898584366	-0.06300787627696991	0.041068077087402344	-0.06351606547832489	-0.05575581267476082	-0.05650104209780693	0.004931984003633261	0.04672856628894806	-0.05615316703915596	-0.02477164752781391	-0.05961892753839493	0.005370915401726961	0.020563557744026184	-0.007396448403596878	0.011221243999898434	-0.05909552052617073	-0.05797912925481796	0.06223369762301445	0.006955997087061405	0.008950202725827694	-0.007327921688556671	0.024532940238714218	-0.06630440801382065	0.006167021580040455	-0.05009438842535019	-0.02878396213054657	-0.015377920120954514	0.061634164303541183	0.043640974909067154	0.04267507046461105	0.028749458491802216	0.04002852365374565	0.012189281173050404	-0.01736622117459774	-0.07132256776094437	-0.058481283485889435	-0.022922424599528313	-0.02265450730919838	-0.02683226764202118	-0.09959675371646881	-0.012254389002919197	-0.025681963190436363	0.03536045551300049	-0.05208112671971321	-0.007510609459131956	0.02673497423529625	-0.03634857013821602	0.0809299573302269	0.04020063206553459	-0.05667577683925629	0.05486532300710678	0.03896096721291542	0.08515655249357224	-0.07315009832382202	-0.05541834607720375	-0.02754729986190796	0.03166116029024124	0.10341125726699829	-0.038322385400533676	0.005626052618026733	-0.036356233060359955	0.07783391326665878	0.0325196199119091	0.01342051848769188	-0.03546943515539169	-0.007611891254782677	0.0553295761346817	-0.04214479774236679	0.04549746587872505	0.05179000645875931	-0.029503945261240005	0.00263702729716897	-0.026411354541778564	-0.06762244552373886	-0.05020445957779884	-0.0006689002038910985	0.006544046103954315	-0.04788472503423691	-0.01056526880711317	-0.032694220542907715	-0.03895970806479454	0.015542441047728062	-0.01737353764474392	-0.029478175565600395	-0.0004703881568275392	0.004333370830863714	-0.0074549089185893536	-0.0049463906325399876	-0.006631558761000633	0.011918003670871258	0.024084076285362244	-0.027091681957244873	0.059816449880599976	-0.0857822448015213	-0.02899092063307762	-0.0840536579489708	0.036656733602285385	0.02091340348124504	-0.008725889027118683	0.026620643213391304	0.05711304396390915	0.0633217915892601	0.0791473463177681	-0.009713076055049896	-0.0029267878271639347	-0.04476934298872948	0.0142125329002738	-0.04206202179193497	-0.032910268753767014	-0.0030144283082336187	-0.04033871740102768	-0.010827625170350075	-0.12663669884204865	-0.06811855733394623	0.003971185069531202	0.034610506147146225	-0.09634701907634735	0.016542550176382065	-0.03841156139969826	0.008204306475818157	-0.02944556437432766	-0.06228666007518768	0.07408326119184494	-0.0512494221329689	-0.023569097742438316	-0.033173296600580215	0.024244189262390137	0.011925031431019306	0.054912544786930084	-0.05515240505337715	-0.05267466604709625	0.015385366976261139	-0.037972331047058105	-0.0436040423810482	-0.0459464006125927	-0.036279983818531036	-0.03176448494195938	0.034944113343954086	0.020012089982628822	0.057018812745809555	0.030178654938936234	0.024927424266934395	0.01414478849619627	-0.04561628773808479	-0.05912701040506363	-0.0013458867324516177	0.03697336092591286	0.06569795310497284	-0.019496893510222435	-0.01342234667390585	0.02089669182896614	0.031404364854097366	-0.02422725223004818	0.032681338489055634	0.010172835551202297	0.037156835198402405	-0.009492519311606884	-0.004163589794188738	-0.027313144877552986	-0.016168005764484406	0.03681420534849167	0.006737434770911932	0.024945449084043503	0.08400937914848328	0.041299715638160706	0.0030940871220082045	0.029596591368317604	0.04993712157011032	0.007347074802964926	-0.003477790392935276	0.025655480101704597	-0.02211909182369709	0.03858546167612076	-0.019903605803847313	-0.02882431633770466	-0.0253189355134964	-0.01671222411096096	0.0031108020339161158	-0.026960382238030434	0.020400015637278557	0.014997689984738827	-0.07324406504631042	0.023053115233778954	0.03671485185623169	0.07763352990150452	0.0277866143733263	-0.0017237699357792735	-0.028196632862091064	-0.023451991379261017	-0.01027585007250309	-0.029868116602301598	-0.0892292782664299	0.09295659512281418	-0.05206412449479103	0.08279130607843399	0.014906708151102066	-0.0764576643705368	-0.030813109129667282	0.03898695111274719	0.10575410723686218	-0.07756775617599487	-0.012309298850595951	0.03248243406414986	-0.01365103293210268	-0.061383698135614395	-0.0031903949566185474	-0.07807106524705887	-0.09040958434343338	-0.03325175493955612	-0.017640920355916023	0.034968920052051544	-0.0011820136569440365	-0.08465497940778732	-0.005936361849308014	-0.025050165131688118	0.007743672002106905	0.09342997521162033	-0.06381530314683914	0.00583223719149828	-0.017361938953399658	-0.03355943039059639	-0.05048919841647148	0.02376719005405903	-0.00997565034776926	-0.06685306876897812	0.010407901369035244	0.045755598694086075	-0.018281953409314156	0.0158949363976717	0.005310367327183485	-0.024501590058207512	0.007614674046635628	-0.04730335250496864	0.02246558479964733	-0.06033501401543617	-0.006369245704263449	0.03220512345433235	0.03420643508434296	0.0033518020063638687	0.002560017164796591	-0.06406844407320023	0.014999156817793846	0.002104852581396699	-0.04464422166347504	-0.06743822991847992	0.0711037889122963	0.01505034789443016	0.05360470712184906	-0.03722640126943588	-0.009453007951378822	-0.10913431644439697	-0.022972876206040382	0.023531224578619003	0.05569516122341156	0.036890726536512375	0.00744888698682189	0.07511386275291443	0.03495527803897858	-0.006612017750740051	-0.003477668622508645	-0.05132484808564186	0.07794223725795746	-0.043699607253074646	0.006226911675184965	-0.013684272766113281	-0.032853953540325165	0.005110543221235275	-0.004028966184705496	-0.06919301301240921	0.031635526567697525	-0.00010989877046085894	-0.000537694024387747	-0.05120569467544556	0.02820410020649433	-0.07062452286481857	0.07165348529815674	-0.06411968171596527	0.0008152704103849828	0.020710745826363564	-0.0329924151301384	-0.0516224205493927	0.041973572224378586	0.01081532146781683	-0.06342361122369766	-0.0163535438477993	-0.010019888170063496	-0.006000744178891182	0.04717157781124115	-0.019894858822226524	0.080448217689991	-0.025460142642259598	0.08405127376317978	-0.019347280263900757	-0.08518288284540176	0.03302684798836708	-0.012416617944836617	-0.06309976428747177	0.03426338732242584	0.018916906788945198	-0.015454466454684734	0.02049415558576584	0.023045193403959274	0.005850303918123245	0.056264787912368774	0.08671145141124725	-0.061489932239055634	-0.06348980963230133	-0.034188345074653625	0.07871353626251221	0.0269145667552948	0.07598044723272324	-0.02825908176600933	-0.07871808856725693	-0.08334740251302719	-0.013327636756002903	-0.031171226873993874	-0.08954106271266937	-0.07825097441673279	0.03568486496806145	0.04155714437365532	-0.11170803755521774	0.039189066737890244	-0.08983595669269562	-0.09658891707658768	0.028760109096765518	-0.0011827429989352822	-0.019172482192516327	-0.03420413285493851	0.05618029832839966	-0.01372253056615591	0.07583256810903549	0.014963450841605663	-0.019663803279399872	0.039875585585832596	0.004184892401099205	0.007495200727134943	-0.045506082475185394	-0.051275599747896194	-0.005320457275956869	-0.05151817202568054	0.008760424330830574	0.001301914919167757	-0.020680783316493034	-0.04901650547981262	0.020133240148425102	0.030915962532162666	0.056638628244400024	0.04537251591682434	-0.009301861748099327	0.014199294149875641	0.0925159677863121	0.046529799699783325	-0.022445669397711754	-0.060096051543951035	-0.01513709221035242	0.04002569243311882	0.024237068369984627	0.048732686787843704	-0.05313974618911743	0.11409801989793777	-0.021777715533971786	-0.03248047083616257	0.014018332585692406	0.03195829316973686	0.121965192258358	0.029871031641960144	-0.05099503695964813
+0.0441698394715786	-0.06849054247140884	-0.038723159581422806	0.07149077951908112	-0.06063409522175789	-0.025898732244968414	-0.014867404475808144	0.021043479442596436	0.007584326900541782	-0.0732915922999382	-0.03679623827338219	0.010745804756879807	0.026966875419020653	0.04355024918913841	0.04814176261425018	-0.01962522231042385	-0.04220923036336899	0.04908458888530731	-0.012688792310655117	-0.012929323129355907	0.031837042421102524	-0.029537536203861237	-0.04470483586192131	0.06388586759567261	0.013943076133728027	0.03900735080242157	-0.04203372821211815	0.012351739220321178	0.02512945793569088	0.021831655874848366	0.03170314431190491	-0.03892019763588905	-0.009696565568447113	-0.06954209506511688	-0.06611726433038712	0.060699138790369034	-0.04554348066449165	0.107021264731884	-0.007180419284850359	0.004222470801323652	-0.07010138779878616	0.0241476409137249	-0.10546133667230606	-0.10628306865692139	0.03086245059967041	0.006086540874093771	0.03346596285700798	-0.002509797690436244	0.024707607924938202	0.017032794654369354	0.04086114093661308	-0.005634700413793325	-0.026595402508974075	-0.00667383149266243	-0.05902044102549553	-0.000841988658066839	0.045265380293130875	-0.045775748789310455	-0.011029727756977081	0.055273279547691345	-0.087293341755867	-0.026571059599518776	0.027327943593263626	0.039045438170433044	-0.01747424714267254	0.0035969307646155357	0.020865367725491524	0.04348568990826607	-0.011684004217386246	-0.054171137511730194	-0.0178539976477623	0.05248066037893295	-0.0023273653350770473	-0.027575718238949776	0.03412503004074097	-0.06965029984712601	-0.011258002370595932	0.03861355781555176	0.0031791594810783863	-0.00010768134234240279	0.022471843287348747	-0.030066991224884987	0.14424610137939453	0.017228050157427788	-0.036397602409124374	-0.028337387368083	0.019823530688881874	-0.01539075281471014	0.0195427518337965	0.013985623605549335	0.02135012298822403	0.09327049553394318	-0.048509079962968826	0.057119935750961304	0.03987240791320801	0.008098985068500042	0.013989550061523914	-0.13091397285461426	-0.0548255480825901	0.005164328031241894	-0.005265805404633284	-0.03910892456769943	0.04620087146759033	0.008996873162686825	0.05484814569354057	-0.011730581521987915	-0.06391425430774689	4.932941010338254e-05	0.025458091869950294	0.003504654625430703	0.03582385182380676	0.09226839244365692	0.03005259670317173	0.055255793035030365	0.0666700005531311	-0.03358564153313637	-0.005245100241154432	-0.06115567311644554	-0.05495446175336838	0.013498570770025253	0.013049768283963203	0.03137080371379852	0.0019600926898419857	-0.0988505557179451	-0.018184207379817963	-0.021744882687926292	-0.1394423246383667	-0.004685600288212299	0.03365059569478035	-0.03491932153701782	0.10510627925395966	0.040633831173181534	-0.03341821953654289	0.027594244107604027	0.029260432347655296	0.05785954371094704	0.016888882964849472	0.043542344123125076	0.009725719690322876	-0.015386913903057575	0.007075428031384945	0.06578196585178375	0.01850125379860401	-0.022411223500967026	0.09023115783929825	0.0115302549675107	-0.01418238878250122	-0.0040129697881639	-0.04399013891816139	-0.009700873866677284	-0.06290663033723831	-0.035285864025354385	0.029995381832122803	-0.04262472316622734	0.01162258256226778	0.008507243357598782	0.058738622814416885	0.030269071459770203	-0.052143119275569916	-0.040789514780044556	-0.06549433618783951	0.004168784245848656	-0.0037919713649898767	-0.07184415310621262	-0.004194315988570452	0.0075422897934913635	-0.028866374865174294	0.008764040656387806	-0.05534727871417999	-0.022241530939936638	-0.008879981935024261	-0.019769685342907906	0.06390725076198578	0.04046899080276489	0.0748409852385521	-0.017149396240711212	0.0011272126575931907	-0.08413972705602646	-0.00036725247628055513	0.009047763422131538	-0.0019253977807238698	-0.02362128719687462	-0.002489047357812524	-0.05455698072910309	-0.03431392088532448	-0.0006142212077975273	0.007994094863533974	-0.05674085021018982	-0.01423459779471159	-0.05177271366119385	-0.07447472959756851	-0.05598161742091179	0.06376143544912338	0.061064887791872025	-0.01879354566335678	0.043152522295713425	-0.03913925588130951	0.06494423002004623	0.00306002888828516	0.002572476165369153	-0.052621956914663315	0.04797264561057091	-0.032936904579401016	0.0759783536195755	0.014975756406784058	-0.06540600955486298	0.05535287410020828	0.02242516539990902	0.05704592913389206	-0.05991070345044136	0.003363577416166663	0.0136363934725523	-0.04184018820524216	0.0651903823018074	0.07452581077814102	-0.00843329168856144	-0.04053782299160957	0.05992100387811661	0.056155476719141006	-0.06245929375290871	-0.0004910512361675501	-0.008060435764491558	-0.08582985401153564	-0.03749997913837433	-0.02149282768368721	-0.05041883513331413	-0.04585172235965729	0.05565682053565979	-0.037573281675577164	-0.027744129300117493	-0.012136324308812618	0.03683805465698242	-0.0010199215030297637	-0.05287384241819382	0.002722016302868724	-0.016326282173395157	-0.07064658403396606	0.06534899026155472	0.0052381642162799835	-0.09550465643405914	-0.06231163814663887	-0.004391162656247616	0.060254745185375214	0.011623851954936981	0.031037596985697746	0.06523606926202774	0.044290151447057724	0.026331009343266487	-0.02810973860323429	0.0242164246737957	-0.015523653477430344	-0.037761393934488297	0.0018418862018734217	0.05797286331653595	-0.020469510927796364	-0.02428758144378662	-0.0141776567324996	-0.04765404760837555	-0.002548458520323038	0.012878334149718285	-0.005461063235998154	-0.03550172969698906	-0.04366883635520935	-0.042231056839227676	0.011808489449322224	-0.04039851576089859	0.027482450008392334	-0.004482796881347895	-0.033706631511449814	0.06330886483192444	-0.058400508016347885	-0.06274212151765823	0.036694154143333435	0.0011026561260223389	0.020206863060593605	-0.05055902153253555	0.03342491015791893	-0.017415767535567284	-0.08001012355089188	0.0033532697707414627	-0.06791441142559052	-0.04283049330115318	-0.048668812960386276	0.013301242142915726	-0.04881366714835167	0.10806959867477417	0.011607452295720577	-0.05004458874464035	-0.03870916739106178	-0.030691904947161674	0.012366287410259247	-0.08165394514799118	0.018174225464463234	-0.013410823419690132	-0.056019511073827744	-0.02709769271314144	0.042833611369132996	-0.011257817968726158	-0.030348701402544975	0.0022887815721333027	0.013337010517716408	-0.07878059148788452	0.0612824410200119	0.009850963950157166	-0.07280102372169495	-0.00492201978340745	-0.09530367702245712	0.019720768555998802	0.09162587672472	0.012657801620662212	-0.008210992440581322	0.013899765908718109	0.03349968418478966	0.08727876842021942	-0.02879265695810318	0.007907145656645298	0.00022423028713092208	-0.05781625956296921	-0.027542119845747948	0.07669100910425186	-0.0469621978700161	0.00839150045067072	0.0019210975151509047	-0.01113240234553814	0.05385769158601761	-0.005248613189905882	-0.019098347052931786	-0.003909839317202568	0.01618756726384163	0.010443847626447678	0.03789021447300911	-0.02013956382870674	0.022027011960744858	-0.053283218294382095	-0.041024960577487946	-0.09316513687372208	0.00014101008127909154	0.041797224432229996	-0.013204069808125496	0.049622077494859695	-0.005309558939188719	-0.07292382419109344	0.009689859114587307	-0.01789925992488861	0.11687404662370682	-0.056864868849515915	-0.006520562805235386	0.06544528156518936	0.02209884114563465	-0.06786862015724182	0.04110366851091385	0.010934345424175262	-0.05573900789022446	-0.05746820941567421	-0.018954360857605934	0.02783873677253723	-0.06578000634908676	-0.017134135589003563	-0.007154053542762995	-0.009086276404559612	0.021799791604280472	0.02984776347875595	0.026107393205165863	0.03873145580291748	-0.006058206781744957	-0.022922856733202934	0.04697049781680107	0.004312976263463497	0.012776761315762997	-0.049692027270793915	0.008297118358314037	0.0008829249418340623	0.0764569416642189	-0.02782794088125229	-0.014902000315487385	-0.042185451835393906	0.057907216250896454	0.007195069454610348	-0.019573358818888664	-0.03113102726638317	-0.05320926383137703	-0.022551516070961952	0.0012327708536759019	0.040304820984601974	-0.04773436486721039	-0.048080332577228546	-0.09030564874410629	-0.06634611636400223	-0.07646230608224869	-0.025482170283794403	0.026276404038071632	-0.02132631465792656	0.023208903148770332	-0.06228525936603546	-0.022312836721539497	0.05687379091978073	-0.008729593828320503	0.0016292522195726633	0.038141846656799316	0.027457311749458313	-0.05401533097028732	0.04231316223740578	-0.039281848818063736	0.060045722872018814	0.03567357733845711	-0.015612722374498844	0.019647907465696335	0.03281332924962044	-0.04853786900639534	0.05189225822687149	-0.016360241919755936	0.004018847364932299	0.03188452497124672	-0.01936882920563221	0.0004069558926858008	-0.0013981279917061329	0.016085248440504074	-0.052869196981191635	0.025639574974775314	-0.06861941516399384	-0.039190225303173065	0.012206477113068104	-0.05507058650255203	-0.0898565948009491	-0.07000227272510529	0.014952911995351315	-0.014466826803982258	0.010273019783198833	-0.013478469103574753	0.02378462813794613	0.07620108872652054	-0.027860797941684723	-0.021691443398594856	0.017320571467280388	-0.043817922472953796	0.044896479696035385	-0.017192140221595764	0.04859862104058266	0.03767542913556099	0.021826205775141716	0.018541639670729637	0.024674033746123314	-0.012557176873087883	-0.0420725978910923	0.004933701362460852	0.010165300220251083	-0.06421127915382385	0.008590823039412498	0.012791136279702187	0.020677734166383743	-0.04720774665474892	-0.03006814420223236	-0.032643042504787445	0.09061820060014725	0.052101366221904755	0.023248691111803055	0.03985874354839325	-0.032089754939079285	0.036860834807157516	0.033018529415130615	-0.04103526100516319	-0.08061540126800537	-0.013966682367026806	-0.04840438812971115	0.040348686277866364	0.001674311002716422	0.00024082687741611153	-0.012207671999931335	-0.06945125758647919	0.04645943269133568	-0.03489058092236519	-0.07937371730804443	0.02098751999437809	0.03001294657588005	-0.016416138038039207	-0.03676991909742355	0.005639692768454552	-0.07848615199327469	0.05405551567673683	0.03903938829898834	0.07473709434270859	-0.021944737061858177	-0.012166204862296581	-0.018106525763869286	-0.025109324604272842	0.030260177329182625	-0.023457329720258713	-0.05249335244297981	0.015786660835146904	0.09507390856742859	-0.06664125621318817	0.01041876059025526	0.045247163623571396	0.010320782661437988	0.04906423017382622	0.01851881667971611	-0.051752474159002304	0.06662245094776154	0.007198929321020842	-0.03158888220787048	0.0706419050693512	-0.06284081935882568	0.06538745015859604	-0.07696673274040222	0.08727510273456573	-0.017301777377724648	0.014148159883916378	0.09277445822954178	0.058545373380184174	0.004151305183768272	0.04775843024253845	-0.03589735925197601
+0.04156508669257164	-0.01802360825240612	-0.008733125403523445	0.03384571522474289	-0.060429129749536514	0.01203183364123106	-0.05224640294909477	0.03554414585232735	-0.0024780170060694218	-0.07635565847158432	-0.0656658187508583	-0.028935344889760017	-0.04409584775567055	0.003073156112805009	0.05748274549841881	-0.04755164682865143	-0.05477017164230347	0.03626587241888046	-0.04084927588701248	-0.037410419434309006	0.0009544255444779992	0.06667830049991608	-0.023838143795728683	0.07359418272972107	0.013830428011715412	0.04199691116809845	-0.03682142496109009	0.036597222089767456	-0.030514098703861237	0.08628912270069122	0.05065498501062393	0.002887590089812875	0.07751308381557465	-0.061449505388736725	-0.0018759076483547688	0.057646751403808594	-0.012775549665093422	0.09655864536762238	0.06380616873502731	-0.04048362374305725	-0.012907645665109158	-0.00235736183822155	-0.04141630604863167	-0.1196998730301857	0.03201994299888611	-0.02670811116695404	-0.02991093322634697	-0.04097535088658333	0.0009629242122173309	0.0399281308054924	0.06872009485960007	-0.02036449871957302	-0.04167507588863373	-0.007249267306178808	-0.022918688133358955	0.025657612830400467	0.04049103707075119	-0.06359163671731949	0.019120197743177414	0.029985465109348297	-0.08541722595691681	-0.003237583674490452	0.019978316500782967	0.011734644882380962	-0.028210705146193504	-0.009441407397389412	0.041075512766838074	0.05052598565816879	0.03862825036048889	-0.014305775985121727	0.008240269497036934	0.06942746043205261	-0.05519460514187813	-0.033805422484874725	0.02106611616909504	-0.04390452429652214	-0.021094080060720444	0.011443781666457653	0.011453877203166485	0.015192339196801186	0.06631745398044586	-0.040355660021305084	0.1438693404197693	-0.04005158692598343	0.003030759282410145	-0.034957531839609146	-0.03721670061349869	-0.07198227941989899	0.018910346552729607	-0.026881812140345573	0.01917828433215618	0.03799288347363472	0.02759328857064247	0.009361364878714085	0.019377317279577255	0.01651555299758911	-0.005800401791930199	-0.10569426417350769	-0.006039232946932316	-0.0041397628374397755	0.007029072847217321	0.030393444001674652	0.04466386139392853	0.04441998526453972	0.07408524304628372	0.039358336478471756	-0.03874373063445091	-0.019573140889406204	0.041888248175382614	-0.002689226297661662	0.005358710419386625	0.08871430158615112	0.06827867031097412	-0.0061406418681144714	0.0545569472014904	-0.0016637988155707717	0.006937291007488966	-0.05261818692088127	-0.0016330081271007657	0.0029395611491054296	-0.03269703686237335	0.0377982035279274	-0.023145580664277077	-0.07398540526628494	0.0006427256157621741	0.0379134863615036	-0.07573457807302475	-0.04819025471806526	0.0042174141854047775	-0.0474836640059948	0.11487934738397598	0.04317047446966171	-0.06493216007947922	-0.0010272231884300709	0.020504744723439217	0.0243049468845129	-0.004693310707807541	0.006042132619768381	0.021295903250575066	-0.025894273072481155	-0.015017209574580193	0.04731171950697899	-0.022319626063108444	-0.035433243960142136	0.05808093771338463	0.005461483262479305	-0.008929855190217495	-0.04395066574215889	-0.10374461859464645	-0.004266954027116299	-0.08053030073642731	-0.07080807536840439	0.024930447340011597	-0.058712951838970184	0.005964746233075857	0.03955730050802231	0.01930573582649231	0.013255016878247261	-0.029518403112888336	-0.05391857773065567	-0.06652380526065826	-0.008167421445250511	-0.009536799974739552	-0.09073847532272339	-0.018447231501340866	-0.012535287998616695	-0.008364260196685791	0.010232968255877495	-0.06500091403722763	-0.02575874887406826	-0.0453839935362339	0.0048575433902442455	0.027354631572961807	0.08689021319150925	0.08969604223966599	0.016268689185380936	0.01447692047804594	-0.06918362528085709	-0.020160311833024025	-0.01814888045191765	0.017036838456988335	-0.04007734730839729	0.03471514210104942	-0.030606327578425407	0.011973143555223942	-0.03231359273195267	0.03934972733259201	0.012155400589108467	0.06738677620887756	0.015890059992671013	-0.012137128971517086	-0.04479701817035675	0.05999825894832611	0.060956116765737534	-0.0027473410591483116	0.034333735704422	0.008796336129307747	0.08247607201337814	-0.015148712322115898	-0.01696079969406128	-0.06954624503850937	0.0562734492123127	0.051837678998708725	0.05991564691066742	0.03986212983727455	-0.04202745854854584	0.06812066584825516	0.037892259657382965	0.02202899381518364	-0.014539201743900776	0.04167407751083374	0.0298375952988863	-0.015974633395671844	0.04584247246384621	0.06160802021622658	-0.012215692549943924	0.000885403249412775	-0.026615183800458908	0.01872895285487175	-0.07651087641716003	-0.02314019203186035	0.006383699364960194	-0.09638765454292297	0.034871865063905716	-0.05658767744898796	-0.07357988506555557	-0.011343155987560749	0.011347027495503426	-0.006116093136370182	-0.027338184416294098	0.046328842639923096	0.02987641841173172	-0.027022138237953186	0.003858489217236638	0.008572722785174847	-0.05615833401679993	-0.07602924853563309	0.07548554241657257	-0.0331437774002552	-0.06942124664783478	-0.051383018493652344	0.008696307428181171	0.04212947562336922	-0.011841613799333572	0.027475843206048012	0.060210056602954865	0.08108647167682648	0.030081531032919884	-0.04578149691224098	0.012740234844386578	-0.02592957206070423	-0.061385925859212875	-0.010283291339874268	0.01815921440720558	0.00971655361354351	-0.07983372360467911	0.015015541575849056	-0.06696352362632751	-0.012346240691840649	0.012900012545287609	0.045081727206707	-0.03762255609035492	0.038878556340932846	-0.06171596422791481	0.016486231237649918	-0.018209831789135933	-0.008249380625784397	0.08919594436883926	-0.026103109121322632	0.004282503388822079	-0.02785755693912506	-0.014421574771404266	0.05205400660634041	-0.01865532249212265	0.022167595103383064	-0.02277364954352379	0.07756534218788147	-0.003967992030084133	-0.0500834621489048	-0.038887497037649155	-0.05433512106537819	-0.03699292987585068	-0.02288174256682396	-0.019593508914113045	-0.03967423737049103	0.044605087488889694	0.055481769144535065	-0.03591961786150932	-0.05362476408481598	-0.03457784652709961	-0.05491126701235771	-0.07299209386110306	-0.02747388184070587	-0.023126598447561264	-0.06473691016435623	-0.02199716679751873	0.010824579745531082	-0.03995819389820099	-0.03711770474910736	0.003404236165806651	0.009535795077681541	-0.10021660476922989	0.026360996067523956	-0.00962879229336977	-0.05558750405907631	0.014453594572842121	-0.07386612147092819	0.02487477846443653	0.07635634392499924	0.052236467599868774	0.010469458065927029	0.021689366549253464	0.042435988783836365	0.08464610576629639	-0.04470427334308624	0.01559781190007925	-0.08383920788764954	-0.005307849030941725	-0.058944057673215866	0.06941892951726913	-0.004356519319117069	-0.026286978274583817	0.023640740662813187	0.003998664673417807	0.031938597559928894	0.00011332968279020861	-0.033242881298065186	-0.058061882853507996	0.0340043380856514	-0.021638013422489166	0.05228507146239281	-0.05634687468409538	0.02853451296687126	-0.027109624817967415	-0.058100342750549316	-0.037725962698459625	-0.03411586210131645	0.05138179287314415	-0.0014380313223227859	0.014905593357980251	-0.010139977559447289	-0.07123713940382004	-0.023179464042186737	-0.008602604269981384	0.09023939818143845	-0.07080644369125366	0.030013691633939743	0.05185645818710327	0.009941197000443935	-0.08880859613418579	-0.015619940124452114	0.012428989633917809	-0.06317225843667984	-0.06727276742458344	0.0020104236900806427	0.041879523545503616	-0.07457588613033295	-0.06575807929039001	-0.04130159318447113	-0.01631246879696846	-0.011168940924108028	0.02860952354967594	0.027056539431214333	0.03117942437529564	0.005647334270179272	-0.019640596583485603	0.0008802319061942399	0.002415768336504698	0.005834757816046476	0.0127644632011652	0.007597549352794886	-0.01970973238348961	0.016217665746808052	-0.06415896117687225	-0.02658390812575817	-0.02817847952246666	0.040037669241428375	-0.016756853088736534	0.03327489271759987	0.013147995807230473	-0.030097313225269318	-0.04221349209547043	0.016498150303959846	0.011535110883414745	-0.03195236250758171	-0.04828890040516853	0.0041862353682518005	-0.05326182767748833	-0.06147359311580658	-0.003036500420421362	-0.002815870800986886	-0.006055929232388735	0.027855627238750458	-0.046341150999069214	0.001566207385621965	0.04039661958813667	0.013224581256508827	0.034280285239219666	0.057756923139095306	0.027400873601436615	-0.035807061940431595	0.013053533621132374	-0.03596995770931244	0.051093753427267075	0.05681091547012329	-0.06272784620523453	-0.03810578212141991	-0.018636854365468025	-0.045237038284540176	0.03713428974151611	-0.03869377076625824	-0.05264666676521301	0.04785466939210892	-0.019543668255209923	0.003926492296159267	-0.020251808688044548	0.050579510629177094	-0.049895279109478	0.062270570546388626	-0.08598066866397858	-0.031164756044745445	-0.024082105606794357	-0.024761667475104332	-0.09088170528411865	-0.046877097338438034	-0.03281698748469353	-0.0282845851033926	0.04940536618232727	-0.05122654512524605	0.0064261071383953094	0.045468758791685104	-0.046226900070905685	-0.06265276670455933	-0.028638578951358795	0.016449136659502983	0.011019785888493061	0.02713920548558235	0.04177163541316986	-0.0028981510549783707	-0.006595395039767027	-0.038390692323446274	-0.03446485474705696	-0.022786546498537064	-0.05128650739789009	-0.0011591528309509158	0.019397152587771416	-0.04546510800719261	0.027959348633885384	0.017372284084558487	0.010304841212928295	-0.03473182022571564	-0.03621324524283409	-0.026582861319184303	0.05775976553559303	0.0008510909392498434	0.02076449804008007	0.023777825757861137	-0.047352295368909836	-0.003412042511627078	0.07378926873207092	-0.08311738073825836	-0.07094556838274002	-0.05437187850475311	-0.017850534990429878	0.05892670527100563	-0.004767498001456261	0.00429051136597991	-0.07246201485395432	-0.037485454231500626	0.028421318158507347	-0.018597833812236786	-0.05216073989868164	-0.028981657698750496	0.04384402930736542	-0.04809029772877693	-0.05405258759856224	0.013873215764760971	-0.09408902376890182	0.044911399483680725	0.0572766475379467	0.08024429529905319	-0.02632918581366539	-0.012107028625905514	-0.011182954534888268	-0.02911457233130932	0.023700783029198647	-0.011284801177680492	-0.04584476351737976	-0.0018124462803825736	0.10107411444187164	-0.029611904174089432	0.02002466842532158	0.07412724941968918	-0.02442939579486847	0.028844431042671204	0.052296899259090424	-0.007241916377097368	0.011642040684819221	-0.01122297439724207	-0.030448460951447487	0.08742015808820724	-0.060546521097421646	0.08612165600061417	-0.04494217783212662	0.12224221974611282	-0.010607227683067322	-0.00650649331510067	0.0857023224234581	0.08270330727100372	0.04591219872236252	0.035504862666130066	-0.09172710031270981
+0.01036520954221487	-0.03737454116344452	-0.02819015271961689	0.037554781883955	-0.08455086499452591	-0.04254794493317604	0.00025630451273173094	0.03929785639047623	-0.006725276354700327	-0.04647339880466461	-0.05219518393278122	0.0315852016210556	0.015303942374885082	0.03478532284498215	0.03105429746210575	-0.04825742170214653	-0.06707344949245453	-0.000963343947660178	-0.01471334882080555	-0.03447249159216881	0.029531430453062057	-0.015793707221746445	-0.05978371575474739	0.05897054076194763	0.03548397496342659	0.021201716735959053	-0.03417249396443367	-0.0021316767670214176	0.03197620064020157	0.017411934211850166	0.07343641668558121	-0.043042540550231934	-0.003960041794925928	-0.05508650466799736	-0.08003079891204834	0.06143869459629059	-0.024999286979436874	0.12290342152118683	0.006601071450859308	0.016653262078762054	-0.04297229275107384	-6.672923336736858e-05	-0.05237375572323799	-0.03616148605942726	0.021190714091062546	-0.03058108314871788	0.0007976830238476396	-0.019190475344657898	0.020025892183184624	0.0014207800850272179	0.07337416708469391	0.009863674640655518	-0.019672401249408722	-0.026617852970957756	-0.04737198352813721	0.016712982207536697	-0.0030208968091756105	-0.029733730480074883	-0.048408087342977524	0.01716584712266922	-0.07771050184965134	-0.0038767396472394466	-0.0010769375367090106	0.005955005530267954	-0.009918497875332832	0.00789335835725069	-0.012965479865670204	0.0802718847990036	-0.015062480233609676	-0.04022761806845665	0.021218908950686455	0.05528974160552025	-0.033976320177316666	-0.04365575313568115	0.04445374757051468	-0.052908167243003845	0.006917500402778387	0.02892838977277279	-0.009443232789635658	-0.009722298011183739	0.00013956028851680458	-0.016967345029115677	0.13271771371364594	-0.02116919867694378	-0.04806927964091301	-0.02487737312912941	-0.008988319896161556	-0.03335662558674812	0.04833175987005234	0.028346413746476173	0.042386867105960846	0.07870620489120483	-0.06196263059973717	0.053557686507701874	0.0061646876856684685	-0.022876430302858353	0.027759937569499016	-0.1450488567352295	-0.04666601121425629	0.01604541204869747	0.006167056504637003	-0.03056565299630165	0.08437993377447128	-0.04453955963253975	0.04457700252532959	0.045076366513967514	-0.07401207089424133	0.003082015784457326	0.023381847888231277	0.008219695650041103	0.04633553326129913	0.11104704439640045	0.04410107061266899	0.09880954772233963	0.08467521518468857	6.804987060604617e-05	-0.006134055554866791	-0.04397721216082573	-0.060251716524362564	-0.006087171845138073	0.007065097335726023	0.0663008838891983	-0.0006425922038033605	-0.061122193932533264	-0.03111003339290619	-0.004443239886313677	-0.13823895156383514	0.027412021532654762	0.07641568034887314	-0.007793622091412544	0.09417948126792908	0.04602934047579765	0.011015200987458229	0.04587506502866745	0.05288819968700409	0.01941544935107231	0.017245573922991753	0.01106460765004158	0.003004875034093857	-0.04134345427155495	-0.04121142998337746	0.05169029161334038	0.03704536333680153	-0.010684529319405556	0.03536667674779892	0.04789470508694649	0.014061924070119858	-0.0005985720199532807	-0.007593452464789152	-0.01579020544886589	-0.08271438628435135	-0.05606169253587723	0.026936763897538185	-0.06761244684457779	0.04522152617573738	0.03032596968114376	0.07260576635599136	0.033357586711645126	-0.015932220965623856	-0.0012480276636779308	-0.034211743623018265	-0.03061625175178051	0.003943463787436485	-0.07359772175550461	0.027572039514780045	0.02852598764002323	-0.024128101766109467	-0.004789142403751612	-0.04162653535604477	-0.01713157445192337	-0.01018373854458332	-0.03783423826098442	0.02754071354866028	0.0859202966094017	0.021064508706331253	-0.03288061171770096	0.02858903259038925	-0.06913365423679352	-0.015722937881946564	0.0003065517812501639	0.025164540857076645	-0.051010459661483765	0.02769569680094719	-0.0710211917757988	-0.04760044813156128	-0.008497707545757294	0.0033232583664357662	-0.047105465084314346	-0.006920217536389828	-0.04008249565958977	-0.05422957241535187	-0.051627352833747864	0.028099309653043747	0.059029992669820786	-0.026433171704411507	0.05777464807033539	0.013995648361742496	0.07027019560337067	0.02154560200870037	0.000484529125969857	-0.07906004786491394	-0.002959376899525523	-0.009111082181334496	0.04197164624929428	0.025651976466178894	-0.07003070414066315	0.0876699760556221	0.02867067977786064	0.05885334312915802	-0.08859695494174957	-0.011470114812254906	0.031081365421414375	0.000430974701885134	0.08142496645450592	0.07868294417858124	-0.04582364857196808	-0.032061781734228134	0.03054444119334221	0.039434291422367096	-0.07429178804159164	-0.008090212009847164	0.00011099621769972146	-0.043980732560157776	-0.01815616898238659	-0.027482783421874046	-0.07359953224658966	-0.04534348100423813	0.01580345630645752	-0.005017280112951994	-0.0497150793671608	-0.03851921111345291	0.06461615860462189	-0.003796183969825506	-0.02068052813410759	-0.0032149655744433403	0.01449571643024683	-0.059673722833395004	0.06235986575484276	-0.023900097236037254	-0.09595286846160889	-0.07734301686286926	-0.005303751677274704	0.02776111103594303	0.04601196572184563	0.02600952610373497	0.04651426151394844	0.09110620617866516	0.026906028389930725	-0.05899593234062195	0.00818476639688015	-0.018589124083518982	-0.019448542967438698	0.002698940923437476	0.03876843303442001	-0.038565024733543396	-0.06629829853773117	0.015762828290462494	-0.07796213775873184	-0.013584622181952	-0.023425770923495293	0.047904521226882935	-0.013168229721486568	-0.07748541235923767	-0.017596689984202385	0.022724907845258713	-0.0027386839501559734	-0.01140481699258089	-0.03530282527208328	-0.052702415734529495	0.08015573769807816	-0.05856730416417122	-0.03868207335472107	-0.007897200994193554	0.0014642250025644898	0.005637770984321833	-0.04999825358390808	0.04997390881180763	-0.04321504384279251	-0.042623866349458694	-0.010559756308794022	-0.05024200677871704	-0.06777936220169067	-0.032307568937540054	0.001862460863776505	-0.020703429356217384	0.09871380031108856	-0.004033369943499565	-0.04889220371842384	0.0024953170213848352	-0.031039919704198837	0.006563978269696236	-0.03926703333854675	0.005906717386096716	-0.00439588725566864	-0.055770352482795715	0.006174291949719191	0.03757547587156296	-0.02220230922102928	-0.017224358394742012	0.08040034770965576	-0.0037603238597512245	-0.030862247571349144	0.04779680445790291	-0.02606731280684471	-0.03325508162379265	-0.03348083794116974	-0.08218778669834137	0.03346624970436096	0.08063965290784836	0.023495573550462723	0.03364648297429085	0.029693404212594032	0.03856611251831055	0.07766233384609222	0.002285337308421731	-0.029172709211707115	0.045746006071567535	-0.06984399259090424	-0.06871318072080612	0.06941332668066025	-0.016651717945933342	0.011937793344259262	-0.026588603854179382	0.005496852099895477	0.04967579245567322	-0.0006521102623082697	0.0015628956025466323	-0.01935405842959881	0.017499666661024094	0.0025269631296396255	0.022107915952801704	-0.008375137113034725	-0.00800078734755516	-0.0057739028707146645	-0.0459464006125927	-0.09800851345062256	-0.05400506407022476	0.05437830463051796	0.013716421090066433	0.05848712474107742	-0.018797773867845535	-0.07105988264083862	0.014702651649713516	0.004793865606188774	0.10083886981010437	-0.06903474032878876	0.004468422383069992	0.005070373881608248	0.04087633267045021	-0.07993147522211075	0.00539071811363101	0.009217488579452038	-0.0399121530354023	-0.06749375909566879	-0.00020409289572853595	0.04769999161362648	-0.05950288474559784	-0.024160906672477722	-0.0105390390381217	-0.02329763025045395	0.03785112872719765	0.021763332188129425	-0.010866059921681881	0.06278349459171295	0.03964883089065552	-0.0321081280708313	0.05392103269696236	0.03196563199162483	0.024511830881237984	-0.04899558424949646	0.04557947814464569	-0.035724569112062454	0.11230172961950302	-0.010151494294404984	-0.04000227153301239	-0.09266948699951172	0.028074730187654495	-0.014673243276774883	0.0002787289849948138	-0.07667072862386703	-0.013248530216515064	0.007193115074187517	0.0255780927836895	0.019034966826438904	-0.07787048816680908	-0.018272673711180687	-0.06800757348537445	-0.06613843888044357	-0.06641019880771637	-0.05145096033811569	0.03512725606560707	-0.04706619307398796	0.03333884850144386	-0.04934243485331535	-0.018277877941727638	0.0235222689807415	-0.019512256607413292	0.022958867251873016	0.013286848552525043	0.04003145918250084	-0.02177582122385502	0.06691049784421921	0.0017552857752889395	0.053355872631073	-0.015547641552984715	-0.02939746342599392	0.059407565742731094	0.05677741393446922	-0.02762397937476635	0.05201674625277519	-0.03466189280152321	0.010460463352501392	-0.013047636486589909	-0.025744473561644554	0.012105881236493587	0.021330170333385468	-0.026411576196551323	-0.06489614397287369	0.026186693459749222	-0.07109732180833817	-0.04620833694934845	0.04829396679997444	-0.04743867740035057	-0.08613085001707077	-0.05377982556819916	-0.0306050106883049	-0.034409407526254654	0.028306415304541588	0.01992850936949253	0.03882720693945885	0.0492868535220623	-0.024332890287041664	-0.006525012198835611	0.025729598477482796	-0.04173848405480385	0.007469542324542999	-0.000960455450695008	0.05559548735618591	0.02144811861217022	0.032524511218070984	0.049901045858860016	-0.004385926760733128	0.017945263534784317	-0.06163204461336136	-0.0018366535659879446	0.008083997294306755	-0.04305735230445862	-0.006233133841305971	0.04827958717942238	0.033532630652189255	-0.06246240809559822	-0.010874170809984207	-0.07580424845218658	0.05799483135342598	0.026230130344629288	0.06104036048054695	0.008680014871060848	-0.04386846721172333	0.00187731406185776	-0.008311486802995205	0.01312168873846531	-0.08490464836359024	0.03177031874656677	-0.05809604376554489	0.004863305948674679	-0.026587391272187233	0.009690066799521446	0.031452447175979614	-0.10915254801511765	0.02006499655544758	-0.010974233970046043	-0.03049507737159729	0.028249118477106094	0.014651145786046982	-0.02111160010099411	-0.0541636161506176	-0.014421291649341583	-0.0642346441745758	0.04263315722346306	0.00921980757266283	0.024875380098819733	-0.03627678006887436	-0.01304696500301361	0.01449707429856062	0.0029029345605522394	0.030615368857979774	-0.024938318878412247	-0.07167497277259827	-0.006046167109161615	0.09475484490394592	-0.020675720646977425	0.018304493278265	0.055654413998126984	-0.009532795287668705	0.031525205820798874	0.013176915235817432	-0.035356998443603516	0.03546540439128876	-0.0004891594289802015	-0.00226487684994936	0.052702099084854126	-0.047699544578790665	0.06129450723528862	-0.05567661672830582	0.08330225944519043	0.007975244894623756	0.0010703192092478275	0.09063372761011124	0.049799006432294846	0.013433707877993584	0.05770938843488693	-0.032042019069194794
+0.060610901564359665	-0.02120746485888958	-0.027652863413095474	0.016901126131415367	-0.04040061682462692	-0.06646287441253662	-0.014212161302566528	0.0266600102186203	0.013888499699532986	-0.03903563320636749	-0.056963831186294556	-0.03039846569299698	-0.006216157227754593	0.01888374797999859	0.03659962862730026	-0.04622090607881546	-0.05279788374900818	0.022445011883974075	-0.010611609555780888	-0.02238200046122074	0.039679188281297684	0.005922435782849789	-0.05536641180515289	0.06873572617769241	0.021118760108947754	-0.006123703438788652	-0.029250506311655045	-0.004942983388900757	0.02945009432733059	0.02686714194715023	0.0960150733590126	-0.009996370412409306	0.047135740518569946	-0.006890306249260902	-0.023872626945376396	0.02085231989622116	-0.05351419746875763	0.08881989121437073	0.008830900304019451	0.01190901268273592	-0.06177191436290741	-0.027080561965703964	-0.040982093662023544	-0.014620614238083363	0.03174138814210892	0.016922177746891975	-0.011458393186330795	-0.01219556387513876	0.011938763782382011	0.0420408695936203	0.09027353674173355	-0.009753787890076637	0.022222569212317467	0.003174565499648452	-0.016858207061886787	-0.008414247073233128	0.057577669620513916	-0.06579961627721786	-0.039011694490909576	0.013577811419963837	-0.09637949615716934	-0.009172368794679642	0.017089689150452614	-0.004163747187703848	-0.006554503459483385	-0.06274701654911041	-0.024473320692777634	0.10370272397994995	-0.013962927274405956	-0.027648385614156723	0.022998984903097153	0.06017766520380974	-0.09262771904468536	-0.043567366898059845	0.06461748480796814	-0.06418372690677643	-0.008074571378529072	0.03566258028149605	-0.03176918253302574	0.00720199616625905	0.004255059640854597	-0.014941719360649586	0.1042187362909317	-0.02456226758658886	-0.07201589643955231	-0.03955484926700592	-0.008697673678398132	-0.05829000845551491	0.055435311049222946	0.022512810304760933	0.04630670323967934	0.05981530621647835	-0.02835623174905777	0.012955007143318653	0.03263815864920616	0.006842209957540035	0.013446833938360214	-0.07254526019096375	-0.05884723365306854	-0.0003119885514024645	0.01912865601480007	0.03184103965759277	0.06800210475921631	-0.007167053874582052	0.023320358246564865	0.03147004917263985	-0.061369553208351135	0.023540204390883446	0.028874613344669342	0.04988847300410271	0.044694624841213226	0.07046036422252655	0.05290112644433975	0.01653202436864376	0.0545995756983757	0.012933327816426754	0.0019482183270156384	-0.034164514392614365	-0.030790245160460472	0.027991799637675285	0.014559966512024403	0.08188176155090332	-0.014005214907228947	-0.06815911084413528	-0.04362756013870239	0.06171190366148949	-0.09272050857543945	-0.05382421612739563	0.07093650847673416	-0.02197311818599701	0.0966397374868393	0.06373622268438339	-0.0345953106880188	0.03716565668582916	0.06849309056997299	0.009437373839318752	-0.011979363858699799	0.0024688676930963993	-0.016000285744667053	-0.044366080313920975	-0.04517577216029167	0.04807955399155617	-0.02886047214269638	-0.07726245373487473	0.0950033888220787	0.059075161814689636	0.0405784472823143	-0.0201751459389925	-0.02949548326432705	-0.053378842771053314	-0.056592945009469986	-0.028676874935626984	0.017868105322122574	-0.043369609862565994	-0.024354329332709312	0.022129062563180923	-0.030669106170535088	0.0290022324770689	0.02835669368505478	-0.04944618046283722	-0.08440089225769043	-0.026167461648583412	-0.008297433145344257	-0.05174532160162926	-0.020049018785357475	-0.004254657309502363	-0.015130024403333664	0.04677607864141464	-0.07406648248434067	-0.031438637524843216	0.012526708655059338	-0.02623600885272026	0.04314463958144188	0.08254088461399078	0.076631098985672	0.030077092349529266	-0.01806403510272503	-0.038138289004564285	-0.013797035440802574	-0.04910315200686455	0.025206860154867172	-0.012771910056471825	-0.0045165084302425385	-0.06318584829568863	0.03627751022577286	0.0094429487362504	0.04149959608912468	-0.034259337931871414	0.009302773512899876	-0.011770677752792835	-0.010889842174947262	-0.07607969641685486	0.05466420203447342	0.062118176370859146	-0.03554312884807587	-0.0059524658136069775	-0.05000543221831322	0.055987484753131866	-0.01888255961239338	-0.013142515905201435	-0.04513021931052208	0.023449715226888657	0.03254794329404831	0.0625091940164566	0.040396250784397125	-0.07344476878643036	0.10966815799474716	0.0005678338347934186	0.048409830778837204	-0.024514034390449524	-0.0011962158605456352	0.02959950640797615	-0.040567170828580856	0.07614536583423615	0.08343716710805893	0.005380334332585335	0.020465489476919174	-0.016260478645563126	0.09322933107614517	-0.07744055986404419	-0.0058495826087892056	-0.013221818953752518	-0.09125857800245285	0.03807700797915459	-0.05156031250953674	-0.084991954267025	-0.008588980883359909	0.035125188529491425	-0.024450885131955147	-0.018668727949261665	-0.018664300441741943	-0.0056485929526388645	0.04656914621591568	0.004729257896542549	0.015352320857346058	-0.034342020750045776	-0.10104374587535858	0.09243708103895187	0.02992643415927887	-0.07799367606639862	-0.06258431822061539	-0.01226176880300045	0.03601080924272537	-0.03844434395432472	0.04178891330957413	0.036802370101213455	0.08863124251365662	0.015905793756246567	-0.014576463028788567	0.04057040065526962	-0.01996873877942562	-0.058243926614522934	0.00020524697902146727	0.005154494196176529	-0.042475808411836624	-0.05603726580739021	0.06273617595434189	-0.05235515534877777	-0.0005094325169920921	-0.015599886886775494	-0.011127274483442307	-0.031167907640337944	-0.02687668427824974	-0.05728738754987717	0.04046465456485748	-0.07885799556970596	-0.021915076300501823	0.03332431614398956	-0.026096155866980553	0.03540245443582535	-0.006578847765922546	-0.0036874618381261826	0.02074243128299713	0.010685612447559834	-0.006195543799549341	-0.04030372574925423	0.07763618230819702	0.026940371841192245	-0.05465231463313103	-0.015091112814843655	-0.060531821101903915	-0.06640096753835678	0.03307723626494408	-0.001815731287933886	-0.008911814540624619	0.06151141971349716	0.023571372032165527	-0.07633781433105469	-0.05905337259173393	-0.02631429396569729	-0.0381055511534214	-0.021537087857723236	-0.012572032399475574	-0.03152656927704811	-0.04726701229810715	-0.03800234943628311	0.03461141511797905	-0.03578069806098938	-0.0020121843554079533	0.014976575039327145	-0.003569297958165407	-0.06770434230566025	-0.008690593764185905	-0.014604432508349419	-0.011370218358933926	0.00428480701521039	-0.043572258204221725	0.03215959668159485	0.09194999188184738	0.08232438564300537	-0.024477634578943253	0.03697558119893074	0.025807050988078117	0.12492092698812485	-0.02223515510559082	-0.02851778082549572	-0.004046163521707058	-0.06041312217712402	0.012041672132909298	0.03531564772129059	-0.017929451540112495	-0.03311711549758911	0.015651585534214973	-0.034220192581415176	0.0418490506708622	-0.005823641084134579	-0.02368262968957424	-0.025824738666415215	0.010036160238087177	-0.030948713421821594	0.006642697844654322	0.009582859463989735	0.020071642473340034	-0.019746320322155952	-0.07885714620351791	-0.04603637009859085	-0.05940583348274231	0.029900802299380302	-0.012969493865966797	0.026743289083242416	-0.048696987330913544	-0.04784529656171799	0.0006359458202496171	0.006633572746068239	0.12490317970514297	-0.043591275811195374	-0.005294191651046276	0.047789566218853	0.038699835538864136	-0.06344249099493027	0.0018708494026213884	-0.0007585135754197836	-0.06145787984132767	-0.03194282948970795	0.02290622889995575	0.00643579475581646	-0.06494356691837311	-0.009629280306398869	-0.003994964994490147	-0.0446556955575943	0.011287366971373558	-0.03068312257528305	0.01611553132534027	0.05264994874596596	0.0036261167842894793	-0.041028931736946106	-0.019954219460487366	0.022636592388153076	0.0559893473982811	-0.042396336793899536	0.027554312720894814	-0.018223116174340248	-0.0017645212356001139	-0.07742947340011597	-0.05108479782938957	-0.05231127515435219	0.02462719939649105	-0.013032963499426842	0.0020551683846861124	-0.023141436278820038	-0.018599875271320343	-0.037255413830280304	-0.004253698978573084	0.010956366546452045	-0.1127198189496994	-0.00909061823040247	-0.06731252372264862	-0.025354988873004913	-0.06812148541212082	-0.0525318942964077	-0.008733493275940418	-0.014961468987166882	0.04149631783366203	-0.00010908079275395721	0.013051547110080719	-0.021741103380918503	-0.014961965382099152	0.06865707039833069	0.09378761798143387	0.003499777987599373	0.034793101251125336	0.04131021350622177	0.02811693400144577	0.0229953583329916	0.04940227419137955	-0.06082724779844284	-0.0086780721321702	0.02057466469705105	-0.07676409184932709	0.024954834952950478	-0.02186322957277298	0.035459261387586594	-0.040889348834753036	-0.015896961092948914	0.03381139039993286	-0.05694466084241867	0.027640126645565033	-0.05766821652650833	0.054524753242731094	-0.06183980777859688	-0.0557563379406929	0.036358460783958435	-0.04063830524682999	-0.04547801613807678	-0.05074203759431839	-0.006343993358314037	-0.010811406187713146	0.049566980451345444	-0.05297664552927017	0.012466386891901493	0.03192705661058426	-0.03377252072095871	-0.08349297940731049	-0.022581765428185463	0.02726125530898571	0.023229701444506645	0.008490429259836674	0.020143110305070877	0.01959502510726452	-0.007552580442279577	0.014163510873913765	-0.06756461411714554	0.010535815730690956	-0.0691138431429863	-0.01631847210228443	0.00027964217588305473	-0.032140959054231644	-0.002768691163510084	0.0267013106495142	0.0255251694470644	-0.04902166128158569	0.016275813803076744	-0.09746087342500687	0.10690560936927795	-0.0021725203841924667	0.05290425941348076	-0.039396919310092926	-0.04116605594754219	0.015482205897569656	0.04231317341327667	-0.03897622600197792	-0.07463987171649933	-0.036032967269420624	-0.01561459619551897	0.017879432067275047	-0.0020081044640392065	0.01278237346559763	-0.02267671562731266	-0.05625569447875023	0.06095074489712715	0.04277312383055687	-0.04136716574430466	-0.0009121008333750069	0.027090132236480713	-0.04576064646244049	-0.021823205053806305	-0.002941451035439968	-0.0625373050570488	0.07864805310964584	0.009712686762213707	0.026631904765963554	-0.030865900218486786	-0.00857474748045206	-0.012593669816851616	-0.05899538844823837	-0.028996072709560394	-0.017587652429938316	-0.01788228191435337	0.0052418699488043785	0.036148540675640106	-0.03476196527481079	-0.003851920133456588	0.08587905764579773	-0.044073209166526794	0.027246424928307533	0.05492246150970459	-0.051730550825595856	0.04624013975262642	0.01369973924010992	-0.0016261825803667307	0.06333482265472412	-0.0948915109038353	0.09774813055992126	-0.06257136911153793	0.10648875683546066	0.03939276933670044	-0.02591043710708618	0.06535965204238892	0.030166981741786003	-0.018606098368763924	0.05057057738304138	-0.09192506223917007
+0.0018680266803130507	-0.07862680405378342	-0.03500379994511604	0.04676136001944542	-0.0024066271726042032	-0.022728458046913147	-0.0005973025690764189	0.019074758514761925	-0.00047567387809976935	-0.1015775203704834	-0.019914066419005394	0.018425021320581436	-0.027532028034329414	-0.01635974831879139	0.016045911237597466	0.015758445486426353	-0.008147588931024075	0.028984367847442627	0.00428435392677784	-0.05433772876858711	-0.018414940685033798	0.03739016130566597	-0.07872765511274338	0.004237632267177105	-0.021038111299276352	-0.037876781076192856	-0.03490239009261131	0.005887727718800306	0.022321512922644615	0.0950673371553421	0.021033875644207	-0.058301571756601334	0.08005993068218231	-0.049441494047641754	-0.05136025324463844	0.021734360605478287	-0.0428968220949173	0.0975286215543747	0.00628247857093811	0.016633493825793266	-0.04182529076933861	-0.027376020327210426	-0.021634066477417946	-0.08829828351736069	0.002198884030804038	-0.02747558243572712	0.009361456148326397	0.004014044068753719	-0.014047801494598389	0.0731557086110115	0.07834944874048233	0.03259504213929176	0.028228892013430595	-0.011727775447070599	0.003659344045445323	-0.004347567446529865	0.032867055386304855	-0.00270069926045835	-0.021436281502246857	0.06286367774009705	-0.062313470989465714	-0.023094866424798965	0.05546784773468971	0.031347356736660004	-0.054146233946084976	0.018910201266407967	0.013633590191602707	0.07536829262971878	-0.014129528775811195	-0.08716079592704773	-0.025404775515198708	0.03396378085017204	-0.007514210417866707	-0.050729598850011826	-0.0005016748327761889	-0.037338852882385254	0.041760288178920746	0.0014858429785817862	0.031255315989255905	0.020045064389705658	-0.023465288802981377	-0.039937276393175125	0.10927218943834305	-0.01627812348306179	0.026171637699007988	-0.07826016843318939	-0.009954973123967648	-0.055718958377838135	0.04293569177389145	-0.030355267226696014	0.048442643135786057	0.03152383491396904	-0.003141955006867647	0.021514220163226128	-0.0024414509534835815	0.00047423416981473565	0.011104379780590534	-0.06984648108482361	-0.03497615084052086	0.020667703822255135	0.0036344672553241253	-0.01410373393446207	0.06303131580352783	0.021944133564829826	0.08118882030248642	0.01734975539147854	-0.02862267754971981	0.014229953289031982	0.09148956090211868	-0.013466652482748032	0.02904144674539566	0.11577600985765457	0.018625007942318916	0.007517841644585133	0.06993293762207031	0.03707984834909439	0.039338964968919754	-0.03296735882759094	0.030315924435853958	0.007997427135705948	-0.015904510393738747	0.041348446160554886	-0.0038052916061133146	-0.07772915065288544	-0.0036283410154283047	0.0546564906835556	-0.06413058191537857	-0.07015412300825119	0.04951232299208641	0.023294003680348396	0.0674939975142479	0.05889572203159332	0.011872886680066586	0.0017975153168663383	-0.028409872204065323	-0.005565696395933628	-0.045225102454423904	0.04212290421128273	0.08740007132291794	-0.03252361714839935	0.0038829294499009848	0.04684104397892952	-0.02877015434205532	-0.03010871447622776	0.03720269724726677	0.013562999665737152	-0.015397152863442898	-0.04810037091374397	-0.08249245584011078	0.023074187338352203	-0.007492367643862963	-0.0664103627204895	-0.0127255879342556	-0.07286462187767029	0.032963018864393234	0.010343227535486221	0.034833334386348724	0.02863810770213604	-0.012797771953046322	0.031332068145275116	-0.05163813382387161	-0.06843665987253189	0.017701424658298492	-0.08297919481992722	-0.01557133998721838	0.04093069210648537	0.010869964025914669	0.060524117201566696	-0.08494120836257935	-0.001892800210043788	-0.0017587481997907162	-0.0007403155905194581	0.0791502296924591	0.03730643540620804	0.0676942840218544	0.022895827889442444	0.02446349710226059	-0.08538199216127396	0.023601211607456207	-0.0356617197394371	0.005720026325434446	-0.03288266435265541	-0.0007122307433746755	-0.06323480606079102	-0.03607596829533577	0.014676065184175968	0.009517891332507133	-0.0008522223797626793	-0.0168033204972744	0.02559300884604454	-0.047720495611429214	-0.057869862765073776	0.04470854997634888	0.0408148355782032	0.005626763217151165	0.03528517857193947	0.025547634810209274	0.13144581019878387	-0.0578259713947773	-0.06076851114630699	-0.05433778464794159	-0.010755314491689205	0.06514367461204529	-0.025683065876364708	-0.01713726297020912	-0.06351875513792038	0.07036296278238297	0.05125008523464203	0.010614035651087761	-0.04067634046077728	0.0008122158469632268	0.0044424463994801044	0.0077153583988547325	0.05589543282985687	0.040471937507390976	-0.03224774822592735	-0.032651156187057495	0.03494821488857269	0.012954930774867535	-0.05938776955008507	-0.05860770493745804	-0.02115168608725071	-0.052301883697509766	-0.03586782515048981	-0.01595788262784481	-0.07928428798913956	-0.010848878882825375	0.004801181610673666	-0.020662162452936172	-0.008055108599364758	0.025523420423269272	0.023956123739480972	-0.03536403551697731	-0.0319308303296566	-0.011405499652028084	-0.04915852099657059	-0.04988386854529381	0.04183986037969589	-0.013623778708279133	-0.0851072147488594	-0.034447114914655685	-0.060757994651794434	0.03930820897221565	-0.020116956904530525	0.055899109691381454	0.057795681059360504	0.05586230754852295	0.006223090924322605	-0.05918607860803604	-0.055330097675323486	-0.013546423986554146	-0.011741102673113346	-0.0041496302001178265	0.026922354474663734	-0.033003512769937515	-0.04397699609398842	-0.014154745265841484	-0.08328109979629517	0.013403943739831448	0.00421571172773838	-0.002211174927651882	-0.02681443654000759	0.003496066899970174	-0.003952002618461847	0.0028258254751563072	0.0029503903351724148	0.011761646717786789	0.017974408343434334	-0.03607764467597008	0.019509892910718918	-0.06816592812538147	0.01586848683655262	0.026284409686923027	-0.039338842034339905	-0.025953615084290504	-0.036151863634586334	0.07814142107963562	-0.015309248119592667	-0.0808306410908699	-0.04708327353000641	-0.0478888563811779	0.015357714146375656	-0.006380920764058828	-0.014993172138929367	-0.047325845807790756	0.05777183920145035	0.004838970955461264	-0.045128751546144485	-0.022137057036161423	-0.06323546171188354	-0.06109444424510002	-0.0764145776629448	0.0025459816679358482	-0.032701436430215836	-0.0044769104570150375	0.035504017025232315	0.026033421978354454	-0.049493931233882904	0.035189319401979446	0.031898077577352524	0.05692845582962036	-0.041365012526512146	0.07426496595144272	-0.0007509533315896988	-0.0758238434791565	0.024518154561519623	-0.057345740497112274	0.005446900147944689	0.04158208891749382	0.02874191664159298	0.01237616129219532	0.046473655849695206	0.07437065988779068	0.06959137320518494	0.009896110743284225	-0.01728193648159504	-0.054450612515211105	-0.016145460307598114	-0.037711452692747116	0.04734779894351959	0.012340422719717026	0.044613562524318695	-0.016416674479842186	0.0077967592515051365	0.0720025897026062	-0.0020360087510198355	-0.023929709568619728	-0.0005801671650260687	0.02818276919424534	0.020465794950723648	0.035831499844789505	0.015376079827547073	0.007713404484093189	0.0066314213909208775	-0.07890834659337997	-0.06558728218078613	-0.006091197486966848	0.012173372320830822	-0.035673532634973526	0.0949312224984169	0.02087443321943283	-0.06870294362306595	0.0015584062784910202	-0.006515234708786011	0.08099576830863953	-0.09391976147890091	-0.022244710475206375	-0.00763368234038353	0.036318231374025345	-0.1438097357749939	-0.011138261295855045	0.003937771078199148	-0.04837788641452789	-0.09303584694862366	-0.023032700642943382	0.03117435798048973	0.002177560469135642	-0.0272035151720047	-0.04950143024325371	-0.02347855642437935	-0.03685948997735977	0.02560568042099476	-0.02020653709769249	0.05037360265851021	-0.003760990221053362	-0.012034481391310692	-0.040252380073070526	-0.009121883660554886	-0.02007089927792549	-0.020850256085395813	-0.0104023776948452	-0.016212264075875282	-0.012600164860486984	-0.03323440998792648	-0.014713377691805363	-0.027972042560577393	0.0019480016781017184	-0.03976866975426674	-0.005727696232497692	-0.022421667352318764	-0.07718852162361145	-0.018772155046463013	-0.03753922879695892	0.006648866459727287	-0.024109361693263054	-0.023766161873936653	-0.002891665091738105	-0.049848370254039764	-0.06141876056790352	-0.05409328639507294	0.04532267898321152	0.004119974561035633	0.014357107691466808	-0.05811415985226631	0.009834581054747105	0.034194231033325195	0.03284738212823868	0.033226728439331055	0.10544417798519135	0.0300332922488451	-0.032065052539110184	0.05830857530236244	-0.025607354938983917	0.020729869604110718	0.007536289747804403	-0.05275842547416687	-0.008015844039618969	-0.03271231800317764	-0.037536460906267166	0.015220647677779198	-0.015623798593878746	0.0052216993644833565	-0.008861646056175232	-0.07912635803222656	-0.017560403794050217	-0.005092231556773186	-0.00992369931191206	0.01850990019738674	0.08237966150045395	-0.05555921047925949	-0.032513346523046494	-0.021608253940939903	-0.01143462024629116	-0.05883149057626724	-0.06022987887263298	-0.033505216240882874	-0.0356985479593277	0.09003718942403793	-0.023310227319598198	0.044288210570812225	0.018507253378629684	-0.019128695130348206	-0.0419183075428009	0.0189981609582901	-0.0300486758351326	-0.03992107883095741	0.03460137918591499	0.01292525976896286	-0.010583057068288326	0.03580700606107712	0.00133548304438591	-0.045588552951812744	0.021997306495904922	-0.08240853250026703	-0.022954562678933144	0.03676888346672058	-0.013432064093649387	0.0015353873604908586	0.0032083343248814344	-0.0210798941552639	-0.06588409841060638	-0.033057380467653275	-0.04027940332889557	0.03999900817871094	-0.006397713907063007	-0.02523506060242653	0.020002521574497223	-0.08129027485847473	-0.06269002705812454	0.08867558091878891	-0.03757541999220848	-0.09573061764240265	-0.020702892914414406	-0.014986896887421608	0.05226375162601471	-0.04112153872847557	0.06625722348690033	-0.07034553587436676	-0.09829917550086975	-0.00792890228331089	-0.036882732063531876	-0.08025659620761871	-0.006257121451199055	0.04996315389871597	-0.11769294738769531	-0.029153402894735336	0.034601446241140366	-0.06432412564754486	0.036050308495759964	0.023688366636633873	0.030073115602135658	-0.05409868061542511	0.004114694893360138	-0.05103617534041405	-0.014908206649124622	0.06593403965234756	-0.045387253165245056	-0.07543869316577911	-0.0577310174703598	0.10159188508987427	-0.056917283684015274	0.06456064432859421	0.031769026070833206	-0.035734277218580246	0.06747934967279434	0.058590661734342575	0.006665559019893408	0.04487564414739609	-0.012240150943398476	0.004510102793574333	0.024959130212664604	-0.02795633114874363	0.05115456134080887	-0.04665961489081383	0.1336655616760254	0.024986006319522858	0.057755328714847565	0.049018941819667816	0.0603308230638504	0.042360592633485794	0.013630733825266361	-0.0975051075220108
+0.03512358292937279	-0.02378249540925026	-0.0179046168923378	0.04794175550341606	-0.07486961781978607	-0.022050131112337112	0.02078304998576641	0.027609309181571007	-0.007281750440597534	-0.04858716204762459	-0.030399447306990623	0.0011725876247510314	-0.010933281853795052	0.012266851961612701	0.011120914481580257	-0.04760105907917023	-0.047236911952495575	-0.008337454870343208	-0.010064918547868729	-0.03535715490579605	0.02379537560045719	-0.004531193058937788	-0.08059075474739075	0.07594721764326096	0.021154504269361496	0.0016777368728071451	-0.032405201345682144	0.016284234821796417	-0.003212643088772893	0.05158400163054466	0.07152558863162994	-0.04601534456014633	0.007054909132421017	-0.06259100139141083	-0.06126740202307701	0.03772446885704994	-0.06932585686445236	0.12039514631032944	0.029514892026782036	-0.008009999059140682	-0.0578899122774601	0.023917799815535545	-0.057683221995830536	-0.037277232855558395	0.04977487772703171	-0.006962785962969065	0.0017160590505227447	-0.025699209421873093	0.06313832849264145	0.036159686744213104	0.0974888950586319	0.012472281232476234	-0.014020129106938839	-0.022491058334708214	-0.03588828071951866	0.045248426496982574	0.012855315580964088	-0.05455558001995087	-0.06290875375270844	0.03382681682705879	-0.056045036762952805	-0.007622610777616501	0.04932471737265587	0.010259806178510189	0.0018280638614669442	-0.009836160577833652	-0.02453884296119213	0.08150877803564072	-0.002103805309161544	-0.0264422670006752	-0.0012739631347358227	0.1274133175611496	-0.08657222986221313	-0.050869591534137726	0.04650689661502838	-0.041141100227832794	-0.016630612313747406	0.018182093277573586	0.023229796439409256	0.0071077896282076836	-0.0100841810926795	-0.030819155275821686	0.09275228530168533	-0.02100980095565319	-0.01786119118332863	-0.03843481093645096	0.004299952182918787	-0.06939104944467545	0.09546739608049393	0.03588190674781799	0.036933016031980515	0.04701119288802147	-0.0495915412902832	0.04558531194925308	0.026048164814710617	-0.020983239635825157	0.02534932643175125	-0.11874962598085403	-0.02219070866703987	-0.009959273971617222	0.041067734360694885	-0.03195292130112648	0.06116170808672905	-0.01758173666894436	0.07342970371246338	0.06682085245847702	-0.09698241949081421	0.03938417509198189	0.04517972841858864	0.024262994527816772	0.05330703780055046	0.10897298902273178	0.07408623397350311	0.05482868850231171	0.06684187799692154	-0.016217438504099846	-0.010216746479272842	-0.046457502990961075	-0.044650111347436905	0.002693519461899996	-0.013274907134473324	0.057807620614767075	-0.0018109690863639116	-0.07648198306560516	-0.019019152969121933	0.032712023705244064	-0.10125242918729782	-0.015853961929678917	0.06170085072517395	0.006864335387945175	0.08329013735055923	0.05060010030865669	-0.0011229716474190354	0.014062559232115746	0.053807053714990616	-0.0065125408582389355	-0.006300480104982853	0.04508869722485542	0.01157307717949152	0.00869842804968357	-0.01169558521360159	0.07662893831729889	0.0233612023293972	-0.04870222136378288	0.01637364737689495	0.03775664046406746	0.042577896267175674	0.01111847348511219	-0.06993377208709717	0.008867545984685421	-0.08048320561647415	-0.07736833393573761	0.019709747284650803	-0.07071851193904877	0.005910149775445461	0.055278223007917404	0.05731730908155441	0.04545684903860092	0.009796224534511566	-0.008978440426290035	-0.028546273708343506	-0.049049604684114456	-0.02321830578148365	-0.08680547773838043	-0.017231445759534836	-0.012295852415263653	0.03051428683102131	0.011318124830722809	-0.03194978088140488	-0.025530675426125526	-0.0061575062572956085	-0.00418401462957263	0.05217533931136131	0.06262184679508209	0.060939136892557144	0.045802805572748184	-0.002215748652815819	-0.05165465548634529	0.014139313250780106	-0.03146437183022499	0.0033286502584815025	-0.0675307959318161	0.015980154275894165	-0.055194634944200516	-0.025981631129980087	0.007108134683221579	0.03344087675213814	-0.015539858490228653	-0.03251706808805466	-0.014363161288201809	-0.0481361486017704	-0.08873958885669708	0.05288185924291611	0.04310017451643944	-0.01497432217001915	0.01158090215176344	0.0029893091414123774	0.04909437149763107	0.00577264791354537	-0.0315951369702816	-0.06561242043972015	0.024409078061580658	0.0021479781717061996	0.02124890312552452	0.04967329278588295	-0.0715063214302063	0.06976400315761566	-0.000262018438661471	0.05084771662950516	-0.08788581192493439	-0.024383913725614548	0.004312496166676283	0.012412548996508121	0.006639947183430195	0.06577221304178238	-0.0329178124666214	-0.038855768740177155	0.03502769395709038	0.0661233440041542	-0.05712183192372322	-0.005113684106618166	-0.0064576598815619946	-0.07509490102529526	0.040103621780872345	-0.007247249595820904	-0.09333068877458572	-0.023814592510461807	0.025310184806585312	-0.018462346866726875	-0.04820569232106209	0.0055586062371730804	0.03362330049276352	-0.02005017176270485	-0.045962076634168625	0.021077388897538185	0.0024087156634777784	-0.08296497166156769	0.07823159545660019	-0.027179554104804993	-0.056861549615859985	-0.04688216745853424	-0.015415788628160954	0.010509084910154343	0.008303603157401085	0.05203524976968765	0.03381897881627083	0.07728320360183716	0.02652653679251671	-0.03615614026784897	-0.0008425020496360958	-0.04240837320685387	-0.013333398848772049	-0.003827203530818224	0.0681845024228096	-0.040438346564769745	-0.0520317517220974	-0.009831673465669155	-0.050562575459480286	-0.00423740828409791	0.005683801136910915	0.03171541541814804	-0.026503659784793854	-0.012052861042320728	-0.009681475348770618	0.045263588428497314	-0.0002479645481798798	-0.027374671772122383	0.01473522000014782	-0.024878837168216705	0.05961734056472778	-0.04241976886987686	-0.027927640825510025	0.012651505880057812	0.018634416162967682	0.009448815137147903	-0.03650546818971634	0.05315811559557915	-0.04436351731419563	-0.10954781621694565	-0.08013874292373657	-0.061636537313461304	-0.06164954602718353	0.008837108500301838	-0.0208437480032444	-0.03588227555155754	0.09040815383195877	-0.0022030649706721306	-0.03748105838894844	-0.06685708463191986	-0.03661863133311272	-0.023089392110705376	-0.06513158231973648	-0.02227797731757164	-0.022861627861857414	-0.04708075523376465	-0.010478685609996319	0.01781461387872696	-0.03872948884963989	-0.005839641205966473	0.004127849824726582	0.02609654702246189	-0.01920085959136486	0.059910841286182404	-0.04806401953101158	-0.02188817784190178	0.007078598719090223	-0.07910425215959549	0.044605933129787445	0.09892120957374573	0.07561033219099045	0.05011618882417679	0.004848940763622522	0.05297718569636345	0.10994666814804077	0.03563416376709938	-0.08422353863716125	-0.021340036764740944	-0.025371797382831573	-0.10733283311128616	0.0400235541164875	-0.032069429755210876	0.005045666825026274	-0.017803115770220757	-0.013867028057575226	0.07173842936754227	0.0014635089319199324	0.0003235414915252477	-0.033444952219724655	0.04153059795498848	-0.002412485657259822	0.0038617358077317476	0.004879975691437721	-0.015085640363395214	0.031169075518846512	-0.03755805641412735	-0.07369101047515869	-0.04524577409029007	0.04839691147208214	0.007202166598290205	0.0834905207157135	0.009872499853372574	-0.057183559983968735	0.039390355348587036	0.008553064428269863	0.072664774954319	-0.056657545268535614	0.0251313503831625	0.008805925026535988	0.017159409821033478	-0.10326742380857468	-0.00557043869048357	0.022799186408519745	-0.03392383083701134	-0.08653652667999268	-0.019512981176376343	0.04695936664938927	-0.014881936833262444	-0.04630245268344879	-0.005979293957352638	-0.07635287940502167	-0.014126668684184551	0.012676470912992954	0.006409905850887299	0.05731715261936188	0.006032383069396019	-0.015630701556801796	0.035723548382520676	-0.007487112656235695	0.016454162076115608	-0.024373240768909454	-0.010396507568657398	-0.014298752881586552	0.0866248831152916	-0.014130388386547565	-0.015914790332317352	-0.07829635590314865	0.03805400803685188	-0.025898903608322144	-0.024383673444390297	-0.03319525346159935	-0.0026382687501609325	-0.015544258058071136	-0.022236265242099762	0.022470518946647644	-0.0673302486538887	0.01353511493653059	-0.009602165780961514	-0.028180262073874474	-0.08272770047187805	-0.03682661056518555	0.016606753692030907	-0.044636182487010956	-0.01894121617078781	-0.046521201729774475	-0.01624392904341221	0.023758219555020332	-0.021220451220870018	0.007410610094666481	0.041109319776296616	0.03389235958456993	-0.024768656119704247	0.05026436969637871	0.013787509873509407	0.03984852507710457	-0.004426545463502407	-0.034337058663368225	0.01432777289301157	0.05230389162898064	-0.03610417991876602	-0.0002741822972893715	-0.02868764102458954	0.03803067281842232	-0.03693711757659912	-0.02382814697921276	0.012582167983055115	-0.03457934781908989	-0.026481682434678078	-0.019512329250574112	0.048565540462732315	-0.059547729790210724	-0.03738503158092499	0.03886685147881508	-0.04448910430073738	-0.07377666980028152	0.0010468455730006099	-0.03716934099793434	-0.015366572886705399	0.037624210119247437	-0.0071069011464715	0.030404796823859215	0.029246820136904716	-0.0621974840760231	-0.027486221864819527	-0.008593905717134476	-0.052503906190395355	0.009294594638049603	0.0008751183049753308	0.07704287767410278	0.013701967895030975	0.013909606263041496	-0.0040227072313427925	-0.02845555916428566	0.02591859921813011	-0.09531727433204651	-0.009089230559766293	-0.010905938223004341	-0.017967285588383675	0.03093644604086876	0.020424554124474525	0.0037612109445035458	-0.038582928478717804	-0.03884349763393402	-0.016910430043935776	0.06667661666870117	-0.02733498439192772	0.0027507920749485493	0.03041723370552063	-0.02247564122080803	-0.016036124899983406	-0.0008786363177932799	-0.03567861393094063	-0.07669389247894287	0.019019192084670067	-0.008940479718148708	0.018725132569670677	-0.0016504921950399876	0.03255987539887428	-0.00377472210675478	-0.08634695410728455	-0.04893285036087036	-0.016643274575471878	-0.07198503613471985	0.016994819045066833	0.007808077614754438	-0.09850422292947769	-0.06015867367386818	0.004405392333865166	-0.06204191967844963	0.05944899842143059	-0.041997574269771576	0.044370319694280624	0.0038053521420806646	-0.006342268083244562	0.004626401700079441	-0.018804242834448814	0.04014082998037338	-0.03593766689300537	-0.06306029111146927	-0.012813279405236244	0.07905398309230804	-0.017581192776560783	0.0341385081410408	0.06397677212953568	-0.018006643280386925	0.0377231203019619	0.05416618660092354	-0.042080219835042953	0.0012972602853551507	-0.0009545966750010848	-0.007369304541498423	0.06943313777446747	-0.056340791285037994	0.09687750786542892	-0.06673562526702881	0.09743254631757736	0.010745682753622532	0.012818622402846813	0.10323938727378845	0.07737501710653305	-0.017423825338482857	0.020597493276000023	-0.06075329706072807
+0.017751673236489296	-0.06229736655950546	-0.018199458718299866	0.06329984217882156	-0.06663612276315689	0.011808495968580246	0.00494147464632988	0.048425257205963135	-0.014828761108219624	-0.042653638869524	-0.011186014860868454	0.02749401517212391	0.005292415618896484	0.005090950056910515	0.01822075992822647	-0.028755342587828636	-0.05214328318834305	0.04072074592113495	-0.06683648377656937	-0.04569442197680473	0.002726293634623289	0.03538083657622337	-0.0522402860224247	0.014792362228035927	-0.045813556760549545	0.015070592053234577	-0.05539345368742943	0.011293058283627033	-0.004310305695980787	0.035692885518074036	0.07709105312824249	-0.026096144691109657	0.031066706404089928	-0.05560218542814255	-0.04846028983592987	0.06964915245771408	-0.012226302176713943	0.08469890803098679	0.0015603316714987159	-0.0017697371076792479	-0.020149728283286095	0.0029744261410087347	-0.024274460971355438	-0.07219170033931732	0.06431159377098083	0.001984394621104002	0.034753262996673584	-0.022220801562070847	0.03534836694598198	-0.004498280119150877	0.06810522824525833	0.034489382058382034	0.006636457983404398	-0.014188061468303204	-0.030886515974998474	0.02004658430814743	0.027886781841516495	-0.04312562197446823	-0.03147302567958832	0.04857195168733597	-0.06341499090194702	-0.008772848173975945	0.027516935020685196	0.005931236781179905	-0.04409667104482651	0.011141559109091759	-0.001625375123694539	0.08831121772527695	0.022101443260908127	-0.03148453310132027	-0.002896649995818734	0.08699935674667358	-0.03499320521950722	-0.06239703297615051	0.06317219138145447	-0.07012636959552765	-0.03655683994293213	0.016335872933268547	-0.031302303075790405	-0.040759701281785965	-0.011927792802453041	-0.029084188863635063	0.12357032299041748	-0.029242344200611115	-0.06775018572807312	-0.03061150200664997	0.004594358149915934	-0.05854317545890808	0.0687420442700386	0.009835717268288136	0.02578543871641159	0.06905461847782135	-0.05123802274465561	0.03992370516061783	0.022157888859510422	0.02201256714761257	0.02297333814203739	-0.11225514858961105	-0.06410625576972961	-0.008484726771712303	0.03560671582818031	-0.02563927136361599	0.08102938532829285	-0.007022982928901911	0.05151847377419472	0.015935160219669342	-0.051636822521686554	-0.04793445020914078	0.0004872442514169961	0.02307054027915001	0.028867758810520172	0.15053807199001312	0.02808995731174946	0.04363367706537247	0.09137945622205734	-0.026622237637639046	-0.0012891468359157443	-0.05495402589440346	-0.027150874957442284	-0.004137069918215275	-0.022324930876493454	0.03180530294775963	-0.008712343871593475	-0.09464068710803986	-0.044004373252391815	0.01665707677602768	-0.12921802699565887	0.0039042015559971333	0.08228552341461182	-0.04347006231546402	0.1185966208577156	0.028048984706401825	-0.01934068463742733	0.025574659928679466	0.03749380260705948	0.015276514925062656	-0.037221431732177734	0.028497492894530296	0.031071919947862625	0.009740646928548813	-0.012248899787664413	0.08352386206388474	0.019209271296858788	-0.004628796596080065	0.04315349459648132	0.01933480054140091	0.017553910613059998	-0.020339632406830788	-0.042299576103687286	0.059380773454904556	-0.03325541317462921	-0.08925533294677734	-0.0057573989033699036	-0.07021458446979523	0.02757340669631958	0.031409185379743576	0.018749698996543884	0.011293342337012291	-0.02169903554022312	-0.041037287563085556	-0.052415765821933746	-0.040358930826187134	-0.050215546041727066	-0.061790645122528076	-0.0108345253393054	0.008037472143769264	-0.01824064739048481	-0.006375760771334171	-0.017050830647349358	-0.019205298274755478	-0.012629232369363308	0.0051614693365991116	0.030471911653876305	0.09351465106010437	0.06338221579790115	0.025749526917934418	0.013392867520451546	-0.04305802285671234	0.018121622502803802	-0.03676469251513481	-0.04256663843989372	-0.03851056098937988	0.037359900772571564	-0.07546666264533997	-0.023623453453183174	0.002895117737352848	0.0589250847697258	-0.03144121915102005	0.016605393961071968	-0.0489472933113575	-0.003763714572414756	-0.056751035153865814	0.04777192324399948	0.07311368733644485	-0.019576050341129303	-0.005602492485195398	-0.0009474113467149436	0.05675126239657402	-0.007114966865628958	-0.03075779415667057	-0.08209926635026932	0.03972609341144562	0.0032672120723873377	0.011371663771569729	0.013453690335154533	-0.06023489683866501	0.07345423847436905	0.017096474766731262	0.03414422273635864	-0.08298759907484055	-0.016506250947713852	0.019570881500840187	0.019312992691993713	0.056048501282930374	0.0623135082423687	-0.022442549467086792	-0.047740548849105835	0.027023447677493095	0.045476265251636505	-0.04308854416012764	-0.008748062886297703	-0.019436411559581757	-0.08560039103031158	0.0017952332273125648	0.0008531890343874693	-0.08270596712827682	-0.031698815524578094	0.005571666173636913	0.005085342563688755	-0.0037427544593811035	0.024894822388887405	0.051904212683439255	-0.00788402184844017	-0.032858990132808685	0.0024629742838442326	-0.022306188941001892	-0.06366930902004242	0.08784947544336319	-0.009584066458046436	-0.07665672898292542	-0.05747493728995323	0.004429981578141451	0.07976881414651871	0.012773248367011547	0.024324029684066772	0.008556884713470936	0.09927196055650711	0.020520485937595367	-0.0398474745452404	0.0016462597995996475	-0.029144734144210815	-0.05327419191598892	-0.010226448997855186	0.05358671396970749	-0.024511590600013733	-0.05967049300670624	0.005385281052440405	-0.064319908618927	0.03145712614059448	0.018335023894906044	0.054669756442308426	-0.03619542345404625	-0.0174727663397789	-0.030752969905734062	0.010408628731966019	-0.03302615135908127	-0.008992588147521019	0.020492587238550186	-0.029180143028497696	0.014196070842444897	-0.018636634573340416	-0.026472395285964012	0.03647690638899803	0.0010883754584938288	0.04738159850239754	-0.07354850322008133	0.044330205768346786	-0.023402340710163116	-0.08588772267103195	-0.021017035469412804	-0.053090352565050125	-0.03997613862156868	-0.016406835988163948	-0.04613166302442551	-0.08854273706674576	0.08664020895957947	-0.002940072678029537	-0.03718956187367439	-0.0455123595893383	-0.021489154547452927	-0.013481417670845985	-0.03309067338705063	-0.03476574644446373	0.003246353706344962	-0.05409183353185654	0.0065599060617387295	0.034891292452812195	-0.05874824523925781	-0.030482394620776176	0.0052638608030974865	0.03542632982134819	-0.03368522599339485	0.0828777626156807	-0.014185968786478043	-0.018917879089713097	-0.02276705577969551	-0.06992750614881516	0.024366451427340508	0.07471020519733429	0.07126696407794952	0.07646377384662628	0.00868445634841919	0.027160214260220528	0.09967947751283646	0.03158264607191086	-0.032835785299539566	-0.012028456665575504	-0.030525825917720795	-0.051264531910419464	0.038348183035850525	-0.016885774210095406	-0.0264276210218668	-0.007986069656908512	0.026004396378993988	0.0780363604426384	-0.015237689949572086	0.01668410189449787	-0.027356382459402084	0.0507848747074604	0.02901516482234001	0.029982849955558777	-0.026490947231650352	7.165626448113471e-05	-0.030856970697641373	-0.05552245303988457	-0.04629269242286682	0.012262219563126564	0.04734228551387787	0.00982640590518713	0.0493074432015419	0.005463501904159784	-0.11089159548282623	0.03802826255559921	0.003512275405228138	0.08250822871923447	-0.06872011721134186	0.00526626780629158	0.017235850915312767	0.02535775862634182	-0.08735387772321701	0.017327921465039253	0.01742546260356903	-0.028299076482653618	-0.051962725818157196	0.01663283072412014	0.04504578560590744	-0.023512279614806175	-0.002695007249712944	-0.026422999799251556	-0.04759540408849716	-0.026149200275540352	0.01744069531559944	-0.005153548903763294	0.035789694637060165	0.015939950942993164	-0.0008130103233270347	0.010103327222168446	0.059611521661281586	-0.004306751303374767	-0.01635931432247162	0.02404949814081192	-0.033758848905563354	0.05125401169061661	-0.04022245109081268	-0.058388851583004	-0.03699136897921562	0.07697305828332901	-0.012691776268184185	0.0019740271382033825	-0.03725067526102066	-0.056337982416152954	-0.009385605342686176	0.01768161542713642	-0.01288590021431446	-0.041571859270334244	0.012630465440452099	-0.029905492439866066	-0.10808281600475311	-0.10262812674045563	-0.050437137484550476	0.03688399866223335	-0.016421237960457802	-0.018797067925333977	-0.048768434673547745	-0.0049055530689656734	0.05431501939892769	-0.002001678803935647	0.03441733121871948	0.04318571463227272	0.06649930775165558	-0.03217767924070358	0.0308647770434618	0.0019566307310014963	0.10030186921358109	0.002186867641285062	-0.03705237805843353	0.01996470056474209	0.03174707666039467	-0.04173000529408455	0.04222455993294716	-0.010452057234942913	-0.002922592218965292	0.021627075970172882	-4.407543383422308e-05	-0.011221838183701038	-0.00984919536858797	-0.01831783913075924	-0.04944736883044243	0.06980674713850021	-0.07942665368318558	-0.0331350676715374	0.04076104238629341	-0.02954152785241604	-0.10618937760591507	-0.05445709452033043	-0.005731930490583181	-0.001832923968322575	0.058677393943071365	-0.03355042636394501	0.014499238692224026	0.031546834856271744	0.0006004645838402212	-0.02867165394127369	-0.00592848239466548	-0.03513573855161667	0.027563175186514854	0.01148719061166048	0.09854524582624435	0.01696135103702545	0.01884934864938259	-0.032220806926488876	-0.00438283896073699	0.005519302561879158	-0.09368610382080078	0.00037067316588945687	-0.015688830986618996	-0.037700898945331573	-0.009465906769037247	0.006860154215246439	-0.015657246112823486	-0.052893105894327164	-0.027935676276683807	-0.016707098111510277	0.09001278132200241	-0.02124844305217266	0.011853311210870743	0.015452860854566097	-0.025201937183737755	-0.02496591955423355	0.02424127236008644	-0.06068810820579529	-0.06932073086500168	0.04575911909341812	-0.02342533878982067	-0.005873866379261017	-0.004543349612504244	0.033946238458156586	-0.01812591776251793	-0.09413737058639526	-0.020750004798173904	-0.00640153931453824	-0.0611795112490654	0.03398555889725685	0.0367795005440712	-0.06294801086187363	-0.0340145044028759	-0.0011728095123544335	-0.06498783081769943	0.07226987928152084	0.007089091464877129	0.060254570096731186	-0.01356382854282856	-0.004998368676751852	-0.02086290717124939	-0.009931440465152264	0.0357513353228569	-0.05322747677564621	-0.09424413740634918	-0.021132903173565865	0.05828607827425003	-0.07614531368017197	0.017859255895018578	0.043580129742622375	-0.03321274742484093	0.059318434447050095	0.02133731171488762	-0.01597866788506508	0.04705845192074776	0.01655604876577854	0.03094780631363392	0.04526378959417343	-0.07045163959264755	0.08694379031658173	-0.06424979865550995	0.05045749247074127	0.01207028515636921	0.023618366569280624	0.07934930175542831	0.10469339787960052	0.0021566725336015224	-0.007092159241437912	-0.03527699410915375
+0.02835571952164173	0.01047481782734394	0.015852205455303192	0.05113505944609642	-0.06820625811815262	-0.036698561161756516	0.00029199771233834326	0.01607831008732319	0.026058891788125038	-0.050312504172325134	-0.004326630849391222	0.006673856172710657	-0.023353401571512222	0.016097115352749825	0.01358956377953291	-0.027437129989266396	-0.055530451238155365	-0.009071656502783298	-0.03817439079284668	-0.06426393985748291	0.02961578220129013	0.013071504421532154	-0.08346505463123322	0.054189037531614304	-0.006003131624311209	0.00025400894810445607	-0.06068652123212814	0.004173749592155218	-0.03181213513016701	0.02727111056447029	0.0706377774477005	-0.01847478747367859	0.07939731329679489	-0.0552220344543457	-0.06773139536380768	0.05039084702730179	-0.004219407215714455	0.0908544585108757	0.0565156452357769	-0.03269856795668602	-0.04294018819928169	-0.0032540280371904373	-0.020712411031126976	-0.05096723884344101	0.011069286614656448	-0.0036951012443751097	-0.015892906114459038	-0.04193531349301338	0.06885471940040588	0.01786351576447487	0.08876635134220123	0.011282742954790592	-0.028291484341025352	-0.035102106630802155	-0.0029225151520222425	-0.0016850352985784411	0.0480632558465004	-0.10070875287055969	-0.07454727590084076	0.007184550166130066	-0.11707785725593567	0.020012812688946724	-0.005959609989076853	0.017169861122965813	-0.0111527806147933	-0.023991283029317856	-0.02570381760597229	0.08956205099821091	0.03691831976175308	-0.04130825400352478	0.03430015966296196	0.08746127784252167	-0.06296253204345703	-0.039360642433166504	0.0521576851606369	-0.04769354313611984	-0.00880818348377943	0.03927486017346382	-0.036841340363025665	0.03219612315297127	-0.012819463387131691	-0.05868726596236229	0.09394276887178421	-0.03253692016005516	0.0066926805302500725	-0.027010323479771614	-0.016299210488796234	-0.08098014444112778	0.05887772515416145	0.00807278323918581	0.0036942139267921448	0.07386601716279984	-0.05207681655883789	0.05968646705150604	0.017812006175518036	0.030634939670562744	0.015649979934096336	-0.1096506416797638	-0.008081050589680672	0.01726178638637066	-0.010376689955592155	0.028371956199407578	0.04558415338397026	-0.018334364518523216	0.07411548495292664	0.028619278222322464	-0.0577535405755043	-0.0009245340479537845	0.05721478909254074	0.030484268441796303	0.07512463629245758	0.08035681396722794	0.06811284273862839	0.0499483123421669	0.021394066512584686	0.003164628054946661	0.01591446064412594	-0.06582783907651901	-0.011774911545217037	0.018006617203354836	-0.014830254018306732	0.051569826900959015	-0.032009340822696686	-0.05295678228139877	-0.02983851544559002	0.027027133852243423	-0.06285273283720016	-0.04418240115046501	0.07301808148622513	-0.025531938299536705	0.10941456258296967	0.07718782126903534	-0.010574109852313995	0.04586566612124443	0.055943287909030914	0.03924575820565224	-0.01586519367992878	-0.010785013437271118	0.0008636683342047036	-0.03811714053153992	-0.02666940726339817	0.06922432780265808	-0.013077857904136181	-0.05798117816448212	0.03704486042261124	0.026810429990291595	0.0058961291797459126	-0.02208813466131687	-0.05299864336848259	0.0005669905804097652	-0.06680872291326523	-0.07815191894769669	0.022331221029162407	-0.07272395491600037	0.02859657257795334	0.10545489192008972	0.03208623826503754	0.04793033376336098	-0.0032318714074790478	-0.020899716764688492	-0.03580216318368912	-0.028071532025933266	-0.009520815685391426	-0.07309603691101074	-0.04250285401940346	-0.027401141822338104	0.02071782387793064	0.06417105346918106	-0.05199897661805153	-0.018329322338104248	-0.01832970231771469	-0.0012719695223495364	0.012988012284040451	0.08790722489356995	0.05944738909602165	0.02413828670978546	-0.004334644880145788	-0.054518889635801315	0.026185881346464157	-0.024357765913009644	0.03867826238274574	-0.07345257699489594	0.026004990562796593	-0.05320505052804947	-0.027209529653191566	-0.01678585074841976	0.02554251067340374	0.009946546517312527	0.019165512174367905	0.03430349752306938	-0.07011985778808594	-0.05960138887166977	0.006147101521492004	0.05659977346658707	-0.02385067008435726	0.03144615516066551	0.007433386519551277	0.06834641098976135	0.013933300040662289	-0.016956090927124023	-0.06633224338293076	0.019722918048501015	0.038133326917886734	0.045232634991407394	0.0425502210855484	-0.05481095239520073	0.07011187076568604	0.013292916119098663	0.02825508825480938	-0.05444937199354172	0.007153888698667288	0.028885001316666603	0.049699727445840836	0.028942912817001343	0.06416435539722443	-0.036912549287080765	-0.008207057602703571	-0.025877948850393295	0.06306014955043793	-0.09263619035482407	0.017198415473103523	-0.012367566116154194	-0.034587312489748	0.048420291393995285	-0.03589223325252533	-0.11086622625589371	-0.03069116920232773	0.0034337867982685566	0.006323883309960365	-0.04295859858393669	0.004341762978583574	0.026157842949032784	-0.02720821648836136	-0.0004212191270198673	0.005537050310522318	-0.05090901628136635	-0.07139210402965546	0.11287923902273178	0.00010176654905080795	-0.04259011894464493	-0.027768654748797417	-0.0013699684059247375	0.0419277660548687	-0.0018940252484753728	0.01354142650961876	0.039605140686035156	0.1088709607720375	0.03306691348552704	-0.03639089688658714	-0.009575880132615566	-0.05007030442357063	-0.08058886975049973	0.03574651479721069	0.03910328447818756	-0.038053203374147415	-0.05167873203754425	0.004959057550877333	-0.07464577257633209	0.01976831629872322	-0.029402511194348335	0.017291095107793808	0.024429962038993835	-0.008860197849571705	-0.0390470027923584	0.057149942964315414	-0.0017379571218043566	-0.05884026736021042	0.018389636650681496	-0.021233918145298958	0.02905845455825329	-0.030266117304563522	-0.019884711131453514	0.037742894142866135	-0.0019076879834756255	0.03725680708885193	-0.010070965625345707	0.04929841682314873	-0.016101446002721786	-0.08389836549758911	-0.03148311376571655	-0.067754365503788	-0.03631077706813812	0.0031235883943736553	-0.0029707136563956738	-0.04432987421751022	0.06996302306652069	-0.000248305092100054	-0.03870508819818497	-0.014694424346089363	0.01313575729727745	-0.04473569989204407	-0.07828452438116074	-0.030432485044002533	-0.007926754653453827	-0.04966883733868599	-0.01940811239182949	0.011721651069819927	-0.0671006441116333	-0.019687743857502937	0.013507185503840446	-0.04995445907115936	-0.042022235691547394	0.024963490664958954	-0.030330028384923935	-0.04044302925467491	0.006142294965684414	-0.08296342194080353	0.03203639015555382	0.10867483913898468	0.01123696006834507	0.04140159487724304	0.03812173381447792	0.054411835968494415	0.07785345613956451	0.01815170794725418	-0.043607573956251144	0.02760794572532177	-0.012082405388355255	-0.062136147171258926	0.05279789865016937	-0.025192510336637497	0.0038211066275835037	0.007806565146893263	-0.009962103329598904	0.07971039414405823	0.002582163317129016	-0.015444543212652206	-0.010580378584563732	0.04694242402911186	-0.027410825714468956	0.02429298311471939	0.012296481989324093	-0.007139535620808601	0.029527515172958374	-0.07941938191652298	-0.03255714848637581	-0.05277799442410469	0.014467629604041576	0.009204860776662827	0.043037064373493195	0.00013395046698860824	-0.08075197786092758	0.001574514084495604	-0.005525641608983278	0.09586136788129807	-0.08750613033771515	0.03382338583469391	-0.008415499702095985	-0.001281562726944685	-0.1326519399881363	-0.031170133501291275	0.04031117260456085	-0.03119490295648575	-0.05905485153198242	0.06144411489367485	0.02976130321621895	-0.0792585015296936	-0.01861777901649475	-0.008439278230071068	-0.04741205275058746	-0.01906725764274597	0.007658148650079966	0.028457826003432274	0.026450462639331818	0.019445979967713356	-0.016592511907219887	-0.030938727781176567	0.0011917056981474161	0.018361110240221024	-0.001295894500799477	0.03415573015809059	-0.0668007954955101	0.060501791536808014	-0.04760441556572914	-0.04066648706793785	-0.06953447312116623	0.053295452147722244	0.010452576912939548	-0.015046079643070698	-0.00012983367196284235	-0.031884707510471344	-0.02299167402088642	0.001655604806728661	0.035688843578100204	-0.07748717069625854	0.011438153684139252	-0.032291706651449203	-0.010876884683966637	-0.041736673563718796	-0.030172904953360558	-0.010534577071666718	-0.057598914951086044	0.03023955039680004	-0.009530994109809399	-0.0129372738301754	0.02023858204483986	-0.020916927605867386	0.011726419441401958	0.03742138668894768	0.008351224474608898	0.005185224115848541	0.0030543541070073843	-0.009813081473112106	0.024245521053671837	0.0416165255010128	-0.044678956270217896	0.008336678147315979	0.048824530094861984	-0.05996290221810341	0.02419566921889782	-0.02555643394589424	-0.014492915011942387	0.006036621052771807	-0.021923869848251343	0.020123140886425972	0.002669408218935132	0.006578014697879553	-0.04252276197075844	0.05225568637251854	-0.04351050406694412	-0.03970953822135925	0.021470405161380768	-0.03876486420631409	-0.11671507358551025	-0.029469864442944527	-0.005353233776986599	-0.0687890350818634	0.07128024101257324	0.01450416911393404	0.0433560311794281	0.029131675139069557	-0.044068560004234314	-0.06162058934569359	0.007276564836502075	-0.0007915716269053519	-0.009549260139465332	-0.014333637431263924	0.05354563519358635	-0.00630372017621994	-0.009522031992673874	0.03230014815926552	0.001187139772810042	0.026837192475795746	-0.0920790359377861	-0.01801677979528904	-0.015986325219273567	-0.02214932255446911	0.015227786265313625	0.01399829052388668	0.018909959122538567	-0.08199597895145416	-0.04082435369491577	-0.040709398686885834	0.07033909112215042	-0.05064170062541962	-0.001329260179772973	0.02264159917831421	-0.03307204693555832	0.001279716962017119	0.036397408694028854	-0.013076121918857098	-0.07165064662694931	-0.0007060147472657263	-0.05883024260401726	0.0010894094593822956	-0.0106441555544734	0.027734674513339996	-0.023511016741394997	-0.06908661872148514	-0.023627087473869324	0.0088798301294446	-0.07178770750761032	-0.008815622888505459	-0.004783693701028824	-0.06829437613487244	-0.09185715764760971	-0.021506663411855698	-0.06766907870769501	0.0727684274315834	0.005670784041285515	0.025211738422513008	-0.0035436146426945925	-0.02145564742386341	-0.02069850265979767	-0.06526892632246017	0.0020205220207571983	0.015760550275444984	-0.04435201361775398	-0.04678236320614815	0.07232934981584549	0.009530923329293728	0.028160028159618378	0.07904971390962601	-0.022082149982452393	0.03997252881526947	0.0450412854552269	-0.050802960991859436	0.034315451979637146	0.00519158598035574	-0.01760099083185196	0.07136086374521255	-0.044151920825242996	0.0898292288184166	-0.03419032692909241	0.10456803441047668	0.007086149882525206	-0.004674724768847227	0.07807285338640213	0.1025264635682106	-0.018186017870903015	0.011744971387088299	-0.043918635696172714
+0.028440212830901146	-0.021824466064572334	0.0005659805028699338	0.0480647087097168	-0.04625632241368294	-0.026807120069861412	-0.01559600979089737	-0.002952723065391183	-0.005153239239007235	-0.0325293242931366	0.012271714396774769	-0.014897461980581284	-0.024278366938233376	-0.020023319870233536	-0.025966528803110123	-0.015835216268897057	-0.059979941695928574	0.03518405184149742	-0.0319606252014637	-0.03881433606147766	0.01598784141242504	0.04963444545865059	-0.07178983837366104	0.022322678938508034	-0.022907929494976997	-0.000483450829051435	-0.05370163172483444	-0.005831393878906965	-0.004850267898291349	0.054748211055994034	0.011355554684996605	-0.0031770183704793453	0.09344898164272308	-0.0552961491048336	-0.030109237879514694	0.08259246498346329	0.019386811181902885	0.08163783699274063	0.057290829718112946	-0.0018026857869699597	-0.07792479544878006	0.02449779026210308	-0.018026070669293404	-0.09457029402256012	-0.001112743397243321	0.026765922084450722	-0.036801233887672424	-0.02440962754189968	0.05534506216645241	0.0630105659365654	0.08696427196264267	-0.030221672728657722	-0.03959895297884941	0.01650753803551197	0.010363029316067696	-0.006324856076389551	0.062297675758600235	-0.011432778090238571	-0.03971751779317856	0.009991632774472237	-0.04638107493519783	-0.0022544239182025194	-0.002347805304452777	-0.027126723900437355	-0.02543596737086773	-0.013737618923187256	0.014191030524671078	0.09118076413869858	0.029529396444559097	-0.010278506204485893	0.010807903483510017	0.08551905304193497	-0.045042894780635834	-0.02387937903404236	0.06587795913219452	-0.08294876664876938	-0.02916904166340828	0.09936177730560303	-0.031522106379270554	0.016995016485452652	-0.022660110145807266	-0.05276239290833473	0.14545145630836487	-0.06904894858598709	0.0002591257216408849	-0.03220289945602417	0.007792085409164429	-0.0581609345972538	0.025958359241485596	0.02287525311112404	0.019976209849119186	0.011913331225514412	-0.03880881518125534	0.04197809472680092	-0.009563306346535683	0.01263325847685337	0.09054995328187943	-0.0797043889760971	-0.047755610197782516	0.06858605146408081	-0.020298374816775322	-0.008421983569860458	0.052137430757284164	-0.02261747047305107	0.05511786416172981	-0.03275890648365021	-0.02326945587992668	-0.02013205923140049	0.051573675125837326	0.006249952130019665	0.06401611864566803	0.0859224796295166	0.073684923350811	-0.010291828773915768	0.05168130621314049	0.01611229032278061	0.06324543803930283	-0.06319676339626312	0.03641008585691452	0.028056439012289047	-0.02359585464000702	0.041239768266677856	-0.05825839191675186	-0.0353989340364933	-0.005226519424468279	0.016320284456014633	-0.07626768946647644	-0.04094894602894783	0.036886490881443024	-0.006577143911272287	0.10319480299949646	0.030730053782463074	-0.04342903569340706	0.038254816085100174	0.013035718351602554	0.011908024549484253	-0.005189014598727226	0.053932685405015945	0.027830583974719048	-0.013939760625362396	0.011681578122079372	0.049613021314144135	-0.023720404133200645	-0.019249649718403816	0.03462015092372894	0.040820058435201645	0.0019976929761469364	-0.03586346656084061	-0.059551507234573364	0.016745254397392273	-0.023899279534816742	-0.08383525162935257	-0.03553534671664238	-0.06475012004375458	0.036572981625795364	0.05728783458471298	0.0018554971320554614	0.08602981269359589	-0.0316905751824379	-0.013799525797367096	-0.07212097942829132	-0.045288585126399994	-0.054438360035419464	-0.06199843809008598	-0.03153535723686218	0.00069718953454867	0.034690942615270615	0.0012534719426184893	-0.045525722205638885	0.010393493808805943	-0.0246624443680048	-0.027348045259714127	0.0283811055123806	0.06042994558811188	0.10570373386144638	0.013693547807633877	0.03383985161781311	-0.058811962604522705	-0.011800172738730907	-0.056563474237918854	0.04261139780282974	-0.06976964324712753	0.025831906124949455	-0.029394257813692093	0.0035649784840643406	0.026230864226818085	0.05261870473623276	-0.02579112909734249	0.005267815664410591	0.04580455645918846	-0.05137358233332634	-0.021579938009381294	0.032865967601537704	0.06249804422259331	-0.009972131811082363	0.017464246600866318	-0.043305397033691406	0.11418494582176208	0.004987421445548534	-0.014576883055269718	-0.09056615829467773	0.046246882528066635	0.007537506520748138	0.04050949215888977	0.04321364685893059	-0.05634263530373573	0.04096481576561928	0.013910707086324692	0.019134046509861946	-0.049432773143053055	0.010542958043515682	-0.04664294421672821	0.050210874527692795	0.03017430007457733	0.04937247186899185	0.009229286573827267	-0.0038941223174333572	-0.01370400469750166	0.04534735530614853	-0.03097144141793251	0.00929231010377407	-0.025933243334293365	-0.05422041565179825	-0.008665302768349648	-0.0582546703517437	-0.09320299327373505	-0.0569579154253006	0.02309618704020977	0.021936772391200066	0.002736624563112855	0.03728162869811058	0.06909438967704773	-0.06784136593341827	-0.024814331904053688	0.03275991603732109	-0.044247105717659	-0.06995346397161484	0.058736544102430344	-0.005469754803925753	-0.039795149117708206	-0.032727669924497604	0.022132163867354393	0.042913567274808884	0.022509433329105377	0.03780454024672508	0.02275710739195347	0.05405421555042267	0.010228807106614113	-0.005843640770763159	-0.03412030637264252	-0.019427891820669174	-0.05134681612253189	-0.007742741145193577	0.0013781915185973048	-0.0210738368332386	-0.0659981518983841	-0.02103741094470024	-0.07727767527103424	0.03598203510046005	0.019480984658002853	-0.013701038435101509	0.003145730821415782	-0.017102431505918503	-0.013895616866648197	-0.004996612202376127	-0.04876210540533066	-0.01507770735770464	0.03789094462990761	-0.04119433835148811	-0.009616917930543423	-0.011023953557014465	0.028374390676617622	0.008466868661344051	0.008428881876170635	0.025187784805893898	-0.025122391059994698	0.07110282778739929	-0.005572002846747637	-0.08942817896604538	-0.04501067474484444	-0.0371960811316967	0.009699233807623386	-0.03329955041408539	-0.010744649916887283	-0.03454701974987984	0.011459280736744404	0.0332539901137352	-0.0486147403717041	-0.0657908022403717	-0.03228284791111946	-0.08765145391225815	-0.08492210507392883	-0.05214415863156319	0.003974140156060457	-0.030347805470228195	-0.026873018592596054	0.00920590478926897	0.006378150079399347	-0.030828392133116722	0.007501242682337761	0.017599817365407944	-0.029398348182439804	0.03404099866747856	0.047539517283439636	-0.07057037949562073	-0.008910014294087887	-0.04907659441232681	0.025357671082019806	0.0854254737496376	0.033969201147556305	0.049253806471824646	0.03607449680566788	0.05036543309688568	0.057044897228479385	0.03528263419866562	-0.02075195126235485	-0.0054017906077206135	-0.04621550813317299	-0.05388950556516647	0.043752532452344894	0.0055460091680288315	0.021180206909775734	-0.006671462208032608	0.012308591045439243	0.0331982858479023	0.017203811556100845	-0.04426518827676773	-0.048499178141355515	0.0337674617767334	-0.035187676548957825	0.02044321410357952	-0.030033865943551064	0.03941380977630615	-0.002568235620856285	-0.05776141583919525	-0.007693593390285969	-0.04962960258126259	0.004242949187755585	-0.01380089670419693	0.03968769311904907	0.02535398304462433	-0.0211965162307024	-0.01650421880185604	-0.028125440701842308	0.1072901114821434	-0.062422629445791245	-0.04320088401436806	0.0027504186145961285	0.05648919567465782	-0.13566818833351135	-0.01628277264535427	0.028537873178720474	-0.053176600486040115	-0.007627355400472879	-0.012415156699717045	4.915232057101093e-05	-0.026328111067414284	0.005014793481677771	-0.049787621945142746	-0.008635974489152431	-0.0373278371989727	0.01008414663374424	0.0020881888922303915	0.03276100382208824	0.0054143923334777355	-0.030769238248467445	0.0069298953749239445	0.005561713129281998	-0.04340207204222679	-0.0012959165032953024	-0.007341963239014149	-0.03338952735066414	-0.038447022438049316	-0.05230820178985596	-0.051591090857982635	-0.06981457024812698	0.03695252910256386	-0.030014611780643463	-0.03794654831290245	-0.010756690055131912	-0.016089484095573425	-0.026145312935113907	-0.008957727812230587	0.031641311943531036	-0.016640679910779	-0.01816067099571228	-0.07179601490497589	-0.055759068578481674	-0.08438006788492203	0.029561618342995644	0.022210542112588882	0.00950782559812069	0.020545948296785355	-0.0519026517868042	-0.031168822199106216	0.03263888880610466	0.06874778866767883	0.0024602788034826517	0.08336953073740005	0.028177212923765182	-0.0027206195518374443	0.03156038001179695	-0.0242691021412611	0.0417325533926487	0.07193117588758469	-0.07444320619106293	-0.05030640959739685	-0.005647465121001005	-0.0367952324450016	0.042833153158426285	-0.010975778102874756	-0.05722790211439133	-0.007946229539811611	-0.013716913759708405	-0.017624767497181892	-0.02800895646214485	0.022944292053580284	-0.00720120407640934	0.04351898655295372	-0.04012983292341232	-0.029174325987696648	0.017170727252960205	-0.018662452697753906	-0.11682258546352386	-0.04629254713654518	0.02932438999414444	0.009690595790743828	0.03239245340228081	-0.060382310301065445	0.06538919359445572	0.06377391517162323	0.00033422300475649536	-0.09796373546123505	-0.01811932772397995	-0.00653522415086627	0.02354750782251358	0.011511744931340218	0.04398169741034508	0.004263238050043583	-0.007212681230157614	0.01379754301160574	0.005778142251074314	-0.0004593539924826473	-0.07177544385194778	-0.014381366781890392	-0.03585374355316162	-0.047377604991197586	0.05280574411153793	-0.010763872414827347	-0.05299360677599907	0.007264384999871254	-0.040118418633937836	-0.039808228611946106	0.07910356670618057	0.0110502103343606	-0.0048280078917741776	0.03767886012792587	-0.04476005584001541	0.019126811996102333	0.06535720080137253	-0.0343017503619194	-0.045955583453178406	0.014379072934389114	-0.03243992105126381	0.01952948234975338	0.02216130681335926	0.0584682933986187	-0.040437936782836914	-0.07120777666568756	-0.0445348359644413	1.1516286576807033e-05	-0.09210850298404694	0.00672603864222765	-0.0024013258516788483	-0.07266426086425781	-0.06454011052846909	0.012253114022314548	-0.10353612154722214	0.06009616330265999	0.03160122409462929	0.06866604834794998	0.03467319905757904	0.011430568061769009	-0.02904421091079712	-0.025588661432266235	0.007208806928247213	0.011403067037463188	-0.025740623474121094	-0.04975097253918648	0.0846736803650856	0.007336883805692196	0.053834468126297	0.06556157022714615	-0.015269930474460125	0.0868588238954544	0.07955333590507507	-0.04007784277200699	0.09036917239427567	0.006453555542975664	-0.04432429373264313	0.05002434179186821	-0.021041639149188995	0.14228174090385437	-0.05758792906999588	0.13754943013191223	0.0001761441380949691	-0.0030093141831457615	0.06820033490657806	0.08431567996740341	-0.011923449113965034	0.009749372489750385	-0.024927152320742607
+0.01674993894994259	-0.07807806879281998	0.003571521257981658	0.05214259400963783	-0.07360649853944778	-0.0003524578642100096	-0.0060873981565237045	0.025613389909267426	-0.005950626917183399	-0.054046280682086945	-0.011446192860603333	0.032967206090688705	-0.004717279225587845	0.02461456134915352	0.026033297181129456	-0.029037291184067726	-0.05065567046403885	0.025178659707307816	-0.06350894272327423	-0.03995351493358612	0.02194664254784584	0.025363462045788765	-0.03887682780623436	0.02293981797993183	-0.04728534445166588	0.01071009412407875	-0.05245736241340637	0.018155325204133987	0.0026362386997789145	0.02812524326145649	0.0744013637304306	-0.028963685035705566	0.02992563135921955	-0.04555324465036392	-0.0557040236890316	0.08209773898124695	-0.020539216697216034	0.10292332619428635	-0.00024697190383449197	0.021498478949069977	-0.008398858830332756	-0.017684027552604675	-0.02941618487238884	-0.06529808044433594	0.06214933097362518	-0.004327195696532726	0.04113338142633438	-0.022557910531759262	0.02771661803126335	0.010030010715126991	0.08436764031648636	0.040629319846630096	0.0020918429363518953	-0.005773817654699087	-0.013482225127518177	0.01064915768802166	0.031241316348314285	-0.035614047199487686	-0.01255526952445507	0.046172209084033966	-0.0714106634259224	-0.035252053290605545	0.027959473431110382	0.004588036797940731	-0.021116500720381737	0.026336144655942917	0.0004999077063985169	0.08907774835824966	-0.0010001860791817307	-0.04070442542433739	0.005304432939738035	0.08466101437807083	-0.024532027542591095	-0.06195434555411339	0.05751556530594826	-0.06478696316480637	-0.014649608172476292	0.01855027675628662	-0.04276995733380318	-0.026140566915273666	-0.017547544091939926	-0.027397271245718002	0.13402962684631348	-0.031100044026970863	-0.060389403253793716	-0.03166770190000534	0.008962499909102917	-0.06068965047597885	0.06045861542224884	0.016456495970487595	0.03589387238025665	0.06950390338897705	-0.056047551333904266	0.036953747272491455	-0.004705929663032293	0.007870221510529518	0.027834679931402206	-0.09377691149711609	-0.04349889978766441	-0.012536558322608471	0.03833800554275513	-0.030245451256632805	0.08174211531877518	-0.01930982619524002	0.05861947685480118	0.02875763550400734	-0.03988541662693024	-0.036680079996585846	-0.008163414895534515	0.02050134167075157	0.021026691421866417	0.1447487771511078	0.03383905440568924	0.057023074477910995	0.09746469557285309	-0.019155019894242287	-0.004311850760132074	-0.0700814500451088	-0.023517053574323654	-0.01509202178567648	-0.016410566866397858	0.052664484828710556	-0.025005066767334938	-0.08588699996471405	-0.033363863825798035	0.008789210580289364	-0.13549357652664185	0.01002164650708437	0.09529227763414383	-0.04297249764204025	0.11386048048734665	0.03194885328412056	-0.023075375705957413	0.04726158827543259	0.03212585672736168	0.009324304759502411	-0.04629233106970787	0.028939859941601753	0.02809106558561325	-0.013137869536876678	-0.00016520541976206005	0.08350193500518799	0.03275448828935623	-0.0005926824524067342	0.041531845927238464	0.03287731856107712	0.015960339456796646	-0.013486329466104507	-0.04689113050699234	0.053570233285427094	-0.04092640057206154	-0.07280483096837997	-0.015557502396404743	-0.07489494234323502	0.015628891065716743	0.023203479126095772	0.019119830802083015	0.007188367657363415	-0.026139454916119576	-0.03532189875841141	-0.06032474711537361	-0.05143275484442711	-0.03499120473861694	-0.055784013122320175	-0.008370359428226948	0.020043697208166122	-0.016229556873440742	-0.01889171078801155	-0.025371141731739044	-0.03419950231909752	-0.00999141950160265	-0.01635965146124363	0.036032967269420624	0.08039072901010513	0.06235485151410103	0.014378917403519154	0.009190320037305355	-0.055969640612602234	0.010468279011547565	-0.031981512904167175	-0.019348710775375366	-0.040312301367521286	0.02933170273900032	-0.08277356624603271	-0.04127158597111702	-0.0027072415687143803	0.032816700637340546	-0.031478848308324814	0.007439993787556887	-0.049181416630744934	-0.004825080279260874	-0.047071073204278946	0.03626953437924385	0.08305346220731735	-0.03381984308362007	0.010302460752427578	0.001130877062678337	0.06796324998140335	-0.025226332247257233	-0.04000338912010193	-0.08926952630281448	0.03623788803815842	0.005989365745335817	0.021310513839125633	0.009926030412316322	-0.07546313852071762	0.08432584255933762	0.025091033428907394	0.04581194743514061	-0.08559151738882065	-0.004649632144719362	0.030292866751551628	0.016883401200175285	0.07038906961679459	0.057079728692770004	-0.03852967545390129	-0.04080613702535629	0.018334440886974335	0.037727080285549164	-0.0356447771191597	-0.014961629174649715	-0.02081741765141487	-0.0756133422255516	-0.021354543045163155	-0.013407116755843163	-0.07132992893457413	-0.043491557240486145	0.012795940041542053	0.0061494274996221066	-0.010329402051866055	0.01284205075353384	0.05137082189321518	-0.013777777552604675	-0.01585087925195694	0.009899993427097797	-0.029363976791501045	-0.05961659550666809	0.08493474125862122	-0.015323998406529427	-0.07901319861412048	-0.05868672952055931	0.008536596782505512	0.08424985408782959	0.02133822999894619	0.01904330775141716	0.00988516490906477	0.09061779081821442	0.020187484100461006	-0.04720989242196083	-0.015067944303154945	-0.016468148678541183	-0.04200684279203415	-0.01958419941365719	0.028932135552167892	-0.020006805658340454	-0.058941762894392014	-0.0011142962612211704	-0.0833057314157486	0.012901155278086662	0.019395500421524048	0.05842718854546547	-0.039485376328229904	-0.030538035556674004	-0.019623294472694397	-0.002885272726416588	-0.023890458047389984	0.0005433118203654885	0.0056528858840465546	-0.034136123955249786	0.021985096856951714	-0.02280622348189354	-0.0014428402064368129	0.022313017398118973	-0.005502188578248024	0.03761567547917366	-0.08298713713884354	0.047654520720243454	-0.02656424418091774	-0.06712596118450165	-0.017935296520590782	-0.049982450902462006	-0.052445538341999054	-0.017381535843014717	-0.040584586560726166	-0.060861535370349884	0.09018567949533463	-0.005324623081833124	-0.03943413868546486	-0.029175788164138794	-0.02889413572847843	-0.0006851683720014989	-0.007876593619585037	-0.004578618798404932	-0.011968852952122688	-0.046841248869895935	0.017493318766355515	0.02662704512476921	-0.04891323670744896	-0.016784198582172394	0.011138241738080978	0.03362894803285599	-0.03110126033425331	0.0671548843383789	-0.011598018929362297	-0.020387230440974236	-0.034119416028261185	-0.06064031645655632	0.035322386771440506	0.08655549585819244	0.07349412888288498	0.06645414233207703	0.008364252746105194	0.03160938620567322	0.09184364974498749	0.015864089131355286	-0.021201075986027718	-0.0017305229557678103	-0.035885993391275406	-0.04545051231980324	0.061179887503385544	-0.016516072675585747	-0.010541224852204323	-0.006616145838052034	0.01132588554173708	0.08108663558959961	-0.027149075642228127	0.0030224532820284367	-0.023674774914979935	0.04912228137254715	0.03850672021508217	0.02287227101624012	-0.026048801839351654	0.011184895411133766	-0.03784225508570671	-0.052076555788517	-0.05713281407952309	-0.008840575814247131	0.05496254935860634	0.0010511785512790084	0.048894964158535004	-0.015701226890087128	-0.11272836476564407	0.022070718929171562	0.01055966317653656	0.09666788578033447	-0.05833316221833229	-0.008862837217748165	0.014368075877428055	0.03395521640777588	-0.09600042551755905	0.025928523391485214	-0.015268484130501747	-0.03214605152606964	-0.05370209366083145	0.011345124803483486	0.04963783174753189	-0.041635289788246155	-0.004956756718456745	-0.04510754346847534	-0.018017800524830818	-0.01678612269461155	0.02417905256152153	-0.005336992908269167	0.049453672021627426	0.024540772661566734	-0.028566323220729828	0.006728451699018478	0.0641130581498146	8.149624773068354e-05	-0.0390099436044693	0.03491658344864845	-0.028570502996444702	0.04121057316660881	-0.03564091771841049	-0.060992948710918427	-0.04518558830022812	0.06510544568300247	-0.022058002650737762	-0.002184848301112652	-0.043037619441747665	-0.0652410089969635	-0.004080701153725386	0.007460637018084526	-0.015100831165909767	-0.05174601078033447	0.006024261470884085	-0.03533367067575455	-0.10365337133407593	-0.10619712620973587	-0.055617835372686386	0.04147779569029808	-0.015163094736635685	0.005955058615654707	-0.04051157832145691	-0.008085159584879875	0.03229496255517006	-0.0014230977976694703	0.0322335846722126	0.04692433401942253	0.05873715505003929	-0.017700182273983955	0.050671592354774475	0.011404342018067837	0.07924865186214447	0.001604126300662756	-0.04253978282213211	0.02856631763279438	0.011273398995399475	-0.049995727837085724	0.05740906298160553	-0.026104720309376717	-0.012023803777992725	0.012901240959763527	-0.0005258318851701915	0.0012277489295229316	-0.006125904619693756	-0.02486983686685562	-0.05777086690068245	0.06108390539884567	-0.08460594713687897	-0.03348207101225853	0.04151498153805733	-0.022821614518761635	-0.10729789733886719	-0.0717797726392746	-0.02171083725988865	-0.008632785640656948	0.05143451690673828	-0.025515655055642128	0.001189649454317987	0.033013489097356796	0.01192151103168726	-0.012727185152471066	-0.00277691544033587	-0.032711680978536606	0.016944726929068565	0.019078155979514122	0.09562838822603226	0.018684346228837967	0.029319968074560165	-0.009390240535140038	-0.006625698413699865	0.018867632374167442	-0.08479290455579758	-0.0027782830875366926	-0.00986490398645401	-0.024044668301939964	-0.025502806529402733	0.011662856675684452	0.00655327970162034	-0.06677758693695068	-0.021214662119746208	-0.03958965465426445	0.1040748655796051	-0.008917509578168392	0.04059015214443207	0.011664682999253273	-0.04987436160445213	-0.02630542404949665	0.01771281287074089	-0.05806911736726761	-0.09306469559669495	0.04601563885807991	-0.03241663798689842	-0.011490224860608578	-0.03801672160625458	0.027829818427562714	0.0018891762010753155	-0.10274606943130493	0.0028851411771029234	-0.002959177363663912	-0.04899156466126442	0.043755482882261276	0.04634983465075493	-0.05134267359972	-0.02936408296227455	0.012359959073364735	-0.06649240851402283	0.06648646295070648	0.009359065443277359	0.05285906046628952	-0.013427670113742352	-0.014584432356059551	-0.021732177585363388	-0.006625144276767969	0.0351654589176178	-0.026100927963852882	-0.08381301909685135	-0.01769261620938778	0.06146375834941864	-0.07431217283010483	0.028107762336730957	0.0611022524535656	-0.029039165005087852	0.042825691401958466	0.043129656463861465	-0.014004496857523918	0.052716802805662155	0.022675396874547005	0.03287516161799431	0.040453437715768814	-0.05391785874962807	0.07685039192438126	-0.0658331885933876	0.06766392290592194	0.01482535619288683	0.025964723899960518	0.06579266488552094	0.07816849648952484	0.02413671463727951	0.011845945380628109	-0.033688727766275406
+0.04412132129073143	-0.045821305364370346	0.019640304148197174	0.06079516559839249	-0.06398286670446396	-0.05185166746377945	-0.030105549842119217	0.03868525102734566	0.008569428697228432	0.0105955945327878	-0.0389665812253952	0.007740893866866827	-0.0020390732679516077	0.016098791733384132	0.04892578721046448	-0.01631239429116249	-0.08533031493425369	-0.012364654801785946	-0.04160464555025101	-0.024900322780013084	0.058750323951244354	-0.008068765513598919	-0.06416376680135727	0.061451807618141174	-0.024412427097558975	0.06621421873569489	-0.0491088330745697	0.03137383237481117	-0.020011816173791885	-0.028708888217806816	0.020808424800634384	-0.008707590401172638	0.03772540017962456	-0.08738268166780472	-0.05889664962887764	0.03658628091216087	-0.025002699345350266	0.09725917130708694	-0.01089110504835844	-0.04097607359290123	-0.04898931831121445	0.06125184893608093	-0.031554579734802246	-0.05999941751360893	0.04826664179563522	0.028639888390898705	0.05624346062541008	-0.0027117629069834948	-0.0014847497222945094	0.01824609562754631	0.08762945234775543	0.02394890785217285	0.001364426570944488	-0.032013047486543655	-0.03371107578277588	0.009452379308640957	0.06422468274831772	-0.046990275382995605	-0.08173827826976776	0.04539557546377182	-0.115605428814888	-0.02003341354429722	-0.02809992991387844	-0.015365063212811947	-0.04867640882730484	-0.007428577169775963	0.0025742710568010807	0.08343510329723358	0.020290682092308998	-0.03517618030309677	0.06971604377031326	0.07732643187046051	-0.05155106261372566	-0.02648470364511013	0.022777575999498367	-0.035105060786008835	-0.009098934009671211	0.07015866786241531	-0.04694613814353943	0.01833907514810562	0.023050537332892418	-0.008782252669334412	0.12453699111938477	-0.022628940641880035	-0.08530312031507492	-0.017241766676306725	-0.0013155760243535042	-0.03426479920744896	0.022184228524565697	0.048115454614162445	-0.009506163187325	0.09685447067022324	-0.04511330649256706	0.042370785027742386	0.024073554202914238	0.06341883540153503	0.022619925439357758	-0.11299891769886017	-0.03778071701526642	-0.02177654206752777	-0.03692727908492088	-0.0220907311886549	0.032662950456142426	0.012216181494295597	0.05265083163976669	0.011229874566197395	-0.03970569744706154	-0.005286604631692171	0.00044254123349674046	0.027351219207048416	0.051394984126091	0.05470800772309303	0.04669324681162834	0.06357832252979279	0.06424999982118607	-0.002875762525945902	0.02702252008020878	-0.051376912742853165	-0.023595470935106277	0.014401677064597607	0.015362565405666828	0.022750910371541977	0.011600695550441742	-0.1050884872674942	0.0037577173206955194	0.011181198991835117	-0.11268151551485062	-0.011193347163498402	0.05211137980222702	-0.08596459776163101	0.11694169789552689	0.060170747339725494	-0.009023028425872326	0.02553359977900982	0.032666582614183426	0.04257180541753769	0.00210025766864419	0.030866824090480804	-0.0114035839214921	-0.030611414462327957	-0.021310225129127502	0.02276703529059887	0.0063047027215361595	-0.054705630987882614	0.056923940777778625	0.04029739648103714	0.017091745510697365	-0.018961599096655846	-0.05009838193655014	-0.004261424299329519	-0.08249107748270035	-0.04892180860042572	-0.022599106654524803	-0.06016726791858673	-0.02331295795738697	0.05119641497731209	0.012953457422554493	0.04898901283740997	-0.031081967055797577	-0.030151305720210075	-0.02986614592373371	-0.03527480363845825	-0.024481629952788353	-0.07541831582784653	-0.023011377081274986	-0.022098861634731293	-0.01822594180703163	-0.011045373044908047	-0.02580125257372856	-0.015625974163413048	-0.01251449529081583	-0.019543472677469254	0.00718496460467577	0.0247378908097744	0.07889508455991745	-0.0025351964868605137	0.030754294246435165	-0.05064302310347557	0.002745546866208315	-0.04218696057796478	-0.01803443767130375	-0.006146756000816822	0.013584118336439133	-0.06680438667535782	-0.017656750977039337	-0.0258927084505558	-0.00593162328004837	-0.03737446665763855	0.040858469903469086	-0.00914543867111206	-0.024385327473282814	-0.03414597734808922	0.05618315562605858	0.09015565365552902	-0.021895617246627808	0.06553997844457626	-0.0023000517394393682	0.03263910487294197	-0.06024402752518654	-0.01275193877518177	-0.04163437709212303	0.04477456584572792	-0.02114218845963478	0.056379273533821106	-0.025169173255562782	-0.034729838371276855	0.07804695516824722	0.029836900532245636	0.004315034952014685	-0.06386200338602066	0.02907242253422737	0.047753315418958664	0.0005947756581008434	0.03753058612346649	0.10284243524074554	-0.025957873091101646	-0.034255776554346085	0.023367775604128838	0.031349875032901764	-0.04799562692642212	0.006028186064213514	-0.013663648627698421	-0.07977066189050674	-0.0031107880640774965	-0.07045159488916397	-0.06713499873876572	-0.037888556718826294	0.04312373325228691	-0.0038376101292669773	-0.0059117283672094345	-0.0015954803675413132	0.016302531585097313	0.0037143996451050043	-0.038073766976594925	-0.01676497980952263	-0.0006260225200094283	-0.0708477795124054	0.08663978427648544	0.010372372344136238	-0.05649934709072113	-0.06262217462062836	0.039793550968170166	0.039071109145879745	0.014536145143210888	0.011416698805987835	0.031049324199557304	0.09212322533130646	0.04691566526889801	-0.007342389319092035	0.028415463864803314	-0.04187973216176033	-0.05167297273874283	0.028973305597901344	0.034215401858091354	-0.02962867170572281	0.014472175389528275	-0.021349603310227394	-0.03411182388663292	-0.015928082168102264	0.01246203389018774	0.034017570316791534	-0.029405955225229263	-0.0421912781894207	-0.02263088896870613	0.014467747882008553	-0.027127720415592194	-0.03637397661805153	0.021526146680116653	0.00923010241240263	0.09446345269680023	-0.057131778448820114	-0.02407011389732361	0.04202825948596001	-0.01018017902970314	0.03913160413503647	-0.07601570338010788	0.032821692526340485	0.015671193599700928	-0.07011721283197403	0.009896334260702133	-0.09775753319263458	-0.04270000755786896	-0.01086476631462574	-0.046065520495176315	-0.04479050636291504	0.11537652462720871	0.05354320630431175	-0.033368539065122604	-0.06280545145273209	-0.0504373274743557	0.02832428738474846	-0.0346364751458168	0.003869079053401947	0.04228891059756279	-0.07326984405517578	-0.037489909678697586	0.059756800532341	-0.033068038523197174	-0.04061201214790344	0.00011005981650669128	-0.056878939270973206	-0.0831301137804985	0.04784174636006355	-0.01795278862118721	-0.011379698291420937	0.003132607089355588	-0.09444102644920349	0.03125928342342377	0.10544479638338089	0.027575865387916565	0.029377669095993042	0.04422391578555107	-0.019574735313653946	0.12109725922346115	-0.019099337980151176	0.005358716007322073	0.0031200917437672615	-0.03024671971797943	-0.035089001059532166	0.05928054079413414	-0.028888342902064323	0.00938402209430933	-0.0059420326724648476	-0.005371538922190666	0.061220500618219376	-0.0165460966527462	-0.03294674679636955	-0.010661638341844082	0.005393036175519228	-0.003409381490200758	0.020247403532266617	0.021850714460015297	0.026182685047388077	-0.027700502425432205	-0.04307575151324272	-0.05198018252849579	-0.024454614147543907	0.047840606421232224	-0.02260836772620678	0.020170031115412712	-0.026204904541373253	-0.04373028129339218	0.019171934574842453	0.014398026280105114	0.09929191321134567	-0.09211540967226028	0.023112976923584938	0.07250862568616867	0.03461171314120293	-0.06948443502187729	0.009131557308137417	0.0497024804353714	-0.06144869700074196	-0.031007925048470497	-0.001981722889468074	0.0234367772936821	-0.07371177524328232	-0.009291878901422024	0.017271196469664574	-0.005672180559486151	0.04483635351061821	0.04313456639647484	0.04067854955792427	0.015798380598425865	-0.044991832226514816	-0.01933165453374386	-0.015263190492987633	0.007048564963042736	0.08554064482450485	-0.04864979162812233	0.04454705864191055	-0.004106780514121056	0.05210118368268013	-0.05255356431007385	-0.03579748794436455	-0.01234631147235632	0.06521113961935043	-0.0073011694476008415	0.013695823960006237	-0.021650657057762146	-0.03694186732172966	-0.020437203347682953	0.05073581635951996	0.024562858045101166	-0.06195346638560295	-0.02718305215239525	-0.032099705189466476	-0.09592656046152115	-0.09460804611444473	-0.08990179002285004	0.0054074120707809925	-0.055923447012901306	0.04752051830291748	-0.046062443405389786	0.022484229877591133	0.007160707842558622	-0.01819862425327301	0.02899610996246338	0.06526169925928116	0.0426061674952507	-0.03388001769781113	0.025748929008841515	-0.01866062358021736	0.04196679964661598	0.0022286635357886553	-0.0365346223115921	0.04823862761259079	0.04031006991863251	-0.03927651420235634	0.051442671567201614	-0.013204609975218773	-0.010944339446723461	0.015970826148986816	-0.021733075380325317	0.021528298035264015	0.039572037756443024	-0.010982164181768894	-0.06577941030263901	0.035515882074832916	-0.058314014226198196	-0.010916863568127155	0.04823143407702446	-0.016126010566949844	-0.09530562907457352	-0.06966156512498856	-0.01743893511593342	-0.03134578466415405	0.015118000097572803	0.0007111531449481845	-0.0042822943069040775	0.04538847133517265	-0.025214359164237976	-0.012283035553991795	-0.002718130126595497	-0.02699490264058113	0.03909306973218918	-0.007187738548964262	0.0900445431470871	0.024927983060479164	-0.011003888212144375	-0.0007471514400094748	0.007004871033132076	-0.0012316986685618758	-0.07106255739927292	-0.007892398163676262	-0.01631162501871586	-0.015193804167211056	0.00725238723680377	-0.004654846154153347	-0.0012966332724317908	-0.08790502697229385	-0.0026776802260428667	-0.030523035675287247	0.07283545285463333	0.022236213088035583	0.03263694792985916	0.07217609137296677	-0.03382658585906029	0.002929854905232787	-0.027457496151328087	-0.019559303298592567	-0.09624211490154266	-0.015541103668510914	-0.05190744623541832	0.046491384506225586	0.0015448677586391568	-0.024538634344935417	0.019566984847187996	-0.08223135024309158	0.016034528613090515	0.025498786941170692	-0.04346463084220886	0.0025370570365339518	0.026228001341223717	-0.03846311941742897	-0.02617078460752964	0.023124558851122856	-0.033428262919187546	0.06555747985839844	0.054628886282444	0.023560963571071625	-0.011667915619909763	-0.012772566638886929	-0.010764718055725098	-0.03073253482580185	-0.005510103423148394	-0.0035763601772487164	-0.06582168489694595	0.011435209773480892	0.06669336557388306	-0.04983954504132271	0.037320371717214584	0.07111141830682755	-0.04471593350172043	0.047063156962394714	0.006959473714232445	-0.026652861386537552	0.005658309441059828	-0.027614137157797813	-0.02993937022984028	0.039816226810216904	-0.06619054824113846	0.04852425307035446	-0.05942215397953987	0.07611348479986191	-0.009299593046307564	-0.05042514577507973	0.06955651938915253	0.08068356662988663	0.01119124423712492	0.008248548954725266	-0.06995582580566406
+0.02632606402039528	0.038879476487636566	0.005463321693241596	0.03738723322749138	-0.08339522778987885	-0.06275558471679688	-0.01207956112921238	0.029920438304543495	0.04506640508770943	-0.01159784197807312	-0.014732785522937775	-0.0021240513306111097	-0.011481160297989845	0.02258460968732834	0.03235233575105667	-0.04997938126325607	-0.07981803268194199	-0.008520036935806274	-0.04801502823829651	-0.030113469809293747	0.04350338876247406	0.04170937463641167	-0.0712929368019104	0.04233833774924278	-0.016251463443040848	0.021278489381074905	-0.052524298429489136	0.0005336240283213556	-0.03532709181308746	0.01390389446169138	0.07112692296504974	-0.0006557322922162712	0.06400726735591888	-0.055247046053409576	-0.026625946164131165	0.05525319650769234	-0.016627488657832146	0.1009565219283104	0.04299013316631317	-0.034024544060230255	-0.0423419252038002	-0.014215288683772087	-0.02595353312790394	-0.030968766659498215	-0.007461098022758961	-0.007922869175672531	-0.023774845525622368	-0.018985159695148468	0.02633476071059704	0.006673290394246578	0.09329566359519958	-0.01357429288327694	-0.05081937462091446	-0.04231373220682144	0.0023705405183136463	0.009448378346860409	0.043057557195425034	-0.0959315374493599	-0.06107834726572037	0.02659457176923752	-0.08492008596658707	0.02785372920334339	0.011857608333230019	-0.009750347584486008	-0.020278146490454674	-0.035877395421266556	-0.00716796750202775	0.0728088840842247	0.04901374131441116	-0.012042270973324776	0.05444039776921272	0.07617930322885513	-0.07130200415849686	-0.060328103601932526	0.06419739127159119	-0.05558394640684128	-0.03144615516066551	0.036920759826898575	-0.013699442148208618	0.02511603571474552	0.013441439718008041	-0.04782576486468315	0.09910565614700317	-0.045402608811855316	-0.06519722938537598	-0.042848341166973114	-0.011067036539316177	-0.06946676969528198	0.05017377808690071	0.010652984492480755	-0.01276632584631443	0.08886128664016724	-0.05110234394669533	0.05088484287261963	0.009928957559168339	0.024672837927937508	0.0068444302305579185	-0.11929409950971603	-0.0456874705851078	-0.006732435431331396	0.008503069169819355	0.010915299877524376	0.050164904445409775	-0.005732065532356501	0.04574336111545563	0.057323116809129715	-0.04145286604762077	-0.008507650345563889	0.022698311135172844	0.0577264130115509	0.032985735684633255	0.056320417672395706	0.08010698854923248	0.06442277133464813	0.04266774281859398	-0.021010832861065865	0.021605612710118294	-0.029171647503972054	-0.05101906880736351	-0.0017078144010156393	-0.022093044593930244	0.038781605660915375	-0.0281527116894722	-0.04960894584655762	-0.06047317385673523	0.01904231496155262	-0.11018247157335281	-0.03176508843898773	0.052807435393333435	-0.004174954257905483	0.10357585549354553	0.049798037856817245	-0.0074477423913776875	-0.0030884467996656895	0.06654137372970581	0.045624684542417526	-0.004017835017293692	0.009944072924554348	-0.010141139850020409	-0.01976989209651947	-0.011616402305662632	0.05493845418095589	0.013288798741996288	-0.05991900712251663	0.03743910789489746	0.05802064761519432	0.02216392755508423	-0.018975336104631424	-0.07289036363363266	0.0017133323708549142	-0.08354420959949493	-0.08040904253721237	0.0022517526522278786	-0.07392159104347229	0.008212354965507984	0.04259093850851059	0.013216288760304451	0.03372776508331299	-0.04538464546203613	-0.04027241840958595	-0.026444772258400917	-0.022624464705586433	-0.013389657251536846	-0.06008273735642433	-0.024732081219553947	-0.05226226896047592	0.004809660837054253	0.0285149198025465	-0.03463650122284889	-0.024390367791056633	-0.010744049213826656	-0.004469148349016905	0.018645623698830605	0.11061864346265793	0.03380856290459633	-0.01135972235351801	0.015981195494532585	-0.04440702870488167	0.00865592435002327	-0.0328405424952507	0.011139489710330963	-0.04935803264379501	0.03912393003702164	-0.0737420916557312	-0.016012022271752357	-0.0015304869739338756	0.05477680265903473	-0.005577939096838236	0.014652770012617111	0.006004061549901962	-0.0382641963660717	-0.04256230965256691	0.051894962787628174	0.09217128902673721	-0.02104431949555874	0.0278182253241539	0.0032890622969716787	0.04121160879731178	0.0212650578469038	-0.024148182943463326	-0.06673908233642578	0.033921316266059875	0.025571396574378014	0.05646005645394325	0.03454616293311119	-0.06355046480894089	0.1036645770072937	0.011470183730125427	0.02016918733716011	-0.04841042309999466	0.010243352502584457	0.035164013504981995	-0.003155807498842478	0.04174482077360153	0.07318209111690521	-0.04037456214427948	0.0008750167908146977	-0.025684671476483345	0.048188094049692154	-0.055844996124506	0.019475746899843216	0.007878611795604229	-0.04723638668656349	0.06075547635555267	-0.01983337663114071	-0.09633984416723251	-0.02826879918575287	-0.008113018237054348	-0.008477955125272274	-0.02198699489235878	-0.0019299943232908845	0.015604095533490181	-0.010612414218485355	-0.022382818162441254	-0.02328922227025032	-0.0005481367115862668	-0.10748567432165146	0.12526528537273407	-0.015884919092059135	-0.05988025665283203	-0.04335343465209007	0.0033763053361326456	0.04823186248540878	-0.004943060223013163	0.010300865396857262	0.07234473526477814	0.1121247336268425	0.04149129241704941	0.01510196179151535	0.0025627699214965105	-0.07273242622613907	-0.07425536215305328	0.007209069561213255	0.03883315250277519	-0.053473200649023056	-0.048656292259693146	0.010494545102119446	-0.03931377828121185	0.00013089313870295882	-0.015260773710906506	0.05330219492316246	-0.004906394053250551	-0.019827483221888542	-0.05776641145348549	0.05028630420565605	-0.010719412006437778	-0.05303328111767769	0.0499119870364666	-0.01799699291586876	0.04821363463997841	0.014461804181337357	-0.06505054235458374	0.026890520006418228	0.019486701115965843	0.047974664717912674	0.008302873931825161	0.06490422785282135	-0.020539551973342896	-0.07493925094604492	-0.006559756584465504	-0.05189325287938118	-0.05047990009188652	-0.024204200133681297	-0.04202738776803017	-0.05907619372010231	0.08041201531887054	-0.009717362001538277	-0.04017087072134018	-0.045907162129879	0.029813870787620544	-0.02353273145854473	-0.06607968360185623	-0.029629021883010864	-0.00784365739673376	-0.09694091230630875	-0.03583851829171181	0.024730414152145386	-0.08324796706438065	-0.029101498425006866	0.012042089365422726	-0.06044550985097885	-0.04760703444480896	0.017842112109065056	-0.052910659462213516	-0.04012656956911087	-0.018438391387462616	-0.05284370481967926	0.0342593677341938	0.08546076714992523	0.02794378250837326	0.04804930090904236	0.03143841028213501	0.0454496368765831	0.08897126466035843	0.01005074568092823	-0.04276367649435997	0.008261879906058311	-0.044761717319488525	-0.06140650808811188	0.029049770906567574	-0.0552784688770771	-0.025637811049818993	0.0020422153174877167	0.01686905138194561	0.06797441840171814	0.0061875260435044765	-0.004523055162280798	0.0007562318933196366	0.049009911715984344	0.01053511630743742	0.051798898726701736	0.0024949652142822742	-0.040127161890268326	2.3759708710713312e-05	-0.07319674640893936	-0.03861338645219803	-0.04805520921945572	0.01117695402354002	0.01021940540522337	0.031961601227521896	-0.007693168241530657	-0.08221277594566345	-0.006734680384397507	-0.0015119095332920551	0.10677853971719742	-0.08138763159513474	-0.010143415071070194	0.03682582452893257	-0.022272814065217972	-0.08447159081697464	-0.02660651132464409	0.042318135499954224	-0.04017624631524086	-0.04011710360646248	0.014631137251853943	0.04351729899644852	-0.07892849296331406	0.0007801916217431426	0.012486874125897884	-0.05175577476620674	-0.0014408213319256902	0.04792080819606781	-0.0020402376540005207	0.00922134704887867	-0.0024364213459193707	-0.0064199804328382015	-0.014653350226581097	0.049518465995788574	0.015729911625385284	-0.020296480506658554	0.017080700024962425	-0.05780636519193649	0.07277897000312805	-0.028588902205228806	-0.05130289867520332	-0.06070926785469055	0.05621690675616264	0.016520563513040543	-0.025305919349193573	-0.016627201810479164	0.00741185387596488	0.0009179011103697121	0.04105190932750702	0.003465053392574191	-0.06607259809970856	-0.016243312507867813	-0.03801102191209793	-0.04090188071131706	-0.08796722441911697	-0.030142968520522118	0.019024498760700226	-0.09813456237316132	-0.012109215371310711	-0.0249616801738739	-0.007327395956963301	-0.014522303827106953	-0.045263953506946564	0.02363891713321209	0.029447780922055244	0.034242622554302216	-0.01800365373492241	0.019064651802182198	0.007663011085242033	0.06535007804632187	0.029407424852252007	-0.026270825415849686	0.029822001233696938	0.028564242646098137	-0.03210270032286644	0.05301802605390549	-0.06046585738658905	0.0074339876882731915	0.03272093087434769	0.010999472811818123	0.006442510988563299	0.01177934929728508	0.004922516644001007	-0.0531003400683403	0.040030982345342636	-0.06906193494796753	-0.02763872593641281	0.031741876155138016	-0.03860443830490112	-0.10724470019340515	-0.008687525056302547	0.0032855598255991936	-0.05716197192668915	0.04126478731632233	0.007654726039618254	0.036399152129888535	0.012780210003256798	-0.07917941361665726	-0.0465252660214901	-0.043207377195358276	-0.013331371359527111	0.030785514041781425	-0.029595185071229935	0.05583672225475311	-0.004561888985335827	0.022765235975384712	-0.0012280781520530581	-0.0036655731964856386	0.003619606141000986	-0.07798751443624496	-0.010935846716165543	-0.014089476317167282	-0.04439782351255417	-0.0018447606125846505	-0.015719745308160782	0.020625974982976913	-0.05780058354139328	-0.019881002604961395	-0.05762035399675369	0.0843145027756691	-0.07144174724817276	-0.012945987284183502	0.006757310591638088	-0.015647100284695625	0.014427687972784042	0.034961264580488205	-0.022327950224280357	-0.06932397186756134	0.021283382549881935	-0.03188392147421837	-0.005582259967923164	0.019255539402365685	0.0061755552887916565	-0.02302657440304756	-0.07702980190515518	-0.030459249392151833	0.01684386469423771	-0.04965052008628845	0.03416755795478821	-0.012262470088899136	-0.061571262776851654	-0.05843280255794525	-0.024078350514173508	-0.04324238374829292	0.07305122166872025	0.01080660242587328	0.03748030215501785	-0.009132037870585918	-0.021704435348510742	0.013950152322649956	-0.04033491760492325	0.001921088551171124	-0.010362121276557446	-0.04871315509080887	-0.04264038801193237	0.05554332584142685	-0.020649459213018417	-0.010550135746598244	0.07600957900285721	-0.04014782980084419	0.016690775752067566	0.03237594664096832	-0.021363161504268646	0.029870931059122086	-0.02638198807835579	-0.05611307919025421	0.07917310297489166	-0.056012775748968124	0.10225039720535278	-0.039894767105579376	0.08107634633779526	-0.0035114691127091646	-0.02944740653038025	0.07818622887134552	0.08675912022590637	-0.006192707922309637	0.013825413770973682	-0.05008542537689209
+-0.002135628368705511	-0.025840377435088158	-0.03037729114294052	0.02164996787905693	-0.0646250769495964	-0.028055846691131592	-0.034770477563142776	0.04149337857961655	0.009618840180337429	-0.0777876153588295	-0.06304951757192612	-0.0023835108149796724	0.0095121581107378	0.009742422960698605	0.019466321915388107	-0.028358856216073036	-0.043172989040613174	-0.007038792595267296	-0.001222080085426569	-0.03060467727482319	0.04968732222914696	-0.008502045646309853	-0.08354108780622482	0.09456309676170349	0.05805332213640213	0.00804691668599844	-0.031578581780195236	0.050784457474946976	-0.00018226275278721005	0.09561924636363983	0.037894878536462784	-0.05948212742805481	0.010324675589799881	-0.07040519267320633	-0.0556400790810585	0.03162137791514397	-0.05996495112776756	0.08743750303983688	0.03728345036506653	-0.015531022101640701	-0.053666528314352036	0.033992964774370193	-0.03452321141958237	-0.057095833122730255	0.020592132583260536	0.007296930067241192	-0.025254273787140846	-0.04142976552248001	0.02890738472342491	0.043375164270401	0.08299509435892105	-0.00716064590960741	-0.019791414961218834	-0.01704183779656887	-0.026390811428427696	0.03217582777142525	0.011628192849457264	-0.04673569276928902	-0.0249739158898592	0.014068668708205223	-0.05691208690404892	0.02687535434961319	0.00619930075481534	0.010576334781944752	-0.015703564509749413	0.03010290302336216	-0.014293121173977852	0.06753372400999069	0.0009619214688427746	-0.014542377553880215	-0.016021745279431343	0.07108829915523529	-0.05553879216313362	-0.03027145378291607	0.03849465399980545	-0.005335407797247171	-0.005460693500936031	0.020751522853970528	0.004307846073061228	0.02218807116150856	0.019756842404603958	-0.051478464156389236	0.15026916563510895	-0.027700090780854225	0.014270813204348087	-0.07283180207014084	-0.0289609432220459	-0.04997509345412254	0.05477503314614296	0.024018412455916405	0.05455601587891579	0.05969745293259621	0.02079843170940876	0.05474982038140297	0.007556180004030466	-0.0012270971201360226	0.026774700731039047	-0.13376423716545105	0.017410995438694954	0.02353283390402794	0.004418003838509321	-0.020209206268191338	0.06678391247987747	0.008652853779494762	0.0394502654671669	0.06330006569623947	-0.08058091998100281	0.027156058698892593	0.05306188017129898	0.034219417721033096	0.019511377438902855	0.10529232770204544	0.055447228252887726	0.05761222541332245	0.03632097691297531	-0.00982406735420227	-0.028026912361383438	-0.018604714423418045	-0.02369997836649418	0.015079250559210777	0.004468053113669157	0.055834852159023285	-0.01038967166095972	-0.08316779136657715	0.04553729668259621	0.04687642306089401	-0.08312056958675385	-0.0006056389538571239	0.01397558394819498	-0.017303597182035446	0.06076120585203171	0.06370887905359268	-0.014189662411808968	0.025515273213386536	0.038986485451459885	0.026067662984132767	0.035463567823171616	0.022980716079473495	-0.011494245380163193	-0.038789283484220505	-0.04706300422549248	0.03122214786708355	-0.009692784398794174	-0.03758952021598816	0.06293929368257523	0.011810378171503544	0.00890006311237812	-0.010997306555509567	-0.061989136040210724	-0.019817180931568146	-0.09782708436250687	-0.08495207130908966	0.04075595736503601	-0.024658752605319023	0.025014949962496758	0.026807991787791252	0.0640651136636734	0.04418914020061493	0.0009095052955672145	0.0051932139322161674	-0.006805179174989462	-0.05620129778981209	-0.022759970277547836	-0.06698404252529144	0.037375230342149734	0.013062364421784878	0.04125697538256645	-0.011598863638937473	-0.05660216510295868	-0.028323763981461525	0.003277987241744995	0.01901037059724331	0.04407019168138504	0.058973025530576706	0.047450028359889984	-0.007265050429850817	0.061806418001651764	-0.058854840695858	-0.05848625302314758	-0.029003437608480453	0.03712926805019379	-0.06262394040822983	-0.010854995809495449	-0.04982776939868927	-0.044255100190639496	0.008545495569705963	-0.009362713433802128	-0.021395036950707436	-0.009458606131374836	-0.020054765045642853	-0.048787832260131836	-0.06814544647932053	0.05789944902062416	0.009867409244179726	-0.02275018021464348	0.04219586402177811	0.04257470741868019	0.0893111377954483	-0.02681170217692852	-0.008270715363323689	-0.06253248453140259	0.04464436694979668	0.019902020692825317	0.020773816853761673	0.03708279877901077	-0.03916742280125618	0.04814665764570236	-0.0033383199479430914	0.04616175964474678	-0.03573903441429138	0.054310690611600876	0.04201445356011391	0.01458406075835228	0.03781019523739815	0.08445874601602554	-0.0348108634352684	-0.05289402976632118	0.02242845855653286	0.04216080904006958	-0.10741066187620163	-0.004532946273684502	-0.008151886984705925	-0.07482948899269104	0.005207007750868797	-0.034729573875665665	-0.08334784209728241	-0.03162519633769989	0.004873163066804409	-0.014656958170235157	-0.04776684194803238	0.008191293105483055	0.027635719627141953	-0.03407575562596321	-0.022614169865846634	0.04363798722624779	0.017100118100643158	-0.06260544061660767	0.04888011887669563	-0.07269372791051865	-0.04168200120329857	-0.032377999275922775	0.027469942346215248	0.004803963005542755	0.027397116646170616	0.022921497002243996	0.08193787187337875	0.07924230396747589	0.06680822372436523	-0.05312168598175049	0.0071412427350878716	-0.05558657646179199	-0.0221724770963192	-0.008143714629113674	0.02471906878054142	0.018191572278738022	-0.055959973484277725	0.00964824203401804	-0.07741999626159668	0.0019614812918007374	-0.015755545347929	-0.002535969717428088	-0.0347927063703537	-0.004021060653030872	-0.03268280252814293	0.04274052008986473	-0.00346121983602643	-0.052789513021707535	0.02474459633231163	-0.06505052745342255	0.05221448466181755	-0.06035096198320389	-0.03211956098675728	0.015230447985231876	0.007102007046341896	0.024561166763305664	-0.02872495725750923	0.08310685306787491	-0.03514979034662247	-0.0798112079501152	-0.08243190497159958	-0.04906364157795906	-0.07531244307756424	-8.563383744331077e-05	-0.0066840206272900105	-0.019613703712821007	0.059867944568395615	0.012939696200191975	-0.04535861685872078	-0.06178702041506767	-0.054881274700164795	-0.027284694835543633	-0.05641508847475052	-0.03942743316292763	-0.025227369740605354	-0.04516560211777687	-0.025175686925649643	0.04816336929798126	-0.03650917485356331	-0.020817816257476807	0.06572584062814713	0.034423667937517166	-0.04414881765842438	0.07024119794368744	-0.036453235894441605	-0.02108794078230858	0.04840627312660217	-0.06253157556056976	0.027571700513362885	0.0870291069149971	0.00934774149209261	0.007202502340078354	0.034010495990514755	0.04666228964924812	0.07181945443153381	-0.0020217588171362877	-0.05162150785326958	-0.04983250051736832	-0.03178859502077103	-0.08495055884122849	0.06681308150291443	-0.01619674637913704	-0.0005780697683803737	-0.020135123282670975	-0.03330451250076294	0.019949017092585564	0.006980334408581257	0.010243121534585953	-0.05258065089583397	0.008117524906992912	-0.02589813619852066	0.029282668605446815	-0.03419407084584236	0.0023626748006790876	0.021013855934143066	-0.04296453669667244	-0.07493089139461517	-0.07501517981290817	0.03502759337425232	0.004244665615260601	0.05563774332404137	0.008993152529001236	-0.016842080280184746	0.019475603476166725	-0.00017775337619241327	0.08883435279130936	-0.07084904611110687	0.04162484034895897	0.03544847294688225	0.018510619178414345	-0.08840783685445786	-0.01672607660293579	0.004387918394058943	-0.038912151008844376	-0.06701791286468506	-0.029714778065681458	0.03794579580426216	-0.02799677848815918	-0.06889042258262634	-0.02075095660984516	-0.03045685403048992	0.010214559733867645	0.03904346749186516	-0.007738654967397451	0.06585460901260376	0.02704341523349285	-0.023067282512784004	0.030236952006816864	-0.030753040686249733	-0.0005816942430101335	-0.016269292682409286	0.025362897664308548	-0.012313347309827805	0.07996850460767746	-0.01412225142121315	-0.030306963250041008	-0.08060630410909653	0.014055091887712479	-0.060840100049972534	-0.020072724670171738	-0.022531136870384216	0.01817019097507	-0.029351290315389633	0.025002852082252502	0.02892816998064518	-0.07885725796222687	-0.008120834827423096	-0.0143502838909626	-0.046203259378671646	-0.04997284337878227	-0.026381345465779305	0.018209092319011688	-0.022737670689821243	0.0279970932751894	-0.06178693100810051	0.00661046477034688	0.04857330024242401	0.028891077265143394	0.013079509139060974	0.05402394011616707	0.018101191148161888	0.001877644332125783	0.06830322742462158	-0.0013812638353556395	0.0029582404531538486	-0.012776557356119156	-0.049318403005599976	0.022307923063635826	0.052834294736385345	-0.03191050887107849	0.02281356416642666	-0.04230000451207161	0.009450349025428295	0.018750052899122238	-0.029919834807515144	0.0116276228800416	-0.03012806735932827	0.016469115391373634	-0.017756115645170212	0.050018683075904846	-0.033762332051992416	-0.060687679797410965	0.013263325206935406	-0.049555107951164246	-0.08379821479320526	0.007127966731786728	-0.04419131204485893	-0.04409242421388626	0.03387558460235596	0.0017753986176103354	0.02654794603586197	0.049909282475709915	-0.06234148517251015	-0.030052902176976204	0.026565775275230408	0.0011419106740504503	-0.0024933849927037954	0.06119731068611145	0.06134546920657158	0.019931644201278687	-0.02347448654472828	0.029926203191280365	-0.05012655630707741	0.017069503664970398	-0.04665544256567955	0.0018075999105349183	0.0423893965780735	-0.02673659473657608	0.02186313271522522	0.0365811251103878	0.043570782989263535	-0.05679143965244293	-0.030619598925113678	-0.028745271265506744	0.02315422147512436	0.001506411237642169	0.020723165944218636	0.039359595626592636	-0.06717479974031448	-0.018944833427667618	0.020142097026109695	-0.02370954491198063	-0.0899990051984787	-0.039846040308475494	-0.07091452926397324	0.08540382236242294	-0.006952669471502304	-0.010653450153768063	-0.030003301799297333	-0.09042022377252579	-0.01778700202703476	-0.029734795913100243	-0.02704070508480072	-0.00950690172612667	0.015345588326454163	-0.05467572808265686	-0.054186753928661346	-0.01334335282444954	-0.07754050940275192	0.05207169055938721	-0.003561978926882148	0.029360070824623108	-0.04179753363132477	-0.002349959686398506	0.01235169731080532	-0.025298548862338066	0.04572822526097298	-0.04826994985342026	-0.0687524750828743	-0.024170905351638794	0.12442504614591599	0.0058620888739824295	0.04386606067419052	0.0883636549115181	0.022271284833550453	0.0551861897110939	0.050370119512081146	-0.025291230529546738	-0.027737481519579887	-0.02118847332894802	-0.011039881967008114	0.044724393635988235	-0.0395432785153389	0.06859266757965088	-0.08053842931985855	0.1296084076166153	0.02986263670027256	0.01113596186041832	0.09091383963823318	0.07022710889577866	0.04829806089401245	0.05758815258741379	-0.09195204824209213
+0.014514723792672157	-0.06686318665742874	-0.09423527121543884	-0.02848677709698677	-0.07896509021520615	-0.0742996484041214	0.02728314884006977	0.028932681307196617	-0.02107941545546055	-0.006352986674755812	-0.07302562892436981	0.008288521319627762	0.02620280161499977	-0.05777790769934654	0.03226453438401222	-0.018602143973112106	-0.04397454112768173	0.0052793147042393684	-0.03476522117853165	0.019341226667165756	0.005726413801312447	0.023859841749072075	-0.055827293545007706	0.0483211949467659	0.01944299414753914	0.04404695704579353	-0.017538484185934067	0.00930043775588274	0.013000432401895523	0.03328638896346092	0.00026171948411501944	-0.012908478267490864	0.03212476894259453	-0.04432956129312515	-0.046887174248695374	0.010840021073818207	-0.025657540187239647	0.11620936542749405	0.03144148364663124	-0.01547953486442566	-0.06515811383724213	0.015554946847259998	-0.05293746292591095	-0.09800534695386887	-0.0076920329593122005	0.03899994120001793	0.011909186840057373	-0.03811600059270859	0.02041834220290184	0.056228190660476685	0.03478262573480606	0.005698557943105698	-0.07746621966362	-0.05661962926387787	-0.024168042466044426	0.021988602355122566	0.01813950203359127	-0.022835614159703255	-0.03142625465989113	0.06108696386218071	-0.10422008484601974	0.01924269087612629	-0.0271590705960989	-0.00926651619374752	-0.023121781647205353	-0.036268819123506546	0.023075228556990623	0.027107486501336098	-0.031938113272190094	-0.04627346992492676	-0.00656490633264184	0.03888915106654167	-0.03673579916357994	-0.011388839222490788	0.011359483003616333	-0.04654809832572937	0.00059009890537709	0.023981522768735886	-6.283393304329365e-05	-0.041758403182029724	-0.04219697043299675	-0.035873912274837494	0.1754796952009201	-0.08872116357088089	-0.06433583796024323	-0.06232825666666031	-0.05320300534367561	-0.05336452275514603	-0.02643786184489727	-0.02024858258664608	-0.0016625059070065618	-0.009078718721866608	-0.008382409811019897	0.01738765463232994	-0.018822237849235535	-0.004488404840230942	0.03853722661733627	-0.13426339626312256	-0.023956026881933212	0.010569585487246513	-0.077456533908844	-0.01637420430779457	0.07110580056905746	0.031183170154690742	0.012918966822326183	-0.0399889312684536	-0.07117358595132828	0.002129769418388605	0.027558187022805214	0.0024359209928661585	-0.014803348109126091	0.02928268164396286	0.06371302902698517	0.03848777338862419	0.09645622968673706	0.011655761860311031	0.02505127526819706	-0.014539415016770363	0.009298127144575119	-0.014240847900509834	0.029939059168100357	0.023286890238523483	0.02495943009853363	-0.07379168272018433	0.01953161507844925	0.020780475810170174	-0.10366740077733994	-0.026448547840118408	-0.014375356957316399	-0.06199780851602554	0.0826587900519371	0.025032643228769302	-0.061844609677791595	0.018564069643616676	-0.01600600965321064	-0.040713559836149216	0.005921093747019768	0.03847605735063553	0.028448475524783134	-0.05200676992535591	0.002913879230618477	0.018979903310537338	-0.004037314094603062	-0.019849568605422974	0.0452263206243515	0.023442266508936882	-0.009202142246067524	-0.06730090081691742	-0.06245562061667442	0.023319920524954796	-0.08771207183599472	-0.03701893985271454	0.04087696596980095	-0.01723933406174183	0.018680527806282043	-0.02535077929496765	0.04583467170596123	0.016952427104115486	-0.03591674193739891	0.009896841831505299	-0.04548916965723038	-0.009511942975223064	-0.03130483254790306	-0.1038137897849083	0.010461674071848392	0.04030745476484299	0.049757763743400574	-0.03928647190332413	-0.04619109630584717	-0.02719215676188469	-0.03724875673651695	0.04140616953372955	0.05655529350042343	0.02729136310517788	0.05796933174133301	-0.028598541393876076	0.04609375819563866	-0.06021459028124809	0.010967669077217579	-0.02556658908724785	0.06319120526313782	-0.041426289826631546	0.06132711097598076	0.001713695120997727	0.007851739414036274	0.0026262751780450344	-0.0011181479785591364	-0.07021374255418777	0.019166873767971992	0.0018871224019676447	-0.04071759805083275	-0.03448936343193054	0.027403445914387703	0.06698965281248093	-0.01988772489130497	0.0592302568256855	0.03095141053199768	0.09598536044359207	-0.027823923155665398	-0.03682078421115875	-0.0835358276963234	0.022568969056010246	-0.01710398867726326	0.010147510096430779	0.0039818743243813515	-0.04895815998315811	0.04595782980322838	0.013282430358231068	0.001137383165769279	-0.0410747304558754	0.08389750868082047	0.03523814678192139	-0.043815553188323975	0.02096737176179886	0.0993555337190628	-0.015097261406481266	-0.029146244749426842	-0.03355831652879715	-0.0036454685032367706	-0.0379536934196949	0.01286362111568451	0.007420503068715334	-0.09000737220048904	-0.05579607933759689	-0.07116822898387909	-0.08063448220491409	-0.0024990313686430454	0.05574026703834534	0.027260448783636093	0.00514516094699502	-0.045073699206113815	0.029826471582055092	-0.015843283385038376	-0.045683201402425766	-0.02756362222135067	0.025156693533062935	-0.0604335218667984	0.021229669451713562	-0.003666035830974579	-0.10562264919281006	0.0008097847457975149	0.0073010241612792015	-0.008999664336442947	-0.025946365669369698	-0.00019398897711653262	0.04936380684375763	0.030831316486001015	0.04498058184981346	-0.039558958262205124	-0.014882269315421581	-0.021653836593031883	0.03602908179163933	0.01930432952940464	0.012045382522046566	-0.024231165647506714	-0.037756819278001785	0.014681132510304451	-0.08839856833219528	-0.0390671044588089	0.01252299826592207	0.038447801023721695	-0.036785874515771866	-0.10504868626594543	0.0028653177432715893	-0.010381081141531467	-0.02479277364909649	-0.02521684020757675	0.029206015169620514	-0.024572273716330528	0.009025010280311108	-0.031524889171123505	0.02166822925209999	0.020320281386375427	-0.019490597769618034	0.020581794902682304	-0.01794959418475628	0.05476115643978119	0.0020633188541978598	-0.04505962133407593	0.02460261806845665	-0.04351750761270523	-0.04881714656949043	-0.0465487502515316	-0.018358822911977768	-0.006121898535639048	0.04109691083431244	0.07394997030496597	-0.03748234733939171	-0.09326303750276566	-0.04274020716547966	-0.05393088608980179	-0.05436844751238823	-0.020564325153827667	0.015477824956178665	-0.030430754646658897	-0.023597300052642822	0.017726508900523186	-0.000610123504884541	0.010424005798995495	0.035460442304611206	0.023301880806684494	-0.10302708297967911	0.039071135222911835	-0.02803058922290802	-0.08749804645776749	-0.008754724636673927	-0.037021901458501816	-0.00402434729039669	0.0686308965086937	0.02978578396141529	0.026392841711640358	0.0012522070901468396	-0.01500849612057209	0.00609987648203969	-0.06790121644735336	0.018584737554192543	-0.04442376270890236	-0.03219226002693176	-0.03262030705809593	0.023911332711577415	-0.015915755182504654	-0.007795094512403011	-0.007274051196873188	0.01188839040696621	0.005001612938940525	-0.014963794499635696	-0.07809050381183624	-0.08034192025661469	-0.03079853765666485	-0.030845381319522858	0.007730451412498951	-0.020121973007917404	0.07923517376184464	-0.02114810422062874	-0.04696628823876381	-0.05175436660647392	-0.036233335733413696	0.012460214085876942	-0.015947584062814713	0.10025183111429214	-0.01889355108141899	-0.029338078573346138	-0.04341757297515869	-0.004245420917868614	0.11220049858093262	-0.060069698840379715	0.009919038973748684	0.04834097623825073	0.060758814215660095	-0.02704782970249653	0.019808329641819	0.04440722241997719	-0.030180394649505615	-0.04776442050933838	-0.03000803478062153	0.01815899647772312	-0.05595726892352104	-0.05039402097463608	-0.037249963730573654	0.010722082108259201	0.018385734409093857	-0.007921742275357246	0.012529049068689346	0.006501671392470598	-0.0009761392720974982	-0.0011439683148637414	0.05107908695936203	0.008038579486310482	0.014520755968987942	-0.03168748691678047	0.01132283080369234	0.015059199184179306	0.014488080516457558	-0.05853841453790665	-0.029804175719618797	-0.06998676061630249	0.04001114144921303	-0.02181619219481945	-0.03225811943411827	-0.06258154660463333	-0.05243523418903351	-0.003445504931733012	0.03559551388025284	-0.005206224974244833	-0.02071540057659149	-0.0469585619866848	-0.014859165996313095	-0.09045034646987915	-0.07251474261283875	-0.0242056455463171	0.007932639680802822	0.01168644055724144	0.0091745276004076	-0.04480480030179024	0.021849853917956352	0.04034204035997391	0.05992025136947632	0.015965715050697327	0.05996442213654518	0.021733123809099197	-0.006604858674108982	0.05890843644738197	-0.03056793287396431	0.10440734028816223	-0.009693237021565437	-0.060556329786777496	-0.010701277293264866	0.0157639030367136	-0.09160540997982025	0.032653506845235825	-0.07709015905857086	-0.04692653566598892	0.01639922708272934	-0.009948804043233395	-0.01578254997730255	0.039827924221754074	0.07309883832931519	-0.019978953525424004	0.01764259859919548	-0.07597164064645767	-0.020187169313430786	0.057587869465351105	-0.01719365268945694	-0.03830886259675026	-0.04848458617925644	-0.0398835651576519	0.013400956057012081	0.005777995567768812	-0.04434416443109512	0.07059818506240845	0.027523458003997803	-0.0387544110417366	-0.04676807299256325	-0.005761601962149143	-0.023049434646964073	0.050759367644786835	0.02140626683831215	0.06005582958459854	0.05318787321448326	0.041365865617990494	-0.03393243998289108	0.03241627290844917	0.010166725143790245	-0.061826035380363464	0.046706125140190125	-0.034779880195856094	0.03855770453810692	-0.016737600788474083	-0.01735905557870865	0.011789599433541298	-0.02294110134243965	0.0007893474539741874	0.004240485839545727	0.04654749110341072	0.0396929606795311	0.024852722883224487	0.08694471418857574	-0.053605202585458755	-0.01740563102066517	0.008764535188674927	-0.023426704108715057	-0.05246727541089058	0.0004353571857791394	-0.054278429597616196	0.06870289891958237	0.045247990638017654	0.0009899731958284974	-0.022472528740763664	-0.026312872767448425	-0.007363944314420223	0.018444236367940903	-0.0026112196501344442	0.010431387461721897	0.10260868817567825	-0.017850510776042938	-0.009172579273581505	0.02699313685297966	-0.05938258767127991	0.04784431308507919	0.05221003293991089	0.06877875328063965	-0.0481979139149189	0.0013148203724995255	-0.0026605187449604273	0.06293535232543945	0.022666320204734802	-0.02678227238357067	-0.06346653401851654	-0.01813480630517006	0.13349458575248718	-0.018534861505031586	0.025659698992967606	0.0659300908446312	-0.035797834396362305	0.08002656698226929	0.03329020366072655	-0.004073762334883213	0.026400215923786163	-0.015119294635951519	-0.06031167507171631	0.05501178279519081	-0.08372089266777039	0.06338493525981903	-0.05140657350420952	0.09000064432621002	0.012993081472814083	-0.05272487550973892	0.11784465610980988	0.07799223065376282	0.04641678184270859	0.009131792932748795	-0.05459349602460861
+0.03753487393260002	0.03175141662359238	0.019970647990703583	0.04383163899183273	-0.10950936377048492	-0.03334587439894676	-0.022381270304322243	0.0019954147282987833	0.015319068916141987	-0.05399281904101372	-0.0076100705191493034	-0.01432129554450512	-0.0044732242822647095	-0.011907453648746014	0.02724982239305973	-0.021785257384181023	-0.07248137146234512	-0.020784607157111168	-0.020231623202562332	-0.044032204896211624	0.04636513441801071	-0.013317643664777279	-0.056188199669122696	0.07351343333721161	0.011895077303051949	0.025763453915715218	-0.03612798824906349	0.027110232040286064	-0.03574349731206894	0.0544513575732708	0.05665380507707596	-0.012955761514604092	0.04490495100617409	-0.0657443031668663	-0.04560475796461105	0.05890722945332527	0.017700985074043274	0.1164264976978302	0.05027831718325615	-0.04407001659274101	-0.03343375027179718	0.0005627053324133158	-0.038455285131931305	-0.0437946543097496	0.02889823354780674	-0.007763080298900604	-0.0151379918679595	-0.011798354797065258	0.06299982964992523	0.028909489512443542	0.06893287599086761	0.009391283616423607	-0.05912186950445175	-0.034006666392087936	0.02129123918712139	0.028345119208097458	0.07107851654291153	-0.052501384168863297	-0.038636405020952225	0.06440840661525726	-0.0677003413438797	-0.015227897092700005	0.014569681137800217	-0.019713204354047775	0.008599353954195976	4.5356220653047785e-05	0.0157930925488472	0.07540648430585861	0.012526744045317173	-0.027083227410912514	0.04163254424929619	0.11333048343658447	-0.06062285974621773	-0.023546921089291573	0.047887708991765976	-0.0722314640879631	-0.05106863006949425	0.05024860054254532	-0.01248930487781763	0.042015183717012405	-0.019078915938735008	-0.07172998785972595	0.1227472797036171	-0.04035494849085808	-0.05671378970146179	-0.05780607834458351	-0.02830669656395912	-0.03981620445847511	0.05777933821082115	0.02036171220242977	0.00015803540009073913	0.05810884013772011	-0.07579140365123749	0.043325960636138916	-0.011025544255971909	0.0024796866346150637	0.04816999286413193	-0.10215526819229126	-0.0161249078810215	0.019823063164949417	-0.041049957275390625	-0.04235035181045532	0.005059321876615286	-0.001578018651343882	0.04815712943673134	0.048672500997781754	-0.040511272847652435	0.011490252800285816	0.028343481943011284	0.05249633267521858	0.04582751542329788	0.0627664104104042	0.08249775320291519	0.061008039861917496	0.07257655262947083	0.0003253735776524991	0.023879459127783775	-0.07117415964603424	-0.027936281636357307	-0.01006509829312563	-0.010704264976084232	0.06414151191711426	-2.9043416361673735e-05	-0.04339102283120155	-0.006719004362821579	0.0332721546292305	-0.10802307724952698	-0.023975705727934837	0.04460180923342705	-0.02565794810652733	0.10185006260871887	0.043481748551130295	-0.00103594153188169	0.03223529830574989	0.034504130482673645	0.028951184824109077	-0.017934150993824005	0.04695199802517891	-0.01070493459701538	-0.040695808827877045	0.0002767886617220938	0.003664347343146801	0.011695284396409988	-0.06910982728004456	0.03227466717362404	0.04712461680173874	0.02917627990245819	-0.023020582273602486	-0.0625714510679245	-0.0017325549852102995	-0.0817015990614891	-0.0515085905790329	0.012923273257911205	-0.08908570557832718	-0.026062868535518646	0.05033409968018532	0.022757375612854958	0.022774361073970795	-0.03395480662584305	0.004857979714870453	-0.02884402871131897	-0.03538699448108673	0.0027252689469605684	-0.07432369142770767	-0.060467422008514404	-0.029475515708327293	0.019172020256519318	0.010075661353766918	-0.02873343788087368	-0.021100878715515137	-0.007978918962180614	0.0059194546192884445	0.05179595202207565	0.06646935641765594	0.06175762787461281	-0.01455681212246418	0.04113852605223656	-0.06046552211046219	0.027607234194874763	-0.045993465930223465	0.025513073429465294	-0.03731800243258476	0.04538251832127571	-0.05416421964764595	-0.026818176731467247	-0.017438262701034546	0.03160740062594414	0.014735011383891106	0.01444070041179657	0.009752192534506321	-0.0680442601442337	-0.05416890233755112	0.03458210825920105	0.07493811845779419	-0.061860691756010056	0.03441470116376877	0.025745483115315437	0.03904842212796211	-0.014873056672513485	-0.023061739280819893	-0.07449811697006226	0.0336570180952549	0.009416504763066769	0.07552602887153625	0.04131199046969414	-0.06716757267713547	0.05715164914727211	0.008402838371694088	-0.013326902873814106	-0.06442976742982864	0.01650642789900303	0.03396102413535118	0.020791994407773018	0.03575057536363602	0.0771038681268692	-0.007194944191724062	0.00942531693726778	-0.028892429545521736	0.058941587805747986	-0.05295355245471001	-0.018314942717552185	-0.026888327673077583	-0.09179221093654633	-0.005061101634055376	-0.06364257633686066	-0.05875976011157036	-0.01589217036962509	0.0419151596724987	-0.006727219093590975	-0.026439711451530457	-0.00016199579113163054	0.021141277626156807	-0.01641065627336502	-0.016794053837656975	0.0018209744011983275	0.005761278327554464	-0.08426596969366074	0.13434745371341705	-0.020137595012784004	-0.08065015077590942	-0.04861242324113846	0.0448458157479763	0.05985773727297783	-0.020154882222414017	0.0463399700820446	0.04671841114759445	0.09040296822786331	0.04546433687210083	0.0015901211882010102	-0.039155252277851105	-0.0480966754257679	-0.06490394473075867	-0.0010782197350636125	0.040596913546323776	-0.029154200106859207	-0.029718054458498955	0.004125209059566259	-0.049317989498376846	0.018604671582579613	0.02267109975218773	0.0552094466984272	-0.035417310893535614	-0.04563511162996292	-0.030454138293862343	0.03167865797877312	-0.022703198716044426	-0.05329171568155289	0.05922326073050499	-0.016315627843141556	0.05011284723877907	-0.012104326859116554	-0.00833930540829897	0.025041984394192696	0.026778122410178185	0.020374195650219917	-0.023024659603834152	0.06379026174545288	-0.003456133184954524	-0.05379711091518402	-0.03623790293931961	-0.07017619162797928	-0.05677488073706627	-0.021396495401859283	-0.02420196495950222	-0.022977974265813828	0.056722767651081085	0.010322083719074726	-0.039660628885030746	-0.06539024412631989	0.0008980081765912473	-0.019940560683608055	-0.06352907419204712	-0.036083072423934937	0.02459818683564663	-0.07157009094953537	-0.00446065841242671	0.011309176683425903	-0.025500234216451645	-0.021606018766760826	0.00424443744122982	-0.042422566562891006	-0.08215821534395218	0.03660665825009346	-0.02878476120531559	-0.040646813809871674	0.007455380633473396	-0.03822098672389984	0.041229166090488434	0.10208573937416077	0.04270705580711365	0.06178295612335205	0.009396246634423733	0.05235816538333893	0.08743389695882797	-0.024198750033974648	-0.007750603836029768	-0.02021072246134281	-0.05240486562252045	-0.09602807462215424	0.05109605938196182	-0.040599413216114044	-0.03369960933923721	-0.026101501658558846	0.03706108406186104	0.07121371477842331	-0.002777926390990615	-0.015453718602657318	-0.031211672350764275	0.03705042973160744	0.010145924054086208	0.06288173049688339	-0.022114966064691544	-0.003916639368981123	-0.023410379886627197	-0.050802234560251236	-0.0413200818002224	-0.05124710127711296	0.04653333127498627	0.029501350596547127	0.0526396706700325	-0.009793037548661232	-0.09118480980396271	-0.016200728714466095	0.021075932309031487	0.07986099272966385	-0.09324754774570465	0.009646350517868996	0.03303277865052223	0.00238543632440269	-0.07557947188615799	-0.015337645076215267	0.0449829176068306	-0.028717579320073128	-0.03451697155833244	-0.005357875023037195	0.06101919710636139	-0.0626833587884903	0.010063276626169682	0.003978464752435684	-0.039577558636665344	-0.01949445530772209	0.029574228450655937	0.012118254788219929	-0.030285412445664406	-0.03022271953523159	-0.02155425027012825	-0.03587818890810013	-0.013131718151271343	0.001548502128571272	0.010093022137880325	0.03580460324883461	-0.03043133020401001	0.024669647216796875	-0.06499233841896057	0.0015702032251283526	-0.06435945630073547	0.09909044951200485	-0.029621563851833344	-0.011402792297303677	-0.009302350692451	-0.02050909399986267	-0.0005295529845170677	0.0405932255089283	0.00857020728290081	-0.05765043944120407	-0.0324082151055336	-0.023707564920186996	-0.058831505477428436	-0.07911719381809235	-0.023052824661135674	0.01866559125483036	-0.0743410736322403	0.008882786147296429	-0.029661787673830986	-0.003976366017013788	-0.019017549231648445	0.012747094966471195	0.022395094856619835	0.019241536036133766	0.042050041258335114	0.0008411119342781603	0.04599643126130104	0.0026636093389242887	0.042029645293951035	0.0031449683010578156	-0.06365692615509033	0.024518297985196114	0.003706471063196659	-0.06224736571311951	0.09519660472869873	-0.046435512602329254	-0.0119468430057168	-0.02419980801641941	0.017495986074209213	0.03815801814198494	-0.007984369993209839	-0.0036324444226920605	-0.017647692933678627	0.050812359899282455	-0.05524544417858124	-0.025595298036932945	0.042286310344934464	-0.021384000778198242	-0.09843304008245468	-0.03060903027653694	0.004087458364665508	-0.03207086771726608	0.044362712651491165	-0.007189327385276556	0.03637462481856346	0.03801480308175087	-0.033929068595170975	-0.043781716376543045	-0.033073555678129196	-0.04194144159555435	0.04758107662200928	-0.010635839775204659	0.0791783258318901	0.012348368763923645	0.020651374012231827	0.01664082705974579	0.02638102136552334	0.006726507563143969	-0.09000427275896072	-0.0061568510718643665	-0.007929137907922268	-0.021728835999965668	-0.005221815779805183	-0.0047782668843865395	0.027560850605368614	-0.02869122102856636	-0.04916355758905411	-0.0521685816347599	0.09216654300689697	-0.029222842305898666	-0.005093584768474102	0.0584108792245388	-0.02792210876941681	0.015653671696782112	0.006728128995746374	-0.057765182107686996	-0.09885861724615097	-0.02508249133825302	-0.019225168973207474	0.0074269515462219715	0.002575451275333762	0.0013329656794667244	-0.012197745032608509	-0.040761422365903854	-0.016609802842140198	0.033888448029756546	-0.055880628526210785	0.03462965413928032	-0.013232128694653511	-0.04499991610646248	-0.071153424680233	-0.014069790951907635	-0.037506990134716034	0.05204997956752777	0.03591493144631386	0.02617858536541462	0.0003006926563102752	0.011399637907743454	0.015964919701218605	-0.052074138075113297	0.013916120864450932	0.023731274530291557	-0.04227906093001366	-0.022040609270334244	0.06323414295911789	-0.03699972853064537	0.06064143031835556	0.018372002989053726	-0.050566427409648895	0.037385329604148865	0.08265625685453415	-0.0486229844391346	0.05051853507757187	-0.020046554505825043	-0.03966908156871796	0.07051709294319153	-0.05489806830883026	0.08970347046852112	-0.03281205892562866	0.0729610025882721	-0.02452288568019867	-0.023935049772262573	0.07228226214647293	0.08864682167768478	0.041060902178287506	0.03059731423854828	-0.07844617962837219
+0.06494390219449997	-0.06458471715450287	-0.048707667738199234	0.02941654436290264	-0.055049143731594086	-0.04506920278072357	0.009624697268009186	0.044259268790483475	-0.005133649799972773	-0.06100679561495781	-0.0673116147518158	0.017289236187934875	0.031445637345314026	0.04254462942481041	0.050232894718647	-0.03910944610834122	-0.03197804093360901	0.02594258077442646	-0.021204737946391106	0.0003321052936371416	0.021186040714383125	-0.03280166909098625	-0.06591923534870148	0.0580696165561676	0.025937141850590706	0.03364361822605133	-0.03804013878107071	0.016815796494483948	0.0252118781208992	0.02708061970770359	0.04737575352191925	-0.04384168982505798	-0.008392516523599625	-0.07512522488832474	-0.07581959664821625	0.02414700575172901	-0.08139607310295105	0.12798550724983215	-0.00563314463943243	0.0002566793991718441	-0.0884714275598526	0.009056759998202324	-0.09933622181415558	-0.0749107077717781	0.041489988565444946	0.0007462609210051596	0.03574909269809723	-0.021558022126555443	0.02574840746819973	0.00838688388466835	0.052905138581991196	-0.024676864966750145	-0.022030383348464966	-0.013825218193233013	-0.07209249585866928	0.017395107075572014	0.006373195443302393	-0.03566141426563263	-0.03324153274297714	0.038617704063653946	-0.10728752613067627	-0.025679847225546837	0.009563376195728779	0.0351964607834816	-0.037404127418994904	-0.009288283064961433	0.00809205137193203	0.04892977327108383	-0.012303057126700878	-0.0519130676984787	-0.00978950597345829	0.04125473275780678	-0.02738732472062111	-0.04738214239478111	-0.00022477543097920716	-0.05968841537833214	0.01920490898191929	0.006546473130583763	0.025910794734954834	-0.005152334459125996	0.0003742414992302656	-0.001110679586417973	0.15281547605991364	-0.008089439943432808	-0.050738826394081116	-0.04180324822664261	-0.008354445919394493	-0.04459410160779953	0.03753739222884178	0.017365606501698494	0.01524944044649601	0.08175905793905258	-0.021947870030999184	0.03799200803041458	0.046890560537576675	-0.011815132573246956	-0.0066796550527215	-0.13108187913894653	-0.04868132993578911	-0.018639296293258667	-0.00844848807901144	-0.0013990654842928052	0.07412457466125488	0.01116082165390253	0.07135120779275894	0.017919393256306648	-0.07677840441465378	0.021821295842528343	0.02439814619719982	-0.0005691250553354621	0.005245136562734842	0.07963818311691284	0.035083066672086716	0.0593232661485672	0.07068678736686707	-0.024682972580194473	0.011846225708723068	-0.04193727299571037	-0.053377725183963776	-0.01852375827729702	0.013709841296076775	0.051535218954086304	0.008465271443128586	-0.10631060600280762	-0.017418788745999336	0.0006179806659929454	-0.13306210935115814	-0.01931033283472061	0.04151640832424164	-0.02754042111337185	0.08447451144456863	0.06726257503032684	-0.032738201320171356	0.026615213602781296	0.04273482412099838	0.007962609641253948	0.016970502212643623	0.032039739191532135	0.026767568662762642	-0.03158702328801155	0.003557598916813731	0.05622226372361183	-0.010399576276540756	-0.03578405827283859	0.0663204938173294	0.03545594587922096	0.004451229237020016	-0.040903232991695404	-0.03878146409988403	0.010155080817639828	-0.06884601712226868	-0.05144823342561722	0.03574874997138977	-0.022134101018309593	0.017202790826559067	0.005394030828028917	0.06276102364063263	0.005767042748630047	-0.024072714149951935	-0.019041430205106735	-0.0665585920214653	-0.020693544298410416	0.0037323941942304373	-0.08448752015829086	0.004605074878782034	0.004496618174016476	0.004281747154891491	0.015112381428480148	-0.05001526698470116	-0.0231041107326746	-0.007792091462761164	0.00043664968688972294	0.034031059592962265	0.05447982996702194	0.054480575025081635	0.0034110855776816607	-0.003687637858092785	-0.06863576173782349	0.02119474485516548	0.00682792579755187	0.017109055072069168	-0.023693611845374107	-0.012415749952197075	-0.05821075290441513	-0.010139228776097298	-0.003513613250106573	-0.0038154597859829664	-0.05050543695688248	0.0037881052121520042	-0.03645892068743706	-0.04228581115603447	-0.07020703703165054	0.0346541628241539	0.05823482200503349	-0.0001319121365668252	0.06849037110805511	-0.020632384344935417	0.06138235330581665	-0.004991564899682999	-0.028351204469799995	-0.03872331604361534	0.029477907344698906	-0.013389265164732933	0.0677420124411583	0.02740972675383091	-0.04873044043779373	0.07343330979347229	0.03449379280209541	0.061357978731393814	-0.06945855170488358	0.005661488976329565	0.009457522071897984	-0.04205090180039406	0.0601191483438015	0.08813611418008804	-0.03877265751361847	-0.0506707988679409	0.05222756415605545	0.04034572094678879	-0.05490053445100784	-0.012859550304710865	0.01856306754052639	-0.08238188177347183	-0.006780666299164295	-0.030701743438839912	-0.05941622704267502	-0.02548626810312271	0.07166420668363571	-0.04055691510438919	-0.029011273756623268	-0.038153309375047684	0.013239209540188313	0.000234850071137771	-0.05726988986134529	-0.005902939476072788	-0.009528683498501778	-0.08575901389122009	0.04537595063447952	0.017391275614500046	-0.1086699590086937	-0.047154657542705536	-0.011771634221076965	0.04610614478588104	-0.001717594568617642	0.03333715721964836	0.04350819066166878	0.05502801761031151	0.008403349667787552	-0.04904234781861305	0.0470680370926857	-0.020189162343740463	0.0016413102857768536	0.0013895874144509435	0.03955917805433273	-0.038766004145145416	-0.03461537882685661	0.02275356836616993	-0.04026918485760689	-0.04842213913798332	-0.023167138919234276	0.016225403174757957	-0.016562534496188164	-0.0419793576002121	-0.02650185115635395	0.038700640201568604	-0.026710039004683495	0.01486513577401638	-0.008905244991183281	-0.025652319192886353	0.06930430233478546	-0.08326231688261032	-0.06126949191093445	0.041842021048069	-0.00446091266348958	-0.014287594705820084	-0.04987477883696556	0.057015057653188705	-0.035969991236925125	-0.06409966945648193	-0.02458634413778782	-0.07344234734773636	-0.029682595282793045	-0.036392223089933395	0.009681740775704384	-0.02765207178890705	0.10983777791261673	0.017126627266407013	-0.03955584764480591	-0.048011962324380875	-0.04808882251381874	-0.01437789760529995	-0.05670267343521118	0.022357294335961342	-0.026667756959795952	-0.028815709054470062	-0.03515827655792236	0.051006853580474854	-0.025499291718006134	-0.006381541956216097	0.005649675615131855	0.013567906804382801	-0.08026190847158432	0.039324965327978134	-0.04260313883423805	-0.07136286795139313	0.018355978652834892	-0.10267792642116547	0.0036876860540360212	0.08814933151006699	0.06267457455396652	-0.0037561082281172276	0.02149306610226631	0.021173549816012383	0.08686723560094833	-0.03963286057114601	-0.018518459051847458	-0.018181560561060905	-0.03177101910114288	-0.03955088183283806	0.05917561799287796	-0.03436300903558731	0.01871613785624504	-0.005855689290910959	-0.022706957533955574	0.04951747506856918	-0.006541382521390915	-0.04388994351029396	-0.008580798283219337	-0.009076274000108242	-0.027474110946059227	0.024531573057174683	0.017633739858865738	0.03307580202817917	-0.03807585686445236	-0.04026997461915016	-0.11284446716308594	-0.018390977755188942	0.030782010406255722	-0.013151988387107849	0.053786758333444595	-0.023517174646258354	-0.03849425166845322	0.004378783982247114	-0.015676448121666908	0.09847238659858704	-0.03137972578406334	0.01404563058167696	0.04404982924461365	0.02688850462436676	-0.08425426483154297	0.038041356950998306	0.02422321029007435	-0.045877620577812195	-0.10586631298065186	-0.015296007506549358	0.028346991166472435	-0.04889025166630745	-0.03074651211500168	-0.002118195639923215	-0.02662775106728077	0.019605405628681183	0.02649172767996788	0.03342987969517708	0.06544366478919983	0.01281745731830597	-0.04339863732457161	0.058527182787656784	-0.004230199381709099	0.05204255133867264	-0.051798105239868164	-0.004161689896136522	-0.009687875397503376	0.09627960622310638	-0.031191881746053696	-0.026986166834831238	-0.041322261095047	0.03256921470165253	0.026449434459209442	-0.025365585461258888	-0.06109791621565819	-0.044868018478155136	-0.04227514564990997	-0.01522747427225113	0.0279917623847723	-0.0624099038541317	-0.028851758688688278	-0.060977302491664886	-0.05661509186029434	-0.08573373407125473	-0.05652962252497673	0.017084231600165367	-0.02444317378103733	0.019597584381699562	-0.03602157160639763	-0.0047918688505887985	0.06648179888725281	-0.011466934345662594	0.007258416619151831	0.0436505526304245	0.030840281397104263	-0.044447124004364014	0.05865469202399254	-0.023906903341412544	0.06025901436805725	0.018574826419353485	-0.018403662368655205	0.013868767768144608	0.02254277653992176	-0.058792173862457275	0.029389357194304466	-0.028803709894418716	0.025229264050722122	0.004650494549423456	-0.04649937525391579	0.018788333982229233	0.030601365491747856	0.02482684515416622	-0.049482621252536774	0.03730738162994385	-0.0758504793047905	-0.0342336967587471	0.01933968812227249	-0.05747227743268013	-0.061009492725133896	-0.05571528151631355	-0.024511052295565605	-0.03251439705491066	0.021658536046743393	-0.011337044648826122	0.031695958226919174	0.047747984528541565	-0.05536404997110367	-0.03425807133316994	0.011639636941254139	-0.018000172451138496	0.04450191557407379	-0.017653431743383408	0.04804164916276932	0.06766466051340103	0.025030024349689484	0.019437873736023903	-0.011936258524656296	0.013567493297159672	-0.04459691420197487	0.002084937645122409	0.023378951475024223	-0.022375572472810745	-0.006311703007668257	0.027558745816349983	0.032288797199726105	-0.06340378522872925	-0.0023265520576387644	-0.05110781639814377	0.06257691234350204	0.05185989290475845	0.04171290248632431	0.018745018169283867	-0.05301988869905472	-0.0018640109337866306	-0.0023977283854037523	-0.028102800250053406	-0.0875522568821907	-0.004546942189335823	-0.050021860748529434	0.028225822374224663	-0.005466535687446594	-0.007450043689459562	0.013206987641751766	-0.07172976434230804	0.03826574608683586	-0.029875382781028748	-0.052379310131073	0.021586673334240913	0.052041150629520416	-0.027877114713191986	-0.03699539229273796	0.0037270733155310154	-0.06646963953971863	0.037048518657684326	0.012891175225377083	0.04161292687058449	-0.03288354352116585	-0.01664424128830433	-0.02628418616950512	-0.0027608931995928288	0.03359835594892502	-0.049336615949869156	-0.0638025552034378	0.023675955832004547	0.10786468535661697	-0.056390419602394104	0.008610954508185387	0.04510575160384178	-0.009862024337053299	0.02253265306353569	0.0030280500650405884	-0.03380168601870537	0.028038853779435158	-0.005192152224481106	-0.013499259017407894	0.0802697092294693	-0.08122772723436356	0.047233812510967255	-0.09262669086456299	0.10496418178081512	0.02031002566218376	0.002669098088517785	0.10490363091230392	0.04323315620422363	0.016479993239045143	0.03706170991063118	-0.05055952072143555
+0.040831539779901505	-0.004017997533082962	0.018907979130744934	0.09516486525535583	-0.09466711431741714	-0.03744667395949364	-0.04045895114541054	0.04476195201277733	0.021330248564481735	-0.07508236914873123	-0.030382268130779266	-0.053654611110687256	-0.015055784024298191	-0.003232451155781746	0.005396252032369375	-0.00755443936213851	-0.05249367654323578	0.043561216443777084	-0.03974000737071037	-0.04746768996119499	-0.02275095134973526	0.01602105237543583	-0.06509318202733994	0.030652707442641258	0.0024613188579678535	-0.004850773606449366	-0.04892987012863159	0.00978081300854683	-0.03716699406504631	0.059576988220214844	0.042052026838064194	0.03172767534852028	0.03526170551776886	-0.04937555268406868	-0.05925450101494789	0.05422380566596985	-0.032868485897779465	0.09188917279243469	-0.02401406690478325	-0.04528876766562462	-0.03555827960371971	0.011131851002573967	-0.011793570592999458	-0.06649690866470337	0.05628734081983566	-0.004610874690115452	-0.00727086840197444	-0.012132934294641018	0.03212076425552368	0.03567146882414818	0.1379644125699997	0.01588192582130432	0.0031008608639240265	-0.006028834730386734	-0.044720359146595	0.029039056971669197	0.011403676122426987	-0.03534549102187157	-0.026210298761725426	0.04147003963589668	-0.07727588713169098	-0.04637037590146065	0.04933327063918114	0.025493523105978966	-0.046938978135585785	0.02061883918941021	-0.03847234696149826	0.11803711950778961	0.035466425120830536	-0.02643640711903572	0.0031182437669485807	0.1060815155506134	-0.050350986421108246	-0.04891602694988251	0.063030906021595	-0.08995146304368973	-0.05720597878098488	0.03706534579396248	0.01020154170691967	0.006718319840729237	0.05202460289001465	-0.04405335336923599	0.10712815821170807	-0.05520784482359886	-0.03032928705215454	0.011340069584548473	0.009907282888889313	-0.06193748116493225	0.09109164774417877	0.06129933521151543	0.004935215227305889	0.07908101379871368	-0.006213075947016478	0.023761877790093422	-0.005235742311924696	0.026505859568715096	0.047479379922151566	-0.10265415906906128	-0.0362650603055954	-0.00036979958531446755	0.024458730593323708	-0.012179799377918243	0.022806448861956596	0.009268932975828648	0.0693909227848053	0.05022839456796646	-0.016060011461377144	-0.03005073219537735	0.0289072934538126	0.030741240829229355	0.03861663118004799	0.08348528295755386	0.07947170734405518	0.03681465983390808	0.03352998569607735	0.0105315912514925	0.010290087200701237	-0.059128712862730026	-0.0584367960691452	0.03219995275139809	-0.016587916761636734	0.05080002546310425	-0.040051840245723724	-0.06443250179290771	-0.0003895955451298505	-0.0022617215290665627	-0.046614669263362885	-0.010286951437592506	0.04803597927093506	-0.01946844533085823	0.1069035530090332	0.010438960045576096	-0.0051601724699139595	0.01756584458053112	0.07194630056619644	0.047479625791311264	-0.020447274670004845	-0.009556172415614128	0.007233563810586929	0.028247401118278503	0.011677621863782406	0.06942550092935562	-0.022512827068567276	-0.04264433681964874	0.05635916069149971	0.03890226036310196	0.05162147432565689	0.01025751419365406	-0.07454105466604233	0.034755121916532516	-0.04993707314133644	-0.08221711218357086	-0.004992284346371889	-0.07337885349988937	-0.0007148042786866426	0.05578974261879921	0.013418000191450119	0.024081088602542877	-0.015596932731568813	-0.025147825479507446	-0.016365783289074898	-0.030841616913676262	-0.013401507399976254	-0.0789949968457222	-0.04763171821832657	-0.05923132598400116	-0.044203877449035645	0.01969623938202858	-0.04720606654882431	-0.03087230958044529	-0.00986013188958168	0.016903551295399666	0.046860188245773315	0.114028699696064	0.07082373648881912	0.044072870165109634	0.015133812092244625	-0.03792700916528702	-0.0325939767062664	-0.03760548308491707	-0.0061678229831159115	-0.0055511873215436935	0.026375843212008476	-0.0479249507188797	0.02274804562330246	0.02080344781279564	0.0655088871717453	0.00998961552977562	0.03807744383811951	0.035173337906599045	-0.00935530848801136	-0.026359565556049347	0.06043354421854019	0.05589917674660683	0.005469353869557381	0.04003102332353592	-0.02625117637217045	0.038187406957149506	0.0014001289382576942	-0.06578587740659714	-0.025171129032969475	0.03397995978593826	0.03209898993372917	0.06991120427846909	0.05171526223421097	-0.08842378109693527	0.0925213173031807	0.06368526071310043	0.015752680599689484	-0.0421682707965374	-0.004901095759123564	0.02273905836045742	0.05719120055437088	0.05298741161823273	0.06838705390691757	0.016986656934022903	-0.00024177222803700715	0.021058974787592888	0.04580439627170563	-0.025175252929329872	0.018649708479642868	0.010691231116652489	-0.10590473562479019	0.04114817455410957	-0.028948424383997917	-0.050491753965616226	-0.03777262195944786	0.03930150344967842	-0.026378251612186432	-0.00025572863523848355	0.04884485900402069	0.001866898383013904	-0.02623932622373104	-0.038574736565351486	-0.028158750385046005	0.0074044992215931416	-0.10144341737031937	0.09561913460493088	0.0009384223376400769	-0.05848366394639015	-0.058981895446777344	0.016181718558073044	0.006212983280420303	-0.007171740289777517	0.048024002462625504	0.07235034555196762	0.0866965800523758	0.0448443703353405	0.030514761805534363	0.0034413288813084364	-0.08167092502117157	-0.06275779753923416	0.0055878739804029465	0.027529381215572357	-0.04108932986855507	-0.018809430301189423	-0.01258531492203474	-0.0904715359210968	-0.011843832209706306	0.03320831060409546	0.022511424496769905	-0.03314290568232536	0.01202573161572218	-0.02322596125304699	0.04729478806257248	-0.05092644318938255	-0.06372305005788803	0.031695444136857986	-0.017933905124664307	0.03577769547700882	-0.04893571883440018	-0.014034206978976727	0.05755827948451042	0.007583362516015768	0.035331111401319504	-0.025467609986662865	0.05824330076575279	-0.04476364329457283	-0.07195057719945908	-0.0027339423540979624	-0.09953179210424423	-0.04234825447201729	-0.014721577987074852	-0.047776754945516586	-0.052740491926670074	0.076675646007061	0.0049357227981090546	-0.01053515262901783	-0.08692286908626556	-0.03746689110994339	-0.04165549576282501	-0.03971456363797188	-0.008951808325946331	-0.01114712655544281	-0.035011887550354004	-0.02878282032907009	0.011516940779983997	-0.03941190615296364	-0.00355715723708272	0.010660194791853428	-0.015595834702253342	-0.04359700530767441	0.07310672104358673	-0.020081549882888794	0.00365848490037024	0.035701826214790344	-0.043180566281080246	0.013286583125591278	0.10255183279514313	0.050499171018600464	0.015297776088118553	0.07676416635513306	0.02668634243309498	0.07381311058998108	0.05881097912788391	-0.06906221807003021	-0.03662577271461487	-0.013424444943666458	-0.04718530923128128	0.028284715488553047	-0.001959337154403329	-0.04904814437031746	0.012070814147591591	0.01873835176229477	0.060988519340753555	-0.003114921972155571	-0.0426783561706543	-0.020081311464309692	0.04463228955864906	-0.0044247121550142765	0.02423391118645668	0.007662045303732157	0.014086127281188965	-0.02385185845196247	-0.04248466342687607	0.031780701130628586	-0.010201974771916866	0.04216381162405014	0.020986638963222504	-0.009710033424198627	-0.02573130466043949	-0.04751776158809662	0.012809843756258488	-0.026108479127287865	0.08879999816417694	-0.07900445908308029	0.01901012286543846	0.08368746191263199	0.022908901795744896	-0.08533546328544617	0.00025535692111589015	0.02099391259253025	-0.04655437171459198	-0.05453085899353027	-0.01365378312766552	0.026713158935308456	-0.05266012251377106	-0.018562545999884605	-0.02241687662899494	-0.04869437590241432	-0.011756733991205692	0.012966901063919067	0.014708153903484344	0.017512233927845955	-0.002886856673285365	-0.05057745426893234	-0.016703229397535324	-0.01792103424668312	0.0031070190016180277	-0.030086608603596687	0.003631220431998372	-0.017264027148485184	0.016705503687262535	-0.017067451030015945	-0.0398847833275795	-0.02068953774869442	0.038690000772476196	-0.007497122511267662	-0.03712339699268341	0.019807137548923492	-0.04917304962873459	0.005237537436187267	0.021966062486171722	0.023130852729082108	-0.04467739164829254	-0.03705272823572159	-0.04550578072667122	-0.026459885761141777	-0.07999221235513687	-0.05507008731365204	0.00891456101089716	-0.06659495085477829	-0.0018610050901770592	-0.052771568298339844	-0.026183288544416428	-0.030838746577501297	0.01063627377152443	0.04335954785346985	0.03923606127500534	0.004106373060494661	-0.01843869499862194	0.004806114826351404	-0.03382765129208565	0.040196046233177185	0.028144855052232742	-0.09432759135961533	-0.0017288047820329666	0.02270868793129921	-0.03626811504364014	0.04688528925180435	-0.013085836544632912	-0.001986253308132291	0.0015462873270735145	-0.014576920308172703	0.015191294252872467	-0.005626930855214596	-0.034943122416734695	-0.007216759957373142	0.03586667403578758	-0.037521980702877045	-0.020801831036806107	0.017856653779745102	-0.008295373059809208	-0.0766378715634346	-0.03557366877794266	-0.020479906350374222	-0.02752804383635521	0.027079470455646515	-0.026029445230960846	-0.018900886178016663	-0.006239541806280613	-0.09805110096931458	-0.03995542600750923	-0.06798025220632553	-0.004127450753003359	0.05116656795144081	0.008274633437395096	0.03281009569764137	-0.015011431649327278	-0.02555534802377224	-0.0367882139980793	-0.008772789500653744	0.033652130514383316	-0.05215967446565628	-0.02048063836991787	-0.00834058690816164	-0.025574220344424248	0.01679563708603382	0.033686261624097824	-0.012816594913601875	-0.06302440166473389	-0.044243354350328445	-0.010866339318454266	0.05763613060116768	-0.026926111429929733	0.0019344700267538428	0.024317920207977295	-0.02883945219218731	0.004273988772183657	0.049365587532520294	-0.04690300300717354	-0.05858900025486946	0.023839985951781273	-0.013628211803734303	0.026012340560555458	0.03618154674768448	0.04651811718940735	-0.08033721894025803	-0.04136252775788307	-0.010586442425847054	0.012861580587923527	-0.09372866898775101	-0.041208457201719284	0.011852345429360867	-0.09530565142631531	-0.04890793189406395	-0.009385463781654835	-0.05262500047683716	0.10043332725763321	0.03267236426472664	0.031569067388772964	-0.029143935069441795	-0.006155898328870535	7.799106242600828e-05	-0.04364063963294029	-0.0014844955876469612	-0.017330504953861237	-0.03641741722822189	-0.012931599281728268	0.05533125624060631	-0.04868827760219574	0.03338561952114105	0.053254712373018265	-0.055862415581941605	0.05471811816096306	0.038985688239336014	-0.03406064584851265	0.049600064754486084	0.038470130413770676	-0.03206820413470268	0.055322058498859406	-0.08693291246891022	0.07083349674940109	-0.042585041373968124	0.09338721632957458	0.02214699052274227	-0.01296122744679451	0.02982441335916519	0.11675065010786057	-0.017806053161621094	0.014574168249964714	-0.07053976505994797
+0.021053364500403404	-0.03655913472175598	-0.03384029492735863	0.02908587083220482	-0.07671231031417847	-0.02947954088449478	-0.012945067137479782	0.06993553787469864	-0.005661049857735634	-0.058705247938632965	-0.025656694546341896	0.012171573005616665	0.01397240161895752	0.015067756175994873	0.007324108853936195	-0.050201449543237686	-0.06529925763607025	-0.004294582176953554	-0.04826296120882034	-0.060367584228515625	0.041480906307697296	0.01161729171872139	-0.051769036799669266	0.06745660305023193	0.012960505671799183	0.005694146268069744	-0.02318987064063549	0.01421511359512806	0.004164690617471933	0.0480809211730957	0.08620846271514893	-0.016044827178120613	0.04089297354221344	-0.04545038938522339	-0.05046701803803444	0.07426268607378006	-0.06077434495091438	0.1199747696518898	-0.0032243365421891212	0.011568143032491207	-0.05748848617076874	-0.005840092431753874	-0.026229199022054672	-0.031717732548713684	0.053137946873903275	-0.0014623733004555106	0.007249535992741585	-0.011216928251087666	0.020032135769724846	0.014374788850545883	0.07528763264417648	0.005137252621352673	0.02807212620973587	0.00511153182014823	-0.006064375396817923	0.06732328981161118	0.027690410614013672	-0.02877708710730076	-0.02157261222600937	0.015515954233705997	-0.06060280650854111	-0.020211128517985344	0.016295425593852997	-0.014950800687074661	0.005455102771520615	0.017191117629408836	-0.021863572299480438	0.09097965806722641	0.012340101413428783	-0.021298568695783615	0.008718404918909073	0.0634641945362091	-0.057563722133636475	-0.05131128430366516	0.06082265451550484	-0.03621453791856766	-0.025779802352190018	0.02553124725818634	-0.02812138758599758	0.01166585087776184	-0.004576996900141239	-0.003235073760151863	0.14763039350509644	-0.02627917192876339	-0.054675716906785965	-0.044925156980752945	-0.011507382616400719	-0.054327815771102905	0.0889308899641037	0.005802219267934561	0.0179713387042284	0.08073684573173523	-0.03336707502603531	0.024828145280480385	0.008571270853281021	-0.005850715562701225	-0.00020854981266893446	-0.11921476572751999	-0.031017186120152473	0.006371319759637117	0.021547749638557434	-0.015863768756389618	0.08741101622581482	-0.02198808081448078	0.05478638410568237	0.08126368373632431	-0.06460076570510864	-0.02174203097820282	0.007379232440143824	0.00868278555572033	0.012332161888480186	0.1443525105714798	0.044305115938186646	0.03133770078420639	0.07879502326250076	-0.0005989805795252323	-0.003779603634029627	-0.026250211521983147	-0.05704358592629433	-0.024267898872494698	-0.03191512078046799	0.05378897115588188	-0.014460360631346703	-0.06471161544322968	-0.0385146290063858	0.03300657868385315	-0.12641775608062744	-0.017530381679534912	0.09882206469774246	-0.04065253213047981	0.09343482553958893	0.05045422166585922	-0.00642276369035244	0.027699505910277367	0.06397542357444763	0.007830279879271984	-0.0025385122280567884	0.02471957542002201	0.0010175592033192515	-0.0372159481048584	-0.02859567292034626	0.05570165812969208	-0.006741236429661512	-0.05803690478205681	0.046848274767398834	0.06480121612548828	0.015418843366205692	-0.010378274135291576	-0.04583459720015526	0.026688557118177414	-0.06433098018169403	-0.09001536667346954	-0.017945798113942146	-0.04192148894071579	-0.003319224575534463	0.03738982230424881	0.015926524996757507	0.02145356871187687	-0.0026100119575858116	-0.04759339243173599	-0.039998117834329605	-0.035334598273038864	-0.016894778236746788	-0.07546596229076385	0.006957515608519316	-0.017425864934921265	-0.004347205627709627	0.007086385041475296	-0.03929468244314194	-0.02340412326157093	-0.00570126436650753	-0.024286698549985886	0.01929575391113758	0.07955951243638992	0.04624996334314346	0.02638069912791252	0.020389791578054428	-0.04868151620030403	-0.032172758132219315	-0.04973596706986427	0.021617088466882706	-0.04192622750997543	0.021449388936161995	-0.06610722839832306	-0.0008861195528879762	0.010354770347476006	0.03144616633653641	-0.02181066758930683	-0.004688158165663481	-0.03539580851793289	-0.01836887188255787	-0.05942409858107567	0.05156935006380081	0.05472352355718613	-0.03307362273335457	0.03321472182869911	0.017034437507390976	0.062639020383358	-0.02457227185368538	-0.021688686683773994	-0.0769832506775856	0.05101558566093445	0.03220582753419876	0.025377532467246056	0.0413225032389164	-0.06607849150896072	0.08014947175979614	-0.008765041828155518	0.04819142818450928	-0.06610237807035446	0.02858240157365799	0.041437000036239624	-0.012221687473356724	0.08053142577409744	0.05493035912513733	-0.007391166407614946	-0.03728373721241951	0.005323546007275581	0.04399408400058746	-0.08607114851474762	-0.0268947035074234	-0.005621185526251793	-0.07304305583238602	0.0268678218126297	-0.027761023491621017	-0.09139015525579453	-0.04550008103251457	0.037187088280916214	-0.009275125339627266	-0.031722795218229294	-0.02451479621231556	0.03326353803277016	-0.009510636329650879	-0.03381015732884407	0.02061496116220951	-0.008068076334893703	-0.08398112654685974	0.0818968117237091	-0.016870278865098953	-0.08539101481437683	-0.056184135377407074	0.020190652459859848	0.04414942115545273	-0.012506076134741306	0.04030332341790199	0.04279780760407448	0.06682951748371124	0.01913057640194893	-0.033326707780361176	0.00382663169875741	-0.03140554949641228	-0.009746143594384193	-0.030259406194090843	0.03487471863627434	-0.006815145257860422	-0.06834028661251068	0.030170045793056488	-0.05155646428465843	0.004213893320411444	0.0032103420235216618	0.06539178639650345	-0.06316061317920685	-0.025303037837147713	-0.018665827810764313	-0.0038095032796263695	-0.05696568265557289	-0.01203357707709074	0.026371773332357407	-0.04234941303730011	0.04614539071917534	-0.04402954876422882	-0.021560439839959145	-0.0015995765570551157	0.01037958450615406	0.025039054453372955	-0.0476309135556221	0.08331532776355743	-0.019759485498070717	-0.08512545377016068	-0.03645631670951843	-0.034586600959300995	-0.066202811896801	-0.027906518429517746	-0.05107211321592331	-0.0256101842969656	0.10254094749689102	0.021470360457897186	-0.059891000390052795	-0.07039094716310501	-0.04757044464349747	-0.004292742349207401	-0.01505438145250082	-0.007480186875909567	-0.01977522484958172	-0.05256402865052223	-0.0020138113759458065	0.04690634831786156	-0.0398595817387104	0.0043657985515892506	0.04852685704827309	0.030281923711299896	-0.02211342379450798	0.056221239268779755	-0.05274852365255356	-0.01973787508904934	-0.012265894562005997	-0.04512486234307289	0.02521332912147045	0.08056437969207764	0.06995052844285965	0.012399420142173767	0.0030119060538709164	0.051897916942834854	0.09665844589471817	0.0027733861934393644	-0.00671991566196084	-0.008428695611655712	-0.029202109202742577	-0.037643007934093475	0.05375008285045624	-0.03310249373316765	-0.035365764051675797	-0.009162288159132004	-0.02238434925675392	0.06592556089162827	-0.001167249632999301	-0.001203044201247394	-0.02911611832678318	0.04574865475296974	0.011930572800338268	0.05186877027153969	0.003145400667563081	0.00045237797894515097	-0.0010545110562816262	-0.0473489873111248	-0.06096293032169342	-0.06258206814527512	0.04856488108634949	0.00513096060603857	0.042832717299461365	-0.008703642524778843	-0.06657297164201736	0.02282704971730709	0.0020072124898433685	0.09866674989461899	-0.06787559390068054	-0.0016318566631525755	0.057590384036302567	0.035831864923238754	-0.08196556568145752	0.016625599935650826	0.0030497792176902294	-0.040369778871536255	-0.08564472198486328	-0.004987072199583054	0.0475434809923172	-0.04743558540940285	-0.038996994495391846	-0.03044065274298191	-0.03964472562074661	-0.01517192367464304	0.00857609324157238	-0.011295653879642487	0.04831364005804062	0.031029094010591507	-0.02246197499334812	0.03718845918774605	0.04204961657524109	0.022749636322259903	-0.047783952206373215	0.029307859018445015	-0.019404981285333633	0.06322943419218063	-0.05350746214389801	-0.058469418436288834	-0.05692586302757263	0.04314139485359192	-0.041230179369449615	-0.017891766503453255	-0.07132106274366379	-0.03252304717898369	-0.023551421239972115	0.013950575143098831	-0.016527468338608742	-0.10250173509120941	-0.010458611883223057	-0.0184163935482502	-0.09752410650253296	-0.09248176962137222	-0.059485048055648804	0.027274049818515778	-0.04026735946536064	0.009678163565695286	-0.058721281588077545	0.009497704915702343	0.022247621789574623	0.021202029660344124	0.04751313105225563	0.07264914363622665	0.041642963886260986	-0.01538144052028656	0.08433862775564194	0.027860518544912338	0.037531450390815735	0.008452563546597958	-0.01730734296143055	0.020466523244976997	0.017830269411206245	-0.039682790637016296	0.055030811578035355	-0.03074677474796772	0.01935810223221779	-0.01528051495552063	-0.0341058224439621	0.000519859604537487	-0.0033323979005217552	0.0068370262160897255	-0.0575886107981205	0.042367447167634964	-0.048975810408592224	-0.023474635556340218	0.05106160789728165	-0.03999234363436699	-0.07103770226240158	-0.04905276373028755	-0.043049413710832596	-0.01632017269730568	0.046098195016384125	0.011096145957708359	0.008274441584944725	0.02070092037320137	-0.03530218452215195	-0.05328897759318352	-0.01645752787590027	-0.024813108146190643	-0.005681609269231558	0.02282235585153103	0.0701674371957779	0.036306239664554596	0.012448519468307495	-0.0026004277169704437	-0.049644775688648224	0.009007925167679787	-0.07063290476799011	0.015443137846887112	0.024127354845404625	-0.05192679166793823	0.010005597025156021	0.006093318574130535	0.015432456508278847	-0.029365550726652145	-0.004230036400258541	-0.04947768524289131	0.06532034277915955	-0.015934603288769722	0.03919032961130142	-0.004591392818838358	-0.03361748903989792	0.005042851436883211	0.016190389171242714	-0.04817265272140503	-0.0747314915060997	0.01365011278539896	-0.029825124889612198	0.02476973459124565	-0.025854697450995445	-0.0008800001232884824	-0.006485689431428909	-0.11149799823760986	-0.00980181060731411	-0.022933388128876686	-0.03301273658871651	0.02508094161748886	0.023724984377622604	-0.07639019936323166	-0.04195920377969742	-0.01421272661536932	-0.0542387031018734	0.06814731657505035	-0.02821981906890869	0.049220602959394455	-0.037949204444885254	-0.03115721419453621	-0.018931090831756592	-0.027498653158545494	0.002797540742903948	-0.03747051954269409	-0.05337945744395256	-0.02681550942361355	0.0603550486266613	-0.028697311878204346	0.013031353242695332	0.08825992792844772	-0.029411599040031433	0.033408526331186295	0.03567078709602356	-0.015432437881827354	0.026406819000840187	0.008038775064051151	-0.012649249285459518	0.03578954562544823	-0.06246670335531235	0.0873575359582901	-0.07137244194746017	0.08802998811006546	0.021735645830631256	0.01961427740752697	0.08781521767377853	0.06855004280805588	0.02707459218800068	0.03570365533232689	-0.06793168187141418
+0.026330340653657913	-0.03916891664266586	-0.0019865846261382103	-0.004150546155869961	-0.04376200586557388	-0.06641820818185806	-0.010271679610013962	0.02031070366501808	-0.009589174762368202	-0.0782369002699852	-0.05115634948015213	-0.028299866244196892	-0.011541252955794334	-0.010238278657197952	0.03711232170462608	-0.04179200902581215	-0.05057666450738907	0.025949066504836082	-0.04160464555025101	-0.02087167464196682	0.03326493501663208	0.02468697912991047	-0.04800890386104584	0.03964363411068916	0.004757030867040157	0.014740919694304466	-0.025223281234502792	0.006363223306834698	0.027403634041547775	0.059715818613767624	0.0640767440199852	-0.01806914433836937	0.049923527985811234	-0.04190649837255478	-0.024450132623314857	0.040005531162023544	-0.0289104413241148	0.09485338628292084	0.014580752700567245	0.009152069687843323	-0.04665729030966759	-0.021898681297898293	-0.01080726832151413	-0.058779630810022354	0.022907530888915062	-0.016404224559664726	-0.0038233152590692043	-0.009490630589425564	-0.009217235259711742	0.04947827011346817	0.09219963103532791	-0.0005414960323832929	-0.014739527367055416	-0.005190548952668905	0.01960090920329094	-0.004422233439981937	0.027108285576105118	-0.032060232013463974	-0.02703876607120037	0.036925338208675385	-0.09387220442295074	-0.01105817873030901	0.008097557350993156	1.9005025023943745e-05	-0.019832424819469452	-0.034920647740364075	-0.014509682543575764	0.10221721231937408	-0.015883417800068855	-0.04866034537553787	0.01717781089246273	0.06848706305027008	-0.061804745346307755	-0.0696486383676529	0.037564169615507126	-0.06812169402837753	0.03820161148905754	0.02168056182563305	-0.020094361156225204	-0.011979005299508572	-0.012983638793230057	-0.03020685352385044	0.16105064749717712	-0.06009560450911522	-0.04322628676891327	-0.056023575365543365	-0.03740297257900238	-0.07169181853532791	0.0642598420381546	0.01613793894648552	0.05485484376549721	0.03852823004126549	-0.008769577369093895	0.011998198926448822	-0.00730959651991725	-0.005918068811297417	0.016164394095540047	-0.08256845921278	-0.025369446724653244	-0.00480391550809145	-0.005479241721332073	0.0029409199487417936	0.06928665190935135	0.025290504097938538	0.03843480721116066	0.03502630442380905	-0.025211116299033165	0.01197719294577837	0.04287593066692352	0.03484947234392166	0.014808328822255135	0.1059335395693779	0.08290529996156693	0.042738914489746094	0.06475057452917099	0.0196351557970047	-0.0011235965648666024	-0.04262259230017662	0.00431973347440362	0.004447047132998705	0.020716138184070587	0.06265892088413239	-0.01257340982556343	-0.0727856382727623	-0.009899997152388096	0.06164705008268356	-0.09921422600746155	-0.04206660017371178	0.06440731137990952	-0.02076948806643486	0.11399302631616592	0.065301813185215	-0.043545566499233246	0.04943972826004028	0.03902999311685562	-5.6567718274891376e-05	-0.021205149590969086	0.003278197953477502	0.005490825977176428	-0.0685005784034729	-0.029538650065660477	0.0316360779106617	-0.04314161092042923	-0.04296783357858658	0.0419209785759449	0.04331256449222565	0.0398971289396286	-0.06218304857611656	-0.05547579005360603	0.0016513183945789933	-0.057237837463617325	-0.06167779117822647	0.0054879020899534225	-0.05011242255568504	0.005782793276011944	0.011061613447964191	-0.004864387679845095	0.002462830627337098	-0.0007550533628091216	-0.0069912998005747795	-0.0695384219288826	-0.06955886632204056	-0.02547634392976761	-0.07955044507980347	-0.0031667435541749	0.025921378284692764	0.013760112226009369	0.017637133598327637	-0.09186117351055145	-0.059807781130075455	-0.009213350713253021	-0.02447371557354927	0.02616301365196705	0.0778271034359932	0.06679881364107132	0.011807220056653023	0.012959879823029041	-0.055973298847675323	-0.012936468236148357	-0.0357777364552021	0.047899533063173294	-0.023910624906420708	0.02718673087656498	-0.06599584966897964	0.001848217099905014	0.0025339454878121614	0.007369657047092915	-0.03570478409528732	0.030906405299901962	0.017054961994290352	-0.014731932431459427	-0.04865610599517822	0.019725581631064415	0.061627186834812164	-0.025921473279595375	0.038316600024700165	-0.018031077459454536	0.10413387417793274	-0.047479018568992615	-0.05435238033533096	-0.07863491028547287	0.0005029379390180111	0.033095408231019974	0.03653424233198166	0.014869973063468933	-0.07603532075881958	0.09501423686742783	0.036603014916181564	0.02317621372640133	-0.051657333970069885	0.04559694230556488	0.02560211718082428	0.014637267217040062	0.06697391718626022	0.05933016538619995	-0.0379292331635952	-0.002418596763163805	-0.006444484926760197	0.036462314426898956	-0.05756659060716629	-0.024352990090847015	-0.01941901445388794	-0.0853591114282608	-0.0022657213266938925	-0.06901510059833527	-0.06577444821596146	-0.023310374468564987	0.03294696286320686	-0.006016931030899286	-0.039651140570640564	-0.004725412931293249	0.032470475882291794	-0.019581709057092667	0.01748514361679554	0.02212916873395443	-0.040083661675453186	-0.10128872841596603	0.06959221512079239	0.0013192174956202507	-0.08620915561914444	-0.06450814753770828	-0.018086416646838188	0.029847830533981323	-0.0027097463607788086	0.036965981125831604	0.034717269241809845	0.08269522339105606	0.022198263555765152	-0.047947224229574203	-0.0018338061636313796	-0.013279062695801258	-0.034389711916446686	-0.013438276946544647	-0.03152163699269295	-0.040544427931308746	-0.07459751516580582	0.0317758210003376	-0.0860721692442894	-0.011708972975611687	-0.02966880239546299	0.022140096873044968	-0.040275223553180695	-0.019084563478827477	-0.010672101750969887	0.03025008738040924	-0.03388439118862152	-0.033596038818359375	0.03960450366139412	-0.030346697196364403	0.026327036321163177	-0.020034167915582657	0.02168569713830948	0.038803085684776306	-0.033370185643434525	0.002322313142940402	-0.04896020144224167	0.10341472178697586	0.015425877645611763	-0.04356905445456505	-0.022134792059659958	-0.06249250844120979	-0.05762587860226631	0.00946713611483574	-0.014836283400654793	-0.012071788311004639	0.04018803685903549	0.03866364806890488	-0.06045902892947197	-0.0341862216591835	-0.05174339935183525	-0.04813561215996742	-0.03763189539313316	-0.04147273674607277	-0.025464380159974098	-0.025414029136300087	0.010034422390162945	0.030982453376054764	-0.05491103231906891	-0.009482241235673428	0.031094815582036972	0.00828253012150526	-0.06418807059526443	0.02486962080001831	-0.02414429746568203	-0.020813189446926117	0.013052895665168762	-0.04673973098397255	0.035919517278671265	0.0781019851565361	0.08322200179100037	0.013687250204384327	0.04775924235582352	0.030433395877480507	0.09242615848779678	-0.014242881909012794	0.0002654453564900905	-0.03981027752161026	-0.044296398758888245	-0.040016304701566696	0.057206012308597565	-0.0021008916664868593	-0.0017844465328380466	0.00855060201138258	0.012065308168530464	0.03867664933204651	-0.021081572398543358	-0.036736391484737396	-0.01711806282401085	0.00622606510296464	-0.034352857619524	0.00858896505087614	-0.008382470346987247	0.05117226764559746	-0.014078624546527863	-0.06548558920621872	-0.07433157414197922	-0.045758992433547974	0.04835402965545654	-0.027491804212331772	0.050242431461811066	-0.04846484214067459	-0.0465797558426857	-0.04411880671977997	0.01316798571497202	0.12603259086608887	-0.030217505991458893	0.015131114982068539	0.02972925826907158	0.06713502109050751	-0.11806222051382065	0.011386857368052006	-0.00834878720343113	-0.020518247038125992	-0.06748682260513306	-0.0010157993528991938	0.03156518563628197	-0.0696607157588005	-0.038993798196315765	-0.021854301914572716	-0.00864273402839899	0.006077388767153025	-0.0007169244345277548	-0.005179580766707659	0.04974357783794403	0.026708489283919334	-0.04732629284262657	-0.013807209208607674	-0.0087614506483078	0.02533770352602005	-0.03042556717991829	0.02091289311647415	-0.0408509187400341	-0.0024900250136852264	-0.05980430915951729	-0.06073395162820816	-0.03476805239915848	0.03904313966631889	-0.02728770487010479	0.004511035047471523	-0.019358597695827484	-0.0296156108379364	-0.032585419714450836	0.00809671264141798	0.00021168428065720946	-0.06382952630519867	-0.008736543357372284	-0.034578002989292145	-0.05718665197491646	-0.07718721032142639	-0.04862019419670105	-0.006261210888624191	-0.00026775395963341	0.03682154417037964	-0.011374931782484055	0.0315527617931366	0.007146118674427271	0.021614449098706245	0.07094060629606247	0.07819222658872604	0.011431197635829449	0.008541744202375412	0.05136795714497566	0.006197783164680004	0.04662594944238663	0.022672023624181747	-0.06928961724042892	-0.02300875633955002	-0.008120259270071983	-0.05420694872736931	0.05525897815823555	-0.03538849949836731	0.003853489877656102	-0.024144615978002548	-0.029313230887055397	0.021381039172410965	-0.020685037598013878	0.024417819455266	-0.026584306731820107	0.06904832273721695	-0.07077440619468689	-0.055343128740787506	0.020300228148698807	-0.03168613091111183	-0.08197562396526337	-0.056363221257925034	-0.03740008547902107	-0.02689202129840851	0.05274565517902374	-0.04517069458961487	0.044986072927713394	0.01682705618441105	-0.016317864879965782	-0.056328706443309784	0.005540578160434961	0.00723438523709774	0.018068654462695122	0.03212420642375946	0.044972099363803864	0.016090216115117073	0.02372928336262703	0.020004533231258392	-0.04786063730716705	0.01955460198223591	-0.08951476216316223	0.009938757866621017	0.003283013589680195	-0.0020354394800961018	-0.0013523470843210816	0.03374243527650833	0.03306486830115318	-0.06333523988723755	-0.008383174426853657	-0.07303982228040695	0.05882350355386734	0.022138694301247597	0.06458505988121033	-0.01203148066997528	-0.0891110971570015	-0.03449888527393341	0.03221835568547249	-0.03169175982475281	-0.08998558670282364	-0.026676278561353683	-0.03448091447353363	0.04572627320885658	-0.02198185585439205	0.03890478238463402	-0.029118865728378296	-0.07855172455310822	0.051138736307621	0.0156844574958086	-0.013681541197001934	-0.005814115982502699	0.06293143332004547	-0.05314495041966438	-0.032418057322502136	0.015161298215389252	-0.060767482966184616	0.026514807716012	0.02415682189166546	0.029746240004897118	-0.04531307518482208	0.004750785883516073	0.0044401343911886215	-0.032786279916763306	0.03213115781545639	-0.006916035898029804	-0.04073762148618698	-0.02175888791680336	0.09360228478908539	-0.03415706008672714	0.03895362466573715	0.0702085942029953	-0.040769800543785095	0.04556048661470413	0.05827438458800316	-0.02914191596210003	0.04955558106303215	0.0073904311284422874	-0.0008835300686769187	0.03347965329885483	-0.09903880953788757	0.07532008737325668	-0.08091804385185242	0.13030317425727844	0.04537101835012436	-0.013568353839218616	0.07387460768222809	0.04860839247703552	0.029950322583317757	0.060200441628694534	-0.09410587698221207
+0.04540681093931198	-0.022060249000787735	-0.024707389995455742	0.009226447902619839	-0.048304252326488495	-0.05102994665503502	-0.02666483446955681	0.031842175871133804	-0.014394782483577728	-0.08322198688983917	-0.05286237969994545	-0.03123554401099682	-0.019326556473970413	-0.00862693227827549	0.03737233206629753	-0.056317828595638275	-0.0529995933175087	0.033054567873477936	-0.01989678107202053	-0.03280166909098625	0.017223725095391273	0.0008602077141404152	-0.050835996866226196	0.06771494448184967	0.02285884879529476	0.00552818039432168	-0.022523915395140648	0.015352127142250538	0.02735026739537716	0.08501690626144409	0.07490287721157074	-0.03848377615213394	0.04760848358273506	-0.05286082625389099	-0.021196847781538963	0.04014609009027481	-0.03995342180132866	0.11100958287715912	0.02689512073993683	0.0007146238931454718	-0.07136353105306625	-0.012550577521324158	-0.035482726991176605	-0.0680302232503891	0.029041768983006477	-0.013623546808958054	-0.020716212689876556	-0.01166424248367548	0.015962934121489525	0.04827500507235527	0.07866480201482773	-0.009207756258547306	-0.016518259420990944	-0.0009695615735836327	-0.006222767289727926	0.027372058480978012	0.029709691181778908	-0.026362352073192596	-0.015203524380922318	0.037548668682575226	-0.10045862197875977	-0.008463867008686066	0.01917305774986744	0.017862312495708466	-0.009946692734956741	-0.040597669780254364	-0.011043285019695759	0.09042620658874512	-0.01082999724894762	-0.03684093430638313	-0.0020179355051368475	0.08337670564651489	-0.07011396437883377	-0.06479351967573166	0.027434779331088066	-0.08532212674617767	-0.007009499706327915	0.003368726000189781	0.013359901495277882	0.014068848453462124	0.005119624547660351	-0.03036201372742653	0.16156664490699768	-0.018382418900728226	-0.02465551532804966	-0.04998157173395157	-0.03861667960882187	-0.050882089883089066	0.07632654160261154	0.017310280352830887	0.05397958680987358	0.04328266903758049	-0.00239773653447628	0.0017825710820034146	0.013576927594840527	-0.016111932694911957	0.012908067554235458	-0.07706966996192932	-0.015179678797721863	-0.004785499535501003	0.01215500570833683	0.010020428337156773	0.05682414397597313	0.03785074129700661	0.057907864451408386	0.03827819973230362	-0.05750742182135582	0.01657700538635254	0.06903979182243347	0.01836840808391571	0.025821123272180557	0.12793710827827454	0.06546523422002792	0.022879809141159058	0.065047487616539	0.022303273901343346	0.015210303477942944	-0.051179997622966766	-0.014995238743722439	0.007629592437297106	-0.00075020786607638	0.06113085523247719	-0.012321527116000652	-0.07798437774181366	-0.00022443674970418215	0.06689678132534027	-0.09244105964899063	-0.0497090220451355	0.03715052455663681	-0.007243048865348101	0.09123998880386353	0.07876966893672943	-0.024030618369579315	0.03100535087287426	0.051237668842077255	0.00042447171290405095	-0.0248134545981884	0.026811156421899796	0.01065440010279417	-0.03820859268307686	-0.03383668512105942	0.037642575800418854	-0.03831036388874054	-0.049095846712589264	0.056762028485536575	0.034250423312187195	0.02451791986823082	-0.03411346673965454	-0.05095350369811058	-0.0030507808551192284	-0.04479384422302246	-0.05145080015063286	0.04070444777607918	-0.04235661029815674	-0.008501207455992699	0.02666436694562435	0.009149322286248207	0.005356884095817804	0.0033048996701836586	-0.03334881737828255	-0.05974583327770233	-0.05641927570104599	-0.01476083043962717	-0.08424843102693558	-0.015798307955265045	0.010383613407611847	0.009314731694757938	0.027038902044296265	-0.09788542985916138	-0.033086441457271576	-0.007106998469680548	-0.028599409386515617	0.03312258422374725	0.07679089903831482	0.06471890211105347	0.026454459875822067	0.012232019565999508	-0.06346780061721802	-0.009175813756883144	-0.04852796718478203	0.0359724797308445	-0.041604772210121155	0.01650283858180046	-0.05068381875753403	0.01530272513628006	0.010123662650585175	0.024265918880701065	-0.006524957716464996	0.015117262490093708	0.011252914555370808	-0.022311493754386902	-0.054582055658102036	0.03901347890496254	0.02819746360182762	-0.013436296954751015	0.02637057565152645	-0.016410773620009422	0.09028659015893936	-0.023120883852243423	-0.03643130883574486	-0.08385162055492401	0.019816001877188683	0.03455471619963646	0.036102671176195145	0.05062440410256386	-0.05460672453045845	0.08211216330528259	0.033650416880846024	0.04112671688199043	-0.04494784027338028	0.007488157134503126	0.01912660337984562	-0.0054061501286923885	0.05148761346936226	0.05988701432943344	-0.026753433048725128	-0.009743516333401203	0.021529771387577057	0.0569026879966259	-0.061692431569099426	-0.04526697099208832	-0.006861328613013029	-0.09969830513000488	0.009132343344390392	-0.05664663016796112	-0.07144910097122192	-0.025746585801243782	0.0490509495139122	-0.03272268548607826	-0.03920518979430199	0.009407375939190388	0.022605637088418007	-0.019732804968953133	-2.8603300961549394e-05	0.034856464713811874	-0.05978638678789139	-0.08892495185136795	0.08332083374261856	-0.008574512787163258	-0.10439416766166687	-0.06284867227077484	-0.011880659498274326	0.03835119307041168	-0.012974533252418041	0.04459654912352562	0.04556020349264145	0.0675327256321907	0.031792618334293365	-0.05362947657704353	0.017508674412965775	-0.011896711774170399	-0.047154899686574936	-0.002387186512351036	0.015566753223538399	-0.03541834279894829	-0.07365527749061584	0.03711767867207527	-0.05228649079799652	0.017228994518518448	-0.016086706891655922	0.02006741613149643	-0.05409252643585205	0.008418525569140911	-0.0240947213023901	0.050939202308654785	-0.058007530868053436	-0.01053426880389452	0.06402497738599777	-0.02773255668580532	0.031396422535181046	-0.033672768622636795	-0.0030817657243460417	0.03946720436215401	-0.008173862472176552	-0.02575128898024559	-0.048305630683898926	0.10150321573019028	0.013848526403307915	-0.048773739486932755	-0.04970797523856163	-0.08377809077501297	-0.04878206178545952	0.007666419725865126	-0.009483406320214272	-0.015131721273064613	0.04709230735898018	0.04184173420071602	-0.053930021822452545	-0.04181133583188057	-0.07060794532299042	-0.04429684951901436	-0.05837327241897583	-0.03949149325489998	-0.021402418613433838	-0.03497534245252609	-0.011179269291460514	0.04247254133224487	-0.03956019505858421	0.0011895758798345923	0.0231380183249712	0.020695416256785393	-0.06716470420360565	0.022530296817421913	-0.03065412864089012	-0.017586255446076393	0.01639445684850216	-0.04558476060628891	0.026915863156318665	0.08266833424568176	0.08593626320362091	0.005880167707800865	0.03201554715633392	0.03308672830462456	0.10227303206920624	-0.003470648778602481	-0.016911841928958893	-0.05876617878675461	-0.050399281084537506	-0.0542544424533844	0.057454291731119156	-0.007731663528829813	-0.009400810115039349	0.004573250189423561	0.013564026914536953	0.030262986198067665	-0.006050156429409981	-0.024601086974143982	-0.0283895805478096	0.020789582282304764	-0.03635278716683388	0.028325477614998817	-0.019879691302776337	0.02810213714838028	-0.021641798317432404	-0.06138349324464798	-0.07980574667453766	-0.05311386287212372	0.06211389601230621	-0.028798110783100128	0.05561867356300354	-0.02254987135529518	-0.05035001039505005	-0.029355525970458984	0.006033014971762896	0.10708001255989075	-0.04010415077209473	0.014289338141679764	0.039526768028736115	0.08448386192321777	-0.0932328999042511	0.014046862721443176	-0.003458939725533128	-0.04315105080604553	-0.07644185423851013	-0.014641146175563335	0.022844670340418816	-0.06007257103919983	-0.028010006994009018	-0.02452969178557396	-0.04633454233407974	-0.008768894709646702	-0.002042226493358612	-0.006859432440251112	0.04124223068356514	0.026310401037335396	-0.05519940331578255	0.013540275394916534	-0.022242870181798935	0.014309282414615154	-0.01402537152171135	0.001548925880342722	-0.04122987017035484	0.004264300689101219	-0.06060656160116196	-0.023671036586165428	-0.04539979621767998	0.04118933901190758	-0.034529637545347214	-1.654881543800002e-06	-0.0031205688137561083	-0.02236839011311531	-0.04770893231034279	-0.019648900255560875	0.030395539477467537	-0.06305532902479172	-0.03240295499563217	-0.028419196605682373	-0.042530227452516556	-0.07312295585870743	-0.03169408440589905	-0.0060870591551065445	0.007253040559589863	0.030628899112343788	-0.025504281744360924	0.029251502826809883	0.019807778298854828	0.02386152744293213	0.058116614818573	0.07364469766616821	0.01286371424794197	0.003712783334776759	0.04592570289969444	0.010579756461083889	0.04042188823223114	0.037235625088214874	-0.06064833328127861	-0.03083127737045288	0.0012707047862932086	-0.06167074665427208	0.04407433792948723	-0.026406507939100266	0.024491826072335243	-0.040583230555057526	-0.020815305411815643	0.016300223767757416	-0.04018364101648331	0.036983903497457504	-0.0014134668745100498	0.04436115920543671	-0.05899721011519432	-0.04879508912563324	0.010141131468117237	-0.050772879272699356	-0.06537144631147385	-0.05105268955230713	-0.017700156196951866	-0.012832987122237682	0.05180280655622482	-0.04382526874542236	0.0382448211312294	0.036665380001068115	-0.022604376077651978	-0.055657897144556046	0.006318237632513046	-0.008679362945258617	0.02827058918774128	0.009737829677760601	0.032873790711164474	0.006950512994080782	0.015502085909247398	0.01939174346625805	-0.04985011741518974	0.017044195905327797	-0.08596796542406082	0.002277662977576256	0.037425849586725235	-0.02344830520451069	0.01883426494896412	0.02854735031723976	0.019483782351017	-0.036906175315380096	-0.00149914575740695	-0.0385705791413784	0.06663336604833603	0.02148277685046196	0.054720908403396606	-0.01146883238106966	-0.06600495427846909	-0.004570568446069956	0.028923295438289642	-0.049582161009311676	-0.0807754173874855	-0.037373580038547516	-0.02739187702536583	0.046996958553791046	-0.02338222973048687	0.03164811059832573	-0.035258807241916656	-0.07706824690103531	0.05163813754916191	-0.003010106971487403	-0.043884553015232086	-0.0004637623205780983	0.03054153546690941	-0.05773262679576874	-0.03443968668580055	0.017335698008537292	-0.08076032996177673	0.03608756512403488	0.0211965162307024	0.046176616102457047	-0.040278200060129166	0.005541463848203421	-0.0033449737820774317	-0.032168805599212646	0.03673715889453888	-0.03679877892136574	-0.030288128182291985	0.005746474955230951	0.07969509065151215	-0.04434843733906746	0.02676364779472351	0.04954613000154495	-0.017940634861588478	0.051153361797332764	0.06154945492744446	-0.0425063855946064	0.04016062244772911	-0.008466673083603382	-0.01594913937151432	0.04784691333770752	-0.09374921768903732	0.08181709051132202	-0.0750846266746521	0.1273079663515091	0.034674081951379776	-0.014371010474860668	0.0971560999751091	0.0607990063726902	0.017548583447933197	0.08012773841619492	-0.1020585149526596
+0.01572122797369957	-0.09283414483070374	-0.037784017622470856	0.01567283272743225	-0.040621329098939896	-0.04826189950108528	-0.018485289067029953	0.030686253681778908	-0.008782359771430492	0.0012149697868153453	-0.0854560062289238	0.016224732622504234	-0.007699445355683565	0.0179882999509573	0.03181327134370804	-0.021488860249519348	-0.03156418725848198	0.016205068677663803	0.0029623275622725487	0.03151789307594299	0.037003010511398315	0.04781381040811539	-0.0509190559387207	0.053680893033742905	0.021248724311590195	-0.0026337443850934505	-0.044039007276296616	0.05355549231171608	0.01082257367670536	0.07994506508111954	0.06607800722122192	-0.04447507858276367	0.02577218785881996	-0.08463349938392639	-0.009109295904636383	-0.013004070147871971	-0.07451038807630539	0.05164099857211113	0.05341767892241478	-0.011038671247661114	-0.021098587661981583	-0.0019656831864267588	-0.024805132299661636	-0.06697636842727661	0.03792453557252884	0.01334504596889019	-0.0409635454416275	-0.08318564295768738	0.006319826003164053	0.06846227496862411	0.09021879732608795	-0.07229165732860565	0.0010173171758651733	0.010631988756358624	-0.07613695412874222	-0.06802605092525482	0.005141228437423706	-0.026986362412571907	-0.0362333282828331	0.006706141866743565	-0.04758287966251373	0.017183691263198853	0.014703501015901566	0.04306793585419655	-0.04831748083233833	-0.005206996574997902	0.0544331893324852	0.011332711204886436	-0.0019476712914183736	0.016083193942904472	0.021685762330889702	0.0420520044863224	-0.0429275743663311	-0.022817159071564674	-0.03950899839401245	0.017319455742836	0.05998356267809868	0.0019028359092772007	0.03859228640794754	0.01767539605498314	0.05632895603775978	-0.006580920424312353	0.1078084260225296	-0.053967226296663284	0.018462728708982468	-0.06771228462457657	-0.011420547030866146	-0.04768180102109909	0.01062075886875391	-0.011824541725218296	0.0786922350525856	0.01802196353673935	0.03674773499369621	0.019743476063013077	0.022753199562430382	0.02343003824353218	0.040257565677165985	-0.0956297442317009	-0.02366233430802822	0.03854847699403763	0.01009729877114296	0.01215017307549715	0.09506537020206451	0.032346028834581375	0.02012128196656704	0.035284433513879776	-0.0504881888628006	0.057111434638500214	0.06034048646688461	0.06069301441311836	0.0023656634148210287	0.04640772566199303	0.058890461921691895	0.002637570956721902	0.030498072504997253	-0.053096648305654526	-0.051889609545469284	-0.004356153775006533	0.023980388417840004	0.05575327202677727	0.03173162043094635	0.033695291727781296	-0.05599050968885422	-0.09802933782339096	0.020182760432362556	0.06672985851764679	-0.08561726659536362	0.0012419060803949833	-0.018696395680308342	-0.01834813319146633	0.07670975476503372	-0.006388673093169928	-0.060273677110672	0.04668493568897247	0.017315467819571495	0.029600828886032104	0.02186436578631401	0.0433955155313015	0.0320187583565712	-0.03406890854239464	-0.05499492585659027	0.055800311267375946	0.007017586380243301	0.0062251100316643715	0.03246396780014038	0.0436282753944397	0.0036339638754725456	-0.024867922067642212	-0.05480052903294563	-0.027753522619605064	-0.10832536965608597	-0.008417499251663685	0.03216779604554176	-0.026674780994653702	0.03435646370053291	-0.029303858056664467	-0.002942246152088046	0.018193261697888374	-0.02355160377919674	0.021577034145593643	-0.08049326390028	-0.07303185760974884	0.015881042927503586	-0.023425966501235962	0.028978362679481506	0.014475593343377113	0.02889898605644703	-0.03670582175254822	-0.013239272870123386	-0.05571979656815529	-0.03264991566538811	-0.027258364483714104	0.02993326634168625	0.08590108901262283	0.038717061281204224	0.020897120237350464	0.02746186964213848	-0.025761669501662254	-0.012244687415659428	-0.04575946554541588	-0.0344783216714859	-0.04936078190803528	-0.011777476407587528	-0.05430768430233002	-0.027599800378084183	-0.03149280697107315	0.008397404104471207	-0.06213244050741196	0.004918849561363459	-0.041995588690042496	0.0016508320113644004	-0.0755489394068718	0.05947404354810715	0.05937467887997627	0.015509365126490593	0.05627404525876045	0.00632842630147934	0.07503114640712738	-0.011670866049826145	-0.012038317508995533	-0.03415115177631378	0.057782623916864395	-0.002089889720082283	0.021237187087535858	0.016404761001467705	-0.026320748031139374	0.11159110814332962	0.028427431359887123	0.04043879732489586	-0.044804349541664124	0.01893896609544754	0.0633106380701065	-0.03239452838897705	0.046579886227846146	0.09159938991069794	-0.029800012707710266	-0.01667112298309803	0.01224695984274149	0.036120202392339706	-0.04038803651928902	0.029375914484262466	0.05094197019934654	-0.05116943269968033	0.04666080325841904	-0.035815298557281494	-0.10056528449058533	-0.003785663517192006	-0.05225284397602081	-0.019014740362763405	-0.025571953505277634	0.04519466683268547	0.023907965049147606	0.04100560024380684	0.011548077687621117	-0.029158633202314377	0.014306051656603813	-0.04986058920621872	0.046872157603502274	-0.08540657162666321	-0.04053695872426033	-0.03964158520102501	0.013961183838546276	0.003011085791513324	0.03745882958173752	0.022798726335167885	0.016055898740887642	0.0891326293349266	0.028086068108677864	-0.03153444826602936	0.04511744901537895	-0.0575532503426075	-0.04573853313922882	-0.025029944255948067	-0.0016699825646355748	-0.018824631348252296	-0.0720786303281784	0.027986420318484306	-0.10495351999998093	-0.0524962916970253	-0.025274386629462242	-0.0005637687863782048	0.007784475106745958	-0.009093591012060642	-0.06991897523403168	0.06927076727151871	0.013331551104784012	-0.05850641429424286	0.03895197808742523	-0.04160545393824577	0.002351670525968075	-0.015097396448254585	-0.017846062779426575	0.041101839393377304	0.003186905523762107	-0.016734670847654343	-0.034478627145290375	0.09291499853134155	-0.03902434557676315	-0.03211211785674095	-0.059910163283348083	-0.011459647677838802	-0.04042612016201019	0.026775741949677467	0.013292545452713966	0.017205029726028442	0.025344830006361008	0.038265760987997055	-0.009118983522057533	-0.05143563076853752	-0.050315119326114655	-0.05441250279545784	-0.02375800348818302	-0.012904495932161808	-0.030997009947896004	-0.05308719351887703	-0.03811565414071083	0.04204858839511871	-0.02362690679728985	-0.05024447664618492	0.051494941115379333	0.051762476563453674	-0.052071209996938705	0.020083541050553322	-0.02292688377201557	-0.03572728857398033	0.06557530909776688	-0.09066969901323318	0.024887213483452797	0.051296260207891464	0.05902162194252014	0.022433320060372353	0.05965602397918701	0.0076499199494719505	0.07335133850574493	-0.06060408428311348	-0.022806016728281975	-0.050768084824085236	-0.03796358034014702	-0.04963240027427673	0.016066724434494972	0.018888643011450768	-0.00909559428691864	0.01997261308133602	-0.05334852263331413	0.025856345891952515	-0.017781300470232964	-0.027298476547002792	-0.07263942807912827	-0.016949504613876343	-0.0630565881729126	0.03659939393401146	0.009817885234951973	0.017408065497875214	-0.014796308241784573	-0.020869337022304535	-0.03773318603634834	-0.08309494704008102	0.045145537704229355	-0.046985771507024765	0.00012207278632558882	0.007150702644139528	0.008479468524456024	0.008688440546393394	0.0061103953048586845	0.09367455542087555	-0.03140450641512871	0.0070961941964924335	-0.020764319226145744	0.02025187574326992	-0.0773543119430542	-0.017294609919190407	-0.04522160068154335	-0.07344747334718704	-0.02009720914065838	-0.022568371146917343	0.02678847685456276	-0.024069489911198616	-0.03472568467259407	0.020686184987425804	-0.007164769805967808	0.028791941702365875	0.06851225346326828	0.01669316180050373	0.12469974160194397	0.05100376158952713	-0.05408593267202377	0.0038853769656270742	0.009166656993329525	0.016387300565838814	-0.018340375274419785	0.03381863236427307	0.012041990645229816	0.018294550478458405	0.01697874628007412	-0.053756702691316605	-0.05832045525312424	-0.026563027873635292	0.010758095420897007	0.022243987768888474	-0.03631244972348213	0.05636458098888397	0.01460842601954937	0.034863218665122986	-0.0040067825466394424	-0.057378001511096954	-0.0009912037057802081	-0.01883602701127529	-0.03337584063410759	-0.09840305149555206	-0.007963106967508793	0.05662459507584572	-0.032857295125722885	0.02722875401377678	-0.05366506800055504	0.04477534070611	0.01652671955525875	-0.03707036375999451	0.008509360253810883	0.07161086052656174	0.022045189514756203	0.020416706800460815	0.027773110195994377	-0.011889315210282803	0.0427580252289772	0.025972744449973106	-0.059768129140138626	0.01607673428952694	0.011207183822989464	-0.0489819310605526	-0.057940300554037094	-0.032097842544317245	-0.029970455914735794	0.024797623977065086	-0.054705556482076645	0.05506798252463341	-0.026586197316646576	0.04800838604569435	-0.02271818183362484	0.034018177539110184	-0.05108530819416046	-0.03544091060757637	-0.01599174365401268	0.01406787522137165	-0.042914118617773056	-0.0010398362064734101	-0.0254651065915823	-0.00112828123383224	0.05919559299945831	-0.06254788488149643	-0.0011639016447588801	0.01993485912680626	-0.024890797212719917	-0.00919380970299244	0.0015659424243494868	0.06312349438667297	0.0019143532263115048	0.0443996787071228	0.032231491059064865	-0.0025741904973983765	-0.015633665025234222	-0.002094097202643752	-0.12773765623569489	0.000602160464040935	0.021806150674819946	-0.05054851993918419	-0.000149219500599429	0.013203519396483898	0.017270684242248535	0.040608134120702744	0.048871636390686035	-0.04399825260043144	-0.029038792476058006	-0.08925902843475342	0.07131221145391464	0.04412457346916199	0.0730186253786087	-0.019827894866466522	-0.12000128626823425	-0.08706170320510864	0.02627987042069435	-0.03981049358844757	-0.10247814655303955	-0.05004982277750969	-0.01247366052120924	0.0566469170153141	0.014792554080486298	0.032258257269859314	-0.024817848578095436	-0.0780787542462349	0.013060067780315876	0.03618903458118439	0.006512115243822336	0.010334312915802002	0.051119234412908554	-0.015139659866690636	0.013411957770586014	-0.03302629664540291	-0.09591446816921234	0.07447027415037155	0.034755438566207886	-0.005914578214287758	-0.051136989146471024	0.009175372309982777	0.019817322492599487	-0.027152612805366516	0.00818097684532404	-0.05543976649641991	-0.05043884366750717	0.00903866346925497	0.09685204923152924	0.02275802753865719	0.05249538645148277	0.07594158500432968	-0.029337549582123756	0.023780403658747673	0.026414573192596436	0.03272268548607826	-0.04772516340017319	-0.04075515270233154	0.005731404293328524	0.08803241699934006	-0.022300373762845993	0.043418508023023605	-0.06747481971979141	0.13399434089660645	0.056484073400497437	-0.04205642640590668	0.05498509481549263	-0.014573012478649616	0.03856267035007477	0.02648814208805561	-0.07846703380346298
+0.014721982181072235	-0.0675918310880661	-0.025469312444329262	0.03835906460881233	-0.09412994980812073	-0.050266724079847336	-0.0007440801127813756	0.03845877945423126	-0.022162407636642456	-0.0754166916012764	0.00884224846959114	-0.00878265779465437	-0.01293317973613739	0.014868607744574547	0.0621139258146286	-0.03067498467862606	-0.036377936601638794	0.033257365226745605	-0.05518985912203789	-0.013069655746221542	0.03364576771855354	-0.006083630956709385	-0.05245107412338257	0.024695970118045807	-0.0225150678306818	-0.0059590726159513	-0.046584468334913254	0.0396282821893692	0.017417266964912415	0.05673641338944435	0.051087018102407455	-0.02808804251253605	0.016167687252163887	-0.05222408473491669	-0.06896959990262985	0.048434097319841385	-0.072954922914505	0.13179509341716766	0.03752144053578377	0.012272803112864494	-0.05154931917786598	-0.01967640034854412	-0.05499815195798874	-0.055460426956415176	0.07280873507261276	-0.031072143465280533	0.029200559481978416	-0.044588521122932434	0.04007994011044502	0.05777042359113693	0.06528466194868088	0.013902713544666767	0.017139026895165443	0.010468102991580963	-0.026663370430469513	0.018768828362226486	0.011232896707952023	-0.036007143557071686	-0.04723209887742996	0.05010312795639038	-0.07834747433662415	-0.03191647306084633	0.007112630642950535	0.036378759890794754	0.009656303562223911	0.009453192353248596	-0.0030277245678007603	0.06075538322329521	-0.03080129250884056	-0.06528779119253159	-0.019778374582529068	0.0954257920384407	-0.05395130813121796	-0.0916348397731781	0.0027309420984238386	-0.05027533695101738	0.016748478636145592	-0.00785901676863432	-0.004846017807722092	-0.008786745369434357	-0.03819720819592476	-0.01973351463675499	0.1306554228067398	-0.03803326189517975	-0.0036660449113696814	-0.04437442123889923	-0.032179564237594604	-0.04986809194087982	0.06221769005060196	0.02188064157962799	0.04772588610649109	0.04588969424366951	-0.055008165538311005	0.002642951440066099	0.0139697827398777	0.011767715215682983	0.01974094659090042	-0.11132914572954178	-0.016391480341553688	-0.018069369718432426	-0.015074769966304302	-0.02573629841208458	0.0820724219083786	0.021583963185548782	0.06955010443925858	0.05233683064579964	-0.060728125274181366	0.023896273225545883	0.00044143409468233585	0.02604888565838337	-0.0021350267343223095	0.1205127015709877	0.08073214441537857	0.04742852598428726	0.045340556651353836	-0.017522456124424934	-0.031859029084444046	-0.03105781599879265	-0.04524153470993042	-0.01674858294427395	-0.010954257100820541	0.04266057536005974	-0.0052424026653170586	-0.04867617040872574	-0.013082748278975487	0.03490374982357025	-0.12543009221553802	-0.02846774086356163	0.05824343115091324	-0.05795721337199211	0.08350730687379837	0.050230514258146286	-0.005544696003198624	0.03365345299243927	0.07486291229724884	0.004324958194047213	-0.0063722217455506325	0.028272336348891258	0.007549980189651251	-0.02499156817793846	-0.008113627322018147	0.06414708495140076	-0.018069785088300705	-0.03693544864654541	-0.007295410614460707	0.07349705696105957	0.003666781820356846	-0.023999048396945	-0.09170284122228622	0.057570621371269226	-0.06858853995800018	-0.04702530801296234	-0.020611237734556198	-0.08809570223093033	0.03216486796736717	0.04314883053302765	0.028303129598498344	0.02207174338400364	-0.0004656895762309432	0.007405126933008432	-0.04585603252053261	-0.06884109973907471	0.008220699615776539	-0.06605516374111176	-0.03765207529067993	-0.006381516344845295	0.016254324465990067	-0.01313929446041584	-0.021405460312962532	-0.050420016050338745	-0.03933523967862129	0.009005043655633926	0.045673172920942307	0.03962713107466698	0.03246600925922394	0.03139742836356163	-0.007363141980022192	-0.0626690536737442	0.009384112432599068	-0.0462755486369133	0.017040517181158066	-0.048787251114845276	0.017184574156999588	-0.05207616090774536	-0.0403008908033371	-0.014886445365846157	0.005631761159747839	-0.019730860367417336	-0.01556291151791811	-0.022451739758253098	-0.03940137103199959	-0.07605670392513275	0.038975298404693604	0.08867909014225006	-0.04834098741412163	0.06419099122285843	-0.02442747727036476	0.06327302753925323	-0.06048073247075081	-0.06084613502025604	-0.05530780926346779	0.044430043548345566	0.014773846603929996	0.008228207007050514	0.006414977367967367	-0.06922926753759384	0.05883151665329933	0.00791323184967041	0.01978100836277008	-0.07285694777965546	0.0320957787334919	0.042537838220596313	0.007650120183825493	0.027958106249570847	0.03083212487399578	-0.05293258652091026	-0.03534199669957161	0.026853088289499283	0.03653848171234131	-0.04819804057478905	0.014200704172253609	0.014923064038157463	-0.07391276955604553	0.008124674670398235	-0.01608351431787014	-0.07679038494825363	0.010655396617949009	0.04161686450242996	-0.056771568953990936	-0.024223919957876205	-0.019877495244145393	0.030393438413739204	-0.01755150593817234	-0.03334100544452667	-0.001947302371263504	-0.0029174669180065393	-0.0630645677447319	0.08090324699878693	-0.010155829600989819	-0.07008004933595657	-0.08184614777565002	-0.006417067255824804	0.018836917355656624	-0.0037644589319825172	0.02022664248943329	0.024017833173274994	0.051776133477687836	0.03137863054871559	-0.032770685851573944	-0.005031079053878784	-0.05422307923436165	-0.003650564467534423	-0.026181211695075035	0.01599586196243763	-0.027058502659201622	-0.03100649081170559	0.016280464828014374	-0.0822632685303688	-0.019575173035264015	-0.0005720581975765526	0.07121523469686508	-0.060329824686050415	-0.02015691064298153	0.02247472293674946	0.03475377336144447	-0.010040944442152977	-0.050348423421382904	-0.007247154135257006	-0.04032357409596443	0.025574853643774986	-0.07268010824918747	-0.012829688377678394	0.04759752377867699	0.007084284909069538	0.02749115414917469	-0.034822218120098114	0.04679704084992409	-0.05221135914325714	-0.09643269330263138	-0.037155233323574066	-0.08377665281295776	-0.049478158354759216	-0.012397987768054008	-0.010630154982209206	-0.024287916719913483	0.11034287512302399	0.04528896138072014	-0.012033707462251186	-0.05453905463218689	-0.057358089834451675	-0.007214994635432959	-0.036332178860902786	0.02700655162334442	-0.021668562665581703	-0.05130819231271744	-0.002194584347307682	0.03196511045098305	-0.05458493158221245	0.006739161908626556	0.0071872989647090435	-0.0034139067865908146	-0.0337541326880455	0.05515208840370178	-0.07411562651395798	-0.01563524641096592	0.013287736102938652	-0.04709859937429428	0.0476444773375988	0.08499781787395477	0.07100635766983032	0.02488788217306137	0.028624022379517555	0.05083901807665825	0.06779993325471878	0.010842244140803814	0.0008000291418284178	-0.03646114096045494	-0.0210096538066864	-0.06687082350254059	0.05955630540847778	-0.0491461344063282	-0.007562416605651379	-0.0020801927894353867	-0.0027651635464280844	0.11317052692174911	-0.04396241530776024	-0.03706987947225571	0.004047223832458258	0.03195622190833092	0.03077773191034794	0.021761454641819	0.018029838800430298	-0.011623634956777096	0.007094731088727713	-0.04481121152639389	-0.050016067922115326	-0.052961528301239014	0.05089268088340759	-0.028906455263495445	0.05860539525747299	-0.017314445227384567	-0.0769914984703064	0.01836167275905609	0.036152634769678116	0.1088532954454422	-0.04062628000974655	0.03708299249410629	0.025920262560248375	0.022724447771906853	-0.093657948076725	0.008185125887393951	-0.0038394173607230186	-0.020093858242034912	-0.10337933897972107	0.03261459991335869	0.058668967336416245	-0.05601727217435837	-0.045863065868616104	-0.01549436990171671	-0.043045226484537125	-0.018759019672870636	0.019803976640105247	-0.016756931319832802	0.043447110801935196	0.02212565764784813	-0.04660384729504585	0.016250433400273323	0.017069309949874878	0.01655137538909912	-0.07045863568782806	0.02022312767803669	-0.02063601277768612	0.08008340746164322	-0.025494717061519623	-0.05136319622397423	-0.03661526367068291	0.06752444803714752	-0.007378020323812962	-0.04940874129533768	-0.08857223391532898	-0.03789711371064186	-0.023207686841487885	0.014175670221447945	-0.020172879099845886	-0.0604020394384861	-0.03208027780056	-0.0075829424895346165	-0.0598650723695755	-0.09497026354074478	-0.05742678791284561	0.03760354593396187	-0.030505267903208733	0.0026588260661810637	-0.04580092802643776	0.0071590496227145195	0.008208340965211391	-0.031475864350795746	0.028503641486167908	0.038811661303043365	0.04313945397734642	-0.022569386288523674	0.0637936219573021	0.0022081283386796713	0.03438296914100647	-0.007414066698402166	-0.014634506776928902	0.025037134066224098	-0.0044256956316530704	-0.06320196390151978	0.011493277736008167	-0.05612960457801819	0.03098408319056034	-0.032171834260225296	-0.047130338847637177	0.02869085595011711	0.012487965635955334	-0.0014582100557163358	-0.03176083415746689	0.05687091499567032	-0.09486592561006546	0.002067280001938343	0.025736011564731598	-0.03136156126856804	-0.06367122381925583	-0.038782425224781036	-0.0447247251868248	0.0012397877871990204	0.04257962852716446	-0.0009311552275903523	0.025112517178058624	-0.008918202482163906	-0.04093364253640175	0.0035284983459860086	-0.029714960604906082	-0.017898723483085632	-0.004071651492267847	0.02341240458190441	0.03749636188149452	0.0012142366031184793	0.012501112185418606	0.009330196306109428	-0.0023698429577052593	0.015495865605771542	-0.07260322570800781	-0.014170840382575989	0.008470549248158932	0.042460206896066666	-0.0021873638033866882	0.004397829063236713	0.04793565720319748	-0.06319859623908997	-0.022806979715824127	-0.027091408148407936	0.07851647585630417	0.010799052193760872	0.031500279903411865	0.04271559417247772	-0.03552680090069771	-0.02835758961737156	0.00027939051506109536	-0.047367021441459656	-0.08431190252304077	-0.005844770465046167	-0.024927666410803795	0.041005369275808334	0.0008298787288367748	0.03992119058966637	-0.017704874277114868	-0.07726503163576126	0.012133592739701271	-0.02306230552494526	-0.045050717890262604	0.021376388147473335	0.04967998340725899	-0.053112491965293884	-0.050571803003549576	-0.01772819086909294	-0.04449152573943138	0.05490459129214287	-0.024610688909888268	0.040462933480739594	-0.0546673908829689	-0.01573619619011879	-0.011331437155604362	-0.039998020976781845	0.05133168026804924	0.00022222919506020844	-0.07416068762540817	-0.019074134528636932	0.08380965888500214	-0.04041849076747894	0.05487300455570221	0.04634374752640724	-0.03989378735423088	0.030558766797184944	0.03446095436811447	0.00621863454580307	0.013429545797407627	0.005037520546466112	-0.0035422397777438164	0.0276040006428957	-0.07175834476947784	0.05582341551780701	-0.062087103724479675	0.10222696512937546	0.03857710584998131	0.019699472934007645	0.09327498823404312	0.06419230997562408	0.02590121515095234	0.012305604293942451	-0.05948282778263092
+0.03272497281432152	-0.03969959914684296	-0.03989733010530472	-0.006375533994287252	-0.06184709072113037	-0.04749022796750069	-0.009455676190555096	0.04375947266817093	-0.01708202064037323	-0.07507863640785217	-0.059310246258974075	-0.007310884539037943	-0.01606842875480652	-0.005444374866783619	0.04266194999217987	-0.04697316512465477	-0.04581847041845322	0.02411152608692646	-0.04368533939123154	-0.02776661515235901	0.013604162260890007	0.019390810281038284	-0.04931676760315895	0.044559214264154434	0.007785807829350233	0.003515535267069936	-0.029848668724298477	0.023472696542739868	0.020838158205151558	0.06248202919960022	0.06497902423143387	-0.04297283664345741	0.04191124811768532	-0.048189882189035416	-0.04959834739565849	0.057826653122901917	-0.04309765622019768	0.11113524436950684	0.031599074602127075	0.004741906188428402	-0.06280667334794998	-0.004693611990660429	-0.01901816762983799	-0.07514301687479019	0.04883414879441261	0.007982825860381126	-0.00041764439083635807	-0.020246105268597603	0.021139763295650482	0.03501364588737488	0.07924731820821762	0.012056408450007439	-0.023143066093325615	-0.025824258103966713	-0.003711136756464839	0.02709287777543068	0.025410989299416542	-0.03906293585896492	-0.00866963341832161	0.03337404876947403	-0.11630994826555252	-0.01121593452990055	0.017638899385929108	0.01008826494216919	-0.016655851155519485	-0.03265920653939247	-0.019419925287365913	0.08655459433794022	-0.020640475675463676	-0.04160706326365471	-0.011557107791304588	0.06397717446088791	-0.042883675545454025	-0.07980616390705109	0.04258099943399429	-0.08493128418922424	0.011332995258271694	-0.00042505052988417447	-0.016909606754779816	-0.015495935454964638	-0.025901369750499725	-0.027716076001524925	0.16433870792388916	-0.04692927375435829	-0.04613509029150009	-0.05716729164123535	-0.05046487972140312	-0.07750069350004196	0.051386117935180664	0.009694647043943405	0.040119994431734085	0.04223036766052246	-0.002903951797634363	-0.007415066938847303	-0.014689709059894085	-0.011476504616439342	0.007861723192036152	-0.08341516554355621	-0.00485633872449398	-0.016409313306212425	0.009221317246556282	0.01068575493991375	0.06993520259857178	0.020617637783288956	0.043953970074653625	0.021915366873145103	-0.05243039131164551	0.005864889360964298	0.05045222118496895	0.02217492088675499	0.0030406101141124964	0.11065670102834702	0.08151716738939285	0.04535955563187599	0.09171224385499954	0.011241806671023369	0.0019466443918645382	-0.05818742886185646	-0.009016751311719418	-0.022712061181664467	-0.006826675496995449	0.052138637751340866	0.003715401515364647	-0.08404426276683807	9.718101500766352e-05	0.038209304213523865	-0.10450749844312668	-0.028163112699985504	0.046107251197099686	-0.022615280002355576	0.09257609397172928	0.06069407984614372	-0.048248883336782455	0.04336225986480713	0.035540129989385605	0.004483257886022329	-0.015578280203044415	0.03158361837267876	0.02009614184498787	-0.058682505041360855	-0.011975442990660667	0.04082358255982399	-0.02587268128991127	-0.039603423327207565	0.0675189346075058	0.026502955704927444	0.03274109214544296	-0.05220145732164383	-0.05929512158036232	0.032736681401729584	-0.060504261404275894	-0.0597384087741375	0.03035535290837288	-0.039002981036901474	0.009021193720400333	0.017323823645710945	0.022007517516613007	-0.019659534096717834	-0.004104134626686573	-0.032365866005420685	-0.05731933191418648	-0.053986016660928726	-0.02568270079791546	-0.09161068499088287	-0.0019210452446714044	0.012913794256746769	0.021691910922527313	0.01615535467863083	-0.09827373176813126	-0.05248558521270752	-0.009233580902218819	-0.008639352396130562	0.04384174570441246	0.08353535830974579	0.07019960880279541	0.011960970237851143	0.019619092345237732	-0.066596120595932	-0.0051616947166621685	-0.024680202826857567	0.03544169291853905	-0.04544973373413086	0.025202998891472816	-0.07064047455787659	-0.00032275242847390473	-0.005977874156087637	0.014367422088980675	-0.03220198675990105	0.030701028183102608	0.003459782572463155	-0.0203140527009964	-0.05418076738715172	0.02618424780666828	0.053822506219148636	-0.014725873246788979	0.03246742859482765	-0.0017405254766345024	0.09020449966192245	-0.03360051289200783	-0.056292083114385605	-0.10357150435447693	0.023597341030836105	0.027852671220898628	0.022533979266881943	0.04085695371031761	-0.07199766486883163	0.09297991544008255	0.015383237972855568	0.03677651658654213	-0.04110011085867882	0.04106726124882698	0.0440293587744236	-0.0003173743316438049	0.03916938975453377	0.06879924237728119	-0.02969496324658394	-0.024081705138087273	-0.013977382332086563	0.050298940390348434	-0.07274694740772247	-0.019779587164521217	-0.005581037141382694	-0.08323056250810623	-0.00018085935153067112	-0.04526004195213318	-0.0825887992978096	-0.026732906699180603	0.0425698347389698	-0.004302929621189833	-0.025614069774746895	-0.0055170520208776	0.02140694670379162	-0.012568253092467785	0.014232898131012917	0.024984432384371758	-0.059710029512643814	-0.08285084366798401	0.06297889351844788	-0.004005483351647854	-0.10266795754432678	-0.047625791281461716	-0.027320390567183495	0.054097436368465424	-0.03088340163230896	0.02541046030819416	0.043185725808143616	0.05391671881079674	0.018972747027873993	-0.05458163097500801	0.013657552190124989	-0.016457991674542427	-0.023728132247924805	-0.017295245081186295	0.0036313519813120365	-0.02419661544263363	-0.07700131833553314	0.031722232699394226	-0.07197149842977524	0.008027736097574234	-0.02376018837094307	0.01715659908950329	-0.040750063955783844	-0.020023344084620476	-0.024259647354483604	0.04057689383625984	-0.044523827731609344	-0.022546378895640373	0.03408246487379074	-0.039346158504486084	0.024338869377970695	-0.033364035189151764	0.009319070726633072	0.026642590761184692	-0.026561319828033447	-0.002638230798766017	-0.058382801711559296	0.09444726258516312	0.014286523684859276	-0.04052411764860153	-0.013412161730229855	-0.05970613658428192	-0.060513030737638474	0.009118861518800259	-0.03220519796013832	-0.020057935267686844	0.06153557449579239	0.04152686521410942	-0.03945046290755272	-0.0469677709043026	-0.04742083698511124	-0.03252662718296051	-0.032920535653829575	-0.04518327862024307	-0.01969381980597973	-0.035850510001182556	-0.0031155806500464678	0.03192237392067909	-0.04197803884744644	-0.0016431414987891912	0.018008744344115257	0.017115255817770958	-0.05775700882077217	0.012283407151699066	-0.046873945742845535	-0.027492230758070946	0.009551582857966423	-0.036538898944854736	0.025667399168014526	0.08528821170330048	0.086626335978508	0.014150233007967472	0.01201506145298481	0.0023349151015281677	0.08220986276865005	-0.015840698033571243	-0.01021559163928032	-0.03791642561554909	-0.06191885471343994	-0.04426167532801628	0.05636226385831833	-0.006318974308669567	-0.014370578341186047	0.007527015637606382	0.0028753927908837795	0.042397212237119675	-0.0117493886500597	-0.0344177782535553	-0.040454234927892685	0.005123922601342201	-0.031576476991176605	0.017282819375395775	-0.019415488466620445	0.04971924051642418	-0.02704262174665928	-0.06938176602125168	-0.0728735476732254	-0.04464706778526306	0.05813426524400711	-0.016354402527213097	0.050918374210596085	-0.041198380291461945	-0.05231279507279396	-0.03004230372607708	0.027760086581110954	0.115464948117733	-0.026247281581163406	0.0266568660736084	0.028468508273363113	0.07804502546787262	-0.08862590789794922	0.023280199617147446	-0.008253178559243679	-0.027954746037721634	-0.07504191994667053	0.0013229699106886983	0.0170177910476923	-0.08274105936288834	-0.03206691890954971	-0.03933025896549225	-0.029118908569216728	0.006817576941102743	-0.015179702080786228	-0.00325794517993927	0.048454344272613525	0.03082231804728508	-0.036224864423274994	0.023135408759117126	-0.0031300599221140146	0.02066950313746929	-0.03195121884346008	0.005176199600100517	-0.03824074938893318	0.015254252590239048	-0.06493552029132843	-0.05419713631272316	-0.0393243283033371	0.03890307620167732	-0.021289896219968796	-0.00428550411015749	-0.024186883121728897	-0.049460627138614655	-0.03779356926679611	-0.011806972324848175	0.010028029792010784	-0.06514275074005127	-0.013055852614343166	-0.02576974593102932	-0.06540443003177643	-0.0886489748954773	-0.044781871140003204	0.009678224101662636	-0.0005144895403645933	0.031214037910103798	-0.021654417738318443	0.022812265902757645	0.039732612669467926	0.02710713818669319	0.039081424474716187	0.07358430325984955	0.030953383073210716	0.018676526844501495	0.038486771285533905	-0.003075241344049573	0.06003277376294136	0.03049137070775032	-0.05054574832320213	-0.03413127735257149	0.006394306663423777	-0.06866537034511566	0.056001629680395126	-0.041824810206890106	0.01945214532315731	-0.011419168673455715	-0.00957383494824171	0.018016891553997993	-0.026676174253225327	0.04656454920768738	-0.01412191428244114	0.05276462063193321	-0.06583652645349503	-0.055113427340984344	0.02422924153506756	-0.037976521998643875	-0.09843457490205765	-0.04922279715538025	-0.030888410285115242	-0.013460513204336166	0.05907409265637398	-0.03620615229010582	0.045484039932489395	0.019344711676239967	-0.02226419933140278	-0.05435733124613762	-0.012665408663451672	-0.009835893288254738	0.02653392031788826	0.01701158471405506	0.046376001089811325	0.02355198748409748	0.025723418220877647	0.0188169963657856	-0.029691053554415703	0.021566463634371758	-0.09073463827371597	0.009279279038310051	0.007592845242470503	-0.015731975436210632	-0.002779952948912978	0.020951643586158752	0.020705172792077065	-0.039227016270160675	0.0005685262731276453	-0.054488617926836014	0.07845941185951233	0.009638024494051933	0.04378635808825493	0.009116251021623611	-0.08313596248626709	-0.009055995382368565	0.02773185633122921	-0.06137575954198837	-0.08634597063064575	-0.016134316101670265	-0.04022200405597687	0.032689161598682404	-0.012828294187784195	0.034676432609558105	-0.0120833246037364	-0.08125120401382446	0.05720876529812813	-0.012524154968559742	-0.018716342747211456	0.021952033042907715	0.05572933703660965	-0.04666641354560852	-0.036918334662914276	0.006522050127387047	-0.07476955652236938	0.053818460553884506	0.01188671588897705	0.03685762733221054	-0.04615546017885208	-0.002854349324479699	-0.012774698436260223	-0.015192564576864243	0.04129095375537872	-0.03621033951640129	-0.03775773197412491	-0.011552682146430016	0.1008160263299942	-0.039222054183483124	0.01940189115703106	0.06557194888591766	-0.02366931363940239	0.03458443656563759	0.06328994780778885	-0.026831958442926407	0.04279078543186188	0.01193862035870552	-0.003614549757912755	0.049056798219680786	-0.08162302523851395	0.0673900917172432	-0.07320617884397507	0.11343264579772949	0.04064629226922989	-0.006666769273579121	0.09153418987989426	0.05856923386454582	0.018406124785542488	0.052276648581027985	-0.08421557396650314
+0.030015522614121437	-0.03010062500834465	-0.01099184900522232	0.031886905431747437	-0.07127949595451355	-0.08735696971416473	0.0006247610435821116	0.058305613696575165	0.043685995042324066	-0.027196595445275307	-0.06035551428794861	-0.015556581318378448	0.03426571190357208	0.029900167137384415	0.03132179379463196	-0.01254779752343893	-0.04624929279088974	0.028117015957832336	-0.04725280776619911	-0.016284316778182983	0.017368024215102196	-0.025810621678829193	-0.070747010409832	0.03529477119445801	0.011702613905072212	0.02614126168191433	-0.07897637039422989	-0.0016416172729805112	0.033739324659109116	-0.0012422245927155018	0.027224082499742508	0.0151741998270154	0.027514319866895676	-0.07658471167087555	-0.052298661321401596	0.04592195525765419	-0.05570395290851593	0.09036373347043991	-0.032145921140909195	0.004697698634117842	-0.0650164783000946	0.014644484035670757	-0.0684615895152092	-0.05342232808470726	0.013545101508498192	-0.0002238977322122082	0.034213192760944366	0.011440572328865528	0.011608711443841457	0.0064378343522548676	0.059155046939849854	-0.023716406896710396	0.002457112306728959	-0.004221918061375618	-0.032628390938043594	0.009458890184760094	0.024330096319317818	-0.04847276210784912	-0.056855708360672	0.017958855256438255	-0.11533592641353607	-0.02829045057296753	-0.015069774352014065	0.030356161296367645	-0.05605859309434891	-0.03379509225487709	-0.021071167662739754	0.09987109154462814	-0.0020067989826202393	-0.05716460198163986	0.027059350162744522	0.029442306607961655	-0.04825321584939957	-0.05773446336388588	0.02882940135896206	-0.07140108197927475	0.002563982969149947	0.07827343046665192	-0.010782711207866669	-0.015878714621067047	0.014701398089528084	-0.029786964878439903	0.11977455019950867	-0.024218866601586342	-0.06979275494813919	-0.01661747135221958	-0.009976760484278202	-0.034316956996917725	0.044662926346063614	0.03168287128210068	0.01539689302444458	0.09365132451057434	-0.03404928743839264	0.03599558770656586	0.03690239042043686	0.036830343306064606	0.004554802551865578	-0.1349429339170456	-0.06438066065311432	0.007517513819038868	-0.022090880200266838	-0.005715150386095047	0.05000479519367218	0.0007193669443950057	0.05154396966099739	0.014591393060982227	-0.03432069718837738	-0.004972132854163647	0.02544168010354042	0.03663567081093788	0.021420931443572044	0.06725185364484787	0.06464414298534393	0.055090609937906265	0.02156190387904644	0.005640407092869282	0.01102349441498518	-0.030117493122816086	-0.06469538807868958	0.001517577562481165	0.035658542066812515	0.014662214554846287	-0.002096593612805009	-0.0824449211359024	-0.02184845507144928	0.0021294422913342714	-0.09722782671451569	-0.030598828569054604	0.03873899206519127	-0.03665310889482498	0.10540049523115158	0.04710214585065842	-0.02790224179625511	0.02212580479681492	0.08947290480136871	0.04923981428146362	0.030386025086045265	-0.005737722851336002	0.00958928931504488	-0.04810239374637604	-0.02994908206164837	0.02789490856230259	-0.023223839700222015	-0.055310845375061035	0.07154691219329834	0.06489332765340805	0.027528168633580208	-0.023462379351258278	-0.057482991367578506	-0.017958056181669235	-0.04828134551644325	-0.059841036796569824	0.011751184239983559	-0.048327893018722534	0.011535040102899075	0.04479747638106346	0.010288465768098831	0.034792155027389526	-0.02320694737136364	-0.024663623422384262	-0.05894065648317337	-0.01168876700103283	-0.010751415975391865	-0.08082903176546097	-0.01874670572578907	-0.025966111570596695	-0.017487404868006706	0.030957646667957306	-0.050286028534173965	-0.02970779873430729	-0.01457685511559248	-0.037979211658239365	0.03069750778377056	0.0740634948015213	0.05493037402629852	-0.0016369959339499474	0.0030522060114890337	-0.051594436168670654	-0.011730324476957321	-0.045117832720279694	0.012203209102153778	0.00637750793248415	0.010320370085537434	-0.05301123484969139	0.017785875126719475	0.009120029397308826	0.018787896260619164	-0.06931870430707932	0.01143836509436369	-0.02626911737024784	-0.03424058482050896	-0.02425001934170723	0.027406373992562294	0.07799772173166275	-0.007069441489875317	0.06345583498477936	-0.060129985213279724	0.03759109973907471	-0.002975062932819128	-0.04193795844912529	-0.0417199544608593	0.03595768287777901	0.010135682299733162	0.05779346451163292	-0.00834442675113678	-0.08768221735954285	0.09313932061195374	0.045429639518260956	0.050836533308029175	-0.059206683188676834	0.003802309511229396	0.02946622297167778	-0.01618821546435356	0.06463129818439484	0.09687076508998871	-0.01585586927831173	-0.010351737029850483	0.030438292771577835	0.03398901969194412	-0.0498301200568676	0.03894646093249321	0.031098779290914536	-0.07922171801328659	0.003899129806086421	-0.07258577644824982	-0.07138784974813461	-0.039747316390275955	0.05185890570282936	-0.039387915283441544	-0.013731531798839569	-0.05749654769897461	-0.005337419454008341	0.0004619208921212703	-0.040433619171381	-0.03411294147372246	0.013522263616323471	-0.0775492712855339	0.06770263612270355	0.02876020222902298	-0.0749926045536995	-0.07573893666267395	0.002496774308383465	0.01847328245639801	-0.00201268563978374	0.012057824991643429	0.037044160068035126	0.07642940431833267	0.00040567797259427607	-0.004386833403259516	0.028525356203317642	-0.01617787405848503	-0.034921228885650635	0.018715038895606995	0.02656155824661255	-0.07601778209209442	0.008885745890438557	0.01593642123043537	-0.061625320464372635	-0.041423749178647995	-0.0011905176797881722	0.014064640738070011	-0.016764046624302864	-0.041203033179044724	-0.015335277654230595	0.03782479837536812	-0.05854783579707146	-0.02997438609600067	0.00393163226544857	-0.030827051028609276	0.06791546940803528	-0.0613875575363636	-0.06680657714605331	0.03346936032176018	-0.0010131534654647112	0.011598780751228333	-0.045862827450037	0.04447563737630844	-0.007978023029863834	-0.06884080916643143	0.034870944917201996	-0.08581310510635376	-0.024614568799734116	-0.02467964217066765	0.02071690931916237	-0.03155450522899628	0.11496350169181824	0.03304367884993553	-0.03283116593956947	-0.04809184372425079	-0.048659976571798325	-0.008271670900285244	-0.0327942930161953	0.012688581831753254	0.001669307705014944	-0.04250179976224899	-0.03109118901193142	0.07237549871206284	-0.051425106823444366	-0.025283049792051315	0.03264375030994415	-0.06108018383383751	-0.06687155365943909	0.0324113667011261	-0.021892383694648743	-0.04874252900481224	0.012259315699338913	-0.06141750514507294	-0.0032647293992340565	0.0989735871553421	0.014293518848717213	-0.03155410289764404	0.04253651946783066	-0.0067343027330935	0.07995350658893585	-0.02000303752720356	-0.017659613862633705	0.023157989606261253	-0.03668438643217087	-0.009390804916620255	0.01985708251595497	-0.06592916697263718	0.0063385311514139175	0.016382908448576927	-0.0028407799545675516	0.03199663385748863	-0.004259544424712658	-0.0674307718873024	0.019129501655697823	-0.014933520928025246	-0.017341217026114464	0.0133433248847723	0.041258394718170166	0.03542451187968254	-0.06143505498766899	-0.057812780141830444	-0.051074765622615814	-0.0036866399459540844	0.041584562510252	-0.014957335777580738	0.018476760014891624	-0.06065722554922104	-0.041112545877695084	-0.012109734117984772	-0.019017914310097694	0.13387885689735413	-0.07062206417322159	0.010641785338521004	0.0754198431968689	0.025638461112976074	-0.07235241681337357	0.05088198930025101	0.05691518262028694	-0.05557143688201904	-0.06550361216068268	0.0026941129472106695	-0.005370711907744408	-0.07884352654218674	-0.00880520325154066	0.031452763825654984	-0.02171890065073967	0.022544119507074356	0.022616997361183167	0.02419523149728775	0.04705710709095001	0.009651117958128452	-0.008549503982067108	0.02174064703285694	0.02702585980296135	0.06522831320762634	-0.046474624425172806	0.02167920395731926	-0.029572630301117897	0.07485134899616241	-0.030306292697787285	-0.053424954414367676	-0.03605864569544792	0.04625783488154411	0.01656300202012062	-0.013025672174990177	-0.05042354390025139	-0.03164919465780258	-0.018728936091065407	0.03619740158319473	0.03515274077653885	-0.06926967948675156	-0.030548570677638054	-0.09486287832260132	-0.05092781409621239	-0.09282571822404861	-0.07242331653833389	0.0256331879645586	-0.06399931758642197	0.023975234478712082	-0.026870565488934517	-0.008132650516927242	-0.013069325126707554	-0.019852520897984505	0.007565892767161131	0.05816149711608887	0.00788072682917118	-0.03393551707267761	0.033793747425079346	-0.0337999127805233	0.06470737606287003	0.02998359315097332	-0.0314674898982048	0.032346561551094055	0.04305035248398781	-0.03394914045929909	0.042408980429172516	-0.023870453238487244	0.02001338079571724	-0.006491808220744133	-0.06658969819545746	0.007933120243251324	0.058419935405254364	0.007903273217380047	-0.07689966261386871	0.041476111859083176	-0.055765073746442795	-0.02129480056464672	0.03775980696082115	-0.027416426688432693	-0.06535597890615463	-0.06067577004432678	-0.009664650075137615	-0.03156327083706856	-0.0024376725777983665	-0.02925446256995201	0.03669445589184761	0.03444132208824158	-0.05879614129662514	-0.030100760981440544	0.005303498357534409	0.008755950257182121	0.04892323166131973	-0.016451170668005943	0.029086461290717125	0.027737077325582504	0.008394279517233372	0.024936754256486893	-0.006850970443338156	0.01496120449155569	-0.06170542910695076	0.0067937131971120834	-0.009814728982746601	-0.026036085560917854	-0.010996932163834572	0.006935274228453636	0.027971763163805008	-0.08271726220846176	-0.017768992111086845	-0.05580713227391243	0.06413421034812927	0.05171908065676689	0.03314743936061859	0.016505779698491096	-0.04314859211444855	0.0029051064047962427	-0.001976719358935952	-0.0006019379943609238	-0.05717009678483009	0.000567686278373003	-0.05354534834623337	0.03670657053589821	0.038451988250017166	0.01706348918378353	-0.02458634227514267	-0.08045383542776108	0.03642522916197777	-0.0129240145906806	-0.07222883403301239	-0.020894065499305725	0.05641831457614899	8.75279656611383e-05	-0.0200594924390316	-0.022426649928092957	-0.03313887491822243	0.06622860580682755	0.05279719457030296	0.05676611140370369	-0.05042530596256256	-0.04040807485580444	-0.01860077865421772	-0.028149276971817017	-0.006884872447699308	-0.0036493914667516947	-0.05528128892183304	0.005868180654942989	0.08575595915317535	-0.04919339343905449	0.0014149550115689635	0.061059750616550446	-0.04826635494828224	0.02247176133096218	-0.010690530762076378	-0.05475638434290886	0.05735310539603233	-0.012758000753819942	-0.04945695027709007	0.03466074541211128	-0.11391276121139526	0.07613450288772583	-0.06733275949954987	0.09122766554355621	0.012888596393167973	-0.04634977504611015	0.07466259598731995	0.08976679295301437	-0.03397641330957413	0.0010199319804087281	-0.031613510102033615
+0.024866092950105667	-0.047276146709918976	0.0034881019964814186	0.005419546738266945	-0.07884451001882553	-0.06125229224562645	-0.019418183714151382	0.029643550515174866	0.0016838132869452238	-0.04928969964385033	-0.033586058765649796	0.0013155477354303002	0.026814013719558716	0.014429769478738308	0.0652502104640007	-0.024588651955127716	-0.049100808799266815	-0.004178277682512999	-0.031336914747953415	-0.007980495691299438	0.07200270891189575	-0.011468318291008472	-0.06638314574956894	0.06533817201852798	0.013917461037635803	0.03029332123696804	-0.05249837785959244	0.020043974742293358	0.020292337983846664	0.03612862154841423	0.03662832826375961	-0.026683853939175606	0.03223631531000137	-0.0774010419845581	-0.03338438272476196	0.03623192757368088	-0.0597839280962944	0.10936494916677475	0.024357542395591736	-0.017560984939336777	-0.04997849836945534	-0.0060544912703335285	-0.06385106593370438	-0.08578620851039886	0.0022812101524323225	-0.005290260072797537	0.023935949429869652	-0.02362973615527153	0.01252823043614626	0.04842545464634895	0.06490345299243927	-0.007949457503855228	-0.02005888521671295	-0.006704780273139477	0.005901736207306385	-0.006411713548004627	0.04043709486722946	-0.0324224978685379	-0.047370512038469315	0.055544883012771606	-0.08643297106027603	-0.007304312661290169	-0.004493624437600374	0.03882919251918793	-0.017005013301968575	-0.02303488180041313	0.008781888522207737	0.05141226202249527	-0.0040397304110229015	-0.04040838032960892	0.010646500624716282	0.07436945289373398	-0.0648365318775177	-0.046745654195547104	0.012927860952913761	-0.024281887337565422	0.004768209531903267	0.026281092315912247	0.008393622934818268	0.0050748963840305805	0.012424291111528873	-0.047212064266204834	0.13929547369480133	-0.045962657779455185	-0.030336203053593636	-0.07416599988937378	-0.015719130635261536	-0.026926901191473007	0.05223843827843666	0.01660335436463356	0.03418390825390816	0.06688416749238968	-0.041665803641080856	0.05183741822838783	0.014689887873828411	0.017929021269083023	0.011932950466871262	-0.12459995597600937	-0.024362048134207726	-0.014239991083741188	-0.015610811300575733	-0.038562364876270294	0.062499646097421646	0.021969454362988472	0.062471888959407806	0.029359225183725357	-0.05046263337135315	0.014877716079354286	0.02990923449397087	0.03912697359919548	0.00016428972594439983	0.10203319787979126	0.06159861758351326	0.04274093732237816	0.045955318957567215	-0.004001342225819826	-0.008041538298130035	-0.03786742687225342	-0.029260659590363503	0.011309312656521797	0.017631666734814644	0.032512813806533813	-0.01377791166305542	-0.10134044289588928	0.015852300450205803	0.06411943584680557	-0.10331884771585464	-0.043848417699337006	0.028459135442972183	-0.04652220383286476	0.10936536639928818	0.07524192333221436	-0.02800154872238636	0.022237498313188553	0.05100838840007782	0.024610672146081924	0.008864317089319229	0.05581164360046387	-0.0011364866513758898	-0.07358935475349426	-0.02648627758026123	0.0332462377846241	0.004770304076373577	-0.051128800958395004	0.0299859419465065	0.05030360445380211	0.014918196946382523	-0.0226307213306427	-0.07755327969789505	0.002834482118487358	-0.07620179653167725	-0.059453628957271576	0.009937944822013378	-0.062426142394542694	0.008558935485780239	0.028314687311649323	0.012197981588542461	0.035721078515052795	-0.018197715282440186	0.0016139763174578547	-0.05939912050962448	-0.07452431321144104	-0.0009102685726247728	-0.0657883808016777	-0.015440655872225761	0.0025781842414289713	0.018460188060998917	-0.0014617423294112086	-0.04342450201511383	-0.04446364566683769	-0.0026990293990820646	-0.034893687814474106	0.04693141579627991	0.03203713893890381	0.03640694171190262	-0.01035990845412016	0.006950598210096359	-0.06826743483543396	-0.006353097967803478	-0.03892065957188606	0.01160253956913948	-0.051415033638477325	0.01975969225168228	-0.06303281337022781	-0.03004138357937336	-0.006380817387253046	0.007419869303703308	-0.030350949615240097	-0.024104801937937737	-0.039388034492731094	-0.046149712055921555	-0.03934350237250328	0.05314813181757927	0.05798168107867241	-0.03508469834923744	0.05785859748721123	0.004450479056686163	0.049075283110141754	-0.044363878667354584	-0.05337603762745857	-0.05165281146764755	0.03933000937104225	0.017935167998075485	0.03831801936030388	-0.018910637125372887	-0.0600208081305027	0.06210080161690712	0.024318795651197433	0.0239045862108469	-0.07710146903991699	0.025620387867093086	0.05068562179803848	-0.019977951422333717	0.042904388159513474	0.08313048630952835	-0.06612072885036469	0.0008397366036660969	0.029717499390244484	0.04347650706768036	-0.05041362717747688	-0.014722522348165512	0.024728471413254738	-0.09070509672164917	-0.011945780366659164	-0.03351328894495964	-0.08221662789583206	-0.04005908966064453	0.03514915704727173	-0.03755112364888191	-0.044687215238809586	-0.017310721799731255	0.02777860313653946	-0.016557039692997932	-0.02794801816344261	0.009918955154716969	0.006006485782563686	-0.06084161996841431	0.07863747328519821	-0.021643396466970444	-0.0841173604130745	-0.06656934320926666	0.020892804488539696	0.02508259192109108	0.02805505320429802	0.023509318009018898	0.047759514302015305	0.08214400708675385	0.010441847145557404	-0.033848658204078674	-0.008634580299258232	-0.033952746540308	-0.04309343919157982	0.0019602312240749598	0.029189608991146088	-0.041706714779138565	-0.035592276602983475	-0.0020399182103574276	-0.06289034336805344	-0.024797264486551285	-0.0015241869259625673	0.039272770285606384	-0.06101010739803314	-0.013593527488410473	-0.021420031785964966	0.02796393260359764	-0.02680589258670807	-0.022651229053735733	0.03974044695496559	-0.02649998478591442	0.059934839606285095	-0.02283954620361328	-0.014902068302035332	0.04001753777265549	-0.006273453123867512	0.0012295839842408895	-0.04646429792046547	0.06179773434996605	-0.025068258866667747	-0.0708041787147522	-0.03269342705607414	-0.08886963129043579	-0.05996482074260712	-0.014925005845725536	-0.005817164201289415	-0.02035568654537201	0.10439180582761765	0.04081308841705322	-0.0644342228770256	-0.05567575991153717	-0.06953851133584976	-0.002299555344507098	-0.03519149497151375	0.011067195795476437	0.005047483369708061	-0.06160605326294899	-0.014302761293947697	0.06694296002388	-0.06713282316923141	-0.023880766704678535	0.034449681639671326	0.00969522725790739	-0.08218522369861603	0.0536634661257267	-0.022358566522598267	-0.03212272748351097	0.009849809110164642	-0.08088985830545425	0.03356427326798439	0.0875055193901062	0.03159632161259651	0.012659703381359577	0.021548038348555565	0.03055448643863201	0.10094737261533737	-0.02818019688129425	-0.011286844499409199	-0.033388007432222366	-0.04912043362855911	-0.08078312128782272	0.047181837260723114	-0.05515618249773979	0.018911372870206833	-0.038245413452386856	-0.014195721596479416	0.042808402329683304	-0.01522641908377409	-0.026023048907518387	-0.0024950935039669275	0.0021918369457125664	0.006040545646101236	0.03634333983063698	0.012412847951054573	0.006586769595742226	-0.04869744926691055	-0.05356147512793541	-0.08772669732570648	-0.04278990998864174	0.04189552366733551	-0.03913307189941406	0.074443019926548	-0.018181070685386658	-0.0710560530424118	-0.02173600159585476	0.010916819795966148	0.13103832304477692	-0.06576211750507355	0.026995094493031502	0.03980180621147156	0.02785339392721653	-0.09634577482938766	0.018280569463968277	0.011132761836051941	-0.04886830225586891	-0.06051598861813545	-0.02344675548374653	0.06366754323244095	-0.04467472806572914	-0.03942383825778961	0.002599158091470599	-0.011332722380757332	0.01331015769392252	0.04191635921597481	-0.0004808578232768923	0.05099964886903763	0.005558613687753677	-0.018888400867581367	0.007802966516464949	0.008596936240792274	0.026314133778214455	-0.016326474025845528	0.028194909915328026	-0.010305834002792835	0.06169360876083374	-0.03366829454898834	-0.02222791314125061	-0.05716366320848465	0.05517028272151947	-0.04195423796772957	-0.01380373165011406	-0.02795087732374668	-0.01373740378767252	-0.020611582323908806	0.045151080936193466	0.013791878707706928	-0.029740488156676292	-0.041609879583120346	-0.028087584301829338	-0.057810597121715546	-0.10079215466976166	-0.03616956248879433	0.025898925960063934	-0.03921455517411232	0.022207733243703842	-0.03525402769446373	0.017875954508781433	0.009163500741124153	-0.02321462705731392	0.024350864812731743	0.04582078009843826	0.03795531392097473	-0.03815753385424614	0.06581057608127594	0.02651973068714142	0.04560689628124237	-0.018834300339221954	-0.03825195133686066	0.04844753444194794	0.03681759536266327	-0.052623651921749115	0.035792455077171326	-0.03335670754313469	-0.002515699015930295	-0.014289629645645618	-0.037455253303050995	-0.0077055771835148335	0.015399203635752201	0.008723966777324677	-0.03444655239582062	0.061855945736169815	-0.07892386615276337	-0.04370614513754845	0.010905778035521507	-0.03729791194200516	-0.06986130028963089	-0.04065801948308945	-0.02926451526582241	-0.02806100808084011	0.02197863534092903	-0.00859429594129324	0.024040790274739265	0.053417354822158813	-0.025092603638768196	0.006999516859650612	0.024203304201364517	0.003233056515455246	0.030599717050790787	0.008065568283200264	0.050967440009117126	0.019425293430685997	0.016356609761714935	0.03648332506418228	-0.03210766986012459	0.03344621881842613	-0.07321804016828537	0.013897033408284187	0.008637508377432823	0.009685290977358818	0.014242514036595821	-0.002739853225648403	0.06254495680332184	-0.08712568879127502	-0.038859885185956955	-0.022378938272595406	0.056037940084934235	0.03366445004940033	0.03999408334493637	0.04648841172456741	-0.06756262481212616	-0.046375375241041183	0.00209559453651309	-0.04461071640253067	-0.09619279950857162	-0.006417504977434874	-0.045890334993600845	0.06038735434412956	-0.010839223861694336	0.01166990865021944	0.0028863048646599054	-0.10089221596717834	0.01126757264137268	-0.01091949176043272	-0.05636061355471611	0.017541110515594482	0.06341173499822617	-0.03154529631137848	-0.02714754454791546	-0.011158065870404243	-0.05016938969492912	0.028343018144369125	0.020273230969905853	0.049498215317726135	-0.05754925310611725	0.0031921747140586376	-0.008438123390078545	-0.017951389774680138	0.030033955350518227	0.0047892555594444275	-0.07568143308162689	0.004195443820208311	0.10979355126619339	-0.05757422372698784	0.03797413036227226	0.07683845609426498	-0.0133244339376688	0.034005001187324524	0.028831847012043	-0.008118439465761185	0.011379098519682884	-0.03425062820315361	-0.033798906952142715	0.0505518838763237	-0.0787397250533104	0.06720256060361862	-0.0893106609582901	0.10527046769857407	0.009930524043738842	-0.02806120179593563	0.08983329683542252	0.06635160744190216	0.044066768139600754	0.026579352095723152	-0.06282146275043488
+0.028446031734347343	0.03912839666008949	0.04627536982297897	0.03882092610001564	-0.1231783777475357	-0.01791594736278057	-0.03695739433169365	-0.00852220505475998	0.018114004284143448	-0.04716504365205765	0.0174945667386055	-0.023018503561615944	-0.013178535737097263	-0.005853667389601469	0.04198071360588074	-0.024406980723142624	-0.07763785123825073	-0.03792363405227661	-0.03484141454100609	-0.04022606462240219	0.05320334807038307	-0.021413937211036682	-0.045973412692546844	0.07319971174001694	-0.004681628663092852	0.03430069610476494	-0.05130124092102051	0.03131352365016937	-0.059243857860565186	0.04377014562487602	0.03789940103888512	0.001507670502178371	0.03792557120323181	-0.06795914471149445	-0.04495713859796524	0.07660820335149765	0.03595449775457382	0.10956735908985138	0.06779143959283829	-0.05777537450194359	-0.014680474065244198	0.00964441429823637	-0.037512630224227905	-0.05402974411845207	0.025691663846373558	-0.018007345497608185	-0.005902520380914211	-0.013565601781010628	0.07114013284444809	0.023110484704375267	0.06865037977695465	0.02521449327468872	-0.061823002994060516	-0.028332030400633812	0.02351188473403454	0.028484027832746506	0.06733085215091705	-0.050262581557035446	-0.03964909166097641	0.06263259798288345	-0.06692676246166229	-0.018404465168714523	-0.006034893449395895	-0.020942358300089836	0.02025841735303402	0.0023923476692289114	0.015310261398553848	0.06697244942188263	0.022444037720561028	-0.01252043154090643	0.042668793350458145	0.12703672051429749	-0.06541161984205246	-0.032107219099998474	0.04513021185994148	-0.06907686591148376	-0.06684808433055878	0.0422530435025692	-0.024498537182807922	0.05126553773880005	-0.02251063659787178	-0.07732464373111725	0.10860076546669006	-0.04080525413155556	-0.035895101726055145	-0.05325561761856079	-0.021065667271614075	-0.03431389853358269	0.04932194948196411	0.034536655992269516	-0.0011809670832008123	0.05917542800307274	-0.08125664293766022	0.04186638072133064	-0.016643688082695007	0.014400659129023552	0.061609815806150436	-0.09684838354587555	0.008772403001785278	0.011024427600204945	-0.04103120416402817	-0.04483312368392944	-0.009767035953700542	-0.0056143347173929214	0.05759434401988983	0.05002209171652794	-0.03759222850203514	0.014677744358778	0.015815654769539833	0.04685210809111595	0.05407923460006714	0.06290547549724579	0.08188559859991074	0.06162642315030098	0.04993646219372749	0.006981931161135435	0.02671435847878456	-0.08760938793420792	-0.02757878042757511	-0.012533540837466717	-0.022632045671343803	0.054661110043525696	-0.01022013183683157	-0.0261499285697937	0.024825312197208405	0.03140873834490776	-0.09665358066558838	-0.008368321694433689	0.0261601060628891	-0.044970862567424774	0.08852778375148773	0.04791319742798805	0.006407030858099461	0.029858505353331566	0.043075378984212875	0.04156000167131424	-0.01811140589416027	0.053381092846393585	-0.023593168705701828	-0.03561639413237572	-0.0031355281826108694	-0.005287799518555403	0.021738752722740173	-0.05672738328576088	0.015655551105737686	0.05053889378905296	0.020070120692253113	-0.006265035830438137	-0.06927735358476639	0.012669107876718044	-0.08228161931037903	-0.04997873678803444	0.0074035353027284145	-0.10633179545402527	-0.019104698672890663	0.08055762201547623	0.039345141500234604	0.04086950048804283	-0.02542496845126152	0.004605026450008154	-0.011241720989346504	-0.05192853510379791	0.0005628142389468849	-0.06728330999612808	-0.06773124635219574	-0.04223325476050377	0.024007471278309822	-0.007120190653949976	-0.012290995568037033	-0.023125922307372093	0.003119289642199874	0.004725935868918896	0.04173029214143753	0.044590871781110764	0.04893055558204651	-0.02077340893447399	0.03704696148633957	-0.06545743346214294	0.01360437273979187	-0.04144415259361267	0.018144182860851288	-0.04461367055773735	0.04093172028660774	-0.05640972778201103	-0.04344847798347473	-0.017687732353806496	0.019065890461206436	0.040495891124010086	0.003512854687869549	0.0023243732284754515	-0.06901330500841141	-0.04990024492144585	0.029002372175455093	0.06144580990076065	-0.06860843300819397	0.03565851226449013	0.03729519248008728	0.02010485716164112	-0.02612500637769699	-0.023511290550231934	-0.07918825000524521	0.04264224320650101	0.003951250575482845	0.07299092411994934	0.033840104937553406	-0.05264275521039963	0.033671412616968155	-0.004134644288569689	-0.02269073948264122	-0.05822247266769409	0.015417602844536304	0.037683457136154175	0.04152487963438034	0.006757441908121109	0.06891920417547226	-0.0159128587692976	0.0251321978867054	-0.028726812452077866	0.06865814328193665	-0.056033212691545486	-0.002043273067101836	-0.022295815870165825	-0.0835396870970726	-0.014089408330619335	-0.04786613956093788	-0.04830889031291008	-0.02719646506011486	0.04081500321626663	-0.005772559903562069	-0.021810077130794525	0.013239175081253052	0.03289748355746269	-0.03923260048031807	0.0003494298434816301	0.023309554904699326	0.004627692513167858	-0.06556912511587143	0.12376488000154495	-0.030262518674135208	-0.06867291033267975	-0.04775914177298546	0.05970829725265503	0.055544301867485046	-0.006843026727437973	0.04326494410634041	0.037480078637599945	0.09133409708738327	0.04869852215051651	0.010498815216124058	-0.05166454240679741	-0.0501791350543499	-0.082123301923275	0.0004082901286892593	0.03314792364835739	-0.00022425834322348237	-0.0280006043612957	-0.006008553318679333	-0.05809508264064789	0.020163744688034058	0.027049437165260315	0.06946059316396713	-0.03918313980102539	-0.017992282286286354	-0.02614164724946022	0.03335481137037277	-0.017448095604777336	-0.06458700448274612	0.05325073376297951	-0.014245055615901947	0.0500459186732769	-0.017342044040560722	0.01016544084995985	0.016600802540779114	0.029588978737592697	0.02171705663204193	-0.029849478974938393	0.040171604603528976	-0.015069695189595222	-0.049757640808820724	-0.049294132739305496	-0.08303855359554291	-0.053300417959690094	-0.00642215134575963	-0.018459836021065712	-0.03299233317375183	0.05972360819578171	0.020323220640420914	-0.028899161145091057	-0.06400086730718613	-0.011840112507343292	-0.004672724287956953	-0.041531872004270554	-0.018645504489541054	0.036795713007450104	-0.08145005255937576	-0.004522667732089758	0.013767522759735584	-0.029212702065706253	-0.03803428262472153	0.0033759968355298042	-0.05575083568692207	-0.07344228774309158	0.027777891606092453	-0.025971967726945877	-0.018060753121972084	-0.00281076249666512	-0.04107781872153282	0.04707072302699089	0.10741977393627167	0.02857957035303116	0.07178561389446259	-0.0058245970867574215	0.03670299053192139	0.06417345255613327	-0.011734125204384327	-0.01265163253992796	-0.01600128598511219	-0.04883287474513054	-0.12051820009946823	0.057232603430747986	-0.04486953839659691	-0.0085865193977952	-0.03814427927136421	0.03463109955191612	0.07380988448858261	-0.008232617750763893	-0.012411602772772312	-0.02283046767115593	0.04553079605102539	0.02271915413439274	0.06222601234912872	-0.026251841336488724	-0.006531201768666506	-0.022118637338280678	-0.04337983950972557	-0.03506239503622055	-0.05681469291448593	0.056909020990133286	0.033484771847724915	0.036857496947050095	-0.0019216308137401938	-0.10359803587198257	-0.014384330250322819	0.029047025367617607	0.07769972831010818	-0.09600470960140228	0.030121004208922386	0.021466342732310295	-0.0004156040959060192	-0.08377764374017715	-0.030680209398269653	0.03517194464802742	-0.03426544368267059	-0.02877766452729702	0.013198178261518478	0.06813529878854752	-0.06482234597206116	0.0022666845470666885	-0.00481933169066906	-0.02501196600496769	-0.005225722678005695	0.04707362875342369	-0.0035147005692124367	-0.04223024845123291	-0.03946249932050705	-0.03119666501879692	-0.03697165846824646	-0.014343032613396645	-0.0029432394076138735	0.011227582581341267	0.049535349011421204	-0.02578950673341751	0.035702433437108994	-0.055158790200948715	0.0014541555428877473	-0.07117870450019836	0.09966646879911423	-0.039171651005744934	-0.009865141473710537	-0.0023470004089176655	-0.017166895791888237	-0.006237119436264038	0.0494672991335392	0.011947563849389553	-0.03811405599117279	-0.03925686702132225	-0.012971551157534122	-0.04734795168042183	-0.07082658261060715	-0.005225867033004761	0.024093108251690865	-0.07017840445041656	0.01698346994817257	-0.024693800136446953	-0.018889818340539932	-0.02982916682958603	-0.0009653376182541251	-0.0015344915445894003	0.0002553498779889196	0.04907587170600891	-0.002948038512840867	0.04128066450357437	0.006608899217098951	0.02466326579451561	-0.012482169084250927	-0.06268466264009476	0.042198460549116135	0.012642832472920418	-0.060540035367012024	0.09173066169023514	-0.04323720932006836	-0.024652281776070595	-0.025866618379950523	0.028975090011954308	0.02425113134086132	-0.0030160637106746435	-0.024296142160892487	-0.016443530097603798	0.04926581680774689	-0.0565127432346344	-0.01548841968178749	0.019156573340296745	-0.029426248744130135	-0.10785013437271118	-0.015401084907352924	0.0024156791623681784	-0.03056575357913971	0.02944905310869217	0.004732956178486347	0.022830896079540253	0.04816288873553276	-0.03014358878135681	-0.003503059968352318	-0.03157920762896538	-0.0359230637550354	0.02713019773364067	-0.007620212156325579	0.08075958490371704	-0.013035996817052364	0.001230894005857408	0.032403334975242615	0.04463856294751167	0.022517839446663857	-0.07481265068054199	-0.012975117191672325	-0.006612236145883799	-0.009441407397389412	0.0013782811583951116	-0.00023309592506848276	0.033186085522174835	-0.04381084442138672	-0.05684688314795494	-0.026068005710840225	0.08006658405065536	-0.027534689754247665	-0.0036911044735461473	0.07634047418832779	-0.019312085583806038	0.018639784306287766	-0.01239174883812666	-0.058032963424921036	-0.09261548519134521	-0.020229609683156013	-0.03629838675260544	0.0023610256612300873	-0.0047447863034904	0.0031636846251785755	0.013830328360199928	-0.04621661454439163	-0.025624720379710197	0.026321273297071457	-0.06182260438799858	0.026671195402741432	-0.015798645094037056	-0.023855412378907204	-0.07338447123765945	-0.0224129781126976	-0.036958880722522736	0.04091254621744156	0.028030341491103172	0.018841566517949104	0.011596588417887688	0.020391734316945076	0.003311462001875043	-0.05201021209359169	0.02036598138511181	0.0497579462826252	-0.049781009554862976	-0.011870752088725567	0.05404650792479515	-0.031311407685279846	0.07462113350629807	0.020141704007983208	-0.025986185297369957	0.028605801984667778	0.09096135199069977	-0.04635772854089737	0.028697874397039413	-0.019538501277565956	-0.03610002249479294	0.056997790932655334	-0.03605543449521065	0.0832357332110405	-0.023301666602492332	0.07906216382980347	-0.03763960674405098	-0.03121344745159149	0.05781898647546768	0.09927047789096832	0.052814584225416183	0.022975346073508263	-0.05618614703416824
+0.03855818882584572	0.025736883282661438	0.0366453193128109	0.048278480768203735	-0.07877831906080246	-0.04754090681672096	-0.02174185775220394	0.021123461425304413	0.01689504086971283	-0.045774150639772415	-0.051728397607803345	-0.0014415455516427755	-0.010619712062180042	0.02260584942996502	0.011092599481344223	-0.047042395919561386	-0.07409103959798813	-0.0021004953887313604	-0.019550858065485954	-0.0675826370716095	0.03215954825282097	0.011068591848015785	-0.0780002772808075	0.043079495429992676	0.021978745236992836	0.021004237234592438	-0.06772308051586151	0.005743630230426788	-0.008060475811362267	0.019162708893418312	0.06150682643055916	-0.02463528700172901	0.0778483971953392	-0.067960724234581	-0.036978092044591904	0.0229609664529562	-0.026265842840075493	0.07330154627561569	0.023565424606204033	-0.036610689014196396	-0.03051522746682167	-0.002771420869976282	-0.026072070002555847	-0.03808446601033211	0.0128086032345891	0.008333698846399784	-0.03467128425836563	-0.017011824995279312	0.027404513210058212	-0.0019135158509016037	0.0882326140999794	-0.009858249686658382	-0.029290465638041496	-0.04049846902489662	0.001615779590792954	-0.011289890855550766	0.053268250077962875	-0.06697846204042435	-0.07882996648550034	0.02724570222198963	-0.1048855409026146	0.002557865111157298	0.008771647699177265	0.010069722309708595	-0.05165326967835426	-0.021012822166085243	4.338846338214353e-05	0.10429399460554123	0.014515969902276993	-0.04450931400060654	0.06576469540596008	0.09222301095724106	-0.06315670907497406	-0.01667586714029312	0.05081355944275856	-0.0738225057721138	-0.008614753372967243	0.06658681482076645	-0.002682712161913514	0.038791462779045105	0.015973631292581558	-0.05900600552558899	0.10281147807836533	-0.03153549134731293	-0.057660430669784546	-0.021184856072068214	-0.009654106572270393	-0.06692921370267868	0.05223638191819191	0.020921841263771057	0.021257812157273293	0.06745614111423492	-0.04166559502482414	0.04125406965613365	0.024595659226179123	0.043566178530454636	0.0307911429554224	-0.0922711044549942	-0.03860100731253624	0.021786410361528397	-0.009954788722097874	0.004984221886843443	0.04502512887120247	0.011880110949277878	0.049838002771139145	0.030865656211972237	-0.021028680726885796	0.020667098462581635	0.030804116278886795	0.036620449274778366	0.07783236354589462	0.060590144246816635	0.08278017491102219	0.059681788086891174	0.04451785981655121	-0.00658032950013876	0.020588664337992668	-0.048690181225538254	-0.02180550992488861	0.01596505381166935	0.008304436691105366	0.07476749271154404	-0.029104674234986305	-0.062126144766807556	-0.02735338918864727	0.03989865258336067	-0.06988905370235443	-0.030100781470537186	0.06900530308485031	-0.020147116854786873	0.11799967288970947	0.06591026484966278	-0.007650778163224459	0.043985042721033096	0.05242066830396652	0.03439464047551155	0.0042941817082464695	0.007597421295940876	-0.006435740739107132	-0.02383536659181118	-0.03898264840245247	0.03055662102997303	-0.007680009584873915	-0.08070643246173859	0.05192555487155914	0.028595229610800743	0.014973323792219162	-0.030873415991663933	-0.05820992588996887	-0.04398166760802269	-0.06218975782394409	-0.06342851370573044	-0.005496421363204718	-0.07947219163179398	0.01105676032602787	0.05260978266596794	0.01998441107571125	0.013679702766239643	-0.041913989931344986	0.013496258296072483	-0.044173747301101685	-0.02277248539030552	0.006769498810172081	-0.0759342834353447	-0.04586472734808922	0.0024648508988320827	0.008571554906666279	0.060133542865514755	-0.08262822031974792	0.005000153556466103	-0.02807963453233242	-0.002963043749332428	0.0363561250269413	0.07362064719200134	0.05264968425035477	-0.008794405497610569	0.02490833029150963	-0.054153211414813995	0.020519515499472618	-0.030557869002223015	0.04293164610862732	-0.037022024393081665	0.0374138206243515	-0.03750722482800484	-0.0032716928981244564	-0.00964383501559496	0.021278182044625282	-0.03019513003528118	0.05928288772702217	0.034360602498054504	-0.04454196244478226	-0.045218776911497116	0.024038473144173622	0.0682225376367569	-0.028426790609955788	0.035646069794893265	-0.006611797492951155	0.04202818498015404	-0.007945259101688862	-0.036646757274866104	-0.05151627957820892	0.009789599105715752	0.013398041017353535	0.07181926816701889	0.00787294004112482	-0.07649312168359756	0.06969662755727768	0.050487738102674484	0.02137279324233532	-0.05511495843529701	0.011981697753071785	0.031373802572488785	0.030240587890148163	0.059810489416122437	0.09388210624456406	-0.02725018933415413	-0.004347240086644888	-0.014282491989433765	0.041871245950460434	-0.0713164433836937	-0.011282727122306824	-0.006711251102387905	-0.07985510677099228	0.02046528458595276	-0.07674473524093628	-0.06152505427598953	-0.009950496256351471	0.010764023289084435	0.0020866221748292446	-0.043358586728572845	0.0015209442935883999	0.02630617469549179	-0.016763299703598022	-0.008324733935296535	-0.027828393504023552	-0.017298096790909767	-0.09392964839935303	0.1115650162100792	0.02494136430323124	-0.05271252989768982	-0.06444984674453735	0.016033543273806572	0.029132230207324028	0.002026383299380541	0.030167870223522186	0.029706664383411407	0.14341047406196594	0.03391661494970322	-0.0160291139036417	-0.017896456643939018	-0.031814150512218475	-0.07630643248558044	0.029062120243906975	0.025793401524424553	-0.037418901920318604	-0.007598687428981066	0.02262519672513008	-0.05093403905630112	-0.011655135080218315	0.009996605105698109	0.025878626853227615	0.027410073205828667	-0.03504377231001854	-0.04006488621234894	0.04970073699951172	-0.0008011607569642365	-0.03943489119410515	0.049341391772031784	-0.0008555222884751856	0.06337526440620422	-0.03236379846930504	-0.03211122006177902	0.057570379227399826	-0.005005760584026575	0.040434058755636215	-0.032398272305727005	0.09349104762077332	0.002326695015653968	-0.032553330063819885	-0.024423856288194656	-0.0612923763692379	-0.03243265673518181	-0.014372649602591991	-0.019525403156876564	-0.03163151815533638	0.07293267548084259	-0.0058169737458229065	-0.044107407331466675	-0.026731066405773163	0.013540362939238548	-0.0244111530482769	-0.08577175438404083	-0.016252225264906883	-0.0020324522629380226	-0.051563043147325516	-0.004628525581210852	0.013545547612011433	-0.05571848899126053	-0.036718446761369705	0.024376628920435905	-0.06334086507558823	-0.09518660604953766	0.050752799957990646	-0.016292845830321312	-0.043253421783447266	0.014970441348850727	-0.06630296260118484	0.002895472338423133	0.10275439918041229	0.02250121347606182	0.00353261805139482	0.05614393204450607	0.06527871638536453	0.12259291112422943	-0.006347199436277151	-0.05072743818163872	-0.018032589927315712	-0.04558593034744263	-0.0659116730093956	0.07725699245929718	-0.02501324936747551	0.010297529399394989	-0.013606405816972256	0.00046644778922200203	0.06352455914020538	-0.026081806048750877	-0.022053325548768044	-0.026129961013793945	0.014321302995085716	-0.019611474126577377	0.02096477709710598	0.015441451221704483	-0.007472265977412462	0.005290981847792864	-0.06424366682767868	-0.06055973470211029	-0.0364217534661293	0.03939357027411461	-0.0011161452857777476	0.0319831557571888	-0.02626516856253147	-0.07000632584095001	0.022619137540459633	-0.008600499480962753	0.06838241964578629	-0.0792502835392952	0.019024820998311043	0.0066737644374370575	0.005380770191550255	-0.09934255480766296	-0.022476309910416603	0.047202274203300476	-0.02832399681210518	-0.03640984743833542	0.017228972166776657	0.02434321492910385	-0.06935390830039978	0.01011259201914072	0.01787538081407547	-0.055809203535318375	-0.0015262083616107702	0.021232645958662033	0.042229145765304565	0.02460300736129284	-0.007383760530501604	-0.033065032213926315	-0.048531558364629745	0.001021407195366919	0.010740786790847778	-0.000720126845408231	0.05765116214752197	-0.06916289776563644	0.04965442791581154	-0.051659878343343735	-0.020476577803492546	-0.04067758470773697	0.05957411974668503	0.014947825111448765	0.00032822994398884475	0.02621198631823063	-0.019403530284762383	-0.01280151680111885	0.024849871173501015	0.03540889173746109	-0.07121574878692627	-0.021055547520518303	-0.04573525860905647	-0.052289534360170364	-0.053041666746139526	-0.030142851173877716	-0.008062782697379589	-0.0655762329697609	0.03944870084524155	0.006564358714967966	0.0011394013417884707	0.0027735780458897352	-0.013794397003948689	0.045048199594020844	0.038158491253852844	0.036092840135097504	-0.021896902471780777	0.01815386489033699	-0.02630094438791275	0.04611515253782272	0.02630334161221981	-0.04926644265651703	0.020900463685393333	0.03285840153694153	-0.0458788126707077	0.05429307743906975	-0.04769296944141388	-0.00017948338063433766	-0.0012986842775717378	-0.019477562978863716	0.042413435876369476	-0.0003766576701309532	0.0033790820743888617	-0.030027177184820175	0.06685153394937515	-0.0655730664730072	-0.055215369910001755	0.036827679723501205	-0.017495885491371155	-0.11425917595624924	-0.04258817061781883	-0.008779033087193966	-0.05958132818341255	0.02786908857524395	-0.00296594831161201	0.014713026583194733	0.04448798671364784	-0.030960023403167725	-0.04690571129322052	0.007640994153916836	-0.0043142023496329784	0.031703703105449677	-0.014006447978317738	0.06517887115478516	0.017949070781469345	0.01622685231268406	0.0337163507938385	0.005944561213254929	0.006836174987256527	-0.08805439621210098	-0.012656155042350292	0.002951953560113907	-0.0343836285173893	-0.014249082654714584	0.021725693717598915	-0.006779660936444998	-0.0923294872045517	-0.04151516035199165	-0.0564938448369503	0.05989282205700874	-0.031526584178209305	0.007125945296138525	0.038391731679439545	-0.041672270745038986	-0.0018219067715108395	0.01022289413958788	-0.025125304237008095	-0.09080041944980621	-0.01707099936902523	-0.047953490167856216	0.03695372864603996	0.011062662117183208	-0.005010567139834166	-0.032562002539634705	-0.04884433373808861	0.011276252567768097	0.03470972925424576	-0.059664878994226456	-0.00044055815669707954	-0.0004824663628824055	-0.0484648235142231	-0.07555148750543594	0.006493124179542065	-0.0577625073492527	0.06395439058542252	0.08376221358776093	0.015163193456828594	-0.0061429343186318874	-0.009299187920987606	0.02710598334670067	-0.05362773686647415	-0.017423227429389954	0.014078638516366482	-0.0538804866373539	-0.009208481758832932	0.06964137405157089	-0.05577024817466736	0.042840681970119476	0.05109994858503342	-0.07256178557872772	0.034597110003232956	0.03785344958305359	-0.05137105658650398	0.05142083764076233	-0.013813845813274384	-0.019890714436769485	0.06517161428928375	-0.0746118426322937	0.08902086317539215	-0.05196183919906616	0.07938932627439499	0.020514370873570442	-0.023845741525292397	0.06258998066186905	0.07597073167562485	-0.025042489171028137	0.017627466470003128	-0.08297919481992722
+0.03040706180036068	-0.023685839027166367	-0.011097284965217113	0.03486006334424019	-0.10418520867824554	-0.01831059716641903	-0.0005910262116231024	0.05556332692503929	-0.006976157892495394	-0.04202686622738838	-0.015021925792098045	0.014803016558289528	0.0047042337246239185	0.022906575351953506	0.006381792016327381	-0.022093039005994797	-0.06662280112504959	-0.024549201130867004	-0.05097696930170059	-0.05353989824652672	0.030318139120936394	-0.0011089229956269264	-0.043182291090488434	0.04126008599996567	-0.000442743010353297	0.011645453050732613	-0.022859016433358192	0.005933009088039398	-0.0160180926322937	0.03143223375082016	0.05169977992773056	-0.02618470788002014	0.01512147393077612	-0.0360412672162056	-0.07320403307676315	0.08276038616895676	-0.03170877695083618	0.1336669623851776	-0.004604567773640156	-0.03424925357103348	-0.04287313297390938	0.004576887935400009	-0.02910991571843624	-0.025493981316685677	0.03973444551229477	-0.011266370303928852	0.01074458658695221	-0.00346747855655849	0.03972841054201126	0.028045469895005226	0.08761367946863174	0.04194192588329315	0.005250594578683376	-0.033850088715553284	-0.03105439618229866	0.059735607355833054	0.01985928975045681	-0.025778379291296005	-0.044775884598493576	0.053246356546878815	-0.05530292168259621	-0.03215404599905014	0.02687695249915123	-0.004498991183936596	0.006924428977072239	0.0007738435524515808	-0.026387760415673256	0.09803634136915207	0.03162294626235962	-0.02009986713528633	-0.010739881545305252	0.10540708154439926	-0.039263252168893814	-0.06312955915927887	0.069965660572052	-0.05410432070493698	-0.05045801028609276	0.00852798018604517	-0.023617208003997803	0.007562772836536169	-0.03538929671049118	-0.05641026049852371	0.13089922070503235	-0.047368232160806656	-0.066790372133255	-0.0388704314827919	-0.001517504220828414	-0.061997342854738235	0.072665736079216	0.03747500851750374	0.008328242227435112	0.06538911163806915	-0.07227734476327896	0.030038397759199142	-0.017458027228713036	-0.016255682334303856	0.049652643501758575	-0.12883338332176208	-0.03536684066057205	-0.01300932839512825	0.03027939796447754	-0.04183456301689148	0.05821520462632179	-0.006877699866890907	0.050990279763936996	0.06284448504447937	-0.05927291512489319	-0.005653387401252985	0.013071345165371895	0.011214455589652061	0.023151079192757607	0.10990163683891296	0.07102037221193314	0.050527311861515045	0.07822848111391068	-0.02124042622745037	-0.0015668224077671766	-0.05786558613181114	-0.03921031579375267	-0.031179483979940414	-0.007044859696179628	0.05957939103245735	-0.01844252459704876	-0.07087339460849762	-0.029360394924879074	0.009416189976036549	-0.11961720138788223	0.0220482237637043	0.08065107464790344	-0.028712112456560135	0.07303890585899353	0.04561610892415047	-0.0008596365223638713	0.028811898082494736	0.0419401191174984	0.004412366077303886	-0.01638961397111416	0.048602428287267685	-0.001632930594496429	0.0020688162185251713	0.0030734820757061243	0.058145731687545776	0.030732151120901108	-0.05156131461262703	0.02265373431146145	0.06441043317317963	0.047093067318201065	-0.008794103749096394	-0.05884175002574921	0.051696229726076126	-0.06903940439224243	-0.08814536035060883	-0.014722772873938084	-0.08262254297733307	-0.004134991206228733	0.05440274253487587	0.026210417971014977	0.04561633616685867	-0.012413246557116508	-0.01603136584162712	-0.01277904212474823	-0.05109066516160965	-0.019093086943030357	-0.07394413650035858	-0.00857831072062254	-0.01726412959396839	0.004971873480826616	-0.020415890961885452	-0.03324350714683533	-0.023340562358498573	0.03366229683160782	-0.0013385881902649999	0.04390251263976097	0.07777376472949982	0.06612330675125122	0.0005336555186659098	0.014741815626621246	-0.05656396225094795	-0.022082526236772537	-0.019312117248773575	0.00030808194424025714	-0.028524862602353096	0.020048541948199272	-0.08821475505828857	-0.02203967235982418	-0.004006965085864067	0.03878908231854439	-0.00821656920015812	-0.02086152695119381	-0.04182330518960953	-0.025413023307919502	-0.05703367665410042	0.03685680031776428	0.038553282618522644	-0.037784360349178314	0.03650211542844772	0.01420389674603939	0.05584952235221863	-0.02830686792731285	-0.027365263551473618	-0.07200685143470764	0.05236120522022247	-0.004399510100483894	0.02270229533314705	0.03826673701405525	-0.06653594225645065	0.07481123507022858	-0.018310289829969406	0.01470611710101366	-0.08724025636911392	0.008332420140504837	0.026381297037005424	0.03492175415158272	0.042381759732961655	0.07429615408182144	-0.012756279669702053	-0.019993845373392105	0.00612277165055275	0.07023979723453522	-0.04119343310594559	-0.016757505014538765	-0.016785819083452225	-0.08101707696914673	-0.007391063030809164	-0.0006051948294043541	-0.06281422078609467	-0.05077245458960533	0.026400387287139893	-0.000127248844364658	-0.022545933723449707	8.197792340070009e-05	0.041889339685440063	-0.026316696777939796	-0.04315304011106491	0.016167733818292618	0.008508973754942417	-0.06008763983845711	0.06939924508333206	-0.022379130125045776	-0.08294323831796646	-0.04454883188009262	-0.00044477154733613133	0.03927500545978546	-0.01751326024532318	0.055828582495450974	0.06654766947031021	0.08418987691402435	0.03444826602935791	-0.00582857197150588	-0.024802161380648613	-0.05426832288503647	-0.030162593349814415	-0.01735294982790947	0.05122677981853485	-0.02283499389886856	-0.057271216064691544	-0.0014398670755326748	-0.05936935171484947	8.990294736577198e-05	0.02339985780417919	0.07911434024572372	-0.04693770036101341	-0.039526719599962234	-0.011379306204617023	0.009339665062725544	-0.04314138740301132	-0.04227466136217117	-0.0076059275306761265	-0.027562923729419708	0.06332939863204956	-0.019078074023127556	0.01151569839566946	0.004906141199171543	0.020499804988503456	0.025771358981728554	-0.04580000787973404	0.05118580535054207	-0.04334161803126335	-0.0848984569311142	-0.04092351719737053	-0.05620212480425835	-0.07935807853937149	-0.018016556277871132	-0.061694398522377014	-0.06745532155036926	0.08308660238981247	-0.004364588297903538	-0.0342395044863224	-0.07209493219852448	-0.03281903266906738	-0.012966894544661045	-0.031273990869522095	-0.011654006317257881	0.004747009836137295	-0.05862535908818245	-0.01588406041264534	0.004218477755784988	-0.03157432749867439	-0.01260434091091156	0.03141625598073006	0.018889250233769417	-0.03591248765587807	0.06945709139108658	-0.037723902612924576	-0.0022694841027259827	-0.02504214458167553	-0.048588890582323074	0.0324510894715786	0.08948735892772675	0.07168871909379959	0.05513006076216698	0.0022427295334637165	0.008306512609124184	0.07462622225284576	0.0418664813041687	-0.03891180083155632	-0.002081679878756404	-0.03189652040600777	-0.08509775996208191	0.030283857136964798	-0.024415360763669014	-0.028552716597914696	-0.04214923083782196	-0.004960137885063887	0.06736503541469574	0.01802433840930462	-0.018267029896378517	-0.0283438041806221	0.051966968923807144	0.010662597604095936	0.06288764625787735	-0.012089868076145649	-0.014956752769649029	-0.004597929306328297	-0.03833957388997078	-0.032959889620542526	-0.033796221017837524	0.05201658606529236	0.011231832206249237	0.04630758985877037	-0.0049071661196649075	-0.07033287733793259	0.03751881793141365	0.013792783953249454	0.08332452178001404	-0.07847192138433456	-0.002487467136234045	0.04197986423969269	0.01997849904000759	-0.0956687405705452	-0.018151134252548218	-0.0012133457930758595	-0.015402519144117832	-0.07083578407764435	-0.013274977914988995	0.05072353035211563	-0.03875551000237465	-0.013224680908024311	-0.047137487679719925	-0.035665202885866165	-0.010781371034681797	0.014716504141688347	-0.01748833805322647	0.019883345812559128	-0.015592629089951515	-0.030168442055583	0.025077026337385178	0.02369355410337448	0.02725943550467491	-0.04233686253428459	0.024774495512247086	0.008883802220225334	0.06884194910526276	-0.02845882624387741	-0.053089726716279984	-0.0792936235666275	0.05108877643942833	-0.03280002996325493	-0.038224246352910995	-0.045578353106975555	-0.04618500545620918	0.01548097375780344	0.02348974533379078	-0.016662636771798134	-0.0443725660443306	-0.013419329188764095	-0.010820981115102768	-0.05993551388382912	-0.1021096482872963	-0.05848193168640137	0.04630954563617706	-0.05140482261776924	-0.01443438045680523	-0.04442385956645012	-0.029276158660650253	-0.01378729660063982	0.01610046997666359	0.02769819088280201	0.0458679124712944	0.04588095471262932	-0.007029391825199127	0.08077812939882278	0.026095816865563393	0.04488721117377281	-0.032710667699575424	-0.04791606590151787	0.03893599286675453	0.014554615132510662	-0.0576864629983902	0.08702614903450012	-0.04166600853204727	0.022310422733426094	-0.035521071404218674	0.025867119431495667	0.0077413534745574	-0.03555728495121002	-0.03186454251408577	-0.03992260619997978	0.051968470215797424	-0.07748264819383621	-0.011002699844539165	0.0620436891913414	-0.0234282985329628	-0.09607033431529999	-0.009077661670744419	-0.025851625949144363	-0.016420092433691025	0.0410030335187912	0.013182798400521278	0.0009746833820827305	0.022480739280581474	-0.05839696526527405	-0.032241180539131165	-0.04464453458786011	-0.04614567384123802	0.010249290615320206	0.01771264337003231	0.09043268114328384	0.02340840734541416	0.003366375109180808	-0.015492796897888184	-0.0011752533027902246	0.0379352904856205	-0.08306793123483658	-0.016957689076662064	-0.004021689761430025	-0.03555998206138611	0.00527713168412447	0.010632605291903019	0.0037647972349077463	-0.029610922560095787	-0.01854763925075531	-0.021275075152516365	0.0771123394370079	-0.022849272936582565	-0.003369047539308667	0.037990618497133255	-0.015356678515672684	0.010940520092844963	0.0025433062110096216	-0.030674461275339127	-0.06514747440814972	0.04860144481062889	-0.024188192561268806	0.012682468630373478	-0.009002039209008217	0.03358149528503418	0.003295623930171132	-0.08657702803611755	-0.054810259491205215	-0.00015161922783590853	-0.0666094496846199	0.055813685059547424	0.007984461262822151	-0.08664585649967194	-0.03927924856543541	0.005009502172470093	-0.03783322125673294	0.06145390123128891	-0.04401645064353943	0.030734572559595108	0.0010207630693912506	0.006111269351094961	-0.02401353046298027	-0.021477803587913513	0.03814704716205597	-0.02386688068509102	-0.07012827694416046	-0.04332933574914932	0.058201033622026443	-0.04393354058265686	0.036313362419605255	0.058142758905887604	-0.04042608290910721	0.05816974490880966	0.06138041242957115	-0.04177480190992355	0.03385213389992714	0.02276833914220333	-0.01558277290314436	0.041722651571035385	-0.04430312663316727	0.0816987082362175	-0.060233309864997864	0.09035749733448029	0.0016231642803177238	0.008175215683877468	0.07537855952978134	0.07644811272621155	0.030139369890093803	0.013480977155268192	-0.067044198513031
+0.05106502026319504	-0.0015907202614471316	9.6830990514718e-05	0.022177154198288918	-0.07949890941381454	-0.025645045563578606	-0.004732299596071243	0.021813206374645233	0.003114142455160618	-0.0252850204706192	-0.007803384680300951	-0.02773430198431015	-0.008456175215542316	-0.018358303233981133	0.035916078835725784	-0.03983428701758385	-0.07387024909257889	0.00026772532146424055	-0.015972433611750603	-0.03878437355160713	0.0345642976462841	0.00840214267373085	-0.059510450810194016	0.028085405007004738	-0.004759624600410461	0.04042332246899605	-0.057049281895160675	-0.005034658592194319	-0.052737802267074585	0.048532601445913315	0.047212108969688416	-0.008878601714968681	0.039173342287540436	-0.03727879747748375	-0.05002544820308685	0.052931737154722214	-0.028697865083813667	0.10443747788667679	0.022864049300551414	-0.0637616366147995	-0.0559869185090065	0.005222853273153305	-0.03658052906394005	-0.04307914152741432	0.03635447099804878	0.024908997118473053	-0.021261541172862053	-0.024236461147665977	0.024650096893310547	-0.004817641340196133	0.10752596706151962	-0.019185638055205345	-0.041655488312244415	-0.016537487506866455	-0.02562093921005726	0.0016326633049175143	0.045978885143995285	-0.03712931647896767	-0.07869241386651993	0.0838678851723671	-0.09108760952949524	-0.01025981642305851	0.013462900184094906	0.006308091338723898	-0.04108106344938278	-0.05017378553748131	-0.015495192259550095	0.0693201944231987	0.008482087403535843	-0.001465180073864758	0.018904633820056915	0.09343114495277405	-0.03785644844174385	-0.07191871851682663	0.06084730476140976	-0.07246598601341248	-0.04703577607870102	0.055190395563840866	0.013247395865619183	0.02338351495563984	0.0032135131768882275	-0.04978151619434357	0.11922229826450348	-0.0635218471288681	-0.060939326882362366	-0.030751604586839676	-0.028278039768338203	-0.04882429540157318	0.07729237526655197	0.0519658699631691	-0.010430194437503815	0.06804997473955154	-0.061648786067962646	0.052738744765520096	0.030192261561751366	0.00495821051299572	0.04490732029080391	-0.06587677448987961	-0.05100564658641815	-0.01630902849137783	0.01076953113079071	-0.018118439242243767	0.035474877804517746	0.03970659524202347	0.0748606026172638	0.012869961559772491	-0.023282503709197044	0.017681023105978966	0.0340384803712368	0.03585914522409439	0.02150011993944645	0.0680815726518631	0.09351582080125809	0.010132196359336376	0.06890404969453812	-0.0030062436126172543	0.006571518257260323	-0.06296652555465698	-0.011558987200260162	0.025428444147109985	-0.0006444416358135641	0.02747214585542679	-0.013675159774720669	-0.07901213318109512	-0.011958116665482521	0.017506808042526245	-0.10314001142978668	-0.01577530801296234	0.05294518172740936	-0.03420981764793396	0.10683716833591461	0.06692355871200562	-0.023150509223341942	0.03137659654021263	0.05057698115706444	0.04779704287648201	0.01790626347064972	0.05187216401100159	-0.036906857043504715	-0.005897650495171547	0.002848757663741708	0.028662461787462234	-0.02181217074394226	-0.0517197884619236	0.029616529121994972	0.0006755245267413557	0.052993521094322205	-0.015080457553267479	-0.07758701592683792	0.026543334126472473	-0.07638997584581375	-0.06674814224243164	0.00016425129433628172	-0.07076416909694672	-0.002907979302108288	0.04300202056765556	-0.004988058470189571	0.02712315134704113	-0.022395316511392593	-0.0270390585064888	-0.03217875212430954	-0.039525847882032394	-0.04462321475148201	-0.08493483066558838	-0.030183428898453712	-0.04625893384218216	0.005835817661136389	0.02279130183160305	-0.043956369161605835	0.00020388653501868248	0.018904484808444977	-0.0020234263502061367	0.03203250467777252	0.08253809809684753	0.06569458544254303	0.020561039447784424	0.018526801839470863	-0.06346814334392548	0.008178340271115303	-0.045725226402282715	0.004763069562613964	-4.160308526479639e-05	0.03739757463335991	-0.04826714098453522	0.0039323135279119015	-0.008390218950808048	0.09039252251386642	-0.004063709639012814	0.0335722453892231	0.013846238143742085	-0.01719566620886326	-0.06684061884880066	0.06599270552396774	0.05204618722200394	0.001418314641341567	5.2022358431713656e-05	-0.005118599161505699	0.05436762422323227	-0.013012250885367393	-0.051054488867521286	-0.021421577781438828	0.027934325858950615	0.02091558650135994	0.07762771844863892	0.017257481813430786	-0.030939841642975807	0.09954960644245148	0.011319332756102085	0.026851767674088478	-0.06168963015079498	0.0033773635514080524	0.005971943493932486	0.011592196300625801	0.007565743289887905	0.09910199791193008	-0.05387042835354805	0.013021957129240036	0.015066946856677532	0.09924453496932983	-0.026479370892047882	-0.014885743148624897	-0.011088895611464977	-0.08275037258863449	0.005346677266061306	-0.01903425343334675	-0.09092670679092407	-0.02537306398153305	0.05579651892185211	-0.03310161456465721	-0.01227665413171053	0.017911911010742188	0.042235467582941055	-0.049964312463998795	0.007316334173083305	0.012505710124969482	-0.02169669419527054	-0.10598058253526688	0.07202519476413727	-0.023588092997670174	-0.07455510646104813	-0.06143222749233246	0.016568582504987717	0.047892238944768906	0.01070514414459467	0.06258422881364822	0.07324597239494324	0.08355549722909927	0.02643904648721218	0.02165374718606472	0.017552657052874565	-0.041215069591999054	-0.04908919706940651	0.025967977941036224	0.010691951960325241	-0.06551538407802582	-0.034324340522289276	-0.012663018889725208	-0.035562664270401	0.014043877832591534	0.003671824000775814	0.019075650721788406	-0.03369311988353729	-0.019231220707297325	-0.015784690156579018	0.04324708506464958	-0.020861122757196426	-0.014088439755141735	0.056398969143629074	-0.019910067319869995	0.04455150291323662	-0.006740436889231205	-0.030782656744122505	0.030308280140161514	0.01750813238322735	0.028077010065317154	-0.034622710198163986	0.07745936512947083	-0.0030828756280243397	-0.09308476746082306	-0.03941987454891205	-0.09449990093708038	-0.011438298970460892	0.0049474104307591915	-0.06685062497854233	-0.06806919723749161	0.06830846518278122	0.01371210440993309	-0.04926999285817146	-0.07973169535398483	-0.023201413452625275	0.00220749294385314	-0.06958947330713272	-0.0482911691069603	0.015964936465024948	-0.06538061052560806	-0.03900009021162987	0.058913443237543106	-0.04464411363005638	-0.04546494409441948	-0.01052093505859375	-0.023000406101346016	-0.06733007729053497	0.012940016575157642	-0.015348929911851883	-0.033761247992515564	0.028026042506098747	-0.0925738587975502	0.04208987206220627	0.12129660695791245	0.058996740728616714	0.029496565461158752	0.025874491780996323	0.03421647846698761	0.10521575808525085	0.024040140211582184	-0.06398917734622955	-0.030321383848786354	-0.06323752552270889	-0.05239548161625862	0.01749979704618454	-0.04516591876745224	-0.007846008986234665	-0.04084750637412071	-0.0034083472564816475	0.04647272080183029	0.010195253416895866	-0.009584221057593822	-0.012490138411521912	0.02977851778268814	-0.01564367301762104	0.047798603773117065	0.011589731089770794	0.00040644113323651254	-0.01695491559803486	-0.03567561134696007	-0.04250068962574005	-0.011659001931548119	0.03722662478685379	-0.011530940420925617	0.017342371866106987	-0.008807147853076458	-0.054277997463941574	0.011552419513463974	-0.0030842749401926994	0.07610899955034256	-0.06701670587062836	0.02031574584543705	0.03157838061451912	-0.0009841350838541985	-0.10005732625722885	0.0025134410243481398	0.056455399841070175	-0.052549343556165695	-0.03700893744826317	-0.007574428338557482	0.046909723430871964	-0.045709237456321716	-0.00399716105312109	-0.010214157402515411	-0.06496111303567886	-0.001822952413931489	0.009005975909531116	0.024252839386463165	0.0006234343163669109	-0.03523585572838783	-0.036437101662158966	-0.004664162173867226	-0.016148649156093597	0.013528773561120033	-0.014879030175507069	-0.00606851652264595	-0.031885478645563126	0.019642531871795654	-0.04417547583580017	-0.0245047677308321	-0.04105331748723984	0.10026631504297256	0.003993213642388582	-0.014608803205192089	0.023821484297513962	-0.0026997653767466545	-0.03836248815059662	0.01711416430771351	0.023293450474739075	-0.03239493817090988	-0.038213834166526794	-0.03901318460702896	-0.04463258013129234	-0.10341949015855789	-0.014930211007595062	0.013853264041244984	-0.06118636950850487	0.010297926142811775	-0.025482794269919395	0.006981320679187775	-0.0013319693971425295	-0.019615547731518745	0.028836192563176155	0.05000535398721695	0.04157067462801933	-0.026509340852499008	0.008340285159647465	0.0277511328458786	0.052734579890966415	0.030330313369631767	-0.040741149336099625	0.021342318505048752	0.02173369936645031	-0.03436368703842163	0.062425415962934494	0.005811529234051704	0.018448414281010628	0.001053779968060553	0.0004738174902740866	-0.0011369059793651104	-0.030085546895861626	-0.003023503115400672	-0.022357279434800148	0.05253623425960541	-0.06857120990753174	-0.030952390283346176	0.011462508700788021	-0.022634334862232208	-0.11213982105255127	-0.035079218447208405	0.03000805340707302	-0.041137415915727615	0.019077956676483154	-0.015553492121398449	0.007732488680630922	0.05388754978775978	-0.049057334661483765	-0.04530101642012596	-0.034855011850595474	-0.001259013544768095	0.06834645569324493	-0.01814885064959526	0.06347335129976273	-0.002215114189311862	0.0062887887470424175	-0.033375926315784454	0.011274772696197033	0.030804703012108803	-0.07381051033735275	0.00015182833885774016	-0.03826785832643509	-0.05733458325266838	0.02446829527616501	0.008500611409544945	-0.015061050653457642	-0.048914551734924316	-0.04298285022377968	-0.04001949355006218	0.07372259348630905	-0.04497861489653587	-0.00543322553858161	0.02371153049170971	-0.00959496945142746	0.01378349307924509	0.024204891175031662	-0.05311145260930061	-0.06753700971603394	0.020920811221003532	-0.024455728009343147	0.034690555185079575	0.027044909074902534	-0.0019438373856246471	-0.015672186389565468	-0.06860599666833878	-0.016418347135186195	0.013371527194976807	-0.10836915671825409	0.010698848403990269	0.0008338376064784825	-0.08772115409374237	-0.04722372442483902	0.013389094732701778	-0.03501572832465172	0.060593921691179276	0.016847098246216774	0.04032105952501297	-0.02181130275130272	0.00749792717397213	-0.002241656417027116	-0.0405423603951931	0.01469224039465189	-0.03118171915411949	-0.03796713426709175	-0.02008420042693615	0.05307437479496002	-0.0711100846529007	0.028430001810193062	0.05051979050040245	-0.027693534269928932	0.03583354502916336	0.08039145171642303	-0.05610789731144905	0.044499993324279785	0.029360469430685043	-0.0399068258702755	0.06276953220367432	-0.08055109530687332	0.1016751304268837	-0.06985616683959961	0.0994202271103859	-0.0051150876097381115	-0.021927624940872192	0.0572163462638855	0.07810480147600174	-0.0188283808529377	0.010273952037096024	-0.07676375657320023
+0.03302812948822975	0.0431031659245491	0.03295046463608742	0.03856504335999489	-0.1213231086730957	-0.06404297053813934	-0.03497052192687988	0.025608455762267113	0.051939111202955246	-0.005111047066748142	-0.008042212575674057	-0.01366434432566166	-0.007315876893699169	-0.0013575187185779214	0.0264278631657362	-0.018281858414411545	-0.06322523206472397	-0.01593152806162834	-0.03375688195228577	-0.01052716001868248	0.058939527720212936	0.0037157016340643167	-0.06199611723423004	0.05595435947179794	-0.001842322526499629	0.036858730018138885	-0.06208835914731026	0.02563546411693096	-0.04493950679898262	0.016011107712984085	0.02587738074362278	0.0057532573118805885	0.024737674742937088	-0.06124797835946083	-0.024597449228167534	0.05910158157348633	-0.007778130937367678	0.06618534028530121	0.049701061099767685	-0.029231613501906395	-0.02322690561413765	0.050164975225925446	-0.029978487640619278	-0.032645951956510544	0.007254968862980604	0.00988108478486538	-0.013220510445535183	-0.00036165217170491815	0.02909008599817753	0.022753924131393433	0.0800853818655014	-0.0030569175723940134	-0.05438116192817688	-0.029402300715446472	0.005135098937898874	0.007488178089261055	0.05123533681035042	-0.0864236131310463	-0.08116927742958069	0.046162042766809464	-0.07022443413734436	0.03700369969010353	-0.0005210282397456467	-0.0038354620337486267	-0.0007212847704067826	-0.022781742736697197	0.004824845585972071	0.08128319680690765	0.04277952387928963	-0.022199071943759918	0.0821475014090538	0.10258524864912033	-0.08799830079078674	-0.034061893820762634	0.05666505917906761	-0.050177644938230515	-0.04343167319893837	0.0942460298538208	-0.02387918345630169	0.005166498012840748	0.019966116175055504	-0.06577565521001816	0.07147828489542007	-0.051307547837495804	-0.06159733980894089	-0.03801700845360756	-0.007670488674193621	-0.04622925817966461	0.04633484035730362	0.04488660767674446	0.006799393333494663	0.0721246674656868	-0.07515973597764969	0.03382933512330055	-0.0017343817744404078	0.04851611703634262	0.03654448688030243	-0.12132421135902405	-0.043392106890678406	0.004293311852961779	-0.02360241487622261	-0.03193099796772003	-0.008220666088163853	0.002024582354351878	0.033917881548404694	0.0531894713640213	-0.011714202351868153	0.025266095995903015	0.01673164591193199	0.07885006815195084	0.06042560562491417	0.014193674549460411	0.11672969907522202	0.08789802342653275	0.033882174640893936	-0.014321930706501007	-0.00837880652397871	-0.03163246065378189	-0.040519606322050095	0.017961544916033745	-0.003530642483383417	0.0255975853651762	0.008035090751945972	-0.06700506806373596	-0.014705518260598183	0.017279338091611862	-0.11286375671625137	-0.006170428358018398	0.005257913842797279	-0.034547075629234314	0.08416057378053665	0.01960439234972	-0.023701943457126617	-0.011669936589896679	0.06514322757720947	0.0731097087264061	0.008428633213043213	0.017079127952456474	-0.025387510657310486	-0.020850438624620438	-0.008320504799485207	0.00511918542906642	0.022211896255612373	-0.06536972522735596	0.018090276047587395	0.032864172011613846	0.041046928614377975	0.022000335156917572	-0.08684662729501724	-0.02200338803231716	-0.09136231988668442	-0.07152052968740463	-0.00517156021669507	-0.07155124098062515	-0.00354329077526927	0.07197638601064682	0.021682962775230408	0.050954706966876984	-0.044930651783943176	-0.020048536360263824	-0.005960436537861824	-0.007095093373209238	-0.04434623941779137	-0.06079588457942009	-0.012353682890534401	-0.04690076410770416	0.015433933585882187	0.005557221360504627	-0.025978701189160347	-0.0275673046708107	-0.035366807132959366	-0.007499978877604008	0.022244693711400032	0.06788045167922974	0.07469851523637772	-0.021039897575974464	0.03589308634400368	-0.04523172229528427	-0.007516174111515284	-0.053360164165496826	-0.019009847193956375	-0.029416801407933235	0.05123275890946388	-0.056202035397291183	-0.0369129478931427	-0.016571011394262314	0.03007102571427822	-0.025432288646697998	0.012240947224199772	-0.0008644178742542863	-0.043054673820734024	-0.043854642659425735	0.06793953478336334	0.08926539123058319	-0.03487083688378334	0.01969628408551216	-0.0058854795061051846	0.020918214693665504	0.0070055024698376656	-0.013925489038228989	-0.05244313180446625	0.04249425232410431	-0.003036139765754342	0.07356877624988556	0.02824963815510273	-0.07459291815757751	0.06399639695882797	-0.003731249365955591	-0.01008363626897335	-0.044575951993465424	0.020365141332149506	0.04549432545900345	0.017455579712986946	-0.004386643413454294	0.08360721915960312	-0.03678121045231819	0.01755150593817234	-0.012518956325948238	0.05871104449033737	-0.048931945115327835	0.04803827404975891	-0.016146648675203323	-0.058553487062454224	0.02202296257019043	-0.044839948415756226	-0.07000663131475449	-0.01629663072526455	0.009617798030376434	-0.02159102074801922	-0.02388816513121128	0.020975708961486816	0.023011399433016777	-0.0030193980783224106	-0.027206694707274437	0.00617646798491478	0.04023974388837814	-0.09239227324724197	0.10893750190734863	-0.03337641432881355	-0.016414949670433998	-0.059604063630104065	0.03824184089899063	0.042530763894319534	0.02069871686398983	0.026264961808919907	0.06558240950107574	0.09896597266197205	0.054573215544223785	0.03601304069161415	-0.017870886251330376	-0.07529541105031967	-0.06534983962774277	0.02420211024582386	0.049462128430604935	-0.055876243859529495	-0.014221996068954468	-0.013435028493404388	-0.05194355919957161	-0.012230613268911839	0.03425880894064903	0.02814370207488537	-0.0343894362449646	-0.03200303018093109	-0.06081769987940788	0.02189101278781891	-0.0012121207546442747	-0.08270386606454849	0.046596165746450424	-0.034229736775159836	0.06725849211215973	-0.0013334817485883832	-0.05070570483803749	0.03401144593954086	0.025187192484736443	0.05590232461690903	-0.01155647449195385	0.02608678676187992	-0.02024998888373375	-0.08945274353027344	-0.021555334329605103	-0.07713285088539124	-0.05726587772369385	0.003379134926944971	-0.022076264023780823	-0.05264166370034218	0.07280503213405609	0.034444574266672134	-0.025655057281255722	-0.08123067766427994	0.017626795917749405	-0.002592487260699272	-0.06037864834070206	-0.046906739473342896	0.02561189793050289	-0.09097152203321457	-0.042229775339365005	0.03485351800918579	-0.04375153407454491	-0.07043518871068954	0.0027476672548800707	-0.07999294251203537	-0.06997797638177872	0.007545347325503826	-0.020963439717888832	-0.02154834195971489	0.00863434188067913	-0.06120135635137558	0.04880721867084503	0.11845213919878006	0.01390093844383955	0.04342777654528618	0.009550216607749462	0.013465316034853458	0.09829474985599518	0.012106490321457386	-0.04436219111084938	0.009812496602535248	-0.05827208608388901	-0.0697830319404602	0.01569923385977745	-0.0672910064458847	-0.023579375818371773	-0.009160700254142284	0.02306552231311798	0.04676053300499916	0.01697562448680401	-0.0027099044527858496	0.007253760937601328	0.027177687734365463	0.024973567575216293	0.03275488689541817	-0.005458674859255552	-0.03333303704857826	0.0012204834492877126	-0.015022425912320614	-0.011200504377484322	-0.04016273468732834	0.04613140597939491	0.020407233387231827	0.03584142029285431	-0.01932486891746521	-0.07010392844676971	0.00524495542049408	0.011138448491692543	0.10077796131372452	-0.08833590149879456	0.04165901616215706	0.049463264644145966	-0.021389754489064217	-0.05861954391002655	-0.016796790063381195	0.06802190095186234	-0.03321165591478348	0.006976274773478508	-0.012466014362871647	0.030790546908974648	-0.06114481762051582	-0.015628479421138763	0.04492725059390068	-0.03337833285331726	0.02500901184976101	0.0443340502679348	0.0038528223522007465	-0.011975599452853203	-0.0413772352039814	0.030522678047418594	-0.015448520891368389	0.012943131849169731	0.030211566016077995	-0.00026876037009060383	0.004256275482475758	-0.017338888719677925	0.0725499838590622	-0.02442500740289688	-0.027673201635479927	-0.03470219299197197	0.09564369916915894	-0.013397560454905033	0.009962241165339947	-0.0011620044242590666	0.03219274431467056	0.009775789454579353	0.06576349586248398	0.014445935375988483	-0.04113034904003143	-0.00979613233357668	-0.03637457638978958	-0.036656007170677185	-0.09110455960035324	-0.028863729909062386	0.017051272094249725	-0.07954341173171997	-0.015448851510882378	-0.03771667554974556	-0.014133509248495102	-0.03589213266968727	-0.04764543101191521	-0.007048327475786209	0.021837811917066574	0.04176883026957512	0.004359307698905468	0.002571726683527231	0.0014648468932136893	0.05321352556347847	0.007927344180643559	-0.05838543921709061	0.04825637862086296	0.05211265757679939	-0.004049528390169144	0.04093978926539421	-0.0327269583940506	-0.007810880430042744	0.020415429025888443	0.010493027046322823	-0.0001633467327337712	-0.014036011882126331	-0.009947135113179684	-0.05846778675913811	0.055987268686294556	-0.055033255368471146	-0.013341661542654037	0.06412074714899063	-0.02472345158457756	-0.10788784176111221	0.013152568601071835	-0.004419032949954271	-0.028823914006352425	-0.0005180939333513379	-0.013285547494888306	0.032829344272613525	0.042496323585510254	-0.062116097658872604	-0.015763161703944206	-0.04223126918077469	-0.02039487659931183	0.0543307401239872	-0.007159967441111803	0.06761490553617477	-0.01751980371773243	0.004115454386919737	-0.0035976769868284464	0.017178170382976532	0.008508347906172276	-0.06623005121946335	-0.0005453215562738478	-0.04859774187207222	-0.029895076528191566	0.015507981181144714	-0.01084346603602171	0.024848265573382378	-0.05705714225769043	-0.061063386499881744	-0.03893991559743881	0.08300270885229111	-0.0403771810233593	-0.011602836661040783	0.05946260690689087	-0.005024703685194254	0.008240900933742523	0.000575220794416964	-0.018337372690439224	-0.0860786885023117	-0.013536777347326279	-0.03948323428630829	0.019377991557121277	0.04910038784146309	0.0149603933095932	-0.01853875443339348	-0.06312112510204315	-0.017321007326245308	0.04385516420006752	-0.0460241474211216	0.026569416746497154	-0.014891591854393482	-0.0376727320253849	-0.049677591770887375	-0.03602642938494682	-0.026929762214422226	0.0662437379360199	0.021636752411723137	0.05266478657722473	0.007834300398826599	0.0062833866104483604	0.052067022770643234	-0.0683460533618927	0.008282821625471115	0.023447368294000626	-0.0377698689699173	-0.022900544106960297	0.0656619593501091	-0.012294666841626167	0.03243521600961685	0.05453411117196083	-0.04024878516793251	0.03178590536117554	0.043627601116895676	-0.050259094685316086	0.022166401147842407	-0.026459017768502235	-0.0710463896393776	0.04971461743116379	-0.04924214631319046	0.0877300351858139	-0.02835015207529068	0.07943947613239288	-0.04480374604463577	-0.07086383551359177	0.07940562069416046	0.08148570358753204	-0.01958046667277813	0.0025549945421516895	-0.045854341238737106
+0.017701320350170135	-0.004326010588556528	-0.020638713613152504	0.059875402599573135	-0.0785340815782547	-0.04496462270617485	-0.003020929405465722	0.015781141817569733	0.00448226323351264	-0.030621351674199104	-0.01848912611603737	-0.007728531491011381	0.0009278322104364634	-0.0074021886102855206	0.013900164514780045	-0.008623836562037468	-0.07501870393753052	-0.002093150280416012	-0.014624794945120811	-0.031965725123882294	0.04632159322500229	-0.023135295137763023	-0.10104355961084366	0.07438415288925171	0.02377627231180668	0.03386164456605911	-0.03763500973582268	-0.009166926145553589	-0.027230946347117424	0.02670593559741974	0.056882161647081375	-0.002441895892843604	0.01596985012292862	-0.03371331840753555	-0.07184365391731262	0.04562177136540413	-0.019929086789488792	0.0991978868842125	-0.005203010514378548	-0.009572487324476242	-0.05682970955967903	0.010137994773685932	-0.06315242499113083	-0.03621484711766243	0.00984072033315897	-0.01405095960944891	0.00334069412201643	0.0021461627911776304	0.032823022454977036	0.030594581738114357	0.09082982689142227	-0.008480947464704514	-0.015757521614432335	-0.027634404599666595	-0.01727796532213688	0.01916947402060032	0.018612796440720558	-0.05246202275156975	-0.06610734760761261	0.05267275869846344	-0.059190887957811356	0.016997942700982094	0.021944498643279076	-0.0001144472262240015	-0.017498543485999107	-0.0046265609562397	-0.014838212169706821	0.09155727922916412	0.003928810823708773	-0.0350835919380188	0.031680136919021606	0.06826423853635788	-0.061105210334062576	-0.011253985576331615	0.04953520745038986	-0.029070382937788963	-0.03395147621631622	0.07519390434026718	0.02043072320520878	0.001686682808212936	0.01949511095881462	-0.03308205306529999	0.1053754985332489	-0.013271204195916653	-0.044642917811870575	-0.04645020142197609	0.012831849046051502	-0.025524703785777092	0.07919623702764511	0.023918932303786278	0.002477092668414116	0.10065990686416626	-0.08180743455886841	0.08338851481676102	0.04097554832696915	0.005583302117884159	0.028685668483376503	-0.14695170521736145	-0.058554571121931076	0.028342019766569138	0.00209568883292377	-0.03347904980182648	0.03740055114030838	-0.026459477841854095	0.052953820675611496	0.04078104719519615	-0.05931783840060234	0.0006138112512417138	0.028596047312021255	0.01623111590743065	0.06630486994981766	0.07418126612901688	0.06119026988744736	0.07684852927923203	0.05561712756752968	0.014266162179410458	0.007324570324271917	-0.03302806615829468	-0.05947582423686981	0.02996193617582321	0.007605001330375671	0.039313945919275284	0.009592623449862003	-0.05865743011236191	-0.03409517928957939	0.01147373579442501	-0.12004519999027252	-0.010220350697636604	0.073884978890419	-0.015145097859203815	0.1020217016339302	0.03873883932828903	0.011791681870818138	-0.0032142840791493654	0.057578667998313904	0.031434860080480576	0.024786194786429405	0.01552655640989542	-0.02546718344092369	-0.008540247566998005	-0.005870806984603405	0.02930174022912979	-0.0022827694192528725	-0.062375087291002274	0.034941382706165314	0.02940542809665203	0.014198454096913338	0.014026875607669353	-0.03811914101243019	-0.02033017948269844	-0.08917376399040222	-0.06412215530872345	0.008867225609719753	-0.08167898654937744	0.016184287145733833	0.04903282970190048	0.050361357629299164	0.0658179298043251	-0.02876042015850544	-0.009700417518615723	-0.03299456462264061	-0.016824135556817055	-0.010586312972009182	-0.07711837440729141	-0.012764361687004566	-0.030454855412244797	-0.018634049221873283	0.03980175778269768	-0.028636090457439423	-0.013053965754806995	-0.0005153407691977918	-0.008867496624588966	0.029913095757365227	0.07994900643825531	0.05018917843699455	0.009925402700901031	0.023524612188339233	-0.04932350292801857	-0.01257618609815836	-0.033842455595731735	0.0022972726728767157	-0.020768487825989723	0.04183162376284599	-0.04533876106142998	-0.03340364247560501	0.0065531618893146515	0.03661210462450981	-0.014514872804284096	-0.017353039234876633	-0.0043046725913882256	-0.06313353031873703	-0.0627591460943222	0.08262680470943451	0.06304357945919037	-0.02333240769803524	0.034799136221408844	0.004031816963106394	0.07251626253128052	0.009203468449413776	-0.0065702046267688274	-0.045353591442108154	0.0015531496610492468	0.00985253881663084	0.07204489409923553	0.034874025732278824	-0.0794234499335289	0.08329421281814575	0.03343464806675911	0.020178012549877167	-0.08025053888559341	-0.007663211319595575	0.0063439616933465	0.0060113826766610146	0.06955260038375854	0.08513623476028442	-0.023002682253718376	-0.018362421542406082	0.048957254737615585	0.05853519216179848	-0.07255642861127853	-6.274264887906611e-05	-0.004053011536598206	-0.056867942214012146	0.015288528986275196	-0.01307255681604147	-0.08213374018669128	-0.03428838402032852	0.033367548137903214	-0.03460352495312691	-0.014340541325509548	-0.015909025445580482	0.04542927071452141	-0.03172075375914574	-0.05699101835489273	0.0025538336485624313	0.02397625707089901	-0.10243285447359085	0.09122948348522186	-0.01726250909268856	-0.08161646127700806	-0.07131675630807877	0.024221932515501976	0.01664590649306774	0.03355993703007698	0.06164427101612091	0.07210570573806763	0.10213954746723175	0.04353715106844902	0.01437648106366396	-0.00532555254176259	-0.0697801262140274	-0.03419322893023491	0.026613280177116394	0.04636339098215103	-0.06019546836614609	-0.04089481383562088	-0.01650950126349926	-0.04364476725459099	-0.007274847012013197	0.01925116404891014	0.02733463980257511	-0.030403153970837593	-0.05521165579557419	-0.022370580583810806	0.015415826812386513	-0.03142635151743889	-0.020783932879567146	0.005386774428188801	-0.021349050104618073	0.07021671533584595	-0.046249013394117355	-0.07113933563232422	-0.0003145192749798298	0.01810416206717491	0.023061292245984077	-0.015886327251791954	0.04443566873669624	-0.03465583547949791	-0.11181586980819702	-0.02160906046628952	-0.08012297749519348	-0.04503469914197922	-0.029546624049544334	-0.006666228175163269	-0.04301224648952484	0.10588684678077698	-0.002707629231736064	-0.0791543498635292	-0.055784668773412704	-0.01724632829427719	-0.012216150760650635	-0.06029224768280983	-0.00675742095336318	0.0009884076425805688	-0.057448145002126694	-0.020258653908967972	0.05957207456231117	-0.036633457988500595	-0.017083166167140007	0.045470818877220154	-0.019533805549144745	-0.052496638149023056	0.05528721585869789	-0.005815095733851194	-0.04241354763507843	0.01765207014977932	-0.09782074391841888	0.03438889607787132	0.0982285887002945	0.00751440180465579	0.02528190240263939	0.034681495279073715	0.062023043632507324	0.08875604718923569	0.016227087005972862	-0.04933326318860054	0.00782173965126276	-0.050053004175424576	-0.047207482159137726	0.03933897987008095	-0.040788643062114716	0.00350018753670156	-0.04458235949277878	-0.000730707251932472	0.0685185045003891	0.016936004161834717	0.0076023852452635765	0.007791778538376093	0.022953584790229797	0.02637643925845623	0.032648179680109024	0.018289675936102867	-0.03570358827710152	0.00719702010974288	-0.03941982239484787	-0.06398206949234009	-0.02968149073421955	0.01998930796980858	-0.0021944409236311913	0.06636171042919159	-0.007012671325355768	-0.06706807017326355	0.04470657557249069	-0.029729284346103668	0.09664347022771835	-0.09328269958496094	0.00740086380392313	0.051264550536870956	0.0023743638303130865	-0.0828118845820427	-0.016604742035269737	0.06311888992786407	-0.061662718653678894	-0.052539266645908356	-0.017488734796643257	0.045990925282239914	-0.0322839580476284	-0.035707633942365646	0.020487559959292412	-0.05582920089364052	0.01145585160702467	0.021480752155184746	0.005922467447817326	-0.0020324240904301405	-0.01858886331319809	-0.004173679742962122	0.006161578930914402	0.0107179069891572	0.008111082017421722	-0.043936409056186676	0.025393648073077202	-0.010715091601014137	0.08135388791561127	-0.02104664035141468	-0.0251646526157856	-0.07398509979248047	0.05557259917259216	-0.012876644730567932	-0.014440197497606277	-0.04033832997083664	-0.006255922373384237	-0.008285287767648697	0.0308978408575058	0.02092672698199749	-0.07087841629981995	-0.0379936546087265	-0.05443086475133896	-0.055046405643224716	-0.07791711390018463	-0.0321337915956974	0.008059692569077015	-0.06292566657066345	0.01309917401522398	-0.0711771547794342	-0.021024322137236595	0.0011666214559227228	-0.042556360363960266	0.021694576367735863	0.042098268866539	0.026535414159297943	-0.03398781642317772	0.04419746622443199	0.0014048921875655651	0.03401898965239525	0.00010420299804536626	-0.03570719063282013	0.06748485565185547	0.05923492833971977	-0.005393229424953461	0.0491507425904274	-0.014185981824994087	0.007109956815838814	0.008999207988381386	-0.03640802949666977	-0.016099974513053894	0.011457632295787334	-0.03093881532549858	-0.05683811381459236	0.029401615262031555	-0.04942672699689865	-0.0296805240213871	0.03166196867823601	-0.052407197654247284	-0.0765528455376625	-0.030733350664377213	-0.007441266439855099	-0.02438393048942089	0.02227410301566124	0.01967199705541134	0.040918439626693726	0.05246345326304436	-0.07682139426469803	-0.034381311386823654	-0.019383249804377556	-0.024518631398677826	0.02911900356411934	-0.014850901439785957	0.05691530555486679	0.005600204225629568	0.010829681530594826	-0.009027699008584023	0.0032408577390015125	0.0005518991965800524	-0.06013229489326477	-0.004147940780967474	-0.006396942306309938	-0.04823493957519531	0.03703184053301811	0.021877849474549294	-0.004400153644382954	-0.04612454026937485	-0.046817127615213394	-0.038360852748155594	0.05978260189294815	-0.021276848390698433	0.008695852011442184	0.021001016721129417	-0.009826134890317917	0.013259166851639748	0.02327798306941986	0.0057555753737688065	-0.06645044684410095	0.0178757943212986	-0.03680785745382309	0.0349038802087307	0.015284037217497826	0.011375878006219864	-0.022249115630984306	-0.09293052554130554	-0.03051319159567356	0.0015305513516068459	-0.0802524983882904	-0.013338658027350903	0.004876279272139072	-0.08649706840515137	-0.0560712032020092	-0.00919250026345253	-0.033472735434770584	0.060427144169807434	0.0019480009796097875	0.05160222202539444	-0.03181390464305878	-0.013767031021416187	0.023275302723050117	-0.02936553955078125	0.0022012460976839066	-0.021978678181767464	-0.07323003560304642	-0.021989107131958008	0.07153111696243286	-0.03511412814259529	0.03531745821237564	0.06153411418199539	-0.012286209501326084	0.04402964934706688	0.019020849838852882	-0.05139375105500221	0.03923099860548973	0.005561470985412598	-0.04707920178771019	0.0497254878282547	-0.05524720624089241	0.09376027435064316	-0.05764537677168846	0.08939891308546066	-0.02882273681461811	-0.017458289861679077	0.08928599208593369	0.07849224656820297	-0.006549326237291098	0.04667797312140465	-0.039322178810834885
+0.07224701344966888	0.030314913019537926	0.049683135002851486	0.08997809886932373	-0.08113428205251694	-0.022584112361073494	-0.03622658923268318	0.006950104609131813	0.026196451857686043	-0.028442980721592903	-0.03465588763356209	-0.03087383694946766	-0.00913745816797018	0.03624076768755913	0.020692652091383934	-0.06192605942487717	-0.044139470905065536	0.016914015635848045	-0.012021949514746666	-0.021500851958990097	0.06811819970607758	0.002215291140601039	-0.07035976648330688	0.07244922965765	-0.010894021019339561	0.014361817389726639	-0.037098824977874756	0.06277737766504288	-0.02350766770541668	0.003605310805141926	0.07862995564937592	-0.018045689910650253	0.036178309470415115	-0.08748757094144821	-0.04191473871469498	0.002430360997095704	-0.040091998875141144	0.07177974283695221	0.04086034744977951	-0.05547644570469856	-0.04341660067439079	0.0627928376197815	-0.01411063876003027	-0.043343912810087204	0.04243439435958862	0.03145599365234375	0.005780935753136873	-0.0005102351424284279	0.027484728023409843	-0.005697628017514944	0.09307307749986649	0.024772342294454575	-0.006763516925275326	-0.03134724497795105	0.012811102904379368	-0.008898609317839146	0.08820264786481857	-0.08766044676303864	-0.033989470452070236	0.04193294048309326	-0.08981754630804062	-0.013394670560956001	0.037566881626844406	-0.02441457286477089	-0.028654346242547035	-0.009303840808570385	0.014021454378962517	0.0849233865737915	0.0288385059684515	-0.016156893223524094	0.05522747337818146	0.10075263679027557	-0.08038004487752914	-0.011507137678563595	0.07034136354923248	-0.06678655743598938	-0.019809257239103317	0.05815254524350166	-0.030275439843535423	0.03457978367805481	0.017943162471055984	-0.03391353413462639	0.08105456829071045	0.0027759179938584566	-0.07873209565877914	-0.025770580396056175	0.0243519376963377	-0.07727956026792526	0.0312926284968853	0.030860602855682373	-0.021136632189154625	0.07338964939117432	-0.03955918550491333	0.06067826598882675	0.008433706127107143	0.05380656197667122	0.014278999529778957	-0.09451405704021454	-0.0015701286029070616	0.006959458813071251	-0.002571734832599759	0.00026288253138773143	-0.00401652604341507	-0.027057403698563576	0.05095004662871361	0.054556798189878464	-0.06308680772781372	0.019486941397190094	0.018858736380934715	0.07000380754470825	0.0893886536359787	0.052315853536129	0.048834990710020065	0.05738721042871475	0.057981666177511215	0.013456005603075027	0.025287970900535583	-0.0720575600862503	-0.04075068235397339	0.01576775498688221	-0.007408805191516876	0.07962459325790405	-0.0035053205210715532	-0.07483559846878052	-0.00817448366433382	0.026923768222332	-0.07338105142116547	-0.027934610843658447	0.051862943917512894	-0.0032232042867690325	0.10182787477970123	0.061426904052495956	-0.01681864820420742	0.03005489706993103	0.049985453486442566	0.06366751343011856	-0.021337119862437248	0.017169222235679626	0.0033894204534590244	-0.035746555775403976	-0.016427747905254364	0.028495635837316513	0.03521067276597023	-0.08596160262823105	0.08020984381437302	0.043429918587207794	0.040505748242139816	-0.015482314862310886	-0.06584244221448898	-0.03729918226599693	-0.07807297259569168	-0.058557868003845215	0.0034441433381289244	-0.05554330348968506	-0.02894759178161621	0.0356873944401741	0.019632577896118164	0.007039164192974567	-0.0430326834321022	-0.04900875687599182	-0.026813644915819168	-0.0015443343436345458	-0.0010181738762184978	-0.059776559472084045	-0.03069794923067093	-0.015263469889760017	-0.006685690488666296	0.03820602968335152	-0.07192914187908173	-0.018179308623075485	0.0028981883078813553	0.00918069202452898	0.06022587791085243	0.04774723947048187	0.06318661570549011	-0.003093248000368476	0.025868194177746773	-0.028080608695745468	0.023688904941082	-0.025789741426706314	0.0061445459723472595	-0.03157510608434677	0.027337413281202316	-0.10022415965795517	-0.0028413666877895594	0.004888210445642471	0.007146529387682676	-0.03315772861242294	0.04363780468702316	-0.006042341236025095	-0.0571349635720253	-0.04632018133997917	0.05222771689295769	0.07376538962125778	-0.01833263225853443	0.022466927766799927	-0.023691298440098763	-0.008225815370678902	-0.0016037721652537584	-0.023030607029795647	-0.05078646168112755	0.05048653483390808	0.0004011066921520978	0.06979480385780334	0.018651308491826057	-0.07819752395153046	0.03981498256325722	0.014068450778722763	0.01115900743752718	-0.03017258271574974	0.022080592811107635	0.05869383364915848	0.003278109710663557	0.03645594045519829	0.07782211154699326	0.025482427328824997	0.017191382125020027	-0.020948870107531548	0.05021805688738823	-0.08624362200498581	0.04166414588689804	-0.025328578427433968	-0.07643204182386398	0.050848234444856644	-0.06710737198591232	-0.06049291417002678	-0.01617034152150154	0.029754221439361572	0.01941768266260624	-0.04755968227982521	0.033021479845047	-0.006660186219960451	0.026821324601769447	-0.016754766926169395	-0.011960858479142189	-0.008471212349832058	-0.11669357120990753	0.12328063696622849	0.03328756242990494	-0.040098849684000015	-0.031512968242168427	0.013079771772027016	0.046150092035532	-0.016639521345496178	0.030855366960167885	0.03216353431344032	0.09681518375873566	0.02957621030509472	-0.0069243102334439754	0.002179916249588132	-0.05676944926381111	-0.08809229731559753	0.019520826637744904	0.048029761761426926	0.0010788521030917764	-0.005059815011918545	-0.00139436696190387	-0.03582285717129707	0.010486679151654243	-0.006606842391192913	-0.01909567043185234	-0.010996160097420216	0.004432972986251116	-0.06833964586257935	0.037402115762233734	-0.028796086087822914	-0.04187380522489548	0.04899441823363304	0.02033761516213417	0.0823882520198822	-0.002553509082645178	-0.02164730243384838	0.050353363156318665	-0.001887463848106563	0.03567192330956459	-0.026648059487342834	0.05081498995423317	0.025011572986841202	-0.050649479031562805	-0.022211289033293724	-0.05247285217046738	-0.06354483217000961	0.00402872497215867	-0.02799028716981411	-0.028033731505274773	0.10094338655471802	0.017493057996034622	-0.04839455336332321	-0.08501814305782318	0.013706229627132416	0.010329175740480423	-0.05381393805146217	-0.042060449719429016	0.010131126269698143	-0.0711490735411644	-0.006241864990442991	0.0041055828332901	-0.04240364208817482	-0.04007195681333542	-0.04748261347413063	-0.028562625870108604	-0.07565217465162277	0.03154356777667999	-0.037975843995809555	-0.017056340351700783	0.020058507099747658	-0.052697498351335526	0.03720110282301903	0.09517627954483032	0.029956841841340065	0.019344722852110863	0.0021615782752633095	0.020066794008016586	0.14658032357692719	-0.024061255156993866	-0.053501635789871216	-0.01402292586863041	-0.07739433646202087	-0.05018985643982887	0.08437690883874893	-0.04524993151426315	-0.0065477387979626656	0.026788681745529175	-0.0010509664425626397	0.07509821653366089	-0.03143800422549248	-0.005178047809749842	-0.0414162203669548	0.027445001527667046	-0.018205173313617706	-0.006414444651454687	-0.005246813874691725	0.004756895825266838	-0.019235655665397644	-0.04720594361424446	-0.052905380725860596	-0.017451874911785126	0.02536024898290634	0.009619130752980709	0.02064398303627968	-0.019618287682533264	-0.07603330165147781	0.008516860194504261	0.0196442361921072	0.0746568813920021	-0.07320911437273026	0.014528985135257244	0.04290854558348656	0.012124833650887012	-0.05831797048449516	0.007342742756009102	0.03369133919477463	-0.04180910065770149	-0.005336669739335775	0.007456628140062094	0.024609046056866646	-0.08204364031553268	0.022341398522257805	0.00114768638741225	-0.008992273360490799	0.03644874319434166	0.0419967919588089	0.025058483704924583	0.014875574968755245	-0.04616708308458328	0.005097134504467249	-0.060088858008384705	-0.008873729035258293	0.03207869827747345	-0.00909450277686119	0.03494426608085632	-0.026750139892101288	0.04692940413951874	-0.042310189455747604	-0.011656524613499641	-0.02553105354309082	0.029789112508296967	-0.006212898995727301	0.02144872210919857	0.022932279855012894	-0.008304177783429623	-0.010519982315599918	0.0031107997056096792	0.03127547726035118	-0.0991128459572792	0.011240299791097641	-0.022528616711497307	-0.056999869644641876	-0.06474719196557999	-0.038941364735364914	-0.005978533532470465	-0.06668230891227722	0.00897522084414959	0.0027578750159591436	-0.010135005228221416	0.02896641567349434	-0.04168839007616043	0.023495277389883995	0.07184907048940659	0.01834424026310444	-0.004612874239683151	0.014797565527260303	-0.010931853204965591	0.03180501610040665	0.04461078345775604	-0.059179555624723434	-0.0004951431765221059	0.06430371105670929	-0.034609604626894	0.0431627593934536	-0.018378792330622673	0.016375241801142693	0.02081325463950634	0.022585153579711914	0.025262946262955666	-0.031442686915397644	-0.020086664706468582	-0.028770167380571365	0.07792245596647263	-0.025957847014069557	-0.052268873900175095	0.020317699760198593	-0.04527823254466057	-0.11977757513523102	-0.019247472286224365	-0.015150678344070911	-0.055508702993392944	0.02246544137597084	-0.007786020636558533	0.022868124768137932	0.026082243770360947	-0.052721261978149414	-0.025107722729444504	-0.021592896431684494	-0.031215179711580276	0.042097508907318115	-0.036046504974365234	0.07556481659412384	0.04144306480884552	-0.007068510167300701	0.03004681132733822	-0.0006633026641793549	-0.009771011769771576	-0.089359350502491	-0.005072992295026779	-0.007921162992715836	-0.04801938310265541	0.006985824555158615	0.0037407679483294487	-0.005784318782389164	-0.07352110743522644	-0.03614097833633423	-0.06259546428918839	0.08311166614294052	-0.04395807534456253	-0.011603699065744877	0.04062967374920845	-0.04019685462117195	0.02581172250211239	0.025449030101299286	-0.058880265802145004	-0.09105469286441803	-0.02857249043881893	-0.017557654529809952	0.005441989749670029	0.020094623789191246	-0.008783158846199512	-0.005571461748331785	-0.06258317083120346	0.03068227507174015	0.040945909917354584	-0.055350493639707565	0.013151847757399082	-0.010918406769633293	-0.045243144035339355	-0.06472380459308624	0.009886189363896847	-0.061870794743299484	0.06884342432022095	0.0551447756588459	0.015951288864016533	0.013188658282160759	-0.008201093412935734	0.007422763388603926	-0.07755129784345627	-0.01478754635900259	0.0011122312862426043	-0.01532050035893917	0.021739274263381958	0.06235021725296974	-0.04288686066865921	0.01465039886534214	0.056335531175136566	-0.014514884911477566	0.0027228198014199734	0.04770982265472412	-0.02445221319794655	0.01597897708415985	-0.03647360950708389	-0.002546863630414009	0.08719334006309509	-0.06490110605955124	0.08923709392547607	-0.040211595594882965	0.05701655149459839	-0.01409642118960619	-0.005540631711483002	0.06498275697231293	0.07601643353700638	-0.0032264129258692265	0.0075427512638270855	-0.07999426126480103
+-0.006825899239629507	-0.04739980027079582	-0.032191064208745956	-0.009084461256861687	-0.08063153922557831	-0.09152744710445404	-0.012586923316121101	0.05591190233826637	-0.01292065903544426	-0.009943847544491291	-0.046181000769138336	0.011778691783547401	-0.00623438972979784	-0.009575853124260902	0.026430534198880196	0.00026795821031555533	-0.06556500494480133	0.008809673599898815	-0.02525367960333824	-0.00039395460044033825	0.0468214713037014	-0.0063310046680271626	-0.06470850855112076	0.029269983991980553	0.024411486461758614	-0.029067769646644592	-0.0637063980102539	0.02061069756746292	0.0009482507593929768	0.08696401864290237	0.035205498337745667	-0.06888213753700256	0.06404544413089752	-0.08169996738433838	-0.023415464907884598	0.016803815960884094	-0.03447258844971657	0.06289553642272949	0.08720768988132477	-0.028581850230693817	-0.0734427198767662	0.016217518597841263	-0.025498749688267708	-0.05879179760813713	0.02494325116276741	0.055369723588228226	-0.03850824385881424	-0.06939543783664703	0.062185391783714294	0.07377295196056366	0.053077030926942825	-0.03621172159910202	-0.0157674178481102	0.0121913468465209	-0.04151012748479843	-0.04301374405622482	0.057364869862794876	-0.025704851374030113	-0.1043103039264679	0.022327076643705368	-0.10960356891155243	0.009171620942652225	-0.03174752742052078	0.049695976078510284	-0.04349203780293465	-0.032165806740522385	0.017473367974162102	0.037839680910110474	0.01929306611418724	-0.0388217531144619	0.00365159148350358	0.05529709905385971	-0.03372050076723099	-0.05262650549411774	-0.04040171951055527	-0.016575615853071213	0.01094814296811819	0.007035427261143923	0.004094835836440325	0.05001704394817352	-0.024082913994789124	-0.07314829528331757	0.1599704772233963	-0.06886257231235504	-0.004373401403427124	-0.04804375767707825	-0.0668296217918396	-0.0335068441927433	0.024157816544175148	0.022923676297068596	0.04180636256933212	0.023621471598744392	-0.022220421582460403	-0.011385168880224228	0.0029573349747806787	0.04118891805410385	0.043573860079050064	-0.0938459113240242	-0.013128300197422504	0.03862195834517479	-0.05466439202427864	0.010922504588961601	0.08426810801029205	0.06704670190811157	0.03973233699798584	-0.009485018439590931	-0.049577727913856506	0.03916630893945694	0.061714477837085724	0.02650158293545246	0.002405534964054823	0.07148341089487076	0.09156344830989838	-0.002341891871765256	0.022674493491649628	-0.02132539264857769	-0.025915252044796944	-0.017074082046747208	0.02520040236413479	0.006032460834830999	0.02590218558907509	0.007153863087296486	-0.010198325850069523	-0.060141418129205704	0.05907436087727547	0.07883980125188828	-0.1061038225889206	-0.02516142837703228	-0.027547961100935936	-0.0704784095287323	0.08125105500221252	0.06096802279353142	-0.049058567732572556	0.04772800952196121	0.0773405134677887	0.029435211792588234	0.015231968834996223	0.06717386096715927	-0.010795742273330688	-0.05268574878573418	-0.06759220361709595	0.025673313066363335	-0.04882408306002617	-0.03625420853495598	0.0327264703810215	0.06728453934192657	0.0030846111476421356	-0.03000519797205925	-0.07135039567947388	0.01634349673986435	-0.07554321736097336	-0.03061656653881073	0.023138415068387985	-0.04772505164146423	0.006728739477694035	0.05286790058016777	0.008314509876072407	0.04885377734899521	-0.03334394842386246	0.0387495718896389	-0.04552968218922615	-0.07422804087400436	-0.014121792279183865	-0.058722659945487976	-0.022505350410938263	0.019459933042526245	0.03791975975036621	-0.008532690815627575	-0.03343211114406586	-0.03818867728114128	-0.037725336849689484	-0.021801764145493507	0.041825830936431885	0.0532219298183918	0.03259021043777466	-0.024250950664281845	0.036252766847610474	-0.09206590801477432	-0.0012342961272224784	-0.08136244118213654	0.012014992535114288	-0.051116943359375	0.031156640499830246	-0.02928457409143448	-0.021054010838270187	-0.032720185816287994	0.025361549109220505	-0.05421428382396698	-0.004533577244728804	-0.034521035850048065	-0.04934470355510712	-0.04639490693807602	0.0266555268317461	0.0592181533575058	-0.030652115121483803	0.042556360363960266	0.005113555584102869	0.05543893203139305	-0.03712262585759163	-0.012635305523872375	-0.08050933480262756	0.07309503853321075	-0.010208401829004288	0.011143182404339314	0.010697864927351475	-0.005168076604604721	0.05739664286375046	0.012002326548099518	-0.004339945036917925	-0.04361303150653839	0.036627188324928284	0.08249708265066147	0.006259436719119549	-0.012615720741450787	0.07796894013881683	-0.041720688343048096	-0.010235044173896313	-0.002407519146800041	0.09142021834850311	-0.051653824746608734	0.00550442049279809	0.037796150892972946	-0.050719503313302994	-0.011241063475608826	-0.043872807174921036	-0.08705904334783554	0.0033708789851516485	-0.005896338261663914	-0.028752125799655914	-0.00316597823984921	0.011898452416062355	0.026369662955403328	0.0026683050673455	0.041423387825489044	0.0017580880085006356	-0.018969111144542694	-0.018839001655578613	0.05558085814118385	-0.05552570894360542	-0.039746832102537155	-0.04330433905124664	0.027312306687235832	0.022820675745606422	0.013260235078632832	-0.000547432922758162	0.016481129452586174	0.05241145193576813	0.05264008045196533	-0.022159725427627563	-0.021746916696429253	-0.029233286157250404	-0.06507611274719238	0.007517375983297825	0.022326482459902763	-0.012612994760274887	-0.033971626311540604	0.045300889760255814	-0.07164153456687927	0.003006471088156104	0.002053887350484729	0.03944060578942299	-0.01863996498286724	-0.0306782778352499	-0.002743176417425275	0.06396637856960297	0.003239738754928112	-0.07665500789880753	0.03587896004319191	-0.041221991181373596	0.03126620873808861	-0.061189595609903336	-0.02278515323996544	0.04531555622816086	0.008164525032043457	0.002780123148113489	-0.022946761921048164	0.09518180787563324	0.004208939615637064	-0.04502028226852417	-0.05438581109046936	-0.08185534179210663	-0.03358861058950424	0.02571084350347519	0.022404994815587997	-0.019986974075436592	0.025211261585354805	0.07915230840444565	-0.020392941311001778	-0.056090597063302994	-0.044563885778188705	-0.04401310160756111	-0.05540941283106804	-0.02947872318327427	0.007210046052932739	-0.06798297166824341	-0.025752153247594833	0.03456898778676987	-0.035138219594955444	-0.05870847776532173	0.06506515294313431	-0.027608858421444893	-0.07066403329372406	0.030711250379681587	-0.028298456221818924	-0.023255135864019394	0.03690351918339729	-0.04529305920004845	0.010379410348832607	0.06073198840022087	0.002867859322577715	0.01697108894586563	0.033982645720243454	-0.009208652190864086	0.041991785168647766	-0.03968174010515213	-0.005324554163962603	-0.06533747166395187	-0.05551645904779434	-0.06869406998157501	0.04103840887546539	-0.03556055203080177	0.007459043525159359	0.004974044393748045	-0.01007347833365202	0.03861904516816139	-0.00021090504014864564	-0.025930535048246384	-0.07140485942363739	-0.01799437403678894	-0.042673151940107346	0.057534828782081604	-0.008551095612347126	-0.0040579685010015965	-0.01832037791609764	-0.05024028941988945	-0.026468312367796898	-0.0549018457531929	0.06580324470996857	-0.03896455094218254	0.01791222393512726	-0.0007783619221299887	-0.02623497135937214	0.007397881709039211	0.03316543251276016	0.08154747635126114	-0.053881868720054626	0.04099663719534874	-0.004027337767183781	0.038538798689842224	-0.0673627033829689	-0.010610848665237427	0.027409087866544724	-0.020688142627477646	-0.04778795689344406	0.03064962662756443	0.011747411452233791	-0.07380308210849762	0.016664063557982445	-0.015331720933318138	-0.05783015489578247	0.016576291993260384	0.024445706978440285	0.015568963252007961	0.0566054992377758	0.06157783418893814	-0.06131359562277794	-0.0051078698597848415	0.0017267807852476835	-0.003424761351197958	0.018818827345967293	0.04025717079639435	-0.027147753164172173	0.04149024188518524	-0.049599360674619675	-0.044213131070137024	-0.06228356435894966	0.050662633031606674	-0.032737601548433304	-0.018274255096912384	0.0011625011684373021	0.026961766183376312	-0.0035736251156777143	0.05952445790171623	0.0012785661965608597	-0.05774643272161484	-0.025555778294801712	-0.01584581844508648	-0.053436070680618286	-0.10351549834012985	0.014583456330001354	0.04123743996024132	-0.03449779003858566	0.012780901975929737	-0.023846417665481567	0.06577614694833755	0.005130603443831205	0.0073713515885174274	-0.00420122966170311	0.0523621030151844	0.033760808408260345	0.02892952971160412	0.0008801623480394483	-0.012881224974989891	0.030978823080658913	0.002025273162871599	-0.0400865375995636	0.024644795805215836	0.023276817053556442	-0.10642501711845398	-0.009442519396543503	-0.08387227356433868	-0.026381507515907288	-0.039687491953372955	-0.020532047376036644	0.014687552116811275	-0.00877496600151062	0.055809203535318375	-0.005715387407690287	0.05824672430753708	-0.049723364412784576	-0.03134699538350105	0.027241507545113564	-0.0017531004268676043	-0.0776539072394371	-0.006414499133825302	0.02101457305252552	-0.00752577418461442	0.05314427241683006	-0.01893189176917076	0.03156480938196182	0.04506722465157509	-0.00767182745039463	0.004980923142284155	0.03390141576528549	0.023092305287718773	-0.003341547679156065	0.03613828867673874	0.04236389324069023	-0.02260984480381012	-0.021697843447327614	0.017337987199425697	-0.05239660665392876	0.016415242105722427	-0.03498866409063339	-0.027805138379335403	0.03729221224784851	0.04350784793496132	-0.024363815784454346	0.009316034615039825	0.021868525072932243	-0.042475201189517975	-0.00992094911634922	-0.019848210737109184	0.06792473793029785	0.025152094662189484	0.02222517691552639	0.07060506939888	-0.06162693724036217	-0.045911747962236404	0.004957206081598997	-0.04349043220281601	-0.09890592098236084	-0.055601779371500015	-0.061406444758176804	0.060322314500808716	0.05935598909854889	0.009895306080579758	-0.017545022070407867	-0.04974684491753578	0.006011229939758778	0.01773916371166706	-0.0032407192047685385	0.015243533998727798	0.03515022620558739	0.0006241274531930685	-0.02374601550400257	-0.05711051821708679	-0.07387614995241165	0.05438249558210373	0.05580086633563042	0.0059337676502764225	-0.0653950497508049	0.03526695445179939	-0.0042116972617805	-0.026207521557807922	0.047645073384046555	-0.0036378041841089725	-0.0685906708240509	0.01084420271217823	0.0951056033372879	-0.030672136694192886	0.05836945399641991	0.03457856923341751	-0.03464813157916069	0.07007262110710144	0.058148372918367386	-0.004219310823827982	0.009451478719711304	-0.03499535843729973	-0.020701024681329727	0.04481607675552368	-0.07859466224908829	0.06400357931852341	-0.03048420138657093	0.1241055428981781	0.07150053977966309	-0.03750103712081909	0.10282276570796967	0.05235089734196663	0.012622201815247536	0.009781607426702976	-0.10529884696006775
+0.08069192618131638	-0.03892521187663078	-0.027607250958681107	0.0333181656897068	-0.08797287940979004	-0.05332806706428528	0.007252350449562073	0.057919301092624664	-0.06982439756393433	-0.011764982715249062	-0.02701825276017189	0.04487839713692665	0.014203422702848911	0.043635379523038864	0.008262883871793747	-0.06089772284030914	-0.07487205415964127	-0.009221682325005531	-0.017355849966406822	0.011134465225040913	0.08255650848150253	-0.027630750089883804	-0.06651327013969421	0.018770337104797363	0.024818360805511475	-0.006678394041955471	0.00669701537117362	0.008045000955462456	0.04121096804738045	-0.03273672237992287	0.07749151438474655	-0.07957101613283157	0.006423326209187508	-0.08723394572734833	-0.059569042176008224	0.03783845901489258	-0.06061394885182381	0.10571523755788803	0.04979619011282921	-0.06606145948171616	-0.08947232365608215	0.04243580996990204	-0.05891753360629082	-0.09071335941553116	0.018685253337025642	0.008149558678269386	-0.033854804933071136	-0.035157907754182816	0.06155489757657051	0.015153314918279648	0.029727190732955933	-0.031161537393927574	0.0018121436005458236	-0.0011612873058766127	-0.05854114890098572	-0.020793423056602478	0.028258739039301872	0.029370969161391258	-0.06429223716259003	0.010034702718257904	-0.04885620251297951	0.0017720084870234132	0.026507653295993805	0.06675027310848236	0.023221047595143318	-0.03999457508325577	0.05307852476835251	-0.00387936900369823	0.042088400572538376	-0.0011081640841439366	-0.03202946111559868	0.07047741115093231	-0.018375182524323463	-0.015952711924910545	0.011862145736813545	-0.04877462238073349	-0.005108220502734184	-0.019538285210728645	0.023486996069550514	0.036955248564481735	-0.0011572533985599875	0.004187068436294794	0.11898984760046005	-0.01269393041729927	-0.02353416010737419	-0.026336409151554108	0.04289485141634941	-0.02143370732665062	0.025817889720201492	0.039850883185863495	0.008081694133579731	0.0462086983025074	-0.0871196985244751	0.027414541691541672	-0.01770653761923313	0.018978571519255638	0.05825335532426834	-0.10832755267620087	-0.05032246187329292	-0.00694527942687273	0.00815283041447401	-0.058384571224451065	0.05514507368206978	-0.012152626179158688	0.033140841871500015	-0.009829655289649963	-0.04732051491737366	0.04129716381430626	0.046656470745801926	0.010796057060360909	0.05970418453216553	0.0898502841591835	0.05480905994772911	0.022696418687701225	0.07666808366775513	-0.057762064039707184	-0.010988104157149792	-0.07065333425998688	-0.014423760585486889	0.024334030225872993	-0.025828611105680466	0.021957194432616234	-0.037748850882053375	-0.06894437223672867	-0.043098390102386475	0.023418482393026352	-0.08269739896059036	-0.018881315365433693	0.0020625328179448843	-0.0037212097086012363	0.07339373975992203	0.050690796226263046	-0.027362432330846786	0.040871791541576385	0.038802746683359146	0.03465777635574341	0.0452776663005352	0.13400842249393463	-0.0033484373707324266	-0.044334784150123596	-0.005717776715755463	0.07050061970949173	0.053715672343969345	-0.006143557373434305	0.005247552879154682	0.0608709454536438	0.014974567107856274	0.004939612932503223	-0.01856025494635105	0.027380123734474182	-0.07532424479722977	-0.016655951738357544	-0.011611291207373142	-0.07088415324687958	0.030153537169098854	0.041870079934597015	0.031362470239400864	0.020737603306770325	-0.06431259959936142	0.024283381178975105	-0.03346477821469307	-0.06250640749931335	0.05732274428009987	-0.060835398733615875	-0.035704001784324646	0.02051570639014244	-0.014452263712882996	-0.0012962054461240768	-0.030732735991477966	-0.007893556728959084	0.009243510663509369	-0.08233339339494705	0.03884415328502655	0.01895999163389206	0.0150674507021904	-0.015781201422214508	-0.027692358940839767	-0.06293811649084091	0.019763978198170662	0.022631308063864708	-0.006409459747374058	-0.09771916270256042	0.017307743430137634	-0.0908365249633789	-0.010944867506623268	-0.01799859292805195	-0.0016167308203876019	-0.06385549157857895	-0.029921939596533775	-0.05567774176597595	-0.09604904055595398	-0.04883838817477226	-0.0004953512107022107	0.02033453993499279	0.010062210261821747	0.08132246136665344	-0.020988186821341515	0.006941540632396936	0.031965021044015884	0.0009554533171467483	-0.07031147181987762	0.05648418888449669	-0.09386950731277466	0.042045336216688156	0.034758683294057846	-0.049418892711400986	0.02609262987971306	0.0011153469095006585	0.023810379207134247	-0.08221399039030075	-0.016294198110699654	0.04777134209871292	-0.002859609667211771	0.0038240887224674225	0.026507897302508354	-0.003934756852686405	-0.012760285288095474	0.06772562116384506	0.08168289810419083	-0.02196764200925827	-0.011558088473975658	0.0561186745762825	-0.04980018734931946	0.0413084551692009	0.024142419919371605	-0.06174003332853317	-0.06437215954065323	0.0033372158650308847	-0.01649785414338112	-0.09732759743928909	0.020275723189115524	0.04953056946396828	0.04111028090119362	-0.06007600575685501	-0.011232688091695309	-0.01071829441934824	-0.026064397767186165	0.07285148650407791	-0.009564503096044064	-0.09407247602939606	-0.02546965889632702	-0.01947665587067604	0.016686899587512016	0.02743944153189659	0.04084339737892151	0.012974989600479603	0.05198850855231285	-0.04940173402428627	-0.031654052436351776	-0.0072061773389577866	-0.0557895302772522	-0.06630688160657883	-0.009804129600524902	0.09921587258577347	-0.014002813026309013	-0.06711927801370621	-0.012501255609095097	0.0012851790525019169	0.017497332766652107	-0.012040230445563793	0.04316684603691101	0.0141294552013278	-0.04618222266435623	-0.0637146532535553	0.06125079095363617	-0.01702166721224785	0.00329569517634809	0.0031528505496680737	0.010802319273352623	0.07380591332912445	0.007625996600836515	-0.0036047922912985086	0.017708970233798027	-0.014900226145982742	0.0005051232874393463	-0.01708095893263817	0.056636590510606766	-0.05955580994486809	-0.01636177860200405	-0.02427927404642105	-0.04635358601808548	-0.06412021815776825	-0.034654222428798676	-0.019492903724312782	-0.012733718380331993	0.09928303956985474	0.022780008614063263	-0.010018532164394855	-0.013259278610348701	-0.012323135510087013	0.00720177311450243	-0.0785880908370018	0.002959775272756815	0.029486212879419327	-0.08616486191749573	-0.01825331337749958	0.02202901616692543	0.009311697445809841	-0.06057826057076454	0.020320923998951912	0.036326125264167786	-0.04577644541859627	0.04582921415567398	-0.0042932480573654175	-0.02451854571700096	-0.023260198533535004	-0.09337738901376724	0.013407040387392044	0.04016781225800514	0.034686122089624405	0.05001576244831085	-0.0004812517436221242	7.318437565118074e-05	0.11331154406070709	-0.01577448658645153	-0.01757526956498623	0.019175289198756218	-0.11439166218042374	-0.116811104118824	0.06449989229440689	-0.01820419915020466	0.005398952402174473	-0.03777512162923813	-0.017847126349806786	0.07080044597387314	-0.03498893231153488	0.005783615168184042	-0.058768901973962784	-0.0047432123683393	-0.05231066048145294	0.06316728889942169	0.022973090410232544	-0.018879851326346397	-0.034384045749902725	-0.007007112260907888	-0.07324465364217758	-0.05155516788363457	0.02161930687725544	-0.03411325067281723	0.022476956248283386	0.05415239557623863	-0.03119712322950363	0.008996252901852131	0.008318486623466015	0.06070577725768089	-0.02395920641720295	-0.004830443300306797	-0.026174193248152733	0.06176268309354782	-0.06455431878566742	-0.01086285151541233	-0.02665737271308899	0.0004665543092414737	-0.012711958959698677	-0.039013005793094635	0.053399860858917236	-0.05442430078983307	0.04383985698223114	0.013788816519081593	0.008464936167001724	0.027228351682424545	0.027213580906391144	0.025921298190951347	0.0832553505897522	0.019737765192985535	-0.030333565548062325	0.06678738445043564	-0.008078341372311115	0.03055867925286293	-0.014567822217941284	0.03404729440808296	-0.010330199263989925	0.08167218416929245	0.006328328046947718	-0.02758307009935379	-0.07982619851827621	0.0037076612934470177	0.02052067592740059	-0.0328093096613884	-0.010999190621078014	0.02427276037633419	0.0468430332839489	0.01875201240181923	0.019694944843649864	-0.0006552448612637818	-0.027278438210487366	-0.016765812411904335	-0.026659222319722176	-0.09382858872413635	0.040966641157865524	0.03278016299009323	-0.06614001095294952	-0.0189775750041008	-0.04093119502067566	-0.016298461705446243	0.07421016693115234	-0.049473680555820465	-0.0048781586810946465	0.018952542915940285	0.042094942182302475	-0.01635662280023098	0.034225281327962875	0.011284434236586094	0.029984360560774803	0.0005098512629047036	0.02197982184588909	0.0011604070896282792	0.048836853355169296	-0.05720493569970131	0.017837172374129295	0.0005066883750259876	0.013381876982748508	-0.04765670746564865	0.021196451038122177	0.006129912566393614	-0.05187804624438286	0.022732866927981377	0.029931366443634033	-0.014452110975980759	-0.047921985387802124	-0.05345354601740837	0.052184853702783585	-0.016500206664204597	-0.08920646458864212	0.005784652661532164	0.011942896991968155	-0.00823633000254631	0.036867592483758926	0.03852534666657448	0.004602196626365185	0.047725990414619446	-0.006503065582364798	0.020052963867783546	-0.00647493964061141	-0.06851654499769211	0.02462293952703476	-0.07824821770191193	0.058345455676317215	0.05444565787911415	0.011970347724854946	0.08261721581220627	-0.020080704241991043	0.005539062432944775	-0.0362621508538723	-0.0401325561106205	0.0061051324009895325	-0.021740853786468506	0.07170247286558151	-0.013723021373152733	0.018338412046432495	0.021788422018289566	-0.04509668052196503	-0.031087718904018402	0.07470137625932693	0.03451316058635712	0.008809668943285942	0.06379377841949463	-0.06768869608640671	0.017852842807769775	-0.01951274462044239	-0.02979777753353119	-0.05527662858366966	0.0555347315967083	-0.02459423616528511	0.00034161825897172093	0.04113197326660156	0.061681054532527924	0.08056336641311646	-0.09165496379137039	0.004118761979043484	0.0027179040480405092	-0.041557133197784424	0.11297697573900223	-0.025719398632645607	-0.013375547714531422	-0.04586482048034668	-0.04454420134425163	-0.07696793228387833	0.03992265835404396	-0.034908294677734375	0.006253231782466173	0.0013935924507677555	0.04797298461198807	0.0022513126023113728	-0.014200636185705662	-0.0018976242281496525	0.0056921932846307755	-0.037171583622694016	0.04001348838210106	0.0845833271741867	-0.02656819112598896	0.028113536536693573	0.03853827342391014	0.022458026185631752	0.019460871815681458	0.0015593613497912884	-0.0028515576850622892	0.008978293277323246	-0.027219710871577263	-0.02640504390001297	0.08471623808145523	0.0018957845168188214	0.009993663989007473	-0.06386569142341614	0.05041270703077316	-0.012396403588354588	-0.009040195494890213	0.08215122669935226	-0.016838088631629944	-0.016522035002708435	0.05000930279493332	-0.012010657228529453
+0.02252422831952572	-0.050792377442121506	-0.009451202116906643	0.007777441293001175	-0.057942505925893784	-0.06568913906812668	0.010568812489509583	0.02944065071642399	0.009848629124462605	-0.04162004217505455	-0.033810436725616455	0.007696535438299179	0.018936948850750923	0.026619330048561096	0.045675553381443024	-0.022013751789927483	-0.05849160999059677	0.007043781224638224	-0.0038292785175144672	0.006754292640835047	0.07181782275438309	-0.02845783904194832	-0.07697629183530807	0.03041381575167179	0.024526620283722878	0.02806887775659561	-0.039118774235248566	-0.01580698974430561	0.02644529566168785	0.023491783067584038	0.0477793887257576	-0.023202739655971527	0.03777068108320236	-0.05198388174176216	-0.04748031497001648	0.016843967139720917	-0.07201606780290604	0.10526494681835175	0.02186674252152443	-0.009391373954713345	-0.06513053178787231	-0.03288581594824791	-0.0955861359834671	-0.059568747878074646	-0.02023635059595108	-0.01907172054052353	-0.0009307409054599702	-0.0001210229893331416	-0.002458045957610011	0.04441358521580696	0.0614151693880558	-0.04006800055503845	-0.01189950667321682	0.016882864758372307	-0.01533481478691101	-0.026830166578292847	0.03286134824156761	-0.017618462443351746	-0.08083566278219223	0.045674946159124374	-0.08859865367412567	0.009142794646322727	-0.001519960816949606	0.06179153174161911	-0.03260162100195885	-0.04390616714954376	0.0027888191398233175	0.03051968477666378	-0.008280646055936813	-0.06471327692270279	0.0010572252795100212	0.04025253653526306	-0.05091370642185211	-0.04370119795203209	-0.008874720893800259	0.0047057499177753925	0.029601886868476868	0.01997467130422592	0.017473215237259865	0.0018519472796469927	0.016779160127043724	-0.03913462907075882	0.13403117656707764	-0.04472517594695091	-0.011190664954483509	-0.05775567889213562	0.0007604951970279217	-0.0352243036031723	0.05075421556830406	0.022032173350453377	0.026416156440973282	0.06751257181167603	-0.05727320536971092	0.06290176510810852	0.05127151310443878	0.021233106032013893	-0.006484093144536018	-0.12462817877531052	-0.07489009201526642	0.004551929421722889	0.007783783134073019	-0.030270490795373917	0.07781902700662613	0.025710241869091988	0.06701800972223282	0.017117522656917572	-0.039397504180669785	0.0406397208571434	0.03647756949067116	0.010845263488590717	0.01544049009680748	0.08957427740097046	0.04969393089413643	0.020496081560850143	0.027482829988002777	0.004288398660719395	-0.016751859337091446	-0.012578040361404419	-0.012979412451386452	0.03426564484834671	0.03193964436650276	0.016591742634773254	0.0031442369800060987	-0.09124534577131271	-0.029049891978502274	0.03598852455615997	-0.0999392420053482	-0.06336305290460587	0.019750552251935005	-0.04241715371608734	0.10124322772026062	0.08741100132465363	-0.039141442626714706	0.008519144728779793	0.05084683373570442	0.0339391753077507	0.03979356959462166	0.04227650910615921	-0.017977938055992126	-0.0646258294582367	-0.022968364879488945	0.042092420160770416	-0.029089314863085747	-0.043193623423576355	0.027976468205451965	0.05365082249045372	0.028493637219071388	-0.006103861145675182	-0.084467314183712	-0.02357640117406845	-0.06209985166788101	-0.036791253834962845	-0.00347061431966722	-0.07496035099029541	0.028806328773498535	0.04612543061375618	0.003843484679237008	0.07450919598340988	-0.017327385023236275	0.019017357379198074	-0.05960199981927872	-0.04755210876464844	0.004463658202439547	-0.0647272989153862	-0.006894808728247881	0.012728307396173477	-0.0016804696060717106	0.02797107957303524	-0.058025069534778595	-0.012448446825146675	0.007723563816398382	-0.04545051231980324	0.04629455506801605	0.024783141911029816	0.04329013079404831	-0.013813205994665623	-0.03210259974002838	-0.09794137626886368	-0.0178610160946846	-0.020403221249580383	0.022217389196157455	-0.016842162236571312	0.008245454169809818	-0.0556778647005558	-0.021975573152303696	-0.017070572823286057	0.031998347491025925	-0.041055385023355484	-0.030676867812871933	-0.04677135869860649	-0.05152374505996704	-0.04059171304106712	0.058497294783592224	0.03661045804619789	-0.00871824286878109	0.04490099474787712	-0.024115614593029022	0.06764546781778336	-0.026349607855081558	-0.0390569232404232	-0.015491606667637825	0.0169198140501976	0.01771969348192215	0.055355146527290344	-0.013661340810358524	-0.04935140907764435	0.07565663009881973	0.029257217422127724	0.03776247426867485	-0.07560570538043976	0.013274194672703743	0.014308638870716095	-0.03028436377644539	0.04154662787914276	0.07248468697071075	-0.09242211282253265	0.007687068544328213	0.06655328720808029	0.07584718614816666	-0.04174929857254028	-0.018153829500079155	0.030921457335352898	-0.05244322121143341	-0.015526832081377506	-0.021344061940908432	-0.07632900029420853	-0.024169372394680977	0.02889692224562168	-0.07708036154508591	-0.05112016201019287	-0.02714221179485321	0.04203104227781296	-0.030484890565276146	-0.022090204060077667	-0.007743653375655413	0.016645872965455055	-0.059167154133319855	0.04981718957424164	-0.033135730773210526	-0.0740860104560852	-0.08568611741065979	-0.0021402921993285418	0.00769174937158823	0.05335408076643944	0.040481045842170715	0.06400170922279358	0.07251746207475662	-0.019456401467323303	-0.03388241305947304	0.003127142321318388	-0.03692667931318283	-0.04306311532855034	0.0305472481995821	0.04436296597123146	-0.06945270299911499	-0.03202900290489197	-0.01147518027573824	-0.04073195159435272	-0.026456758379936218	0.009051012806594372	0.02801787480711937	-0.027232879772782326	-0.01213841699063778	-0.009466206654906273	0.013560442253947258	-0.007307757157832384	0.016284313052892685	0.002966848900541663	-0.031202856451272964	0.06526787579059601	-0.030590631067752838	-0.04438081756234169	0.01949065737426281	-0.008047621697187424	0.007001912221312523	-0.012341855093836784	0.059037577360868454	-0.04037036746740341	-0.08345453441143036	-0.034388717263936996	-0.10408367961645126	-0.030205607414245605	-0.029896071180701256	0.009341023862361908	-0.044442903250455856	0.09721525758504868	0.033483292907476425	-0.07323946058750153	-0.033740367740392685	-0.06294064968824387	-0.00028479844331741333	-0.06389057636260986	0.016117647290229797	-0.01064396183937788	-0.06793411821126938	-0.02968587726354599	0.07419576495885849	-0.07152741402387619	-0.030702263116836548	0.04896239936351776	-0.01105149369686842	-0.07968118786811829	0.041765712201595306	0.0030465018935501575	-0.048439186066389084	0.010701699182391167	-0.125961035490036	0.0497075691819191	0.06912047415971756	0.0017764130607247353	-0.01185764279216528	0.03361371532082558	0.048241421580314636	0.10230889916419983	-0.03444944694638252	-0.01339841727167368	-0.018795961514115334	-0.0647539347410202	-0.051268309354782104	0.043536476790905	-0.05524877458810806	0.04977646842598915	-0.05249139294028282	-0.023860381916165352	0.04633258655667305	0.0024841600097715855	-0.0002722201752476394	0.014824994839727879	-0.01340786088258028	-0.010893628932535648	0.04115338623523712	0.02763448655605316	-0.01618131436407566	-0.02199186384677887	-0.045815639197826385	-0.08834787458181381	-0.00787583738565445	0.01696566678583622	-0.055762480944395065	0.05029091611504555	-0.013360055163502693	-0.057850029319524765	0.011029999703168869	-0.019937055185437202	0.1175725907087326	-0.06941881775856018	0.019221121445298195	0.03909602388739586	-0.0014335777377709746	-0.10309246927499771	-0.00036574408295564353	0.037592995911836624	-0.05339286848902702	-0.0669528990983963	-0.0231949295848608	0.06112455576658249	-0.017819512635469437	-0.033301983028650284	0.008217443712055683	-0.01055727805942297	0.018887188285589218	0.036357857286930084	0.025745602324604988	0.06731027364730835	0.010973988100886345	-0.016252810135483742	0.00948795024305582	0.011475245468318462	0.03656994551420212	-0.02419421263039112	0.01998414844274521	-0.014384884387254715	0.07716237753629684	-0.01488522533327341	-0.033487576991319656	-0.0643000528216362	0.03851141035556793	-0.013550498522818089	-0.013289223425090313	-0.028719952329993248	0.0030528835486620665	-0.01805085875093937	0.03661777079105377	0.019890815019607544	-0.025960756465792656	-0.06548122316598892	-0.04680174961686134	-0.02690345048904419	-0.11327967047691345	-0.020816514268517494	0.031976837664842606	-0.05042232945561409	0.013771025463938713	-0.04803317040205002	0.014486636966466904	0.0036061867140233517	-0.05350368097424507	0.022192537784576416	0.05768604949116707	0.015179837122559547	-0.04665978252887726	0.047835737466812134	0.020605267956852913	0.030677614733576775	-0.0165781881660223	-0.009067722596228123	0.05084019526839256	0.030173666775226593	-0.02482202649116516	0.01981261558830738	-0.02876320853829384	0.0037813158705830574	-0.01854885183274746	-0.06794802844524384	-0.026332378387451172	0.003055162262171507	0.005401470232754946	-0.04162580147385597	0.056251853704452515	-0.0901481956243515	-0.039273470640182495	0.008424059487879276	-0.03811173513531685	-0.056818462908267975	-0.038788530975580215	-0.015315311029553413	-0.04682376980781555	0.024331307038664818	0.014784019440412521	0.016077766194939613	0.07044529914855957	-0.042127400636672974	0.0022336584515869617	0.031198738142848015	0.0020253965631127357	0.015396464616060257	-0.015164797194302082	0.013353797607123852	0.014786504209041595	-0.0012049035867676139	0.021061167120933533	-0.0373852401971817	0.01756354607641697	-0.05079350247979164	-0.012731868773698807	0.016791315749287605	-0.0032572713680565357	0.0229684729129076	-0.009897075593471527	0.05164238065481186	-0.07732239365577698	-0.03426866605877876	-0.04913857951760292	0.042711902409791946	0.030469657853245735	0.02230033464729786	0.02565104514360428	-0.052057377994060516	-0.02472604811191559	0.03591877967119217	-0.009896716102957726	-0.06275548785924911	0.0020703806076198816	-0.04574470594525337	0.06295560300350189	0.020641004666686058	0.007922869175672531	0.006772803608328104	-0.08625857532024384	0.004321559797972441	-0.016379043459892273	-0.08497743308544159	0.017062557861208916	0.04923417419195175	-0.0454706996679306	-0.028477216139435768	-0.008831134997308254	-0.02138654887676239	0.01390200573951006	0.002268662443384528	0.06341595202684402	-0.0718248188495636	0.005014864727854729	0.003436317900195718	-0.021755633875727654	0.044400110840797424	-0.006421416532248259	-0.07033329457044601	0.0011472504120320082	0.10760577768087387	-0.06851089000701904	0.029496321454644203	0.07318494468927383	-0.010799611918628216	0.03653767332434654	0.015305409207940102	-0.03591766208410263	0.002494243672117591	-0.019002454355359077	-0.053580623120069504	0.04699568450450897	-0.08456707000732422	0.06741681694984436	-0.08743901550769806	0.1275806576013565	0.009550400078296661	-0.01299422886222601	0.09525403380393982	0.03200121596455574	0.007059704978018999	0.030473023653030396	-0.05959230288863182
+0.03480322286486626	-0.0704117864370346	0.009192604571580887	0.05128411576151848	-0.03233935683965683	-0.04498939588665962	-0.026623286306858063	0.024688685312867165	-0.02502404898405075	-0.047703977674245834	-0.032178763300180435	-0.0050819264724850655	-0.011967794969677925	0.027510568499565125	0.03564130887389183	-0.03778675198554993	-0.10359200090169907	0.02959851361811161	-0.026649679988622665	-0.03604709357023239	0.02675226517021656	0.020907726138830185	-0.09572748094797134	0.05147142335772514	-0.012914956547319889	0.013174657709896564	-0.06618492305278778	0.014293478801846504	0.005091520957648754	0.001824515755288303	0.05557253956794739	-0.042971089482307434	0.06550091505050659	-0.0592992790043354	-0.05423092842102051	0.03407041355967522	-0.04242749884724617	0.10958979278802872	0.005612777080386877	-0.011021262034773827	-0.06871198117733002	0.04281648248434067	-0.008895082399249077	-0.06420126557350159	0.04590418189764023	0.0283028744161129	0.00233720475807786	-0.0247485414147377	0.010610468685626984	0.02841171994805336	0.10544335842132568	0.027908701449632645	0.0013481524074450135	-0.017748793587088585	-0.02197522111237049	0.005208783317357302	0.04980775713920593	-0.04686116427183151	-0.06837760657072067	0.023563208058476448	-0.09968623518943787	-0.010732683353126049	0.011878040619194508	-0.017514387145638466	-0.04330192133784294	-0.006775532383471727	-0.026392076164484024	0.09197349846363068	-0.0025232122279703617	-0.028116781264543533	0.021836938336491585	0.07751080393791199	-0.04580039158463478	-0.053888749331235886	0.057971592992544174	-0.05919705331325531	0.020445285364985466	0.05174264684319496	-0.02737245336174965	0.012738214805722237	0.001052460283972323	0.004563717637211084	0.13068519532680511	-0.0125604048371315	-0.03220817446708679	-0.024579783901572227	0.02263435535132885	-0.047159723937511444	0.03030700609087944	0.04542994126677513	0.03398798033595085	0.0632694885134697	-0.007965072989463806	0.04413387551903725	0.006000691559165716	0.011867620050907135	0.03552825748920441	-0.09706203639507294	-0.02223285846412182	0.003848664928227663	0.004770438652485609	0.009807035326957703	0.08668804168701172	0.0027064362075179815	0.05390019342303276	-0.007601466961205006	-0.059227101504802704	0.011372745968401432	0.03208228573203087	0.016109148040413857	0.04758566990494728	0.11745666712522507	0.034487925469875336	0.05632695183157921	0.06544791907072067	0.012928328476846218	0.030774032697081566	-0.04529232159256935	-0.021197481080889702	0.014551259577274323	-0.01448892429471016	0.05041375011205673	-0.024050595238804817	-0.06548578292131424	0.0031048262026160955	0.04956641420722008	-0.10055854916572571	-0.02688591554760933	0.08845680207014084	-0.020601417869329453	0.11488977074623108	0.05858637019991875	0.003550806548446417	0.0560673363506794	0.043686624616384506	0.01466869655996561	0.010899757035076618	0.03727506101131439	-0.005934508517384529	-0.032070089131593704	-0.019011737778782845	0.05685557425022125	-0.007567911874502897	-0.04179920628666878	0.07276368886232376	0.025808246806263924	-0.003558270400390029	-0.024780895560979843	-0.037281583994627	-0.003976612351834774	-0.05724376440048218	-0.08061759918928146	-0.01964886486530304	-0.06351544708013535	0.006613271310925484	0.021457280963659286	0.03388925641775131	0.046109698712825775	-0.01925116963684559	-0.019006365910172462	-0.07599140703678131	-0.06811627000570297	-0.0333581380546093	-0.07677139341831207	-0.011065649800002575	0.00885923020541668	-0.014951847493648529	0.02373749017715454	-0.0646369457244873	-0.011095061898231506	0.003043562173843384	-0.013383770361542702	0.016171613708138466	0.0443776398897171	0.06665827333927155	0.0044032372534275055	0.04151435196399689	-0.04659465700387955	-0.025347428396344185	-0.014065450988709927	0.024395165964961052	-0.03601917251944542	0.015555688180029392	-0.07040421664714813	-0.00016234294162131846	0.003058562520891428	0.006892296485602856	-0.05088045448064804	0.022511770948767662	0.0027602557092905045	-0.026286330074071884	-0.04550909250974655	0.05282815173268318	0.06764636933803558	-0.020309709012508392	0.051484644412994385	-0.019768359139561653	0.08213087171316147	-0.038108982145786285	-0.018049273639917374	-0.05349184572696686	0.039014749228954315	-0.002924558473750949	0.02346440590918064	-0.009756785817444324	-0.05113370716571808	0.057306379079818726	0.04813108220696449	0.041499290615320206	-0.04284887760877609	0.01637151837348938	-0.001460125669836998	0.018943320959806442	0.05861444026231766	0.0740082785487175	-0.018899627029895782	-0.04481230303645134	0.026951735839247704	0.05098217725753784	-0.07691586017608643	-0.009498780593276024	-0.006815326400101185	-0.07031991332769394	0.015504338778555393	-0.04829440638422966	-0.08894302695989609	-0.030889209359884262	0.02704240381717682	-0.0004239481932017952	-0.014874172396957874	0.010799403302371502	0.037273652851581573	-0.021576333791017532	-0.024845393374562263	0.019337883219122887	-0.04219493642449379	-0.08785196393728256	0.08159396797418594	0.0326029434800148	-0.061494093388319016	-0.06502090394496918	-0.005387209355831146	0.03366752713918686	0.022642046213150024	0.008412735536694527	0.021909087896347046	0.09878264367580414	0.05325578525662422	-0.04353208467364311	0.03673607110977173	-0.01637112908065319	-0.04829776659607887	-0.00233578123152256	0.01512489840388298	0.0007018357282504439	-0.01630169153213501	-0.011473366990685463	-0.046713687479496	0.009775948710739613	-0.013891786336898804	-0.01311581302434206	-0.014601697213947773	-0.026314806193113327	-0.008684166707098484	0.033946502953767776	-0.034443870186805725	-0.015550882555544376	0.01554560661315918	-0.006454343441873789	0.0762699618935585	-0.06842517852783203	-0.019933298230171204	0.024824537336826324	-0.021408600732684135	0.03760618716478348	-0.079208143055439	0.07557176053524017	-0.008409242145717144	-0.07203938812017441	-0.007984434254467487	-0.07687430828809738	-0.04494180530309677	-0.014935114420950413	-0.04570801183581352	-0.024173010140657425	0.09341157227754593	0.012272548861801624	-0.05981067940592766	-0.026912979781627655	-0.05087985470890999	-0.007340668700635433	-0.04473479837179184	0.0012805262813344598	0.00019531563157215714	-0.017292680218815804	-0.009085956029593945	0.03741658851504326	-0.04575776681303978	-0.019881250336766243	0.021575724706053734	-0.010179081931710243	-0.03590897470712662	0.08083809167146683	-0.010864623822271824	-0.014784451574087143	0.009002921171486378	-0.07415974885225296	0.029381049796938896	0.08725521713495255	0.03419230878353119	0.008507482707500458	0.07528962939977646	0.03946821019053459	0.12928687036037445	0.02601197548210621	-0.033807091414928436	-0.01209387369453907	-0.05912237986922264	-0.047766052186489105	0.0904783234000206	-0.00637847650796175	0.02302294224500656	-0.0016073717270046473	-0.02639349177479744	0.058821920305490494	-0.020553162321448326	-0.03215543180704117	-0.03278256952762604	0.009391534142196178	-0.029410388320684433	-0.017866047099232674	-0.005921666976064444	0.026400987058877945	0.002607637317851186	-0.05701708793640137	-0.06648405641317368	-0.04153919965028763	0.04141568765044212	-0.05452745407819748	0.03990350291132927	-0.022874148562550545	-0.020724764093756676	0.029576504603028297	0.002300721127539873	0.09520263224840164	-0.05351683869957924	0.003247699700295925	0.03396017849445343	0.06883574277162552	-0.09277494251728058	-0.00703677162528038	0.006494189612567425	-0.04609531909227371	-0.02538607083261013	0.010616939514875412	0.006391477771103382	-0.07311229407787323	-0.030695337802171707	-0.016167042776942253	-0.026931744068861008	0.01448986865580082	0.023860972374677658	0.028759486973285675	0.058489300310611725	-0.01984155923128128	-0.05038512870669365	-0.010922028683125973	0.013520164415240288	0.024478474631905556	-0.06129048392176628	0.048460979014635086	-0.038347721099853516	0.02571248821914196	-0.049075447022914886	-0.0626433789730072	-0.04339105263352394	0.022740749642252922	-0.025265520438551903	0.003032136708498001	-0.007560505531728268	-0.029738804325461388	-0.03623504191637039	-0.007783872075378895	0.041229769587516785	-0.07728499174118042	-0.018610021099448204	-0.04125700891017914	-0.0987730398774147	-0.05976412817835808	-0.05933179333806038	0.0008580398862250149	-0.023084405809640884	0.06790314614772797	-0.0454244501888752	0.02006431482732296	0.052127715200185776	0.011207676492631435	0.04568267613649368	0.06921378523111343	0.03779815882444382	-0.01234742533415556	0.036394890397787094	-0.018610119819641113	0.008722675032913685	0.027831440791487694	-0.03583480045199394	0.0016812661197036505	0.0269766952842474	-0.051349371671676636	0.034119654446840286	-0.022978445515036583	0.02704099379479885	0.015314655378460884	-0.03825274109840393	0.025141194462776184	-0.011492523364722729	-0.015105871483683586	-0.0330546610057354	0.04221383109688759	-0.04186141490936279	-0.05965953692793846	0.010151725262403488	-0.06513198465108871	-0.11009518802165985	-0.05282231420278549	-0.020243540406227112	-0.019011953845620155	0.03182682767510414	-0.011777069419622421	0.021514244377613068	0.052346087992191315	-0.014194749295711517	-0.05598216503858566	0.007262301165610552	-0.021144837141036987	0.026638126000761986	0.024911057204008102	0.06406272202730179	0.007534690201282501	-0.006021983455866575	0.03573044389486313	-0.01826053485274315	-0.0028712276834994555	-0.09778573364019394	-0.0040845712646842	-0.011478385888040066	-0.026569906622171402	0.009351029060781002	0.016089648008346558	-0.014689979143440723	-0.07525265961885452	0.010360834188759327	-0.03685497120022774	0.0769137367606163	0.012618652544915676	0.023698804900050163	0.032960064709186554	-0.06636177003383636	-0.0090996865183115	0.014052252285182476	-0.0351649634540081	-0.09003538638353348	0.007577606476843357	-0.05955004319548607	0.046069372445344925	-0.011972150765359402	0.013213468715548515	-0.010576760396361351	-0.10998591780662537	0.024797745048999786	-0.011304586194455624	-0.03913705050945282	-0.023625647649168968	-0.0009740255773067474	-0.07980702072381973	-0.06845516711473465	0.018977927044034004	-0.08351874351501465	0.06712804734706879	0.034471649676561356	-0.003785267472267151	-0.022278346121311188	-0.004058638121932745	-0.013308273628354073	-0.014414261095225811	0.005720075219869614	-0.01756664551794529	-0.07899914681911469	-0.012484638951718807	0.08084272593259811	-0.05403291806578636	0.03483716398477554	0.09552904218435287	-0.013214100152254105	0.05177369341254234	0.020047619938850403	-0.026848891749978065	0.03191286697983742	-0.017492683604359627	0.0037501580081880093	0.03841888904571533	-0.0528239831328392	0.055847372859716415	-0.0852498784661293	0.10032334923744202	0.026396648958325386	0.0072677480056881905	0.06842910498380661	0.07213194668292999	-0.002103293314576149	0.03312214836478233	-0.08841554075479507
+0.041142452508211136	0.004038689192384481	-0.013646704144775867	0.04905233532190323	-0.07767851650714874	-0.056815583258867264	-0.005600369535386562	0.04480323567986488	0.0018946294439956546	-0.039034027606248856	-0.02541101537644863	-0.0036224774084985256	0.023764869198203087	0.01942973956465721	0.006663295440375805	-0.03398900106549263	-0.06951827555894852	0.01799400895833969	-0.01670929417014122	-0.0539635494351387	0.019965190440416336	0.02383861131966114	-0.06915409117937088	0.04689549282193184	0.0298600010573864	0.003692832076922059	-0.046607695519924164	-0.012554466724395752	0.008056696504354477	0.024993935599923134	0.04633432254195213	-0.035707987844944	0.05313107743859291	-0.04894599691033363	-0.055702921003103256	0.07026407122612	-0.04403771460056305	0.12305191159248352	0.004922407679259777	-0.031226670369505882	-0.07346762716770172	0.012016667053103447	-0.008797743357717991	-0.039277397096157074	-0.0002656052529346198	0.026672182604670525	-0.022241396829485893	0.006870342418551445	0.013826440088450909	0.020025676116347313	0.09689993411302567	0.0005136295803822577	-0.012562118470668793	-0.02121991105377674	-0.013322531245648861	0.013859564438462257	0.051266904920339584	-0.04151063784956932	-0.07305756211280823	0.02263793721795082	-0.07205789536237717	0.000595945748500526	0.0285334512591362	-0.0044559575617313385	-0.02545209601521492	-0.041358571499586105	-0.019423846155405045	0.09668248891830444	0.03063243255019188	-0.0014981819549575448	0.030200589448213577	0.08936087787151337	-0.05310610309243202	-0.05764239653944969	0.06911834329366684	-0.08492700010538101	-0.0401473268866539	0.06306758522987366	-0.006479625590145588	0.014249894767999649	0.007189613301306963	-0.0382789745926857	0.1140158474445343	-0.04010172188282013	-0.07079170644283295	-0.013250341638922691	0.0015969007508829236	-0.06202109903097153	0.053117696195840836	0.03226758539676666	0.010417859070003033	0.07539952546358109	-0.06826765835285187	0.04467497020959854	0.02303634211421013	-0.00362986302934587	0.042756590992212296	-0.11719048768281937	-0.06436879187822342	0.008799748495221138	-0.004514898173511028	-0.018018409609794617	0.048875000327825546	0.002201963448897004	0.03282790631055832	0.024834653362631798	-0.03550906851887703	0.014073731377720833	0.04119134321808815	0.04987224563956261	0.03057360090315342	0.07397224009037018	0.08707065135240555	0.03143167495727539	0.058819789439439774	-0.01796216145157814	0.025750422850251198	-0.033879272639751434	-0.05391443148255348	0.0008166695479303598	-0.0072546773590147495	0.049327295273542404	-0.021196691319346428	-0.08005352318286896	-0.05397256463766098	0.031032754108309746	-0.10087823122739792	-0.01433101948350668	0.06381309777498245	0.002081100130453706	0.11325167119503021	0.05644731596112251	-0.016868848353624344	0.032445814460515976	0.04695197567343712	0.031760357320308685	0.00968318060040474	0.04996981471776962	-0.0054120272397994995	-0.013467224314808846	-0.01562528684735298	0.03992408514022827	0.022291218861937523	-0.07609298825263977	0.04678768664598465	0.047652292996644974	0.053794678300619125	-0.029171636328101158	-0.059744711965322495	-0.017898138612508774	-0.0470636785030365	-0.10120058059692383	-0.016119375824928284	-0.05381585657596588	0.0004627388552762568	0.03063938207924366	0.005920458119362593	0.039106909185647964	-0.027913782745599747	-0.01072482019662857	-0.045730046927928925	-0.019155964255332947	-0.011132033541798592	-0.0722569078207016	-0.025645598769187927	-0.024229248985648155	-0.02074274979531765	0.018777834251523018	-0.07395622879266739	-0.01189515832811594	-0.0026217112317681313	-0.018728697672486305	0.04669032245874405	0.09455936402082443	0.07560029625892639	0.00590096740052104	0.03967242315411568	-0.0452243871986866	-0.006930709816515446	-0.026897236704826355	0.009914296679198742	-0.039148084819316864	0.027490613982081413	-0.05880952626466751	0.014006456360220909	0.015041469596326351	0.07785831391811371	-0.026316294446587563	0.014544152654707432	0.005532576236873865	-0.03739049658179283	-0.04845651611685753	0.044468868523836136	0.06146395951509476	-0.020228054374456406	0.033146508038043976	-0.03744465485215187	0.054801687598228455	-0.005875651724636555	-0.012415070086717606	-0.04732127860188484	0.04179047420620918	0.0019143198151141405	0.04879879578948021	0.025877902284264565	-0.06738005578517914	0.08093392103910446	0.01918676868081093	0.02496827393770218	-0.07478291541337967	-0.0019848502706736326	0.013264933601021767	0.005319541785866022	0.05321953818202019	0.07782677561044693	-0.012873096391558647	0.0009805008303374052	0.0027463589794933796	0.06486830115318298	-0.048117853701114655	-0.015138750895857811	-0.025272196158766747	-0.08659593015909195	0.021383078768849373	-0.040331535041332245	-0.08507690578699112	-0.04197108373045921	0.03409745916724205	-0.013420984148979187	-0.04811025783419609	-0.005353094078600407	0.0345008410513401	0.002839548047631979	-0.03969702497124672	-0.015892744064331055	-0.018548499792814255	-0.09503169357776642	0.09149052202701569	0.01792094111442566	-0.07182957231998444	-0.06284117698669434	-0.020983126014471054	0.02056831680238247	-0.011294975876808167	0.04277122765779495	0.07493099570274353	0.09070917218923569	0.03466597571969032	-0.0031867672223597765	0.017443107441067696	-0.043554238975048065	-0.05420517176389694	-0.0031088346149772406	0.04866907000541687	-0.05474985018372536	-0.015899619087576866	0.010813348926603794	-0.04742730036377907	0.004830705467611551	0.008048408664762974	0.007283871527761221	-0.028444413095712662	-0.04434114322066307	-0.03819248825311661	0.024675024673342705	-0.045088715851306915	-0.031183574348688126	0.03281440585851669	-0.010092436335980892	0.0857076421380043	-0.00896257534623146	-0.03990541398525238	0.027593830600380898	0.012848981656134129	0.027436211705207825	-0.018608322367072105	0.08497757464647293	-0.009590770117938519	-0.06972736120223999	-0.010726086795330048	-0.055426958948373795	-0.0629977434873581	-0.0275910384953022	-0.05650489404797554	-0.04180770739912987	0.07687221467494965	-0.010928532108664513	-0.050340745598077774	-0.06131165102124214	-0.011964148841798306	-0.01134983915835619	-0.0909208133816719	-0.027364162728190422	0.004452016670256853	-0.05805401876568794	-0.04566345363855362	0.017728229984641075	-0.027821151539683342	-0.02075098268687725	0.010732138529419899	-0.016344400122761726	-0.057148393243551254	0.05976526066660881	-0.02506842277944088	-0.015936972573399544	-0.006890560500323772	-0.06170326843857765	0.019021065905690193	0.09549069404602051	0.04269222915172577	0.02636953443288803	0.0548640638589859	0.039409954100847244	0.13231565058231354	0.03619886934757233	-0.045593585819005966	-0.0028526345267891884	-0.05983418598771095	-0.06202755495905876	0.04646727442741394	-0.03181722015142441	-0.020933594554662704	-0.010303034447133541	0.009159687906503677	0.04031209275126457	0.03613409399986267	-0.039440326392650604	-0.019456902518868446	0.04287118837237358	-0.026875587180256844	0.0364781953394413	-0.004940368235111237	-0.006953372620046139	-0.014675325714051723	-0.050496190786361694	-0.04324466735124588	-0.022014234215021133	0.02481977269053459	-0.01204331312328577	0.055446188896894455	-0.016996778547763824	-0.03766987472772598	0.002059451537206769	-0.014348052442073822	0.10501440614461899	-0.07447018474340439	-0.021218515932559967	0.05054484307765961	0.03666152060031891	-0.09192433953285217	0.0025836012791842222	0.047949157655239105	-0.03463231772184372	-0.03918744996190071	-0.015340028330683708	0.016822315752506256	-0.06742128729820251	0.005067792255431414	0.004290583077818155	-0.052789412438869476	0.003786579705774784	-0.00030159312882460654	0.013314162380993366	0.023584267124533653	-0.01996113359928131	-0.018242930993437767	0.007413723040372133	0.008323537185788155	0.01978132873773575	-0.04219319671392441	-0.0006029930664226413	-0.048534244298934937	0.045376162976026535	-0.047991249710321426	-0.021836744621396065	-0.04703252390027046	0.04304027557373047	-0.0058462051674723625	-0.035488974303007126	-0.002662832383066416	0.004909535404294729	-0.002757730195298791	0.012736614793539047	0.0304107703268528	-0.054806679487228394	-0.03238542005419731	-0.06583255529403687	-0.04380793124437332	-0.07133843749761581	-0.042144306004047394	0.01636507362127304	-0.07159902155399323	0.0064904410392045975	-0.024101324379444122	-0.026630952954292297	-0.0071179806254804134	0.002394935116171837	0.04160180315375328	0.0597611740231514	0.04479604586958885	-0.02417094074189663	0.04972434043884277	0.00962095707654953	0.03973971679806709	0.03611130639910698	-0.0387997031211853	0.01310246717184782	0.036993689835071564	-0.042997606098651886	0.05698954313993454	-0.03528241440653801	0.03398628532886505	-0.006595277693122625	0.00646876310929656	0.02104300819337368	-0.03292584419250488	0.006760907359421253	-0.018311558291316032	0.051614005118608475	-0.06839936971664429	-0.045924924314022064	0.04099865257740021	-0.04708219692111015	-0.09522410482168198	-0.03127524256706238	0.005131498910486698	-0.02081814967095852	0.031273625791072845	-0.001520685851573944	0.037776969373226166	0.040839534252882004	-0.048762284219264984	-0.07774831354618073	-0.026639774441719055	-0.023039868101477623	0.06271953880786896	-0.02301320619881153	0.04022713005542755	0.018463172018527985	0.006518959067761898	0.013205938041210175	-0.008609116077423096	0.016008520498871803	-0.08923176676034927	-0.009459814056754112	-0.0193556509912014	-0.06592944264411926	0.029315205290913582	-0.0015822222921997309	-0.0035271451342850924	-0.047517336905002594	-0.014719907194375992	-0.05308754742145538	0.06329565495252609	-0.019091293215751648	-0.014434435404837132	0.03421594575047493	-0.020159713923931122	0.017682429403066635	0.024669045582413673	-0.039764586836099625	-0.06705363094806671	0.02120417170226574	-0.0045212325640022755	0.03634025901556015	0.027073722332715988	0.021452646702528	-0.028684400022029877	-0.082585908472538	-0.00202383054420352	0.017984014004468918	-0.08167541027069092	0.023769576102495193	-0.011766458861529827	-0.10230521857738495	-0.07008999586105347	0.01274428702890873	-0.039712242782115936	0.06111079826951027	0.017779920250177383	0.03188898414373398	-0.02595391310751438	-0.0032340127509087324	-0.012175379320979118	-0.051893386989831924	0.011520749889314175	-0.011699194088578224	-0.022769322618842125	-0.03860975056886673	0.07355830818414688	-0.03996313735842705	0.0023356396704912186	0.06356003135442734	-0.04582830145955086	0.04572046548128128	0.032347291707992554	-0.04752129688858986	0.08147669583559036	-0.0026317364536225796	-0.046400606632232666	0.06570146232843399	-0.08053608238697052	0.10850611329078674	-0.05929611250758171	0.08433989435434341	0.015674438327550888	-0.004093796946108341	0.08067649602890015	0.07697618752717972	-0.02095331810414791	0.02307688072323799	-0.07870196551084518
+0.06120112165808678	-0.01962697133421898	0.0040221745148301125	0.0027874575462192297	-0.07945582270622253	-0.08646202832460403	-0.031749311834573746	0.08497671782970428	-0.004497643560171127	-0.04227311164140701	-0.05727672949433327	-0.0036056519020348787	0.017830494791269302	0.008912833407521248	0.04153822734951973	-0.014221149496734142	-0.045901648700237274	0.005349806975573301	-0.041513461619615555	-0.017402129247784615	0.0781669020652771	0.011913876980543137	-0.0559859573841095	0.0531548410654068	0.030496522784233093	0.010763305239379406	-0.03586765378713608	0.03088662214577198	0.002511061495169997	0.028271866962313652	0.02068782038986683	-0.04148854315280914	0.04286433756351471	-0.058666862547397614	-0.020531071349978447	0.023729849606752396	-0.0667264387011528	0.09460868686437607	0.011205680668354034	-0.032303497195243835	-0.0494939386844635	0.03427691385149956	-0.019221140071749687	-0.04607635736465454	0.02709437720477581	0.05233113095164299	0.006530703976750374	-0.014926746487617493	-0.012441830709576607	0.04720142483711243	0.07211451232433319	-0.014785713516175747	-0.010212008841335773	-0.024995582178235054	0.011442714370787144	0.010624362155795097	0.043737608939409256	-0.038429345935583115	-0.054905232042074203	0.021828047931194305	-0.10587359964847565	-0.009412841871380806	-0.009141219779849052	0.012039809487760067	-0.030220642685890198	-0.031562793999910355	0.003220997517928481	0.08013848960399628	0.03857823461294174	-0.04505747929215431	0.032374750822782516	0.06717675924301147	-0.0805019661784172	-0.04416991025209427	0.04895609989762306	-0.04276368021965027	0.010017982684075832	0.04934293031692505	-0.008162971585988998	-0.0016666335286572576	0.014176907017827034	-0.02787412330508232	0.13927815854549408	-0.08259224891662598	-0.09773281961679459	-0.027465054765343666	-0.01915513537824154	-0.057241808623075485	0.040637288242578506	0.0194187443703413	-0.008593365550041199	0.05430135130882263	-0.03290170803666115	0.00419213529676199	-0.022068187594413757	0.028279775753617287	0.012519719079136848	-0.10856803506612778	-0.03399631008505821	-0.02362058125436306	-0.04613572731614113	-0.0027484132442623377	0.044863250106573105	0.038790520280599594	0.03682209923863411	0.02710121124982834	-0.010979650542140007	0.008844653144478798	0.014090300537645817	0.045592308044433594	-0.008635942824184895	0.04842761531472206	0.10220722109079361	0.03379032388329506	0.07432761043310165	-0.012157956138253212	0.007078771013766527	-0.011639535427093506	-0.035786453634500504	-0.014839868992567062	0.011268068104982376	0.060483016073703766	-0.0004048254922963679	-0.10729449987411499	0.009726817719638348	0.06839819252490997	-0.09040811657905579	-0.038563210517168045	0.04030894488096237	-0.0626126378774643	0.10776042938232422	0.05594364181160927	-0.05206867679953575	0.022775568068027496	0.07163021713495255	0.01663016900420189	0.009908351115882397	0.04439062625169754	0.001423325389623642	-0.07150495797395706	-0.008336393162608147	0.019429977983236313	-0.014025619253516197	-0.08220534026622772	0.05413876473903656	0.05778275430202484	0.044966861605644226	-0.03486333042383194	-0.07241256535053253	0.0025882686022669077	-0.08070948719978333	-0.10098037123680115	-0.025686798617243767	-0.03809557855129242	-0.028872819617390633	0.008246766403317451	-0.014762908220291138	-0.0011461729882284999	-0.039552588015794754	0.002403711900115013	-0.06350947171449661	-0.04466075077652931	-0.023991581052541733	-0.05915983021259308	-0.01446892973035574	0.01744658313691616	-0.000646955450065434	0.011223668232560158	-0.07798714190721512	-0.04411998391151428	-0.01453969907015562	-0.015065866522490978	0.035940635949373245	0.061381611973047256	0.07766430079936981	0.005732267629355192	0.02503185160458088	-0.039950840175151825	-0.01601300574839115	-0.02195700816810131	0.03440658375620842	-0.03719625994563103	0.052219923585653305	-0.04936232790350914	0.025435762479901314	-0.021520618349313736	0.008628123439848423	-0.05447996035218239	0.058702822774648666	-0.010496847331523895	-0.0019119374919682741	-0.02592950500547886	0.04457707703113556	0.07836578786373138	-0.017516955733299255	0.06617001444101334	-0.013047573156654835	0.03714685142040253	-0.06816688925027847	-0.05185384675860405	-0.0335080586373806	0.0428449884057045	0.031392741948366165	0.044942066073417664	0.004524869378656149	-0.0539548359811306	0.0650871992111206	0.039380621165037155	0.003692700993269682	-0.05704092979431152	0.07492972165346146	0.07229475677013397	-0.004342477302998304	0.03778831660747528	0.07567286491394043	-0.02877025306224823	0.002467493060976267	-0.03400244191288948	0.024310177192091942	-0.053500063717365265	-0.01870117150247097	0.013364174403250217	-0.10381104797124863	0.022129489108920097	-0.06432542949914932	-0.04632365703582764	-0.02317507565021515	0.04495793953537941	-0.00648256903514266	-0.046669088304042816	-0.03176732733845711	0.003236456774175167	0.018898621201515198	-0.035457320511341095	-0.0005700322799384594	0.016387129202485085	-0.08120308071374893	0.0750039592385292	0.022657182067632675	-0.04294346272945404	-0.059350091964006424	0.02670114114880562	0.03455743566155434	-0.010754204355180264	0.023861927911639214	0.05614370107650757	0.08014348149299622	0.015110352076590061	-0.007468237541615963	0.0008991024806164205	-0.04354635626077652	-0.010410000570118427	-0.010496887378394604	0.005385469179600477	-0.02952319011092186	-0.02512187510728836	0.020710153505206108	-0.04601077735424042	-0.06005053222179413	-0.0031806814949959517	0.02558823861181736	-0.05626038834452629	-0.04050397127866745	-0.03302539139986038	-0.005532110575586557	-0.03207659721374512	-0.04113349691033363	0.04506724327802658	-0.015872379764914513	0.06467393040657043	-0.0369880236685276	0.0073234024457633495	0.06069200113415718	-0.028144359588623047	0.014222543686628342	-0.052237171679735184	0.08124872297048569	-0.014644998125731945	-0.03115859255194664	-0.013440048322081566	-0.0737261176109314	-0.07871966809034348	-0.006241403520107269	-0.055474650114774704	0.010413172654807568	0.10005579888820648	0.07120052725076675	-0.04956429824233055	-0.09058358520269394	-0.024885332211852074	-0.01691177487373352	-0.037613656371831894	-0.005570149514824152	0.023749088868498802	-0.03982599824666977	-0.02772052027285099	0.022727016359567642	-0.04336973652243614	-0.021329062059521675	0.0028500414919108152	-0.013630359433591366	-0.09856821596622467	0.034134991466999054	-0.02174992673099041	-0.020557653158903122	0.03370222449302673	-0.04659692570567131	0.0010546570410951972	0.09035854041576385	0.07026357203722	-0.002913985401391983	0.044643886387348175	0.012694630771875381	0.12328333407640457	-0.024699967354536057	-0.003436160273849964	-0.03636588528752327	-0.03430252894759178	-0.037222616374492645	0.03602660074830055	-0.03826940432190895	-0.022378114983439445	-0.012004587799310684	-0.025855690240859985	0.029508322477340698	-0.004143802914768457	-0.04771901294589043	-0.03152400255203247	-0.010373582132160664	-0.02315298095345497	0.018344713374972343	0.017096221446990967	0.020985117182135582	-0.03345733880996704	-0.03322187811136246	-0.03776436671614647	-0.03796946629881859	0.0461256317794323	-0.028681859374046326	0.03852476552128792	-0.040962181985378265	-0.020758602768182755	-0.024898435920476913	0.03883044421672821	0.08964136242866516	-0.03840119391679764	0.03834117576479912	0.08167341351509094	0.021026795729994774	-0.07846608757972717	0.02332291193306446	0.02972416952252388	-0.0048113251104950905	-0.03771521896123886	-0.023542338982224464	0.0353645421564579	-0.07152356207370758	-0.029043596237897873	-0.00599493645131588	-0.023312965407967567	0.026587193831801414	0.016877273097634315	0.03724270313978195	0.05151462182402611	-0.012909515760838985	-0.008297390304505825	-0.014772159978747368	-0.003715111641213298	0.06548120826482773	-0.01488411333411932	0.01110444962978363	-0.007855908013880253	0.04406159371137619	-0.08555138856172562	-0.05606299266219139	-0.003417566418647766	0.04982813447713852	-0.03539269417524338	-0.007500004954636097	-0.004518292844295502	-0.014555200934410095	-0.012940379790961742	0.06009279191493988	0.012167016044259071	-0.029565131291747093	-0.015816649422049522	-0.0004511841107159853	-0.07658234238624573	-0.09656767547130585	-0.0695922002196312	-0.009599721990525723	-0.037219371646642685	0.018043024465441704	-0.011521355248987675	0.017160579562187195	0.007762897294014692	0.015604889020323753	0.051070764660835266	0.07169442623853683	0.04260997474193573	0.0034049679525196552	0.046614278107881546	0.02084745652973652	0.0431365966796875	0.011817090213298798	-0.052159592509269714	0.00040965978405438364	0.013715438544750214	-0.0485771968960762	0.05546580255031586	-0.046808067709207535	-0.004637742880731821	-0.020012006163597107	-0.014233402907848358	0.014318311586976051	-0.0018698724452406168	0.04792937636375427	-0.0433548279106617	0.09052644670009613	-0.06847485154867172	-0.0366445928812027	0.05314456671476364	-0.0083250030875206	-0.08524118363857269	-0.0447128564119339	-0.05586102977395058	-0.024402417242527008	0.009190059266984463	-0.026516519486904144	-0.0004439564363565296	0.02542976289987564	-0.034336935728788376	-0.04482634365558624	-0.017907002940773964	0.018380677327513695	0.058127086609601974	0.021290024742484093	0.061034344136714935	0.053689513355493546	0.013510859571397305	0.003708590054884553	-0.04147176817059517	0.027583017945289612	-0.06145651265978813	0.03640061244368553	-0.011836809106171131	0.010654681362211704	-0.004023035988211632	0.001515243318863213	0.036229923367500305	-0.07365435361862183	-0.020247885957360268	-0.04497511684894562	0.06395129859447479	0.011197280138731003	0.021990470588207245	0.06467775255441666	-0.06876805424690247	-0.04133150354027748	-0.005617816001176834	-0.03973083570599556	-0.09160546213388443	-0.013574455864727497	-0.04026609659194946	0.054484039545059204	0.021702518686652184	-0.004723010119050741	-0.00789326336234808	-0.05645131692290306	0.041105322539806366	0.03283856809139252	-0.0059436047449707985	0.025542574003338814	0.04772919788956642	-0.05285090580582619	-0.034574445337057114	-0.014216730371117592	-0.02294972352683544	0.04100646451115608	0.03164597973227501	0.008324834518134594	-0.04455462470650673	0.006077433004975319	0.006734127178788185	-0.03475300222635269	0.001949226832948625	0.017631493508815765	-0.04778329282999039	0.007960768416523933	0.10020048916339874	-0.04522665962576866	0.012972036376595497	0.07746502757072449	-0.05658181011676788	0.02683802880346775	0.029248448088765144	0.015398594550788403	0.034532345831394196	-0.017662251368165016	-0.02964191697537899	0.059153009206056595	-0.08876406401395798	0.05692051351070404	-0.09102831035852432	0.08139623701572418	0.031107479706406593	-0.04251628741621971	0.08985266834497452	0.043645698577165604	0.026746658608317375	0.002402976155281067	-0.1090032160282135
+0.0024272336158901453	-0.05989371985197067	-0.045824699103832245	0.0824299231171608	-0.08106473833322525	-0.06343254446983337	-0.0036556601990014315	0.041777003556489944	-0.04475880414247513	-0.010610923171043396	-0.03878934308886528	-0.016087664291262627	-0.03300394490361214	-0.0197706650942564	0.016628319397568703	0.009698471985757351	-0.0627666637301445	0.04547936096787453	-0.04060953110456467	-0.03591732308268547	0.03256164863705635	0.020576344802975655	-0.10058079659938812	0.054856643080711365	0.01218008529394865	0.00458165816962719	-0.04712902382016182	0.005491407122462988	0.012097576633095741	0.026256486773490906	0.018182143568992615	-0.014910570345818996	0.03557729348540306	-0.030860289931297302	-0.06728669255971909	0.04976101219654083	-0.02432123012840748	0.08200372010469437	0.0025163372047245502	0.0017035235650837421	-0.05771378055214882	0.013672486878931522	-0.036158714443445206	-0.06045539304614067	0.04683571308851242	0.019581954926252365	0.017331628128886223	-0.045922089368104935	0.05986132472753525	0.0889120027422905	0.1027723029255867	0.0038554405327886343	0.02657625637948513	0.004016130231320858	-0.021034736186265945	-0.009975006803870201	0.020953528583049774	-0.02512677013874054	-0.05766289308667183	0.006819476373493671	-0.05846400558948517	-0.00951853021979332	0.007870551198720932	-0.0060742502100765705	-0.026609886437654495	0.011711394414305687	0.0061363764107227325	0.11482692509889603	0.009979506023228168	-0.03622236102819443	-0.0022350624203681946	0.0805768221616745	-0.05839627981185913	-0.010188826359808445	0.041345223784446716	-0.02561492845416069	-0.017078498378396034	0.0672762542963028	-0.011438329704105854	-0.011839467100799084	0.013322879560291767	-0.021733080968260765	0.13817466795444489	-0.042362019419670105	-0.05586061254143715	0.001446736278012395	-0.0018369946628808975	-0.043211933225393295	0.059355683624744415	0.028151048347353935	0.02624807134270668	0.07093588262796402	-0.06856538355350494	0.05599680542945862	0.0119763920083642	0.04912002384662628	0.0621773898601532	-0.1398438662290573	-0.06508735567331314	0.03134872019290924	-0.03253795951604843	-0.049099646508693695	0.07882456481456757	-0.03693452104926109	0.013489359989762306	-0.006208224222064018	-0.039720043540000916	-0.040815677493810654	-0.006876056082546711	0.014071500860154629	0.03474276140332222	0.06985043734312057	0.08079124242067337	0.041867319494485855	0.04413097724318504	-0.018839320167899132	0.0006143799982964993	-0.0362900085747242	-0.054122619330883026	0.03979535028338432	0.0030452837236225605	0.03600478917360306	-0.006131862755864859	-0.038968391716480255	-0.023545900359749794	0.024604598060250282	-0.10753452777862549	-0.008726242929697037	0.07541293650865555	-0.052974481135606766	0.1274208277463913	0.005882425233721733	-0.0161377415060997	0.02613206021487713	0.06250064820051193	-0.0027257453184574842	0.02224128134548664	0.028773784637451172	-0.006741413846611977	0.0013297128025442362	-0.02164393849670887	0.06572838872671127	0.0013010113034397364	-0.03949761390686035	0.03988517448306084	0.06973832845687866	0.012056765146553516	-0.021900998428463936	-0.042730581015348434	0.013260853476822376	-0.07713945209980011	-0.06255273520946503	-0.03322222828865051	-0.08662499487400055	0.010862848721444607	0.02712973952293396	0.01352999173104763	0.06456682831048965	-0.025967301800847054	0.012024054303765297	-0.05677231773734093	-0.05352918431162834	-0.01997571811079979	-0.048814959824085236	-0.04603257775306702	0.008512076921761036	-0.042017094790935516	-0.008770844899117947	-0.003546640044078231	-0.030954135581851006	-0.035639867186546326	-0.005827009677886963	0.038714684545993805	0.08513818681240082	0.05022476613521576	0.03505721315741539	0.03059583343565464	-0.0212249755859375	-0.019429316744208336	-0.05893060192465782	0.001380106550641358	-0.05292315408587456	0.0558980368077755	-0.01344774104654789	-0.01922905258834362	-0.009844254702329636	0.046437811106443405	-0.05765344947576523	0.015081353485584259	0.006345583125948906	-0.030778244137763977	-0.03861211612820625	0.057420309633016586	0.1178450733423233	-0.05411611497402191	0.05136020854115486	-0.04076429456472397	0.07561928778886795	-0.04714971408247948	-0.0005443895352073014	-0.06715159863233566	0.05170253664255142	-0.008702153339982033	0.04034584388136864	0.019458144903182983	-0.0976012647151947	0.0884079784154892	0.05263668671250343	-0.00500531168654561	-0.07394088804721832	0.027189774438738823	0.02611628733575344	0.026707187294960022	0.08058404922485352	0.045114047825336456	0.01329654548317194	-0.042443450540304184	0.019774187356233597	0.013588056899607182	-0.03292679786682129	0.022522905841469765	0.0017231006640940905	-0.07811278849840164	0.026214584708213806	-0.042739637196063995	-0.07965116202831268	-0.025871939957141876	0.006933184806257486	-0.0011208576615899801	0.004436952993273735	-0.0007112004677765071	0.03553730994462967	-0.0023358208127319813	-0.05967434123158455	-0.02671987935900688	0.010783410631120205	-0.06081908196210861	0.0968029648065567	-0.004774575587362051	-0.05273503437638283	-0.07999615371227264	0.020729830488562584	-0.014447302557528019	0.037856411188840866	0.021533271297812462	0.03354763239622116	0.09466437250375748	0.04485425725579262	0.02302379347383976	-0.014507139101624489	-0.06933058053255081	-0.024142678827047348	-0.01027576345950365	0.01953992061316967	-0.04694290831685066	-0.032065391540527344	-0.00035566583392210305	-0.07950590550899506	-0.0051970165222883224	0.018821636214852333	0.046325214207172394	-0.019510144367814064	-0.06748862564563751	-0.006453219335526228	0.008707372471690178	-0.0332166962325573	-0.055580299347639084	-0.005555795505642891	-0.03035827726125717	0.03269335255026817	-0.05883152410387993	-0.03454464673995972	0.012706339359283447	0.010747002437710762	0.05154018476605415	-0.03278910741209984	0.03324946388602257	-0.03261195123195648	-0.0883331149816513	0.01980082131922245	-0.06765129417181015	-0.07345956563949585	-0.04578157886862755	-0.014200236648321152	-0.021688880398869514	0.09818385541439056	0.015429025515913963	-0.03452106937766075	-0.059235505759716034	-0.016507258638739586	-0.04171288385987282	-0.04515380039811134	0.017166560515761375	0.0019028818933293223	-0.06331607699394226	-0.025645093992352486	0.019817642867565155	-0.01643446832895279	-0.015857957303524017	0.03969321772456169	-0.010283004492521286	-0.05303439870476723	0.10116919875144958	0.021372798830270767	-0.0033732219599187374	0.0016763329040259123	-0.045893047004938126	0.025258099660277367	0.08077225834131241	0.020138490945100784	0.03978055715560913	0.09059404581785202	0.043795689940452576	0.07907214015722275	0.027186140418052673	-0.01158476434648037	-0.015462945215404034	-0.027538780122995377	-0.029603980481624603	0.05152973532676697	-0.0012020044960081577	-0.019529346376657486	0.011504564434289932	0.004330618306994438	0.0804847702383995	-0.021785136312246323	-0.03348122909665108	-0.0585448332130909	-0.000708036997821182	0.026160795241594315	0.0028434123378247023	0.009036208502948284	-0.012786628678441048	-0.001083133276551962	-0.061398137360811234	-0.005720228888094425	-0.04636237025260925	0.022240621969103813	-0.01414934080094099	0.03505014255642891	-0.0039189597591757774	-0.018467165529727936	0.03858477994799614	-0.017328418791294098	0.10292579978704453	-0.07570730149745941	-0.006029544863849878	0.057499952614307404	0.038873814046382904	-0.07172563672065735	-0.010093600489199162	0.03657237067818642	-0.05259178951382637	-0.04396132752299309	0.011508198454976082	0.03828989714384079	-0.049231402575969696	-0.015161252580583096	-0.0034627707209438086	-0.05077182129025459	-0.014288672246038914	0.0005120398127473891	0.028962455689907074	0.046567242592573166	0.02016649767756462	-0.030311230570077896	0.010996843688189983	0.030120210722088814	0.003392682643607259	-0.056832458823919296	0.04654240980744362	-0.00385523634031415	0.049394391477108	-0.053797706961631775	-0.07944001257419586	-0.044925618916749954	0.025951053947210312	-0.01405002735555172	-0.04651933163404465	-0.06212877482175827	-0.009790030308067799	0.02037992887198925	0.063625268638134	0.012021981179714203	-0.048927128314971924	-0.02111409232020378	-0.05866052210330963	-0.08253955096006393	-0.09057808667421341	-0.029470151290297508	-0.0016184510895982385	-0.04127383977174759	0.025629309937357903	-0.07478581368923187	-0.02534271590411663	-0.0024166402872651815	-0.017484959214925766	0.032179996371269226	0.0578332282602787	0.04128413647413254	-0.015039699152112007	0.02738315984606743	-0.032988909631967545	0.04557310789823532	0.005541963502764702	-0.05277952179312706	0.04469844698905945	0.03374800086021423	-0.04957732558250427	0.028063038364052773	-0.050952017307281494	-0.031217582523822784	-0.0057801916263997555	-0.026786217465996742	0.015893101692199707	0.014365491457283497	0.005689702928066254	-0.05459339916706085	0.03272778540849686	-0.06261226534843445	-0.02919493056833744	0.05917660892009735	0.006271096412092447	-0.06756465882062912	-0.051448922604322433	-0.017221443355083466	0.029960790649056435	0.05818910896778107	-0.02123061940073967	0.010041049681603909	0.034087084233760834	-0.04693301022052765	-0.04340243712067604	-0.04702502489089966	-0.005653988104313612	0.024544361978769302	0.028165021911263466	0.05141483247280121	0.01779758743941784	-0.010864980518817902	-0.028599875047802925	-0.013482930138707161	-0.026176756247878075	-0.048333220183849335	-0.021220995113253593	-0.007778363302350044	0.016543954610824585	0.0399329736828804	0.00710990559309721	-0.026582062244415283	-0.023758353665471077	-0.010574114508926868	-0.029092729091644287	0.06910264492034912	0.010951965115964413	0.006387334782630205	0.05976167693734169	-0.02295384183526039	-0.0036927873734384775	0.027274787425994873	-0.020097574219107628	-0.05480750650167465	0.012400452047586441	-0.009720290079712868	0.0464925691485405	0.042237233370542526	0.03582139313220978	-0.04456670582294464	-0.05232319235801697	-0.02427820675075054	0.014018816873431206	-0.039157114923000336	-0.012085550464689732	0.0436822809278965	-0.06151799112558365	-0.05322176218032837	-0.022226087749004364	-0.05423365905880928	0.09172583371400833	0.0319480337202549	0.03721895441412926	-0.03801478445529938	-0.0007271153153851628	0.008836030960083008	-0.015449040569365025	-0.01838555559515953	-0.00852595828473568	-0.08231407403945923	-0.022898713126778603	0.08308585733175278	-0.02433968149125576	0.040971267968416214	0.07433643937110901	-0.04800812900066376	0.06389392167329788	0.0024299905635416508	0.01616322249174118	0.07127892225980759	0.013860468752682209	-0.010243188589811325	0.04965556040406227	-0.05934612825512886	0.09368078410625458	-0.03401277959346771	0.07679129391908646	0.026339903473854065	-0.026861773803830147	0.09277184307575226	0.08073411881923676	-0.01277836412191391	0.012422218918800354	-0.05558563396334648
+0.03465953469276428	-0.026303991675376892	-0.025571081787347794	0.04255791753530502	-0.07350044697523117	-0.028754649683833122	-0.0035899861250072718	0.04100850224494934	-0.004843811970204115	-0.06125391274690628	-0.0344596691429615	-0.035951897501945496	0.016877857968211174	0.0013826413778588176	0.031482599675655365	-0.06201238930225372	-0.07649169117212296	0.02358057349920273	-0.012243791483342648	-0.058213021606206894	0.0060790786519646645	0.010739177465438843	-0.06601182371377945	0.04501015320420265	0.013739265501499176	0.020042570307850838	-0.044359758496284485	0.0018276649061590433	-0.012144467793405056	0.04154413565993309	0.07479513436555862	-0.012273238971829414	0.05604758486151695	-0.07465095072984695	-0.05860431492328644	0.035096921026706696	-0.040205154567956924	0.13109776377677917	0.012055949307978153	-0.07331854850053787	-0.06922043859958649	0.0035103841219097376	-0.032534170895814896	-0.07154981791973114	0.04348514974117279	-0.016548901796340942	-0.02011222019791603	-0.021059593185782433	0.014275231398642063	0.0029504732228815556	0.09139276295900345	0.009176610969007015	-0.02209819294512272	-0.02008911222219467	-0.04189901426434517	0.02462877333164215	0.0318257100880146	-0.04046614468097687	-0.0605480894446373	0.04190376028418541	-0.08492257446050644	-0.005339822731912136	0.02197359874844551	0.016768110916018486	-0.03552964702248573	-0.03851312771439552	-0.004691663198173046	0.06641305238008499	0.006367582827806473	0.00024350511375814676	-0.0015025106258690357	0.0899290144443512	-0.05765416473150253	-0.06405063718557358	0.04792654886841774	-0.07090645283460617	-0.016806192696094513	0.01354573667049408	0.009359467774629593	0.015636160969734192	0.020306088030338287	-0.009358485229313374	0.13953107595443726	-0.037932269275188446	-0.03031860664486885	-0.01915695145726204	-0.009143232367932796	-0.05646869167685509	0.06642203032970428	0.022143013775348663	0.010699019767343998	0.05442981421947479	-0.018937550485134125	0.057525891810655594	0.030761219561100006	0.02117268554866314	0.025636468082666397	-0.12012838572263718	-0.029319707304239273	-0.009480084292590618	0.009142206981778145	-0.029974626377224922	0.056792426854372025	0.018862634897232056	0.04362170398235321	0.041064776480197906	-0.06608139723539352	0.011044099926948547	0.04740418121218681	0.03121105022728443	0.029685864225029945	0.11743530631065369	0.055068936198949814	0.015294774435460567	0.055489908903837204	0.011287221685051918	0.014948638156056404	-0.0488223060965538	-0.02165449596941471	0.04604503512382507	-0.012324611656367779	0.020713485777378082	-0.0181647390127182	-0.06835437566041946	-0.028572671115398407	0.041220568120479584	-0.07933378219604492	-0.029486386105418205	0.03972319886088371	-0.02730710059404373	0.12780746817588806	0.06262701749801636	-0.01063245814293623	0.03866497054696083	0.05023610219359398	0.03551061078906059	0.014762171544134617	0.02444325014948845	-0.009619612246751785	-0.03715725243091583	-0.04834549129009247	0.0658266693353653	-0.008293496444821358	-0.046206194907426834	0.05068284645676613	0.02837866172194481	0.04641206935048103	-0.025869684293866158	-0.07720352709293365	0.008868138305842876	-0.08443117886781693	-0.07248766720294952	0.02127099223434925	-0.06968054920434952	0.014244609512388706	0.03594214841723442	0.014958648942410946	0.021896615624427795	0.016260016709566116	-0.02815506048500538	-0.03360230103135109	-0.042476002126932144	0.0007562035461887717	-0.10053861886262894	-0.030503280460834503	-0.03240935876965523	-0.014038635417819023	0.022367550060153008	-0.05695740133523941	-0.00887517910450697	0.014685689471662045	0.0039180004969239235	0.04277784749865532	0.07552991062402725	0.03288830816745758	0.02464889921247959	0.014986124821007252	-0.04532531276345253	-0.011228961870074272	-0.024929219856858253	0.03309805318713188	-0.03515954688191414	0.022871598601341248	-0.04951167479157448	0.02961713634431362	0.00439674174413085	0.05316450819373131	-0.004342956934124231	0.03384143114089966	0.0010343913454562426	-0.04727914556860924	-0.07032936811447144	0.040382642298936844	0.04158147796988487	-0.003908188082277775	0.03906422480940819	0.008681739680469036	0.05695343390107155	-0.015320010483264923	-0.03015446849167347	-0.03168051317334175	0.029434222728013992	0.018915416672825813	0.043615978211164474	0.004184254445135593	-0.049581341445446014	0.08057792484760284	0.019821636378765106	0.04027721658349037	-0.05616767331957817	0.01890181191265583	0.025668535381555557	0.0009497826686128974	0.03985913097858429	0.0790054127573967	-0.018714314326643944	-0.005265634506940842	0.023710433393716812	0.0728587731719017	-0.05978686735033989	0.0017353291623294353	0.0046160356141626835	-0.10326908528804779	0.038234785199165344	-0.04670066758990288	-0.1037614569067955	-0.031114822253584862	0.025841593742370605	-0.0327632911503315	-0.06893233209848404	0.011472172103822231	0.03647492453455925	-0.010367527604103088	-0.014102429151535034	-0.022028282284736633	-0.002623768523335457	-0.08464706689119339	0.08293735235929489	-0.0065587288700044155	-0.09579934179782867	-0.05276305228471756	-0.007277250289916992	-0.005326246842741966	-0.011276023462414742	0.0306625384837389	0.05783071741461754	0.09646768867969513	0.02482832968235016	-0.035244207829236984	0.04153168201446533	-0.05470465496182442	-0.07419703900814056	0.013425170443952084	0.04812601953744888	-0.03934672474861145	-0.03980370983481407	0.0059803081676363945	-0.0703788548707962	0.017515338957309723	-0.019204333424568176	0.02799621783196926	-0.026702286675572395	0.0020755452569574118	-0.02889254502952099	0.057372916489839554	-0.0221262127161026	-0.023042144253849983	0.04609747231006622	-0.008567233569920063	0.04845056310296059	-0.016595754772424698	-0.013654207810759544	0.03615843877196312	-0.01027596089988947	0.030478261411190033	-0.0321500338613987	0.08940603584051132	-0.017608432099223137	-0.06403886526823044	-0.025403952226042747	-0.07766145467758179	-0.06156346574425697	-0.012361007742583752	-0.05391758307814598	-0.03159688040614128	0.08815927058458328	0.022078583016991615	-0.030709927901625633	-0.06303329765796661	-0.06513547897338867	-0.0005960856797173619	-0.08029057830572128	-0.032032597810029984	0.007929537445306778	-0.07318668812513351	-0.02090943045914173	0.043853759765625	-0.04312814772129059	0.0009455715189687908	0.02021673135459423	-0.0013369639636948705	-0.05610683187842369	0.06854959577322006	-0.0415654182434082	-0.013268038630485535	0.02033223584294319	-0.0888780876994133	0.03454182296991348	0.0866633877158165	0.05785764381289482	0.01927362009882927	0.05728384852409363	0.05212041735649109	0.1081712394952774	0.004685431718826294	-0.03470807895064354	-0.03092874400317669	-0.04631773382425308	-0.08395009487867355	0.050743717700242996	-0.017214888706803322	-0.023797709494829178	-0.002336792415007949	-0.005650436505675316	0.05017608776688576	-0.01903759501874447	-0.02142704650759697	-0.04064187780022621	0.04714784771203995	-0.0468607097864151	0.04143356531858444	0.011218760162591934	0.02290058694779873	-0.0027965973131358624	-0.06629544496536255	-0.04845389723777771	-0.03562534973025322	0.03507107496261597	-0.01100139133632183	0.026713838800787926	0.004778677597641945	-0.04978179931640625	-0.010472217574715614	-0.007199058774858713	0.09642086178064346	-0.0838095173239708	0.012872987426817417	0.04106971248984337	0.040516503155231476	-0.09612233191728592	-0.0018716574413701892	0.017568985000252724	-0.04975347965955734	-0.05512030050158501	0.0025909016840159893	0.05074106529355049	-0.05717269703745842	-0.03595823422074318	-0.0032520839013159275	-0.03525211662054062	-0.008313392288982868	0.017551323398947716	0.01662120409309864	0.04126853495836258	-0.015195414423942566	-0.03588731214404106	0.016463719308376312	-0.020162619650363922	0.03242658078670502	-0.027019403874874115	0.03210139274597168	-0.03156265988945961	0.03572649136185646	-0.0296173132956028	-0.031132476404309273	-0.06111651659011841	0.0475967638194561	-0.005771331023424864	-0.0168774276971817	-0.010649636387825012	-0.006652844604104757	-0.019847363233566284	0.02350260689854622	0.03126073256134987	-0.05913597345352173	-0.044997844845056534	-0.031950172036886215	-0.04915454983711243	-0.06918085366487503	-0.04115351662039757	0.00231660227291286	-0.07213550060987473	0.025907259434461594	-0.046365100890398026	0.004726580809801817	0.02334539033472538	-0.0049116951413452625	0.052798692137002945	0.05899159610271454	0.016898108646273613	-0.039301320910453796	0.03524365648627281	-0.00636604567989707	0.049552273005247116	0.030196266248822212	-0.03816152364015579	-0.0003000263241119683	0.039349354803562164	-0.05480550602078438	0.02952960692346096	-0.011551221832633018	0.02625710889697075	-0.001640926580876112	-0.020690565928816795	0.02950756810605526	-0.009482651017606258	0.012445272877812386	0.006093916483223438	0.03284498304128647	-0.05038129538297653	-0.049063436686992645	0.0025534541346132755	-0.03257998824119568	-0.07614224404096603	-0.03958314657211304	-0.005242136772722006	-0.04767770320177078	0.05624404177069664	-0.015996085479855537	0.01339677907526493	0.023351319134235382	-0.06495218724012375	-0.05555347725749016	-0.014649570919573307	-0.02652570605278015	0.0413358174264431	-0.013202105648815632	0.04404687136411667	0.005896596238017082	-0.0037648642901331186	0.006688233930617571	-0.025883609429001808	0.01862957514822483	-0.07708209753036499	0.0021749106235802174	-0.015149512328207493	-0.04176371544599533	0.03853931277990341	0.01018248125910759	0.02714805118739605	-0.059819646179676056	-0.012684264220297337	-0.037428971379995346	0.03758123517036438	-0.017832834273576736	0.0014545919839292765	0.01357273943722248	-0.037834860384464264	0.002144810976460576	0.03641941025853157	-0.05660392343997955	-0.05630554258823395	0.003591694636270404	-0.013532636687159538	0.0601966567337513	0.017347371205687523	0.018550937995314598	-0.01675121672451496	-0.07696491479873657	-0.0019167925929650664	0.0017104237340390682	-0.08774279803037643	-0.016734417527914047	0.02248922921717167	-0.0810387060046196	-0.06813331693410873	0.0008948330068960786	-0.06520460546016693	0.07720606029033661	0.017642829567193985	0.027319878339767456	-0.059177808463573456	-0.00702599436044693	-0.011102691292762756	-0.045124538242816925	0.022627010941505432	-0.03413524478673935	-0.04560893028974533	-0.011593363247811794	0.08558940142393112	-0.035236623138189316	0.03371062129735947	0.07177986949682236	-0.028995197266340256	0.0476241260766983	0.028418531641364098	-0.02911240980029106	0.01142781414091587	-0.006812948267906904	-0.03622531518340111	0.07003982365131378	-0.09141944348812103	0.07222705334424973	-0.06726729869842529	0.10032236576080322	0.02603115513920784	-0.012446853332221508	0.0651981309056282	0.09286349266767502	-0.0011139546986669302	0.026033222675323486	-0.08391492813825607
+-0.03343713656067848	-0.0671733170747757	0.04692067950963974	0.0245143361389637	-0.08518032729625702	-0.08525922149419785	0.0008774753659963608	0.009770948439836502	0.020194170996546745	-0.007436403539031744	0.017573852092027664	0.016004420816898346	0.006667011417448521	0.021737918257713318	0.0060540735721588135	0.02776184305548668	-0.08393450826406479	0.019793102517724037	-0.07622405886650085	0.003242064965888858	0.025759808719158173	0.024381374940276146	-0.05421430245041847	-0.005928145255893469	-0.06534816324710846	0.0066043660044670105	-0.07765567302703857	-0.007857740856707096	-0.004878422245383263	-0.011222188360989094	0.011337277479469776	0.015638330951333046	0.07541398704051971	-0.05875543877482414	-0.03587150201201439	0.05389240384101868	-0.007873275317251682	0.07691708207130432	0.013281485065817833	-0.006810142658650875	-0.04101118445396423	0.009050693362951279	0.006367919966578484	-0.0631956085562706	0.00014321814524009824	-0.0023129316978156567	0.037289902567863464	0.022047992795705795	-0.004680500831454992	0.07986574620008469	0.06959190219640732	0.037131547927856445	-0.00612684478983283	-0.006974451243877411	0.023172322660684586	-0.021812576800584793	0.03675073757767677	-0.03428429365158081	-0.0896715447306633	0.06970004737377167	-0.08383125066757202	0.001649235375225544	0.013417893089354038	0.036381568759679794	-0.03882640227675438	-0.014427375979721546	-0.024989068508148193	0.09114521741867065	0.02259925566613674	-0.06181670352816582	0.04355371743440628	0.06287685036659241	-0.012226509861648083	-0.04193764179944992	-0.012140029110014439	-0.021900977939367294	0.02616499364376068	0.052974801510572433	-0.04553340747952461	-0.042946211993694305	-0.010764922946691513	-0.0476953499019146	0.10789273679256439	-0.05622582137584686	-0.027330132201313972	-0.042716726660728455	0.015210196375846863	-0.06091926619410515	0.02632412128150463	0.01660836488008499	0.030882736667990685	0.0640239343047142	-0.05181626230478287	0.02558840811252594	-0.015918105840682983	0.057217322289943695	0.013588463887572289	-0.12428401410579681	-0.040439408272504807	0.0003441237495280802	-0.003181091509759426	-0.01738746277987957	0.04883527010679245	0.026171082630753517	0.03709392994642258	-0.0014243166660889983	-0.00867992453277111	-0.006786251440644264	0.006922251544892788	0.014496982097625732	0.02078942395746708	0.09606978297233582	0.0761924460530281	0.07021738588809967	0.023781463503837585	0.014522615820169449	-0.002366789383813739	-0.007625141646713018	0.0098446449264884	-0.006806737743318081	0.025341175496578217	-0.012874945998191833	0.010728678666055202	-0.0841498076915741	-0.014677298255264759	0.03851344808936119	-0.11545732617378235	-0.013376234099268913	0.04022396728396416	-0.05532631278038025	0.10418173670768738	0.02402470074594021	-0.039602503180503845	0.019567275419831276	0.03486361354589462	0.025708075612783432	-0.0444747619330883	0.017725788056850433	0.003980786539614201	-0.03221331164240837	-0.010130029171705246	0.04892360791563988	0.0007439557230100036	-0.04365531727671623	-0.013786408118903637	0.06949183344841003	0.017089810222387314	-0.0024056660477072	-0.09510704129934311	0.04178693890571594	-0.021577846258878708	-0.06823410093784332	-0.05228656157851219	-0.08532439172267914	0.012246980331838131	0.06985385715961456	-0.02753753401339054	0.07289984822273254	-0.05498356372117996	-0.00728653697296977	-0.050281133502721786	-0.047887206077575684	-0.03620591014623642	-0.06664891541004181	-0.004373328760266304	-0.022514209151268005	0.0001458627375541255	-0.008587252348661423	-0.040531616657972336	-0.04908863455057144	-0.03629850968718529	-0.036294758319854736	0.015422726981341839	0.026419198140501976	0.064546599984169	-0.0330791138112545	0.014522400684654713	-0.052666082978248596	-0.008143402636051178	-0.05204492807388306	-0.02846660651266575	-0.002984986174851656	0.056968919932842255	-0.08479250967502594	-0.05539396405220032	-0.023198813199996948	0.021142737939953804	-0.05745107680559158	-0.03382791206240654	-0.03509195148944855	-0.011407892219722271	0.007644380442798138	0.04909881204366684	0.0936443880200386	-0.033497877418994904	0.04964569956064224	-0.03390827775001526	0.07415521144866943	-0.05034811049699783	-0.043570879846811295	-0.061743635684251785	0.05148008093237877	-0.005651216953992844	0.00111191940959543	-0.034625355154275894	-0.06981285661458969	0.05657713860273361	0.03135281801223755	-0.023846164345741272	-0.08608227968215942	0.03999301791191101	0.03687319532036781	0.03706391155719757	0.03418085351586342	0.062197353690862656	-0.06455675512552261	0.0047840094193816185	0.027140216901898384	0.036989904940128326	-0.011492900550365448	0.029831882566213608	0.013176333159208298	-0.023391729220747948	-0.04546840488910675	-0.02529609017074108	-0.06399741023778915	-0.01698196679353714	0.014235935173928738	-0.029578644782304764	0.014350478537380695	0.03367699310183525	0.0465327650308609	-0.04408595710992813	-0.004001750610768795	-0.023683782666921616	0.007294675800949335	-0.05261445790529251	0.06123807653784752	-0.006359640508890152	-0.033529944717884064	-0.06871923059225082	0.003916929941624403	0.023913584649562836	0.04777838662266731	-0.01034494861960411	0.017346646636724472	0.07045184075832367	0.05316831171512604	-0.007911919616162777	-0.034373894333839417	-0.059517789632081985	-0.07765465974807739	0.010134605690836906	0.04970380663871765	-0.062131330370903015	-0.00494323531165719	-0.05410332977771759	-0.08766623586416245	-0.02060435526072979	0.05639718845486641	0.04383797571063042	-0.05592483654618263	-0.018221460282802582	0.02054367959499359	-0.023611491546034813	-0.018659168854355812	-0.05702928453683853	-0.006810580380260944	0.010555179789662361	0.058521486818790436	-0.008655527606606483	-0.020848188549280167	0.05018167942762375	0.004670125897973776	0.05873076245188713	-0.04054810851812363	0.037384264171123505	-0.00021283238311298192	-0.09301616251468658	0.015848703682422638	-0.10120977461338043	-0.038297925144433975	-0.023313753306865692	-0.036280229687690735	-0.06715502589941025	0.06743448227643967	0.052427738904953	-0.045239418745040894	-0.04605869948863983	-0.05907715857028961	-0.010394353419542313	-0.010951578617095947	-0.014224814251065254	0.014398797415196896	-0.042188361287117004	-0.004924866836518049	0.026676135137677193	-0.0864916741847992	-0.04405954107642174	0.03917624428868294	-0.04180727154016495	-0.030432257801294327	0.08436134457588196	-0.0005305918748490512	0.00782622117549181	-0.00604240782558918	-0.07950612902641296	0.04298848286271095	0.06764008104801178	-0.0005193361430428922	0.043059106916189194	0.04578990861773491	-0.009184237569570541	0.06739654392004013	0.03095981851220131	0.003090565325692296	-0.013049291446805	-0.03175829350948334	-0.04650908708572388	0.0367516428232193	-0.05383710190653801	0.024864202365279198	-0.007335677742958069	0.02973228506743908	0.07332313805818558	0.015858255326747894	-0.03972470387816429	0.024859625846147537	0.01626189798116684	0.012377161532640457	0.01812013052403927	-0.013191320933401585	-0.01285969465970993	-0.01592324487864971	-0.0369209460914135	-0.01835356466472149	0.02179579995572567	0.04643196240067482	-0.06773413717746735	0.0528610423207283	-0.031003067269921303	-0.07077272981405258	0.022394569590687752	-0.019971905276179314	0.14161433279514313	-0.07581201195716858	-0.00031759653938934207	0.04713498055934906	0.048546284437179565	-0.077044777572155	0.006689230911433697	0.011476906947791576	-0.019459156319499016	-0.013243701308965683	-0.002936854027211666	0.016958113759756088	-0.05698183551430702	-0.02499302290380001	-0.001082250731997192	0.012340199202299118	0.013504364527761936	0.03501968830823898	-0.0038005379028618336	0.019960708916187286	-0.0030120769515633583	-0.008183040656149387	-0.03778688237071037	0.05533991754055023	0.002447884762659669	-0.04601491615176201	0.03194992616772652	0.007145560346543789	0.02974153310060501	-0.0016366560012102127	-0.06091558560729027	-0.006702770479023457	0.06250764429569244	-0.04046665132045746	0.0029685867484658957	-0.0080803781747818	-0.035048168152570724	0.031173450872302055	0.0651368722319603	-0.021067211404442787	-0.013339703902602196	-0.032745346426963806	-0.026373501867055893	-0.0733332633972168	-0.13148358464241028	-0.06388411670923233	0.052777212113142014	-0.04077419638633728	0.009350457228720188	-0.0600571446120739	0.04025847837328911	-0.03314005583524704	-0.017354687675833702	0.01696523278951645	0.054486677050590515	0.03125554695725441	-0.016741937026381493	0.0056515466421842575	-0.012481230311095715	0.057073481380939484	-0.02400120161473751	-0.05976850539445877	0.05652150884270668	0.02793825976550579	-0.028163950890302658	0.03535846248269081	-0.062189195305109024	-0.016736378893256187	0.010219598188996315	-0.023629914969205856	-0.02980339713394642	0.021930471062660217	-0.04035060852766037	-0.04018901661038399	0.055174194276332855	-0.06732486188411713	-0.006517106667160988	0.03363877907395363	-0.028257373720407486	-0.09835315495729446	-0.024326590821146965	-0.014174464158713818	0.008536174893379211	0.032701052725315094	-0.008160566911101341	0.04362236708402634	0.02506144531071186	-0.018978888168931007	0.01102403737604618	0.004784611985087395	-0.015917988494038582	0.020262712612748146	0.022496577352285385	0.04364839196205139	-0.03251789137721062	-0.0058031571097671986	-0.0169824305921793	-0.00973226223140955	0.02389913983643055	-0.09783296287059784	-0.009882650338113308	-0.025873428210616112	0.026897069066762924	0.003699294989928603	-0.015929406508803368	0.01301936898380518	-0.0857163742184639	-0.023025918751955032	0.011576012708246708	0.06170611083507538	0.008850925602018833	0.016012251377105713	0.048642903566360474	-0.0561576783657074	-0.0513247475028038	0.02507321909070015	-0.01706857979297638	-0.08717652410268784	0.018612392246723175	-0.046038489788770676	0.04005996882915497	0.022355295717716217	0.06776346266269684	-0.03308863937854767	-0.11169840395450592	-0.014914660714566708	0.008936881087720394	-0.03870478272438049	0.0031324673909693956	0.04963778704404831	-0.06704874336719513	-0.007783239707350731	-0.0006866306648589671	-0.013449996709823608	0.04007699713110924	0.02454949915409088	0.06028193235397339	-0.045272983610630035	-0.0015836969250813127	0.014683734625577927	-0.03747693821787834	0.0522959940135479	0.020193764939904213	-0.07899992913007736	-0.0441654808819294	0.08084211498498917	-0.07030462473630905	0.05891532823443413	0.0647817850112915	-0.06649842113256454	0.0939318835735321	-0.00045168231008574367	-0.02880268357694149	0.04592813923954964	-0.022718170657753944	-0.06407831609249115	-0.016372503712773323	-0.07006655633449554	0.0536283403635025	-0.0385344997048378	0.10899770259857178	0.003977584652602673	-0.042069509625434875	0.07493037730455399	0.09079014509916306	0.01385105587542057	-0.0126634007319808	-0.047290414571762085
+0.034731507301330566	-0.017001312226057053	0.02360996976494789	0.04639144241809845	-0.04833762347698212	-0.018105382099747658	-0.016323326155543327	0.003439826425164938	0.038512565195560455	-0.04796505719423294	-0.02864459902048111	-0.005543762352317572	-0.005990389734506607	0.0016251326305791736	0.023645449429750443	-0.02790752612054348	-0.052650365978479385	0.03583678975701332	-0.0345941036939621	-0.03399508073925972	0.04730556905269623	0.034495167434215546	-0.043281156569719315	0.04103875532746315	-0.023443682119250298	0.053573597222566605	-0.028699439018964767	0.008967028930783272	-0.02666684426367283	0.013617191463708878	0.06380588561296463	0.02000872604548931	0.05763428285717964	-0.049293190240859985	-0.025317315012216568	0.0587485134601593	-0.020584899932146072	0.10711383819580078	-0.0065357983112335205	-0.00791227724403143	-0.020358024165034294	-0.04609013348817825	-0.06024406477808952	-0.06045224890112877	0.0168691985309124	-0.02396990917623043	0.019151883199810982	0.004627732094377279	0.0003538895398378372	0.02597859874367714	0.08760105073451996	-0.008944539353251457	-0.028673354536294937	-0.009694957174360752	0.021359175443649292	0.0177978053689003	0.06204564869403839	-0.0647469013929367	-0.023022696375846863	0.028400808572769165	-0.07494905591011047	-0.012368333525955677	0.0221131332218647	0.007502662017941475	-0.030001120641827583	-0.026939818635582924	-0.0038206574972718954	0.08417357504367828	0.002409809036180377	-0.039513345807790756	0.03675062209367752	0.0801849439740181	-0.07467123866081238	-0.048557575792074203	0.06255245953798294	-0.05726722255349159	-0.02765050157904625	0.05471442639827728	-0.025056753307580948	-0.036400631070137024	0.01727227494120598	-0.04006253182888031	0.12462186068296432	-0.06495943665504456	-0.0791999101638794	-0.03256983682513237	-0.0009074800764210522	-0.07432443648576736	0.0596267394721508	0.009651395492255688	-0.0008244646014645696	0.0821409597992897	-0.0485556498169899	0.06293555349111557	0.03374709561467171	0.028165102005004883	0.005046128761023283	-0.103326216340065	-0.06938179582357407	-0.01188032329082489	0.016815105453133583	-0.0201733261346817	0.0371522456407547	0.0015561458421871066	0.050375133752822876	0.0320771150290966	-0.02337717078626156	-0.021192606538534164	1.445008547307225e-05	0.0572713240981102	0.02120427042245865	0.07869478315114975	0.0590999573469162	0.048668261617422104	0.06316127628087997	-0.008264518342912197	0.013953544199466705	-0.028370501473546028	-0.02430587448179722	0.018771694973111153	0.0024711426813155413	0.028616327792406082	-0.01925789564847946	-0.08049178868532181	-0.06278686225414276	0.024298449978232384	-0.08988671004772186	-0.04396327584981918	0.056098826229572296	-0.053443294018507004	0.1360216587781906	0.04307122901082039	-0.04778895154595375	-0.0031968967523425817	0.03380001708865166	0.03608167916536331	-0.023762637749314308	0.006143350154161453	-0.007063453085720539	-0.034469448029994965	0.0052695926278829575	0.05064374953508377	-0.015018068253993988	-0.04945912957191467	0.04559578001499176	0.028927583247423172	0.055484648793935776	-0.0312493983656168	-0.07635770738124847	0.0009694852633401752	-0.06365185976028442	-0.05753035470843315	-0.011866415850818157	-0.08529231697320938	0.005266840104013681	0.036374300718307495	-0.01983962394297123	0.032707907259464264	-0.006029532290995121	-0.02481822669506073	-0.08010976016521454	-0.021456899121403694	-0.026475487276911736	-0.05106138065457344	-0.05908948555588722	-0.03238658234477043	-0.004645051434636116	0.008893999271094799	-0.04186871275305748	-0.03237740695476532	-0.012866707518696785	-0.008414746262133121	0.036296192556619644	0.08815539628267288	0.08903326094150543	0.03622326999902725	-0.029167812317609787	-0.035394225269556046	-0.0019702406134456396	-0.027439115568995476	0.009869950823485851	-0.009243274107575417	0.019025590270757675	-0.042851049453020096	0.006858444306999445	-0.006819105241447687	0.05166352912783623	0.011835074052214622	0.04417537897825241	0.0055377609096467495	-0.00843384861946106	-0.03440584987401962	0.04890957474708557	0.0863543450832367	-0.0316614955663681	0.013950526714324951	-0.031105829402804375	0.06784207373857498	-0.02523701824247837	-0.057151418179273605	-0.03834236413240433	0.012521454133093357	0.028523053973913193	0.07779021561145782	0.01357226725667715	-0.08290627598762512	0.10115807503461838	0.034179918467998505	0.03428613767027855	-0.08323497325181961	0.02009812369942665	0.004261398687958717	0.0006324528367258608	0.07261060923337936	0.06643256545066833	-0.049529533833265305	-0.00685459841042757	0.0024114325642585754	0.03430463373661041	-0.02842879481613636	-0.009208916686475277	-0.008374427445232868	-0.08869988471269608	0.03292009234428406	-0.047424715012311935	-0.06717909127473831	-0.04343010112643242	0.020065713673830032	-0.019774027168750763	-0.025942137464880943	-0.007702173199504614	0.02592514641582966	-0.008772125467658043	-0.029024317860603333	-0.007723623886704445	-0.003948550671339035	-0.10410791635513306	0.1074371263384819	0.002040326129645109	-0.06411014497280121	-0.08999527990818024	0.021603133529424667	0.04697620868682861	0.00044177647214382887	0.022941607981920242	0.043928228318691254	0.11039387434720993	-0.003810120513662696	0.0003856539260596037	0.018604611977934837	-0.05480900779366493	-0.07556793093681335	0.011032991111278534	0.02210896648466587	-0.05566688999533653	-0.03749465569853783	-0.007356259971857071	-0.049965761601924896	-0.022328602150082588	0.024998510256409645	0.04243626818060875	-0.012747582979500294	-0.01284860260784626	-0.022894715890288353	-0.0018629690166562796	-0.047850098460912704	-0.005762121174484491	0.03693242743611336	-0.012408195994794369	0.03047523833811283	0.01058591902256012	-0.0157041996717453	0.038212183862924576	-0.020936070010066032	0.048128459602594376	-0.024620359763503075	0.06309857219457626	-0.022375255823135376	-0.06831583380699158	-0.010069360956549644	-0.06929827481508255	-0.04981256276369095	-0.03216538205742836	-0.03492071479558945	-0.051143914461135864	0.09445297718048096	0.0019578540232032537	-0.060826752334833145	-0.04968041926622391	-0.014115802943706512	-0.01969403401017189	-0.049290768802165985	-0.01034417375922203	-0.014171049930155277	-0.06671173125505447	-0.03621191158890724	0.0496356338262558	-0.06394295394420624	0.002872226294130087	-0.023798491805791855	-0.02892257086932659	-0.0644189864397049	0.03126299008727074	0.005815587472170591	-0.04280529543757439	-0.0026118021924048662	-0.08146271854639053	0.048577163368463516	0.08988893777132034	0.06309688091278076	0.04582492634654045	0.03802572935819626	0.04872928932309151	0.1151515394449234	-0.0121775446459651	0.004351796582341194	-0.0050506205298006535	-0.016931774094700813	-0.028157873079180717	0.0327027253806591	-0.012606978416442871	-0.027438180521130562	-0.012623692862689495	0.015262143686413765	0.06880763918161392	-0.006633899174630642	-0.0031048827804625034	-0.0024419387336820364	0.04413996636867523	0.007942656986415386	0.018657639622688293	-0.0029738517478108406	0.028834901750087738	-0.04038059711456299	-0.07186093926429749	-0.028459394350647926	0.0004952212912030518	0.016433756798505783	-0.012378516606986523	0.03325166180729866	-0.046934694051742554	-0.09036976844072342	-0.007564791012555361	-0.005793147720396519	0.1139829084277153	-0.05873831734061241	0.0010604384588077664	0.082126684486866	-0.013465388678014278	-0.10454895347356796	-0.003352914936840534	0.04566425457596779	-0.06010466068983078	-0.05054860934615135	-0.01287766546010971	0.0466303825378418	-0.025817716494202614	-0.029826847836375237	0.00807204656302929	-0.022947438061237335	-0.009666625410318375	0.026934145018458366	0.03619696572422981	0.01621525175869465	-0.009290206246078014	-0.007720388006418943	-0.028469128534197807	0.03237028792500496	0.040988922119140625	-0.031379394233226776	0.00350451678968966	-0.02914133109152317	0.014469731599092484	-0.051657434552907944	-0.04483369365334511	-0.03699411451816559	0.07176591455936432	0.006028267554938793	-0.021949540823698044	-0.02199995145201683	-0.041658464819192886	-0.010912454687058926	0.04622526839375496	0.005739707034081221	-0.041852936148643494	-0.04060744494199753	-0.05120793730020523	-0.06230024993419647	-0.10181871801614761	-0.0566672645509243	-0.00607577059417963	-0.05191407725214958	0.015534795820713043	-0.032706595957279205	-0.014635024592280388	-0.011029626242816448	-0.020366525277495384	0.05757373198866844	0.05516214296221733	0.026440301910042763	-0.037250541150569916	0.029735757037997246	-0.006389921065419912	0.06887942552566528	0.04638873413205147	-0.04389572516083717	-0.0038162311539053917	0.014046364463865757	-0.0165039524435997	0.07020751386880875	-0.030927296727895737	-0.004454440902918577	0.0198553204536438	-0.009428833611309528	0.01879599317908287	0.0031928049866110086	0.013656163588166237	-0.06189242750406265	0.05287962034344673	-0.09622542560100555	-0.027182722464203835	0.023327946662902832	-0.016760317608714104	-0.07840459793806076	-0.07009153813123703	-0.025022083893418312	-0.029239727184176445	0.028957655653357506	-0.04025111719965935	0.005163534078747034	0.010516522452235222	-0.04200522601604462	-0.057577237486839294	-0.036942142993211746	0.0008134352974593639	0.05030961334705353	-0.009604614228010178	0.05339984595775604	0.02380073070526123	0.0028340427670627832	-0.015249837189912796	-0.0009445114410482347	-0.0005145614850334823	-0.0931578278541565	-0.0062210639007389545	-0.03834923729300499	-0.031473904848098755	0.011231270618736744	-0.025010284036397934	0.03437773138284683	-0.06703448295593262	-0.02150403894484043	-0.06338149309158325	0.09659615904092789	-0.02915887162089348	0.018327273428440094	-0.0024242918007075787	-0.035854436457157135	-0.0016066216630861163	0.04382152855396271	-0.047412898391485214	-0.05287567526102066	-0.00016702906577847898	0.0010023342911154032	0.02759845182299614	0.009756451472640038	-0.0013639421667903662	-0.03005046769976616	-0.04323076829314232	-0.004709629341959953	0.02076438069343567	-0.08810233324766159	0.007789638824760914	0.053282562643289566	-0.07588929682970047	-0.04402589797973633	0.025874771177768707	-0.017521295696496964	0.04853810742497444	0.00810533668845892	0.07538484781980515	-0.027119852602481842	-0.021151848137378693	-0.0018161663319915533	-0.03712636977434158	0.015048002824187279	0.014274210669100285	-0.05509648472070694	-0.03137265145778656	0.0564127080142498	-0.0462777353823185	0.015715455636382103	0.0785205066204071	-0.05430971086025238	0.03135758265852928	0.04061100259423256	-0.044902700930833817	0.042634572833776474	-0.0033946516923606396	-0.029766082763671875	0.06611474603414536	-0.08934814482927322	0.11197620630264282	-0.07970412075519562	0.09630643576383591	-0.0034332559444010258	-0.016768477857112885	0.060222361236810684	0.07574547082185745	-5.92958640481811e-05	0.009056388400495052	-0.04466184228658676
+0.05859271436929703	0.028600571677088737	0.014369233511388302	0.031596314162015915	-0.10093862563371658	-0.023817172273993492	-0.009915524162352085	0.027630141004920006	-0.008674618788063526	-0.028993431478738785	-0.02718113176524639	-0.010034690611064434	0.017805466428399086	0.028641413897275925	0.03225077688694	-0.029539745301008224	-0.04692989960312843	-0.012405863031744957	0.012324792332947254	-0.05901280418038368	0.023163190111517906	-0.011070666834712029	-0.050009261816740036	0.08394969254732132	0.058925364166498184	0.004427891690284014	-0.057690102607011795	0.0038622950669378042	-0.0029355022124946117	0.031861562281847	0.04762033000588417	-0.04035792127251625	0.010467407293617725	-0.034647684544324875	-0.037754859775304794	0.03795353323221207	-0.04436802864074707	0.09946085512638092	0.01133477222174406	-0.05198875442147255	-0.025852466002106667	0.0048073334619402885	-0.03359433263540268	-0.007621574681252241	0.0008267498342320323	0.012597046792507172	-0.026361025869846344	-0.008300496265292168	0.03425673395395279	0.012191355228424072	0.08222528547048569	-0.010794779285788536	-0.02690780907869339	-0.046957872807979584	-0.006440463941544294	0.009808911941945553	0.039192840456962585	-0.0702638328075409	-0.06164679676294327	0.07995879650115967	-0.07396095991134644	8.967093890532851e-05	0.04733801260590553	0.018709499388933182	0.0002059163380181417	-0.04946119710803032	-0.003946020733565092	0.08784540742635727	0.022020237520337105	-0.008404780179262161	0.04881845787167549	0.10035821050405502	-0.050852712243795395	-0.030699988827109337	0.0571785494685173	-0.057802651077508926	-0.035104963928461075	0.05943530425429344	0.043633121997117996	0.028129253536462784	0.022880081087350845	-0.06915207952260971	0.05349258333444595	0.003961417358368635	-0.05703474581241608	-0.03553123399615288	0.0030321239028126	-0.03890427574515343	0.08052792400121689	0.020241394639015198	0.012098599225282669	0.08113540709018707	-0.09280935674905777	0.05916110798716545	0.022312218323349953	-0.03651157021522522	0.011816111393272877	-0.11100243777036667	-0.04759659618139267	0.01357690617442131	0.016354262828826904	-0.014586606994271278	0.017586378380656242	0.012381237000226974	0.039310675114393234	0.05825694650411606	-0.04873456805944443	0.03999095410108566	0.04556341469287872	0.04998265579342842	0.025784259662032127	0.04272627457976341	0.09182470291852951	0.06245375797152519	0.0331820547580719	-0.014848046004772186	-0.006461319513618946	-0.04053983837366104	-0.0640311911702156	0.002695386530831456	0.025585398077964783	0.06624838709831238	-0.005916376132518053	-0.09254531562328339	-0.029209615662693977	0.056193944066762924	-0.09490809589624405	0.012999257072806358	0.07681526243686676	0.0284337867051363	0.0795198604464531	0.03765653446316719	-0.008564219810068607	0.0404699333012104	0.05455643683671951	0.043894246220588684	7.274618837982416e-05	0.048497311770915985	-0.01839420571923256	-0.004936975426971912	-0.020211687311530113	0.013329206965863705	0.06238105520606041	-0.1146647036075592	0.019901961088180542	0.035369861871004105	0.03671873360872269	-0.0011905854335054755	-0.035139523446559906	-0.050914205610752106	-0.05961357802152634	-0.0751827284693718	0.028665844351053238	-0.06259939819574356	-0.021229561418294907	0.026687387377023697	0.011971483007073402	0.015517453663051128	-0.041665833443403244	-0.00770921166986227	-0.057055335491895676	-0.016431117430329323	-0.0025715017691254616	-0.06152156740427017	-0.012769609689712524	-0.039301130920648575	-0.001398734631948173	0.046173568814992905	-0.07470019161701202	-0.009724644012749195	0.012513828463852406	-0.013559752143919468	0.04473598673939705	0.09079667925834656	0.05219114571809769	-0.0028351745568215847	0.052273478358983994	-0.021600985899567604	0.010626699775457382	-0.020077401772141457	-0.0076736463233828545	-0.012209372594952583	0.05050353705883026	-0.06201814115047455	-0.02020321972668171	-0.026903728023171425	0.07211881130933762	-0.024769630283117294	-0.018012965098023415	-0.028859492391347885	-0.03100815787911415	-0.053070180118083954	0.07063799351453781	0.036667175590991974	-0.026554973796010017	0.02389536425471306	-0.02573375776410103	0.02226889319717884	0.012467137537896633	-0.007490145042538643	-0.02969503588974476	0.04537079855799675	0.028053412213921547	0.06638214737176895	0.02427978254854679	-0.0695987120270729	0.06787123531103134	0.02568044140934944	0.02497326210141182	-0.07555632293224335	-0.031296662986278534	0.02291964553296566	-0.02419479377567768	0.0587814562022686	0.09235375374555588	-0.022848796099424362	0.032027628272771835	-0.0027183492202311754	0.07440657913684845	-0.04110366478562355	-0.00509840901941061	0.005287669133394957	-0.07244472950696945	0.01672288030385971	-0.029404275119304657	-0.060768112540245056	-0.025455068796873093	0.03763018548488617	-0.027593271806836128	-0.03634900972247124	-0.00815652683377266	0.022284654900431633	-0.0025504999794065952	-0.025876875966787338	-0.003655626904219389	-0.008729510009288788	-0.08951283246278763	0.09735502302646637	0.003665237221866846	-0.06495210528373718	-0.07725443691015244	-0.012963800691068172	0.030803503468632698	-0.004953448195010424	0.0767824649810791	0.07146622985601425	0.09717365354299545	0.03879088535904884	0.02527070976793766	0.006582962349057198	-0.027029190212488174	-0.04693378508090973	0.0004393246490508318	0.059023793786764145	-0.0823633000254631	-0.020210355520248413	0.014824377372860909	-0.025643689557909966	-0.026337502524256706	0.00954512506723404	0.0075491019524633884	-0.03428598865866661	-0.03839243948459625	-0.057171307504177094	0.03791643679141998	-0.04124728962779045	-0.03137990087270737	0.03141925856471062	0.0010431178379803896	0.09971324354410172	-0.0031337356194853783	-0.06256339699029922	0.037787534296512604	0.03491879999637604	-0.02353074587881565	-0.02966470457613468	0.04190902039408684	-0.027878910303115845	-0.06607895344495773	-0.03074151836335659	-0.05886272341012955	-0.04973837360739708	-0.022256210446357727	-0.03275644779205322	-0.02990046516060829	0.0873795747756958	-0.03179234638810158	-0.06301762163639069	-0.0473884642124176	-0.0054668826051056385	-0.001677026622928679	-0.06789335608482361	-0.01641935482621193	0.007417941931635141	-0.035333774983882904	-0.027828363701701164	0.0241067074239254	-0.030741754919290543	-0.04830621927976608	0.006317596882581711	-0.014891529455780983	-0.08424010872840881	0.04292678460478783	-0.03364291414618492	-0.01950003392994404	0.010396615602076054	-0.0637354925274849	0.02367909997701645	0.10170967876911163	0.03823832422494888	0.00761005375534296	0.013883971609175205	0.03334687277674675	0.1348850429058075	0.033594850450754166	-0.07986406981945038	-0.0017641788581386209	-0.07065804302692413	-0.06918637454509735	0.021117910742759705	-0.04486621916294098	-0.01911066100001335	-0.029523629695177078	-0.019218917936086655	0.05547681078314781	0.038227055221796036	-0.02331329882144928	0.0009104833588935435	0.03628529980778694	-0.015519711188971996	0.04452912509441376	-0.0008271905826404691	-0.049027714878320694	-0.019366050139069557	-0.009229700081050396	-0.05868489667773247	-0.0221855491399765	0.06165085360407829	-0.029157713055610657	0.07935631275177002	-0.029057059437036514	-0.0548955462872982	0.027070017531514168	-0.0055819968692958355	0.07781033217906952	-0.06906421482563019	0.004212404601275921	0.03235529735684395	0.0004933880991302431	-0.056093864142894745	0.009490112774074078	0.029999662190675735	-0.052952155470848083	-0.017613183706998825	-0.028035925701260567	0.03217405080795288	-0.056051261723041534	0.0009030636283569038	0.01906641758978367	-0.08075955510139465	0.01569058746099472	0.006762997712939978	0.00454253563657403	0.011398842558264732	-0.05435171350836754	-0.00898693036288023	-0.006588974967598915	-0.002342558465898037	0.0025261996779590845	-0.0025835330598056316	0.01049965713173151	-0.020575614646077156	0.06870433688163757	-0.0366479866206646	0.012022479437291622	-0.05668879672884941	0.05898174270987511	-0.01577802561223507	0.009189012460410595	0.009478416293859482	0.014053905382752419	0.0079171322286129	-0.0011754563311114907	0.03959725797176361	-0.05119600147008896	-0.025679592043161392	-0.052658163011074066	-0.009224742650985718	-0.060279328376054764	-0.048952605575323105	0.022366352379322052	-0.07423339784145355	-0.0011276090517640114	-0.01742604561150074	-0.014410650357604027	-0.037807587534189224	-0.044106099754571915	0.00700463866814971	0.026574384421110153	0.05900122597813606	-0.027965031564235687	0.05883226916193962	0.03460332378745079	0.03736981749534607	0.010788786225020885	-0.06188610568642616	0.0768219605088234	0.045892804861068726	-0.01872982457280159	0.06693746149539948	-0.024090435355901718	0.055075909942388535	-0.017991861328482628	0.01721489615738392	0.0342787504196167	-0.06347013264894485	-0.026478828862309456	-0.0320393368601799	0.07092917710542679	-0.057494256645441055	-0.04693333059549332	0.01991303451359272	-0.06456481665372849	-0.0841783881187439	0.008742638863623142	0.0010622491827234626	-0.022036677226424217	0.012835448607802391	0.0016169293085113168	0.02388092689216137	0.07004713267087936	-0.0475163608789444	-0.03884848952293396	-0.021403271704912186	-0.012322597205638885	0.06062517315149307	-0.01466179545968771	0.037159960716962814	0.006638271268457174	0.012442459352314472	0.0014227128122001886	-0.020665382966399193	0.029033392667770386	-0.09493318200111389	-0.007572470232844353	-0.014176545664668083	-0.06192196533083916	0.03117743507027626	0.01753973588347435	0.00622630724683404	-0.05194338783621788	-0.049231141805648804	-0.03391677886247635	0.07038243860006332	-0.026750274002552032	-0.005637293681502342	0.016438443213701248	-9.377491369377822e-05	-0.0071424334309995174	0.007082663476467133	-0.04235038906335831	-0.08136023581027985	0.001206675311550498	0.004132661037147045	0.023965224623680115	-0.008829163387417793	0.019100356847047806	-0.037621255964040756	-0.0816979706287384	-0.01118718646466732	0.03241661563515663	-0.09062239527702332	0.032382551580667496	-0.019276970997452736	-0.07815282791852951	-0.058806464076042175	0.014417017810046673	-0.022593652829527855	0.03051116317510605	0.021297788247466087	0.023644106462597847	-0.012782738544046879	0.003768947906792164	0.008159392513334751	-0.07528453320264816	0.00705379992723465	-0.023650050163269043	-0.03954426571726799	-0.023217329755425453	0.06584551185369492	-0.07448743283748627	0.001283446210436523	0.030063306912779808	-0.05385414883494377	0.006813849788159132	0.02675575390458107	-0.06296250969171524	0.06462327390909195	-0.024726198986172676	-0.03314615413546562	0.08098819106817245	-0.06961189955472946	0.10228085517883301	-0.051303401589393616	0.07224158942699432	-0.03075852058827877	-0.03403152897953987	0.09799809008836746	0.05502475053071976	-0.011177887208759785	0.012307942844927311	-0.09285753220319748
+0.07100369781255722	-0.03133566305041313	-0.03758911043405533	0.02816830202937126	-0.0575435571372509	-0.039236851036548615	-0.013114947825670242	0.035126011818647385	-0.042863260954618454	-0.04712510108947754	-0.03153012692928314	-0.052637044340372086	-0.020632153376936913	-0.03893556445837021	0.05217321217060089	-0.07228536158800125	-0.07472659647464752	0.049377623945474625	-0.02975498139858246	-0.01763521134853363	-0.0027585437055677176	0.0455218069255352	-0.06108660250902176	0.021931763738393784	-0.017740435898303986	0.003831904148682952	-0.01920662447810173	-0.006425101775676012	-0.010798758827149868	0.04480357840657234	0.09309381246566772	0.007172827143222094	0.03681836277246475	-0.0019779056310653687	-0.03486011177301407	0.026295756921172142	-0.02284252643585205	0.11833566427230835	0.0036584928166121244	-0.043180789798498154	-0.040332112461328506	-0.0037803289014846087	-0.01668822020292282	-0.06950892508029938	0.0426396019756794	-0.009544018656015396	-0.03191026672720909	-0.037425171583890915	-0.0014581892173737288	0.009780914522707462	0.08726741373538971	-0.002602496650069952	-0.003776857163757086	-0.01065740641206503	-0.03144189342856407	-0.004657207988202572	0.020416390150785446	-0.027360837906599045	-0.019129445776343346	0.056185778230428696	-0.08548404276371002	-0.001902218908071518	0.046276479959487915	0.002712805289775133	0.001177120371721685	-0.06744685024023056	-0.0042982446029782295	0.06914116442203522	-0.009499408304691315	0.003818740602582693	-0.010475145652890205	0.08067835867404938	-0.0687515065073967	-0.07433313876390457	0.08918943256139755	-0.10394870489835739	-0.03263983502984047	0.010573181323707104	0.018717879429459572	-0.031347859650850296	0.022861385717988014	0.009017243050038815	0.12371866405010223	-0.055939991027116776	-0.06215200200676918	-0.024997932836413383	-0.012103463523089886	-0.05730956047773361	0.06747022271156311	0.013275371864438057	0.02889363467693329	0.044290877878665924	-0.037646207958459854	0.01333512831479311	-0.004244923125952482	-0.00847508292645216	0.05285962298512459	-0.07031173259019852	-0.05914866179227829	-0.023721257224678993	0.02390352450311184	-0.0018786322325468063	0.048394642770290375	0.01907220296561718	0.028193900361657143	0.010599073022603989	-0.03308592364192009	-0.0036806962452828884	0.037484947592020035	0.0470738485455513	0.02426624484360218	0.10709375888109207	0.058507051318883896	0.012500902637839317	0.08901370316743851	0.030632788315415382	0.004890818148851395	-0.04784473776817322	-0.012951580807566643	0.05426013842225075	-0.0316956453025341	0.048199862241744995	-0.022984614595770836	-0.07217565178871155	-0.03866303712129593	0.056957319378852844	-0.07263607531785965	-0.04345471411943436	0.05295960232615471	-0.012699741870164871	0.11133316159248352	0.019051510840654373	-0.014663963578641415	0.045860230922698975	0.052476558834314346	0.01589050330221653	-0.0110122449696064	0.0265598576515913	-0.014765709638595581	-0.031233636662364006	-0.016567444428801537	0.07226955890655518	-0.015692221000790596	-0.009840209037065506	0.047399986535310745	0.024199794977903366	0.03584571182727814	-0.020688220858573914	-0.037087395787239075	0.03203097730875015	-0.07134011387825012	-0.04834191873669624	0.00838848203420639	-0.07306331396102905	0.014102868735790253	0.009569243527948856	-0.026546979323029518	-0.015441114082932472	-0.003346503945067525	-0.04608461260795593	-0.07388342171907425	-0.044294655323028564	-0.015229444019496441	-0.07506539672613144	-0.04814726114273071	-0.0048327320255339146	-0.04819261655211449	0.030549408867955208	-0.058645687997341156	-0.02169181779026985	0.021846532821655273	-0.01649148389697075	0.03661626577377319	0.12158604711294174	0.04638252779841423	0.05834539607167244	0.009417134337127209	-0.022665472701191902	-0.009629722684621811	-0.03441695496439934	0.01982431858778	-0.02785869874060154	0.06716657429933548	-0.05851103737950325	0.05346570163965225	0.0100589320063591	0.07981422543525696	-0.008194412104785442	0.06285222619771957	0.005351942963898182	0.0015581371262669563	-0.06981112062931061	0.0568501353263855	0.05886964872479439	0.010291943326592445	0.016714558005332947	-0.02203199453651905	0.07126636058092117	0.004635970573872328	-0.0444607175886631	-0.04700242727994919	0.0014281414914876223	0.03112667426466942	0.05232382193207741	0.037982724606990814	-0.06248445063829422	0.11145710200071335	0.037573520094156265	0.04401993378996849	-0.037879083305597305	0.00048133585369214416	0.020005837082862854	-0.011200686916708946	0.06236335635185242	0.0670749843120575	-0.0006968158995732665	0.033867839723825455	0.005056330002844334	0.06864967942237854	-0.030200012028217316	-0.009142911061644554	0.014679565094411373	-0.11569180339574814	0.05165212228894234	-0.008551946841180325	-0.09511721134185791	-0.014620442874729633	0.035380326211452484	-0.020936675369739532	-0.025312136858701706	0.019468093290925026	0.04565217345952988	0.015013732016086578	-0.01950725167989731	-0.007247413974255323	-0.03243722766637802	-0.10595755279064178	0.1051245778799057	0.008833453059196472	-0.11904162913560867	-0.05585407093167305	-0.022593040019273758	0.011265798471868038	-0.013097614981234074	0.047161765396595	0.04629248380661011	0.09926936775445938	-0.0030194439459592104	-0.007173897232860327	0.04201691970229149	-0.04161890968680382	-0.05485696718096733	0.00021055532852187753	0.006029103882610798	-0.0564582385122776	-0.08377790451049805	0.015977703034877777	-0.06220075860619545	0.014433080330491066	-0.0300979632884264	0.016307510435581207	-0.040970295667648315	-0.022031666710972786	-0.057633206248283386	0.04349358752369881	-0.06300552934408188	-0.012873626314103603	0.06050741299986839	-0.017104484140872955	-0.010488379746675491	0.0298189427703619	0.02051079086959362	0.03291809558868408	-0.01862686313688755	-0.0018731500022113323	-0.04010063782334328	0.07520608603954315	-0.018678992986679077	-0.03774426132440567	0.014530980959534645	-0.06534449756145477	-0.04518576338887215	0.002458153758198023	-0.05614350363612175	-0.032586727291345596	0.07673589885234833	0.03309188783168793	-0.05389765277504921	-0.048936016857624054	-0.03235365450382233	-0.05027655139565468	-0.043954476714134216	-0.03494279086589813	0.019902413710951805	-0.03760587051510811	-0.03216535970568657	0.04699881374835968	-0.03449513390660286	-0.008179799653589725	0.003087700344622135	0.035211533308029175	-0.05629274994134903	0.009686088189482689	-0.005543809849768877	-0.014886499382555485	0.018108675256371498	-0.05506764352321625	0.03485485166311264	0.07758484780788422	0.11563272029161453	0.03216243162751198	0.05725919082760811	0.03507902845740318	0.11870728433132172	0.020876284688711166	-0.04280892759561539	-0.017354009672999382	-0.07757435739040375	-0.026278382167220116	0.0179261676967144	0.011139629408717155	-0.05454343557357788	0.00403964938595891	0.00761224702000618	0.05416463315486908	-0.04264703020453453	-0.01258179359138012	-0.042834434658288956	0.02815648354589939	-0.03641644865274429	0.022612424567341805	0.0029007401317358017	0.03562207520008087	-0.03849927335977554	-0.06149415299296379	-0.01487842109054327	-0.0485076941549778	0.02674797549843788	-0.017938798293471336	0.023743467405438423	-0.020895149558782578	-0.05031895264983177	-0.02992207370698452	0.0010116735938936472	0.10153661668300629	-0.047479208558797836	-0.00755154388025403	0.034431930631399155	0.06224973127245903	-0.06971988826990128	-0.00985559169203043	-0.005104775074869394	-0.054731328040361404	-0.010455193929374218	-0.014300988055765629	0.043574009090662	-0.06199038401246071	-0.020014753565192223	-0.007330009713768959	-0.02669253759086132	-0.006387230474501848	-0.022133583202958107	-0.003315994283184409	0.041595134884119034	-0.014950809068977833	-0.03069857880473137	-0.00397034827619791	0.00743468850851059	0.0295769851654768	-0.020799333229660988	0.021132856607437134	-0.022672923281788826	-0.02885168418288231	-0.053610771894454956	-0.06579311192035675	-0.054926224052906036	0.05332763120532036	-0.012014014646410942	0.010804512538015842	-0.017238186672329903	-0.015140826813876629	-0.030569056048989296	0.009022695943713188	-0.00651508430019021	-0.04397260770201683	-0.019043894484639168	-0.023095598444342613	-0.0495375320315361	-0.05551416426897049	-0.025749046355485916	-0.009799501858651638	-0.014870071783661842	0.023093659430742264	-0.03741953894495964	0.011075511574745178	0.02591848373413086	-0.01937582716345787	0.07802912592887878	0.06596633046865463	0.015932902693748474	0.009623252786695957	0.030679170042276382	0.026680482551455498	0.06928566843271255	0.06491534411907196	-0.07448343932628632	-0.04061610996723175	0.013525083661079407	-0.05582708492875099	0.05004412680864334	0.01903941109776497	0.022746941074728966	-0.010187742300331593	0.0032807362731546164	0.01505931094288826	-0.05533531680703163	0.011626815423369408	-0.014810671098530293	0.024956537410616875	-0.05652610585093498	-0.06464115530252457	0.011225270107388496	-0.021566132083535194	-0.062111109495162964	-0.051514845341444016	-0.008697240613400936	-0.003508205758407712	0.04961971193552017	-0.06801284104585648	0.02049224078655243	0.012904544360935688	-0.03969820216298103	-0.07160106301307678	-0.064525306224823	0.007335867267102003	0.060185909271240234	-0.020544281229376793	0.025761589407920837	0.007689544931054115	0.014641845598816872	-0.027319375425577164	-0.04125497490167618	0.021814852952957153	-0.07321026176214218	0.009653741493821144	-0.0475214384496212	-0.03485993295907974	0.03965826332569122	0.009762060828506947	0.0015094539849087596	-0.029485126957297325	-0.023138994351029396	-0.061387114226818085	0.08884523808956146	-0.017031574621796608	0.045636072754859924	-0.02704286202788353	-0.05667472630739212	-0.00299450708553195	0.04488586261868477	-0.04215870425105095	-0.036287982016801834	0.03336525708436966	-0.010751300491392612	0.009932895191013813	0.01725132018327713	0.07323592156171799	-0.033500511199235916	-0.07702983915805817	0.03690257668495178	0.037164799869060516	-0.07527514547109604	-0.0020084441639482975	0.02688053995370865	-0.06871373951435089	-0.030864661559462547	-0.0003038022550754249	-0.06745516508817673	0.08112581074237823	0.007531036157160997	0.031922053545713425	-0.04394841566681862	0.014733645133674145	-0.004356267862021923	-0.0196713674813509	-0.02323519065976143	-0.04353950545191765	-0.04464549943804741	-0.011151407845318317	0.06970469653606415	-0.05263371020555496	0.006079551298171282	0.06479131430387497	-0.02956215664744377	0.02196073904633522	0.03453069180250168	-0.01133564580231905	0.05105287581682205	0.03365757688879967	0.002294248202815652	0.06818890571594238	-0.0801566019654274	0.07099489867687225	-0.06599073857069016	0.07334021478891373	0.0026695511769503355	-0.03857412934303284	0.04790540039539337	0.062099434435367584	-0.009483644738793373	0.040039658546447754	-0.06451531499624252
+0.020561151206493378	-0.006282460410147905	-0.011869220063090324	0.030236897990107536	-0.0905773863196373	-0.06239813566207886	0.00688365800306201	0.03631066903471947	0.002796574030071497	-0.031803302466869354	-0.06182866543531418	-0.012076406739652157	-0.007860618643462658	0.002343901200219989	0.05420505255460739	-0.01999322697520256	-0.06525146961212158	0.000820928777102381	-0.013519927859306335	-0.015086529776453972	0.04738001152873039	-0.03164006024599075	-0.08476652950048447	0.056535907089710236	0.04152587801218033	-0.005478444509208202	-0.0454898327589035	0.03441692516207695	-0.018916254863142967	0.04138679802417755	0.058143895119428635	-0.03893519565463066	0.039129938930273056	-0.06989502906799316	-0.07528591901063919	0.020562510937452316	-0.049974989145994186	0.10340836644172668	0.03764037415385246	-0.05813615024089813	-0.06040119379758835	0.03067384846508503	-0.03712962940335274	-0.05189698562026024	0.05689889192581177	0.03834499791264534	-0.0024374849162995815	-0.029456673189997673	0.04577131196856499	0.02860521897673607	0.10390081256628036	0.0072141895070672035	-0.025702528655529022	-0.02888704277575016	-0.029666181653738022	-0.009557855315506458	0.04696673899888992	-0.037086308002471924	-0.0811895802617073	0.04866563901305199	-0.11906354874372482	-0.008936773985624313	0.012124155648052692	0.011904810555279255	-0.043000634759664536	-0.023590316995978355	-0.015773877501487732	0.05711711198091507	-0.006244446150958538	-0.025993620976805687	0.007198624778538942	0.08264587074518204	-0.05478774756193161	-0.05361976847052574	0.0320204496383667	-0.032681725919246674	0.0012490215012803674	0.03308245167136192	-0.0027563900221139193	0.0434233583509922	-0.0008643152541480958	-0.0484396331012249	0.12889938056468964	-0.06323844194412231	-0.04037151858210564	-0.03796791285276413	-0.0395258367061615	-0.0451776385307312	0.04174307361245155	0.05931989476084709	-0.0030890286434441805	0.059365931898355484	-0.0323595255613327	0.0328737273812294	0.011111266911029816	0.02176942117512226	0.023860299959778786	-0.09722071886062622	-0.004753338173031807	-0.0004354815755505115	-0.017273152247071266	-0.02150905318558216	0.03799270838499069	0.016635403037071228	0.05477446690201759	0.03462003543972969	-0.051071930676698685	0.0433943010866642	0.0501302108168602	0.05364914983510971	0.01764725148677826	0.051014747470617294	0.08888379484415054	0.03072397969663143	0.061907172203063965	0.007221205625683069	-0.010085372254252434	-0.06680282950401306	-0.02689477428793907	0.015511680394411087	0.010990946553647518	0.03508848696947098	0.026145022362470627	-0.07140105962753296	0.02311028353869915	0.021685350686311722	-0.08374202996492386	-0.037712693214416504	0.011054816655814648	-0.04833783954381943	0.11265578866004944	0.06980286538600922	-0.019119860604405403	0.03134223818778992	0.06028600409626961	0.04162321239709854	0.047099485993385315	0.047008492052555084	-0.03324827924370766	-0.07136745750904083	-0.014350876212120056	0.019636519253253937	-0.018236080184578896	-0.048745688050985336	0.06988432258367538	0.0345301553606987	0.05591563880443573	-0.02001037448644638	-0.09026960283517838	-0.008462374098598957	-0.11704672873020172	-0.052166491746902466	0.017907872796058655	-0.061006445437669754	-0.015294894576072693	0.03406946733593941	0.021135682240128517	0.026466935873031616	-0.018737170845270157	0.01190462987869978	-0.007197433616966009	-0.03640135005116463	-0.00020990593475289643	-0.08501838892698288	-0.029475213959813118	-0.012688746675848961	-0.006850683130323887	0.016937194392085075	-0.05244063213467598	-0.03732255846261978	-0.0069406223483383656	0.004441831260919571	0.0665445625782013	0.04938242584466934	0.04361886903643608	-0.011083884164690971	0.03642037883400917	-0.0873214527964592	-0.008122161030769348	-0.04114219546318054	0.01594805158674717	-0.026585666462779045	0.03388631343841553	-0.05360012874007225	0.0015294714830815792	-0.03495655208826065	0.03850937634706497	-0.04290469363331795	0.029128599911928177	0.0028726723976433277	-0.06829309463500977	-0.057051096111536026	0.05239426717162132	0.06699671596288681	-0.018157334998250008	0.03941792622208595	0.017574241384863853	0.018442239612340927	-0.029878437519073486	-0.043432291597127914	-0.039374057203531265	0.031095139682292938	0.011270549148321152	0.04887804761528969	0.012749866582453251	-0.06498803943395615	0.07836562395095825	0.019349312409758568	0.004581334535032511	-0.032127007842063904	0.02877192199230194	0.07093246281147003	5.572732334258035e-05	-0.016102546826004982	0.09721735119819641	-0.02704300545156002	-0.0021389340981841087	0.0049523403868079185	0.08536894619464874	-0.06140074506402016	0.006313210818916559	0.011009412817656994	-0.08486805111169815	0.009838328696787357	-0.05824540555477142	-0.09034396708011627	0.004310857970267534	0.039733968675136566	-0.040654506534338	-0.03285668045282364	-0.010409834794700146	-0.006493863184005022	0.002059601480141282	0.009525797329843044	-0.014908830635249615	0.01340580265969038	-0.06817761808633804	0.08476027846336365	-0.033462025225162506	-0.061547040939331055	-0.04718465730547905	0.006687993183732033	0.017384186387062073	-0.0006693775067105889	0.03334487974643707	0.06259503960609436	0.0696718767285347	0.030911311507225037	-0.017839079722762108	0.006121132057160139	-0.047227852046489716	-0.04067542776465416	0.025672929361462593	0.037796057760715485	-0.04009971395134926	-0.0014863163232803345	-0.0009721520473249257	-0.04655735194683075	0.01193890068680048	0.009384894743561745	0.013336650095880032	-0.022164590656757355	-0.03468361496925354	-0.015147766098380089	0.06933993846178055	0.003429640317335725	-0.06256726384162903	0.043511345982551575	-0.013666762970387936	0.07628561556339264	-0.05159259960055351	-0.040058720856904984	0.012874298729002476	-0.004107981454581022	0.03179831802845001	-0.039435189217329025	0.06704234331846237	0.005694225430488586	-0.05947733297944069	-0.011430785059928894	-0.10108702629804611	-0.07212937623262405	0.009394997730851173	-0.032801784574985504	-0.01779376156628132	0.07653676718473434	0.042719271034002304	-0.024285683408379555	-0.08538350462913513	-0.04502928629517555	0.011966320686042309	-0.05416582524776459	-0.031805168837308884	0.004042403772473335	-0.08044354617595673	-0.016074035316705704	0.042716704308986664	-0.035614170134067535	-0.028621183708310127	0.018358750268816948	-0.033097170293331146	-0.08000388741493225	0.03536351025104523	-0.04734500125050545	-0.0019516412867233157	0.04225674644112587	-0.06559417396783829	0.03719358518719673	0.10238716006278992	0.010713955387473106	0.01346465852111578	0.04305592551827431	0.005972872022539377	0.10826840251684189	-0.031635407358407974	-0.04196838662028313	-0.04642219841480255	-0.07593882828950882	-0.08688419312238693	0.04542251303792	-0.04571131616830826	-0.02068597637116909	-0.012603268958628178	0.001198982703499496	0.04926279932260513	-0.014619325287640095	-0.023685360327363014	-0.05377240478992462	-0.009533853270113468	-0.025248579680919647	0.017435166984796524	0.01776360347867012	0.01113992091268301	-0.0232768002897501	-0.06399497389793396	-0.04023578017950058	-0.02191423997282982	0.04536299407482147	-0.0021436791867017746	0.024244338274002075	-0.034589510411024094	-0.040979571640491486	-0.008350495249032974	0.036229584366083145	0.09232359379529953	-0.07431657612323761	0.054738979786634445	0.051820509135723114	0.01861524023115635	-0.06192566454410553	0.018137698993086815	0.058222696185112	-0.029641810804605484	-0.04459959268569946	0.009737001731991768	0.05398023873567581	-0.08679813146591187	0.003674448700621724	0.006043677683919668	-0.03709499165415764	0.020551156252622604	0.010764436796307564	0.03479231148958206	0.02376001700758934	-0.017003994435071945	-0.013831167481839657	-0.0198329146951437	-0.030433671548962593	0.04588091000914574	-0.011186905205249786	0.027823662385344505	-0.03438495844602585	0.06064334884285927	-0.035029880702495575	-0.011665981262922287	-0.04537167772650719	0.055117592215538025	-0.020222539082169533	-0.016032911837100983	0.017835497856140137	0.003367251018062234	-0.005815192125737667	0.032351601868867874	0.024393238127231598	-0.07698079943656921	-0.027750136330723763	-0.018747441470623016	-0.043990228325128555	-0.0953865647315979	-0.02931312471628189	0.0010920427739620209	-0.08716659992933273	0.01664620079100132	-0.02455512434244156	0.022420449182391167	-0.0011588673805817962	-0.023243475705385208	0.012230024673044682	0.05973758548498154	0.028473244979977608	0.004899839870631695	0.008035236969590187	0.0020649682264775038	0.01046687550842762	-0.013901684433221817	-0.04170200973749161	0.03396553546190262	0.03890706226229668	-0.06701333820819855	0.033932432532310486	-0.04470401629805565	0.004648123402148485	-0.011185855604708195	-0.011921650730073452	0.0184171162545681	-0.006289134733378887	0.006860960274934769	-0.005583081394433975	0.05225445330142975	-0.05617928132414818	-0.04584947228431702	0.036679841578006744	-0.01824916899204254	-0.08555576950311661	-0.01583819091320038	-0.005204927176237106	-0.05690419301390648	0.0475689060986042	0.00481035141274333	0.012126162648200989	0.02109510637819767	-0.06343463808298111	-0.014296403154730797	-0.02596203051507473	-0.02554762177169323	0.04533248767256737	-0.019815456122159958	0.0604807548224926	0.005662696436047554	-0.025029484182596207	0.007488580420613289	-0.017076384276151657	0.02395801618695259	-0.06938372552394867	-0.01294872909784317	-0.011765801347792149	0.011646159924566746	-0.004213427659124136	0.000905970751773566	0.035351019352674484	-0.05334850028157234	-0.035480398684740067	-0.051350172609090805	0.0578497052192688	-0.02094232104718685	-0.011076223105192184	0.08585093915462494	-0.03936578705906868	0.006394098978489637	0.011722998693585396	-0.06328294426202774	-0.08979903161525726	-0.021724548190832138	-0.041874345391988754	0.06142658740282059	0.06342907249927521	-0.019506225362420082	0.0014305926160886884	-0.043630123138427734	0.021698683500289917	0.009507403708994389	-0.050000909715890884	-0.004429123364388943	0.01592177338898182	-0.050857096910476685	-0.058847274631261826	-0.018596148118376732	-0.029996564611792564	0.09085451066493988	0.046019047498703	0.014267919585108757	-0.064828060567379	-0.0051979138515889645	0.009575233794748783	-0.021987149491906166	0.02756897732615471	-0.0052178543992340565	-0.05476471781730652	0.027635131031274796	0.10131340473890305	-0.0571170374751091	0.04258444532752037	0.051841624081134796	-0.023970898240804672	0.02825179696083069	0.05395121872425079	-0.032425954937934875	0.003776992205530405	-0.00983391422778368	-0.02336954139173031	0.05718546733260155	-0.09615860879421234	0.05867089703679085	-0.04241906479001045	0.07523495703935623	0.02703963778913021	-0.01448020152747631	0.08302637189626694	0.08442123979330063	-0.003436301602050662	0.017675552517175674	-0.10942770540714264
+0.04177292436361313	-0.026636488735675812	-0.013213098049163818	0.06229618936777115	-0.07512318342924118	-0.042796455323696136	-0.030786730349063873	0.03051573410630226	0.013172670267522335	-0.02851679176092148	-0.023317908868193626	-0.006844594608992338	-0.014485353603959084	-0.002524757757782936	0.033121947199106216	-0.029988978058099747	-0.08471034467220306	0.017897548153996468	-0.034076303243637085	-0.02281651459634304	0.04346621781587601	0.010472861118614674	-0.08142966032028198	0.0550215020775795	-0.0025869563687592745	0.04185697063803673	-0.04830189794301987	0.006423787213861942	-0.03247508779168129	0.01370343565940857	0.05890896916389465	-0.010219748131930828	0.028288068249821663	-0.04789389669895172	-0.06017259508371353	0.06876260787248611	-0.03687940910458565	0.1124122217297554	0.011090153828263283	-0.009954329580068588	-0.05652092024683952	0.01752253621816635	-0.05382797494530678	-0.054498009383678436	0.03551974147558212	0.007910535670816898	0.004965192638337612	-0.026586312800645828	0.027957044541835785	0.020966481417417526	0.11155574023723602	-0.0008506911690346897	-0.011638103984296322	0.0009160270565189421	-0.038044191896915436	0.025809109210968018	0.04832763969898224	-0.06623014807701111	-0.07329191267490387	0.025104016065597534	-0.07509229332208633	-0.0026011846493929625	-1.5326460925280116e-05	0.0021591349504888058	-0.02511494606733322	-0.006766411475837231	-0.011825431138277054	0.0807974636554718	0.016897158697247505	-0.025479359552264214	0.0280630961060524	0.08300212025642395	-0.0684913694858551	-0.05650784447789192	0.053658533841371536	-0.057172201573848724	-0.05320066958665848	0.06243881955742836	-0.010362118482589722	0.007525941357016563	0.019810477271676064	-0.0245947428047657	0.1188117042183876	-0.03780962526798248	-0.056491829454898834	-0.01255556009709835	-0.007600152399390936	-0.04084305092692375	0.05566500872373581	0.06024836748838425	0.006668469402939081	0.09340283274650574	-0.05594629421830177	0.05329710990190506	0.02842005155980587	0.02742336131632328	0.039329543709754944	-0.1188058853149414	-0.055293820798397064	-0.009094148874282837	-0.005777162965387106	-0.017544986680150032	0.05730896070599556	-0.005211957730352879	0.0711774230003357	0.023872297257184982	-0.054607242345809937	-0.009107465855777264	0.0069138603284955025	0.03817381337285042	0.04521865397691727	0.07691929489374161	0.07128839939832687	0.061334554105997086	0.06685767322778702	-0.01142616756260395	0.006643110886216164	-0.04429548978805542	-0.06251438707113266	0.017490467056632042	-0.019246919080615044	0.025848399847745895	-0.017495399340987206	-0.05387153476476669	-0.023862138390541077	0.006311700213700533	-0.12286952137947083	-0.02450517751276493	0.05240869149565697	-0.046469904482364655	0.11226899176836014	0.05599430203437805	-0.01058600191026926	-0.004545337520539761	0.06857533752918243	0.03933975100517273	0.020134204998612404	0.022450167685747147	-0.025725847110152245	-0.0009860813152045012	-0.005439335014671087	0.04289517551660538	-0.009113358333706856	-0.04301151633262634	0.04507233947515488	0.02028292790055275	0.02694515511393547	0.0024300143122673035	-0.06646594405174255	0.0026307213120162487	-0.09092669188976288	-0.06652669608592987	-0.009271850809454918	-0.07761882245540619	0.013640510849654675	0.05391450971364975	0.04601230472326279	0.06372136622667313	-0.018549876287579536	-0.023494889959692955	-0.032611358910799026	-0.02933482825756073	-0.027942854911088943	-0.08491447567939758	-0.0346708744764328	-0.03756973147392273	-0.024401197209954262	0.002857190789654851	-0.01060558669269085	-0.018935251981019974	-0.027556678280234337	-0.005824437364935875	0.02662847749888897	0.08217684179544449	0.07361926883459091	0.019269604235887527	0.009488647803664207	-0.05578922480344772	-0.019080976024270058	-0.03968242183327675	-0.006843619979918003	-0.02714373730123043	0.013536863960325718	-0.04734606668353081	-0.007504210341721773	0.009372379630804062	0.046152181923389435	-0.009425736032426357	0.018102522939443588	0.008007068186998367	-0.045206669718027115	-0.06106327846646309	0.06929352134466171	0.08761420845985413	-0.03052903153002262	0.032082635909318924	-0.013353526592254639	0.0566507950425148	6.8536406615749e-05	-0.023962857201695442	-0.04593444615602493	0.0325297974050045	-0.010117561556398869	0.0676635354757309	0.022576555609703064	-0.06332268565893173	0.09009724855422974	0.0309457890689373	0.03551655635237694	-0.05563737452030182	-0.008089270442724228	0.019182661548256874	0.006788086611777544	0.03578437492251396	0.07387900352478027	-0.03330931067466736	-0.03876316919922829	0.03773827478289604	0.05704973265528679	-0.06018693372607231	0.0022657138761132956	-0.0075434851460158825	-0.06911496073007584	0.04189346730709076	-0.02078903652727604	-0.09455541521310806	-0.04112608730792999	0.025975512340664864	-0.0205887071788311	-0.014409610070288181	0.0030197252053767443	0.02924947626888752	-0.004680325277149677	-0.04579000920057297	0.009952450171113014	-0.009226521477103233	-0.10667292773723602	0.09683812409639359	-0.006453360430896282	-0.060431983321905136	-0.0755576491355896	0.022597163915634155	0.02910177782177925	0.019100172445178032	0.020451294258236885	0.06173763796687126	0.09492315351963043	0.04689569026231766	0.0046698846854269505	0.022753126919269562	-0.058199193328619	-0.046084024012088776	0.02285800501704216	0.030580168589949608	-0.026196129620075226	-0.02418285422027111	-0.004752380307763815	-0.041689515113830566	-0.0024951540399342775	0.01880580000579357	0.039987754076719284	-0.02865162491798401	-0.04526353254914284	-0.01900571957230568	0.03585068881511688	-0.02719339169561863	-0.02942848950624466	0.02352101542055607	-0.034058697521686554	0.07068488001823425	-0.05852644890546799	-0.05831897258758545	0.019757041707634926	-0.0027714259922504425	0.05827014148235321	-0.014103693887591362	0.05825817584991455	-0.034776151180267334	-0.10446170717477798	-0.015218839049339294	-0.092528335750103	-0.04550829902291298	-0.018311290070414543	-0.02384955808520317	-0.05756530538201332	0.11056135594844818	0.006995723117142916	-0.04264012351632118	-0.05568347126245499	-0.007723047398030758	0.001721080276183784	-0.060310959815979004	0.0016644856659695506	-0.0012724404223263264	-0.0761205330491066	-0.03661984205245972	0.055220890790224075	-0.03650163859128952	-0.028009911999106407	0.007844730280339718	-0.0448969304561615	-0.04200970381498337	0.04173097759485245	-0.020768925547599792	-0.03126158192753792	-0.007051702588796616	-0.07836543768644333	0.039007578045129776	0.10685224831104279	0.01999850571155548	0.03333132341504097	0.038198791444301605	0.03984212875366211	0.10585872828960419	0.0214291550219059	-0.03866155445575714	-0.006734922993928194	-0.04194344952702522	-0.044027481228113174	0.06060520187020302	-0.03710435703396797	-0.016443252563476562	-0.00964350625872612	0.007547101937234402	0.06551744043827057	0.00016914965817704797	-0.008673880249261856	-0.013683021068572998	0.02874170057475567	0.024813616648316383	0.02019749954342842	0.0018201543716713786	-0.00919889472424984	-0.009335980750620365	-0.04352152720093727	-0.036000821739435196	-0.029701706022024155	0.028035521507263184	0.00382375530898571	0.0349995419383049	-0.01927555352449417	-0.07033703476190567	0.03341827914118767	-0.004949413239955902	0.09832438081502914	-0.08046956360340118	0.02208670787513256	0.06092344969511032	0.005082090850919485	-0.07764418423175812	-0.012737223878502846	0.06718071550130844	-0.05856692045927048	-0.061006687581539154	0.007034371141344309	0.03155728429555893	-0.06016039848327637	-0.03260320797562599	0.002286257455125451	-0.05857070907950401	0.020208926871418953	0.019264182075858116	0.024915428832173347	0.010437141172587872	-0.004744513891637325	-0.026075759902596474	0.018412146717309952	0.027132444083690643	0.03522180765867233	-0.06194228678941727	0.008169653825461864	-0.03782689943909645	0.07110617309808731	-0.038217995315790176	-0.04162264242768288	-0.05360287055373192	0.06332246214151382	0.0033008672762662172	-0.028872687369585037	-0.026483695954084396	-0.007990754209458828	-0.018590502440929413	0.03481472656130791	0.02097788080573082	-0.07556507736444473	-0.04553326591849327	-0.0539848655462265	-0.06691953539848328	-0.08390707522630692	-0.03637995943427086	0.005909566767513752	-0.06868163496255875	0.008989372290670872	-0.0469837561249733	-0.022914402186870575	0.009983373805880547	-0.03551395982503891	0.032110653817653656	0.03840965777635574	0.033832862973213196	-0.03663642704486847	0.021080082282423973	-0.009768137708306313	0.03026535175740719	0.027516966685652733	-0.01868148148059845	0.028308743610978127	0.046850766986608505	-0.03255808725953102	0.031393349170684814	-0.021624719724059105	0.01402695570141077	0.02233949303627014	-0.02460535801947117	0.0009031648514792323	0.008412202820181847	-0.008191372267901897	-0.06462851911783218	0.029485099017620087	-0.0771508738398552	-0.02514771744608879	0.04400559514760971	-0.0456480011343956	-0.08907920867204666	-0.04393751919269562	-0.0035028946585953236	-0.025445640087127686	0.007144574541598558	0.008052514865994453	0.009803460910916328	0.034852489829063416	-0.06197648495435715	-0.029029252007603645	-0.03692358732223511	-0.023477887734770775	0.03793927654623985	-0.0004998440272174776	0.06581912934780121	-0.00893714465200901	0.0020335682202130556	-0.005668893922120333	0.019730782136321068	-0.012736165896058083	-0.06967712938785553	-0.01935473456978798	-0.015689773485064507	-0.040401242673397064	0.012360701337456703	0.005028681363910437	-0.005234549753367901	-0.05840660631656647	-0.01802508533000946	-0.03686301037669182	0.09083938598632812	-0.027115177363157272	0.007522094063460827	0.04354934021830559	-0.004334987141191959	0.02141682244837284	0.01031485665589571	-0.02929660491645336	-0.07495397329330444	0.01880844309926033	-0.027369892224669456	0.02957666479051113	0.03133346512913704	-0.010109849274158478	-0.021471671760082245	-0.07557998597621918	-0.007336786016821861	0.0015831883065402508	-0.06981934607028961	-0.010643528774380684	0.007584101520478725	-0.07954733818769455	-0.06747054308652878	-0.002993353409692645	-0.05725523829460144	0.07420162111520767	0.0035113266203552485	0.05013345181941986	-0.025369711220264435	-0.011450272053480148	0.0007053883746266365	-0.02072964981198311	0.004291013814508915	-0.0019863322377204895	-0.06462500244379044	-0.013494282960891724	0.05244465917348862	-0.03583044931292534	0.012024334631860256	0.08605024218559265	-0.009985008276998997	0.03562457486987114	0.037670694291591644	-0.040919214487075806	0.02505524829030037	0.0007304218597710133	-0.03305482491850853	0.06081022322177887	-0.06996618211269379	0.08527713268995285	-0.05774741992354393	0.09263575822114944	-0.0029177898541092873	-0.005203717388212681	0.0764908418059349	0.08799636363983154	-0.029054535552859306	0.031981006264686584	-0.04711489751935005
+-0.012312834151089191	-0.024785323068499565	-0.03844685107469559	0.004343963228166103	-0.07653612643480301	-0.03494187071919441	-0.027920322492718697	0.05067683383822441	-0.00649194186553359	-0.07146701961755753	-0.06478908658027649	-0.002633440773934126	0.015199592337012291	0.00013319247227627784	0.016166791319847107	-0.042126867920160294	-0.05288565531373024	-0.015049519017338753	-0.010170376859605312	-0.041279230266809464	0.031032145023345947	0.0006340827094390988	-0.08268658071756363	0.08547914773225784	0.05702732130885124	-0.008626565337181091	-0.032090961933135986	0.03775201365351677	-0.0005972615326754749	0.0975721925497055	0.0539572536945343	-0.04633455350995064	0.02494620531797409	-0.0483599454164505	-0.06259092688560486	0.02407304383814335	-0.06552377343177795	0.0851592868566513	0.03101448155939579	-0.020885080099105835	-0.054399747401475906	0.027376355603337288	-0.009171240031719208	-0.04266975075006485	0.03193185105919838	0.02275962010025978	-0.02660481259226799	-0.043871548026800156	0.0284319669008255	0.028260616585612297	0.08309067785739899	0.012168576940894127	-0.005286748055368662	-0.02447502687573433	-0.02416134625673294	0.03606851398944855	0.0023982336279004812	-0.030766254290938377	-0.02835962176322937	0.02434179186820984	-0.07129012048244476	0.025880716741085052	0.014171955175697803	5.607322964351624e-05	-0.012485639192163944	0.026279378682374954	-0.039050906896591187	0.08021007478237152	-0.011196792125701904	-0.0036219435278326273	-0.011651318520307541	0.06937307119369507	-0.04588088020682335	-0.029509637504816055	0.047768209129571915	-0.009912311099469662	-0.013176565989851952	0.00998845137655735	-0.004782685078680515	0.01651882193982601	0.012916133739054203	-0.04039239138364792	0.15053114295005798	-0.032814376056194305	0.0014248539227992296	-0.07825850695371628	-0.03685130178928375	-0.05488915741443634	0.0679788887500763	0.021331412717700005	0.05624061077833176	0.058275628834962845	0.026411481201648712	0.03847105801105499	-0.001029333332553506	-0.011305442079901695	0.02110050804913044	-0.11500822007656097	0.019867265596985817	0.01596689596772194	0.019768306985497475	-0.0067313723266124725	0.07886641472578049	0.0061481124721467495	0.03292669728398323	0.06674139201641083	-0.07513672858476639	0.021902482956647873	0.04736731946468353	0.024987637996673584	0.011792097240686417	0.11947571486234665	0.047558821737766266	0.049657199531793594	0.04588020592927933	0.011706648394465446	-0.031437184661626816	-0.013747809454798698	-0.017285795882344246	0.00854428205639124	-0.004059982486069202	0.05940084531903267	-0.0016421886393800378	-0.08269239962100983	0.04327000305056572	0.05972570180892944	-0.07577221840620041	0.005832199472934008	0.03623434156179428	-0.026356372982263565	0.061518341302871704	0.05992758274078369	-0.00865982286632061	0.04403051733970642	0.04292090982198715	0.010893836617469788	0.023670019581913948	0.020769476890563965	-0.023885738104581833	-0.04480714350938797	-0.06243413686752319	0.03776182234287262	-0.017628123983740807	-0.043784093111753464	0.06830193847417831	0.015001659281551838	0.008872264064848423	-0.01302783377468586	-0.04756183922290802	-0.0033469614572823048	-0.08645439147949219	-0.08685845881700516	0.03811880946159363	-0.012747016735374928	0.01786554418504238	0.028449825942516327	0.04962308332324028	0.017246246337890625	0.007859637960791588	-0.00903787650167942	-0.0012366923037916422	-0.05340610817074776	-0.026975292712450027	-0.07580099999904633	0.04526465758681297	0.007596973795443773	0.035181671380996704	0.0019993563182651997	-0.06759363412857056	-0.025344915688037872	0.010065404698252678	0.02068234421312809	0.03686223924160004	0.06421031057834625	0.023604271933436394	-0.00558238010853529	0.07305961847305298	-0.05733610689640045	-0.05572980269789696	-0.040704190731048584	0.04560039937496185	-0.0577380545437336	0.012398573569953442	-0.04790665581822395	-0.026972053572535515	0.013939364813268185	0.007630647625774145	-0.02551998384296894	-0.007239897735416889	-0.023039348423480988	-0.02667330950498581	-0.07305745780467987	0.05559275671839714	0.007861397229135036	-0.016999240964651108	0.026216916739940643	0.05565119534730911	0.07792643457651138	-0.02818218432366848	-0.009825151413679123	-0.0680321529507637	0.04000047594308853	0.027259672060608864	0.012728137895464897	0.036878928542137146	-0.04068109765648842	0.05449647456407547	-0.014022700488567352	0.044604554772377014	-0.029739223420619965	0.06054609268903732	0.05893567577004433	0.00814466830343008	0.047080811113119125	0.0913785845041275	-0.026620902121067047	-0.03745901212096214	0.010879572480916977	0.05888567492365837	-0.11286424845457077	-0.013360567390918732	-0.001634735381230712	-0.08121518045663834	-0.0050981012172997	-0.024922948330640793	-0.0918489396572113	-0.021169478073716164	0.021790046244859695	-0.006124002393335104	-0.030343614518642426	0.006701881997287273	0.02327662892639637	-0.03022918291389942	-0.0007333739195019007	0.036640189588069916	0.009440483525395393	-0.06407855451107025	0.0444038026034832	-0.06438765674829483	-0.05836196616292	-0.020464973524212837	0.0220328401774168	0.0026182858273386955	0.014506158418953419	0.028853707015514374	0.0731240063905716	0.07236756384372711	0.06879514455795288	-0.05217652767896652	0.0010607762960717082	-0.04418560117483139	-0.016864614561200142	-0.013108099810779095	0.023280495777726173	0.016215145587921143	-0.0586349293589592	0.030581049621105194	-0.08155722171068192	0.015981709584593773	-0.016017094254493713	-0.0007904816302470863	-0.05037955194711685	-0.011447625234723091	-0.027852049097418785	0.040561921894550323	-0.01448037475347519	-0.05043630301952362	0.030611645430326462	-0.05343262106180191	0.05001749098300934	-0.05046791210770607	-0.022936999797821045	0.0096952049061656	0.015068179927766323	0.021806659176945686	-0.04052266851067543	0.0922359898686409	-0.013505212962627411	-0.07043283432722092	-0.07011034339666367	-0.040438417345285416	-0.07903090864419937	0.01049975585192442	-0.03300885856151581	-0.018011145293712616	0.056607287377119064	0.016350429505109787	-0.05942748859524727	-0.07073570787906647	-0.0655955821275711	-0.02621304802596569	-0.029668355360627174	-0.04741302505135536	-0.027912013232707977	-0.03919438272714615	-0.019653888419270515	0.05205795541405678	-0.042406145483255386	-0.014510136097669601	0.08885446935892105	0.04990019276738167	-0.042081478983163834	0.06915966421365738	-0.048030268400907516	-0.0015251076547428966	0.04359111934900284	-0.04971291497349739	0.018040938302874565	0.08227012306451797	0.022488908842206	0.0004593297780957073	0.02968323789536953	0.03826266527175903	0.07128553092479706	0.0021051564253866673	-0.06759417057037354	-0.055679064244031906	-0.042888447642326355	-0.07112302631139755	0.0636013001203537	-0.014862705953419209	-0.013747765682637691	-0.02327178604900837	-0.03588321432471275	0.02249174937605858	-0.001812333008274436	0.017363356426358223	-0.06478118151426315	0.011952693574130535	-0.030813923105597496	0.028578070923686028	-0.03091641329228878	0.008126427419483662	0.02228856459259987	-0.047886691987514496	-0.0713607668876648	-0.07993318885564804	0.03612899035215378	0.009560874663293362	0.05140245705842972	0.0011262594489380717	-0.016032211482524872	0.023269621655344963	-0.004594998899847269	0.0877169668674469	-0.06715499609708786	0.04063178598880768	0.027408238500356674	0.04267353564500809	-0.06578972935676575	-0.004464115481823683	-0.007549022790044546	-0.03820464387536049	-0.05730808153748512	-0.020851122215390205	0.037524301558732986	-0.0404118150472641	-0.060937441885471344	-0.030316632241010666	-0.03496621176600456	0.0045501310378313065	0.006263034883886576	-0.01741001382470131	0.06519471108913422	0.035348355770111084	-0.030672283843159676	0.028336377814412117	-0.02303837426006794	-0.011028187349438667	-0.012920304201543331	0.03843884915113449	-0.014200970530509949	0.06356938928365707	-0.027901483699679375	-0.02754271775484085	-0.07838519662618637	0.016185108572244644	-0.0741095095872879	-0.012128571979701519	-0.015802135691046715	0.006590820383280516	-0.03671839460730553	0.01695217378437519	0.00996414665132761	-0.09795703738927841	0.0036414789501577616	-0.004232548642903566	-0.05786847695708275	-0.042586274445056915	-0.029618749395012856	0.01313506718724966	-0.01740589551627636	0.03263222798705101	-0.06151479110121727	0.03396860137581825	0.04793349280953407	0.039659224450588226	0.023590436205267906	0.05915216729044914	0.02477915771305561	0.018091341480612755	0.06817477941513062	0.01884738728404045	0.007552723400294781	-0.02272750996053219	-0.06117263063788414	0.02715156599879265	0.06105615571141243	-0.050637874752283096	0.025555836036801338	-0.04145709425210953	0.013949297368526459	0.011236067861318588	-0.01814185455441475	0.0029549964237958193	-0.034758102148771286	0.008963635191321373	-0.005459037609398365	0.04513179510831833	-0.02158639207482338	-0.07283841073513031	0.022625038400292397	-0.04793432354927063	-0.07568447291851044	0.003639333415776491	-0.05243244767189026	-0.04067951813340187	0.044211115688085556	0.0020749387331306934	0.02282288484275341	0.0466030016541481	-0.05752169340848923	-0.036855388432741165	0.02727554552257061	0.0010770871303975582	-0.004683701787143946	0.06121589243412018	0.061322107911109924	0.0236953254789114	-0.02400600165128708	0.01841682195663452	-0.06394651532173157	0.03657102584838867	-0.05780009552836418	0.015417528338730335	0.043107904493808746	-0.026587555184960365	0.01162565965205431	0.04145467281341553	0.03416145592927933	-0.05465133860707283	-0.016967445611953735	-0.02016734890639782	0.016086023300886154	-0.012917980551719666	0.03242911025881767	0.026879796758294106	-0.06535360217094421	-0.02084476314485073	0.0223255418241024	-0.03515314683318138	-0.09629478305578232	-0.026950597763061523	-0.08373606204986572	0.0830087959766388	-0.006960786879062653	-0.010721585713326931	-0.026928354054689407	-0.0970543697476387	-0.010721378959715366	-0.024336425587534904	-0.015070394612848759	-0.009329745545983315	0.017073139548301697	-0.06051518768072128	-0.046638328582048416	-0.016528960317373276	-0.07673000544309616	0.06713956594467163	0.0029057622887194157	0.02570323459804058	-0.05550215765833855	-0.005497728008776903	0.010882046073675156	-0.014398619532585144	0.024986285716295242	-0.0642239972949028	-0.06828160583972931	-0.019466640427708626	0.11589868366718292	-0.014664653688669205	0.040827956050634384	0.08799155801534653	0.0074498956091701984	0.05370667576789856	0.0455402247607708	-0.022803664207458496	-0.01726796105504036	0.0008259251480922103	-0.0011526348534971476	0.025634074583649635	-0.05009405314922333	0.06302519887685776	-0.06539984047412872	0.11154830455780029	0.040520958602428436	0.005438824184238911	0.08066748082637787	0.07511311769485474	0.04838986694812775	0.05886608362197876	-0.10252313315868378
+0.03577898070216179	-0.03528829291462898	0.03575725108385086	0.013862431980669498	-0.06363355368375778	-0.060104645788669586	0.014818469993770123	0.026073893532156944	0.00010257097892463207	-0.01293198298662901	0.003677709959447384	0.024634087458252907	-0.01020672544836998	0.010742425918579102	0.007925350219011307	-0.03267800062894821	-0.07471253722906113	-0.0013409984530881047	-0.06460075080394745	-0.01836589351296425	0.011890853755176067	0.04926879331469536	-0.09954182803630829	-0.008471054024994373	-0.04262687638401985	0.032491300255060196	-0.08824095129966736	-0.010185029357671738	-0.01643129624426365	-0.004324057605117559	0.04434922710061073	-0.028668208047747612	0.10639883577823639	-0.0535070076584816	-0.0451384000480175	0.034735579043626785	-0.0371212437748909	0.09044892340898514	0.06737460941076279	-0.04195500537753105	-0.04703553393483162	-0.026745356619358063	0.011169064790010452	-0.06872197240591049	-0.015570792369544506	0.02136414498090744	-0.015383767895400524	-0.05209767445921898	0.053651828318834305	0.009242258965969086	0.09888216853141785	0.012355383485555649	-0.02957487851381302	-0.01018900889903307	-0.0004376770812086761	-0.02861667424440384	0.02150573767721653	-0.04755396768450737	-0.12635883688926697	0.020905084908008575	-0.12046462297439575	0.012062418274581432	-0.02834351733326912	0.04251345992088318	-0.06968355178833008	-0.05318307504057884	-0.020494522526860237	0.07417605817317963	0.048994895070791245	-0.034550223499536514	0.039291754364967346	0.07801640778779984	-0.0422845222055912	-0.06330005824565887	-0.00875629298388958	-0.05493791401386261	0.0049393498338758945	0.015347765758633614	-0.023944737389683723	-0.00022294717200566083	-0.031235622242093086	-0.03547753766179085	0.10279954969882965	-0.06642453372478485	-0.017879435792565346	-0.007821164093911648	-0.002726437523961067	-0.10333051532506943	0.04454663768410683	0.03554798662662506	0.0013852822594344616	0.043662458658218384	-0.04175671562552452	0.052626751363277435	0.022660085931420326	0.052113257348537445	0.03604141250252724	-0.07911980897188187	-0.03746416047215462	-0.030911527574062347	-0.02895192988216877	0.044353779405355453	0.09341392666101456	0.03048471361398697	0.09108607470989227	-0.04341089352965355	-0.011222302913665771	0.018558410927653313	0.03954453021287918	0.019111257046461105	0.03292253613471985	0.07302092760801315	0.07088304311037064	0.026541955769062042	0.029704982414841652	-0.017084037885069847	0.037321191281080246	-0.04397537559270859	0.00435015931725502	-0.015362092293798923	-0.0062655056826770306	0.03148258849978447	-0.05302697792649269	-0.06690730154514313	-0.03310150280594826	0.04869293421506882	-0.06997468322515488	-0.04815029352903366	0.05543041601777077	-0.03392462804913521	0.10725422948598862	0.10796809941530228	-0.037742603570222855	0.04428146779537201	0.04061952978372574	-0.01374638918787241	-0.024016546085476875	0.027588488534092903	0.016600221395492554	-0.001964639639481902	-0.0016667923191562295	0.05227818340063095	-0.014488747343420982	-0.040241923183202744	-0.020181557163596153	0.01819617860019207	0.012435243465006351	-0.05273096263408661	-0.07528003305196762	0.038977544754743576	-0.003995917737483978	-0.0853610560297966	-0.011611192487180233	-0.06179709732532501	0.030893471091985703	0.08021079003810883	0.017993750050663948	0.03386746346950531	-0.027470532804727554	0.022061673924326897	-0.05595382675528526	-0.061834659427404404	-0.0261517446488142	-0.08434737473726273	-0.039571575820446014	-0.0108457887545228	0.03305811434984207	0.030682839453220367	-0.03721955791115761	0.010115725919604301	-0.029005851596593857	0.008125522173941135	-0.021091202273964882	0.05749999359250069	0.05581135302782059	0.014351267367601395	-0.02231444977223873	-0.05125071108341217	0.0644921287894249	-0.020174818113446236	0.009228172712028027	-0.04838969558477402	0.028175445273518562	-0.04516148567199707	-0.012256399728357792	-0.0059739951975643635	0.044376667588949203	-0.018697349354624748	0.02955404482781887	0.04135476425290108	0.0019949041306972504	-0.029946980997920036	-0.03592599183320999	0.0530756451189518	-0.0068387119099497795	0.03551948443055153	-0.01749381609261036	0.06039341539144516	-0.012933055870234966	-0.06369874626398087	-0.035338666290044785	0.03343947231769562	-0.001421102206222713	0.027037151157855988	-0.005904376041144133	-0.019294409081339836	0.05340978503227234	0.05196831375360489	3.4983324439963326e-05	-0.0798133835196495	0.002028818940743804	-0.010405309498310089	0.06799252331256866	0.0014777507167309523	0.0652133896946907	-0.10043903440237045	-0.019249621778726578	0.016061127185821533	0.04086727276444435	-0.03280321881175041	-0.010246899910271168	0.0001748861832311377	-0.035840872675180435	0.01868760958313942	-0.029801053926348686	-0.09481937438249588	-0.009192727506160736	0.025893205776810646	0.019161181524395943	-0.03189965710043907	0.03320914879441261	0.02525106631219387	-0.05019683018326759	0.0028951040003448725	-0.01720666140317917	-0.06554051488637924	-0.07943609356880188	0.07273288816213608	0.027039511129260063	-0.04034006595611572	-0.035125624388456345	-0.007392784580588341	0.03773839771747589	0.032850220799446106	-0.0003731207689270377	0.004278513137251139	0.11878490447998047	0.02931976690888405	-0.04786873608827591	-0.00790185946971178	-0.040800999850034714	-0.0688028559088707	0.050321437418460846	0.027610288932919502	-0.043245021253824234	-0.009521395899355412	0.005599767901003361	-0.0668288990855217	-0.011091955006122589	-0.016024149954319	0.04272694140672684	0.02217409946024418	-0.007787186652421951	-0.005182673688977957	0.05458960309624672	0.013566075824201107	-0.030900008976459503	0.03852385655045509	0.013725717552006245	0.04603227972984314	-0.03234640136361122	-0.016435319557785988	0.0718345046043396	-0.0024694204330444336	0.06557642668485641	-0.011751159094274044	0.06911784410476685	-0.024879151955246925	-0.07306334376335144	-0.02801791951060295	-0.11032431572675705	0.0020047747530043125	-0.006873705890029669	-0.03691665828227997	-0.07282675057649612	0.050699904561042786	-0.0047814915888011456	-0.010183972306549549	-0.012545312754809856	0.0075910380110144615	-0.03221976011991501	-0.06469854712486267	-0.013076434843242168	-0.00283900392241776	-0.02320241928100586	-0.023301180452108383	0.012445872649550438	-0.09856733679771423	-0.04347992315888405	-0.0018473472446203232	-0.05930080637335777	-0.06159666180610657	0.052439186722040176	-0.02339961938560009	-0.03082612156867981	-0.013473868370056152	-0.10806106775999069	0.012893890030682087	0.09731650352478027	0.020046185702085495	0.06470680236816406	0.056012723594903946	0.029905810952186584	0.08481460064649582	0.03406631201505661	-0.058404482901096344	-0.01176073681563139	-0.004512028302997351	-0.0589570589363575	0.05080278962850571	-0.03453268110752106	0.05602407827973366	-0.012732254341244698	0.009784476831555367	0.0520089752972126	0.001228609587997198	-0.05771675333380699	-0.009934889152646065	0.011472797952592373	-0.04422621428966522	0.0029955196660012007	0.013280544430017471	0.006707793101668358	0.015990806743502617	-0.03206511214375496	-0.04657631367444992	0.002571807010099292	0.00682449946179986	-0.04460841789841652	0.03865484148263931	0.0061837490648031235	-0.05945894122123718	0.01433675829321146	-0.04450253024697304	0.07453712821006775	-0.06313541531562805	0.03460700809955597	-0.03801590949296951	0.02315978892147541	-0.13709251582622528	-0.023444948717951775	0.05587645247578621	-0.011493569239974022	-0.055039383471012115	0.06298484653234482	0.01478544995188713	-0.05475645512342453	-0.008189866319298744	-0.013739610090851784	-0.042921289801597595	-0.01881800964474678	0.01971634477376938	0.0677022710442543	0.03275377303361893	0.023288948461413383	-0.06059575080871582	-0.030999260023236275	0.024916986003518105	0.01609300822019577	-0.02309863083064556	0.02516964077949524	-0.08542485535144806	0.04077823832631111	-0.05131693184375763	-0.04789864644408226	-0.02474876306951046	0.05204955115914345	0.051806990057229996	-0.018650604411959648	0.032404348254203796	-0.026288431137800217	-0.010292393155395985	0.005503376014530659	0.031499769538640976	0.004023243207484484	-0.022108281031250954	-0.019032960757613182	-0.031877901405096054	-0.07387489080429077	-0.01908578909933567	-0.0016662352718412876	-0.04073254391551018	0.015932727605104446	0.01867849752306938	0.0022246858570724726	0.026924891397356987	-0.02649482898414135	0.024200059473514557	0.023695286363363266	0.04298332333564758	-0.016567563638091087	-0.026745833456516266	-0.0011015492491424084	0.04884284734725952	0.006148576270788908	-0.021238770335912704	0.015707217156887054	0.03176300600171089	-0.07514046132564545	-0.009949722327291965	-0.03323065862059593	-0.010353381745517254	0.016322331503033638	-0.03626325726509094	-0.004322086460888386	0.02444888837635517	0.0020934464409947395	-0.01930752582848072	0.05843130499124527	-0.08563467860221863	-0.011904876679182053	0.018160635605454445	-0.03426985442638397	-0.11378419399261475	-0.03947380185127258	-0.011424257420003414	-0.05176225304603577	0.04196343198418617	0.011229485273361206	0.01836230792105198	0.052898380905389786	-0.0158519446849823	-0.03305104374885559	0.03314860537648201	0.022580469027161598	0.03405293449759483	-0.016765553504228592	0.05322302505373955	-0.001785202999599278	0.014337848871946335	0.013413934037089348	0.029153944924473763	0.03457186371088028	-0.09954307973384857	-0.028925132006406784	-0.018921377137303352	0.021580545231699944	-0.0051183016039431095	0.014586403965950012	-0.007946901954710484	-0.11373312026262283	-0.02029605582356453	0.007599606644362211	0.05204790458083153	-0.026471653953194618	-0.0031761364080011845	0.04009805992245674	-0.05961679667234421	-0.059538744390010834	-0.0036070365458726883	-0.025443704798817635	-0.0810389444231987	0.05415648967027664	-0.05073975771665573	0.006526260171085596	0.010669764131307602	0.03552006185054779	-0.018961794674396515	-0.0788990929722786	-0.02958006225526333	0.010148498229682446	-0.060461096465587616	-0.00153833266813308	0.02086053043603897	-0.07693760097026825	-0.0550723597407341	0.005769876763224602	-0.04938393458724022	0.02612815983593464	0.01577705331146717	0.01543648261576891	-0.007536690682172775	0.001473733107559383	-0.02606530487537384	-0.021460263058543205	0.0057007018476724625	0.015116452239453793	-0.053926918655633926	-0.05134681984782219	0.0596492625772953	-0.051178645342588425	0.04155163839459419	0.07527564465999603	-0.03252245485782623	0.05261991173028946	0.030584778636693954	-0.034032389521598816	0.033691078424453735	-0.00753364572301507	-0.02136271446943283	0.06169847771525383	-0.06357113271951675	0.056081708520650864	-0.06030000001192093	0.11217661201953888	0.026811771094799042	-0.017779555171728134	0.06048323214054108	0.0959075540304184	-0.017459820955991745	-0.010884585790336132	-0.035938650369644165
+0.01332886517047882	-0.08328069001436234	-0.012133204378187656	0.04465889558196068	-0.03466068580746651	-0.033764682710170746	0.008302726782858372	0.017292413860559464	-0.00954844057559967	-0.055548567324876785	-0.006804298143833876	-0.025363516062498093	-0.02167133241891861	0.01406702771782875	0.03998921439051628	-0.026079434901475906	-0.07522469013929367	0.04163391888141632	-0.06446563452482224	-0.018044868484139442	0.008695472963154316	0.03923488408327103	-0.05342700704932213	0.021539822220802307	-0.07433051615953445	-0.0021271000150591135	-0.055955372750759125	0.015710245817899704	-0.0021112130489200354	0.02026304230093956	0.03630611300468445	0.011282656341791153	0.06836049258708954	-0.08930619060993195	-0.048694461584091187	0.0483219288289547	-0.013412543572485447	0.11119876056909561	0.03609181568026543	-0.035251859575510025	-0.06240292638540268	0.034595854580402374	-0.028023088350892067	-0.1101033166050911	0.08042390644550323	-0.011154546402394772	0.019388295710086823	-0.013383857905864716	-0.008559070527553558	0.06247719004750252	0.06776218861341476	0.018058819696307182	0.00047756804269738495	-0.0012509037042036653	-0.04688616469502449	0.005532523617148399	0.04352385550737381	-0.03417086973786354	-0.02280670404434204	0.051561810076236725	-0.0795743390917778	-0.023728331550955772	0.02625722438097	0.0046897102147340775	-0.04889444261789322	-0.007481193635612726	-0.0006479641888290644	0.0763164535164833	-0.004761015996336937	-0.027100495994091034	-0.020727206021547318	0.06536292284727097	-0.024387983605265617	-0.05413350462913513	0.029944535344839096	-0.06694534420967102	0.018712753430008888	0.02493438310921192	-0.04299668222665787	-0.015558550134301186	-0.0019470973638817668	0.017977919429540634	0.12654495239257812	-0.03838328644633293	-0.0020895800553262234	-0.0600496344268322	0.012607327662408352	-0.06615407019853592	0.010418107733130455	-0.0013273324584588408	0.03537335991859436	0.042998865246772766	0.012643441557884216	0.009759485721588135	0.006271440535783768	0.035755354911088943	0.013597806915640831	-0.10499216616153717	-0.030386339873075485	-0.013729559257626534	0.015182480216026306	0.0020485662389546633	0.053227439522743225	0.012961002998054028	0.05314875766634941	-0.0006672784802503884	-0.06453769654035568	-0.005318149924278259	0.014381070621311665	-0.009408078156411648	0.04357793554663658	0.14303630590438843	0.03423488885164261	0.014241351746022701	0.07919567078351974	0.013721218332648277	0.024399420246481895	-0.05239890143275261	0.022556515410542488	0.009057706221938133	-0.028284307569265366	0.022872209548950195	-0.0010124484542757273	-0.0822475403547287	-0.0008483025594614446	0.030166663229465485	-0.09425079077482224	-0.04440918564796448	0.04015791788697243	-0.044115908443927765	0.09985854476690292	0.029403885826468468	-0.03022126480937004	0.025708340108394623	0.01894260197877884	0.015006303787231445	-0.018000362440943718	0.031913552433252335	0.025071611627936363	-0.031653180718421936	-0.01351482979953289	0.08533810079097748	-0.031028810888528824	-0.002583236200734973	0.04242130368947983	0.048956576734781265	-0.004345261491835117	-0.02991008572280407	-0.08789747208356857	0.0495535247027874	-0.058668721467256546	-0.06304628401994705	-0.04305555671453476	-0.10223371535539627	0.042451098561286926	0.049801409244537354	-0.019892839714884758	0.05687985196709633	-0.0027195203583687544	-0.04227682203054428	-0.058164387941360474	-0.066217802464962	-0.01265030913054943	-0.08704354614019394	-0.028272680938243866	-0.01794927753508091	0.0025919999461621046	0.006304745562374592	-0.036602482199668884	-0.032871369272470474	-0.015222933143377304	-0.016571763902902603	0.03689737245440483	0.01888797990977764	0.07946206629276276	0.03296515345573425	0.0092897554859519	-0.05863044410943985	-0.0011418134672567248	-0.04017111286520958	0.00413485337048769	-0.04307650402188301	0.006737122777849436	-0.10066955536603928	-0.011738159693777561	-0.008469677530229092	-0.00039347659912891686	-0.035887353122234344	0.0065462891943752766	-0.01229830365628004	-0.028468815609812737	-0.0674370676279068	0.07038215547800064	0.08031558245420456	0.013041272759437561	0.03281278535723686	-0.022591959685087204	0.08714324235916138	-0.057674188166856766	-0.04427540302276611	-0.07155713438987732	0.045388445258140564	0.01788417622447014	0.0014801474753767252	-0.007657886017113924	-0.039464399218559265	0.06578074395656586	-0.0007396764704026282	0.03386847674846649	-0.039047472178936005	0.05145575478672981	0.022876612842082977	-0.0014155869139358401	0.025997400283813477	0.06018565222620964	-0.006856756750494242	-0.024758126586675644	0.011502590030431747	0.04346638545393944	-0.058582305908203125	0.024865396320819855	0.015755925327539444	-0.08111760020256042	0.004385671578347683	-0.023179233074188232	-0.09922485798597336	-0.00865041371434927	0.04027881845831871	-0.036201391369104385	0.00723030511289835	0.024253390729427338	0.04202849790453911	-0.03598110377788544	-0.01825067028403282	-0.006325962953269482	-0.019601015374064445	-0.06547144800424576	0.053063616156578064	0.002265626797452569	-0.07071945071220398	-0.03779355436563492	-0.010590544901788235	0.030318457633256912	-0.0021201367489993572	0.013397261500358582	-0.002025738125666976	0.056685350835323334	0.013911944814026356	-0.05037297308444977	0.0180099718272686	-0.04458002746105194	-0.05547219142317772	-0.03604023903608322	0.022648895159363747	0.0009003209415823221	-0.066324383020401	-0.040569182485342026	-0.07977323234081268	0.004905820824205875	0.010513829998672009	0.015027175657451153	-0.03890044987201691	0.014321147464215755	-0.005931565538048744	0.012898974120616913	-0.030485540628433228	-0.019767334684729576	0.00917325634509325	-0.007792360614985228	-0.0008241007453761995	-0.03615045174956322	0.0129071194678545	0.044848546385765076	-0.009756562300026417	0.03469610586762428	-0.07379022240638733	0.0616307258605957	0.008871912024915218	-0.07983791083097458	-0.02937564067542553	-0.07704982161521912	-0.006896242965012789	0.007408969569951296	-0.05239301919937134	-0.044117849320173264	0.08417078107595444	0.08503081649541855	-0.03246567025780678	-0.06822950392961502	-0.10125134140253067	-0.0437171570956707	-0.029592491686344147	-0.020145900547504425	0.0009845751337707043	-0.04468522593379021	-0.006762659642845392	0.042796410620212555	-0.046082936227321625	-0.016097895801067352	-0.004189180675894022	0.02899722196161747	-0.018919184803962708	0.05753787234425545	-0.01886868104338646	-0.031015440821647644	0.027489500120282173	-0.08421719819307327	0.0199179295450449	0.0817750096321106	0.07290064543485641	0.03764884173870087	0.03765175864100456	0.02552010491490364	0.08449546992778778	-3.451522206887603e-05	-0.0027211231645196676	-0.035320863127708435	-0.02477635070681572	-0.059350818395614624	0.05466708168387413	-0.016156265512108803	0.012454874813556671	0.007761647924780846	-0.0023109072353690863	0.0973004698753357	-0.041114673018455505	-0.0323849581182003	-0.0227385051548481	0.021375028416514397	-0.03395567834377289	0.008511980064213276	-0.013836082071065903	0.04473865404725075	0.00047864075168035924	-0.08875879645347595	-0.05349196866154671	-0.027699263766407967	0.023319687694311142	-0.024345234036445618	0.021332142874598503	0.007633006665855646	-0.05843397602438927	0.012077530845999718	0.009547768160700798	0.12720400094985962	-0.06748265027999878	-0.0026412683073431253	0.03024214506149292	0.055663470178842545	-0.10015694797039032	0.00217183749191463	-0.018789751455187798	-0.05616092309355736	-0.043334584683179855	-0.00286252424120903	0.04130767285823822	-0.048595380038022995	-0.043607521802186966	-0.027698233723640442	0.00846390426158905	-0.0009495046106167138	0.031400684267282486	-0.0033260732889175415	0.06784544140100479	-0.014589661732316017	-0.021936621516942978	-0.00282641826197505	0.017333615571260452	0.020199941471219063	-0.06204763799905777	0.02893795818090439	0.006818393711000681	0.0021160277538001537	-0.0223183985799551	-0.06312666833400726	-0.0145357521250844	0.03771942853927612	-0.04228109493851662	-0.009189693257212639	-0.042061109095811844	-0.054952263832092285	-0.039305709302425385	0.011389981023967266	0.0021559318993240595	-0.045726366341114044	-0.014963783323764801	-0.013485576026141644	-0.07514586299657822	-0.10561677813529968	-0.05879952758550644	0.053540073335170746	-0.00547085190191865	0.031070679426193237	-0.07831049710512161	0.02173510007560253	0.05123318359255791	0.025811269879341125	0.028242213651537895	0.0832124724984169	0.0282958522439003	-0.03506205976009369	0.026251250877976418	-0.03904204070568085	0.04943102225661278	0.032330963760614395	-0.04894070327281952	-0.03726312890648842	-0.005747986026108265	-0.03934097662568092	0.020612390711903572	-0.010021054185926914	0.0031823597382754087	-0.007863798178732395	-0.04246882349252701	-0.0014323715586215258	0.0032145483419299126	-0.015091088600456715	-0.012703219428658485	0.04530045762658119	-0.06159605085849762	-0.04354847967624664	-0.019623801112174988	-0.03427105396986008	-0.11361969262361526	-0.06031430512666702	-0.013528547249734402	0.013012601993978024	0.05454494059085846	-0.056544769555330276	0.043836064636707306	0.0004945009713992476	-0.019301507622003555	-0.03878486901521683	-0.027105940505862236	-0.02601592242717743	-0.01159219816327095	0.01743343099951744	0.052369773387908936	0.011274614371359348	-0.018267294391989708	-0.004822468850761652	-0.02803702838718891	0.0030702739022672176	-0.0850641280412674	-0.017670197412371635	-0.01785905659198761	-0.006695887073874474	0.03971157968044281	0.0029210704378783703	-0.0043912651017308235	-0.05346911773085594	-0.028109727427363396	-0.039253976196050644	0.06708495318889618	0.003678728826344013	0.031503111124038696	0.03155439347028732	-0.06491480767726898	-0.014811351895332336	0.05588550865650177	-0.056436579674482346	-0.07424638420343399	-0.009932258166372776	-0.04163455590605736	0.03711751475930214	0.009321575053036213	0.05607065185904503	-0.01161926705390215	-0.1034649908542633	0.011212444864213467	-0.02200521156191826	-0.06144281104207039	-0.013171770609915257	0.036049287766218185	-0.06502256542444229	-0.027210941538214684	0.01421397551894188	-0.06380386650562286	0.07476357370615005	0.0025192101020365953	0.05125010386109352	-0.03285757079720497	-0.01260413322597742	-0.03132705017924309	-0.02873891033232212	0.03759003058075905	-0.02736789360642433	-0.08051040768623352	0.000397137482650578	0.09150274097919464	-0.03322112187743187	0.0453018844127655	0.07482469081878662	-0.04921332746744156	0.07472708821296692	0.028829272836446762	-0.007814997807145119	0.00324535951949656	-0.004992785397917032	-0.019077448174357414	0.018477950245141983	-0.05301579833030701	0.06414834409952164	-0.0639716237783432	0.127369225025177	0.03239333629608154	0.01682659238576889	0.062314294278621674	0.08494164049625397	0.01791137270629406	-0.0007317385170608759	-0.06598562002182007
+0.028020381927490234	-0.04825945198535919	-0.009985201992094517	0.06819631904363632	-0.08935754001140594	-0.039456695318222046	0.012538784183561802	0.02007361687719822	-0.0020599975250661373	-0.06896766275167465	0.01183493435382843	-0.03744645044207573	0.01655932329595089	0.008441810496151447	0.04217403009533882	-0.00861291866749525	-0.044931355863809586	0.046461071819067	-0.014858396723866463	-0.05340420827269554	-0.011588078923523426	-0.015364835038781166	-0.0555744469165802	0.035067033022642136	-0.002424607053399086	-0.021905750036239624	-0.08064471185207367	-0.004547571297734976	-0.01776670478284359	0.042942196130752563	0.02815890684723854	-0.010271754115819931	0.022531617432832718	-0.0370936244726181	-0.09913606941699982	0.0520213358104229	-0.04697432368993759	0.13228283822536469	-0.01466272585093975	-0.028271906077861786	-0.05669145658612251	0.017404060810804367	-0.03192778676748276	-0.03908952698111534	0.048068832606077194	0.026156313717365265	0.01916971057653427	-0.004804732743650675	0.03220836818218231	0.022481154650449753	0.09900679439306259	0.04219547286629677	-0.0019778183195739985	-0.012885204516351223	-0.03109351545572281	0.006662665866315365	0.051240574568510056	-0.05382698029279709	-0.06111166998744011	0.07116437703371048	-0.11074052006006241	-0.04304683208465576	0.032369423657655716	0.006096414290368557	-0.01335280854254961	-0.015916017815470695	-0.04813786968588829	0.07898633927106857	-0.02179081365466118	-0.03633977472782135	-0.008998525328934193	0.09967687726020813	-0.024070993065834045	-0.08946332335472107	0.06963134557008743	-0.08565875887870789	-0.016970930621027946	0.04667581245303154	-0.007207435555756092	0.011469786055386066	-0.027252083644270897	-0.046910837292671204	0.09628771245479584	-0.0302641149610281	-0.02269105426967144	-0.0032279903534799814	-0.0015473244711756706	-0.04317475110292435	0.05474931001663208	0.037701476365327835	0.0045159184373915195	0.06478461623191833	-0.0663781613111496	0.0478692427277565	0.014025527983903885	-0.02073219045996666	0.034894078969955444	-0.10504423081874847	-0.026324769482016563	0.02399187907576561	-0.009279887191951275	-0.024694113060832024	0.035066232085227966	0.011622739024460316	0.06455965340137482	0.01931781694293022	-0.05784497410058975	0.018082598224282265	0.033094439655542374	0.0357653982937336	0.01621324196457863	0.08971711993217468	0.06395429372787476	0.02856948785483837	0.028580907732248306	0.020112553611397743	0.0050405883230268955	-0.06238012760877609	-0.062153007835149765	0.012613042257726192	0.004217228386551142	0.037506334483623505	-0.006825747899711132	-0.0568978488445282	0.002175702014937997	0.01011037826538086	-0.08857075870037079	-0.007225252687931061	0.08430497348308563	-0.02356060966849327	0.10471600294113159	0.025860657915472984	0.007415966130793095	0.07335595786571503	0.06254512071609497	0.05633821338415146	-0.001388491247780621	0.025659048929810524	-0.005747631192207336	-0.019615378230810165	0.0011261634062975645	0.06006539613008499	0.006333096884191036	-0.05605105310678482	0.056380074471235275	0.04624582454562187	0.03734734654426575	-0.012296181172132492	-0.07170682400465012	0.02833372913300991	-0.053573425859212875	-0.08233031630516052	-0.0041842334903776646	-0.07459864020347595	0.006659605074673891	0.0343598909676075	0.021992916241288185	0.042419519275426865	-0.008306620642542839	-0.014766519889235497	-0.04783978313207626	-0.030408082529902458	-0.02537313848733902	-0.07033266127109528	-0.04192766547203064	-0.030384322628378868	-0.030232476070523262	0.031149236485362053	-0.0656476616859436	-0.034819845110177994	0.017030442133545876	0.012468334287405014	0.07404342293739319	0.07027432322502136	0.05438583716750145	0.028369270265102386	0.03987475112080574	-0.05199754238128662	-0.01402801088988781	-0.024535391479730606	0.023875802755355835	-0.008869405835866928	0.04238198697566986	-0.052891455590724945	0.004080629907548428	0.002048018155619502	0.07302679121494293	-0.022743849083781242	-0.0025841013994067907	-0.01551505271345377	-0.052042704075574875	-0.057850394397974014	0.060368478298187256	0.06014415621757507	-0.024066144600510597	0.03976861760020256	-0.040481723845005035	0.050132155418395996	-0.02816815674304962	-0.042604733258485794	-0.017638372257351875	0.04737842082977295	0.031241977587342262	0.027655713260173798	-0.0008771244902163744	-0.07224303483963013	0.06560433655977249	0.026100419461727142	0.03764873370528221	-0.05439803749322891	0.005478634499013424	0.019785147160291672	0.011137117631733418	0.03626231104135513	0.06599611788988113	-0.004215251188725233	0.015683967620134354	0.0004913851735182106	0.08367830514907837	-0.04288705065846443	0.03447270020842552	-0.01132902316749096	-0.07897554337978363	-0.01299141626805067	-0.03297394886612892	-0.08651138842105865	-0.024843556806445122	0.06937149912118912	-0.04322395473718643	-0.00935540534555912	-0.02493985928595066	0.03322349116206169	-0.031630732119083405	-0.020868942141532898	-0.016439519822597504	-0.01271800883114338	-0.07815168797969818	0.08403800427913666	0.026245979592204094	-0.07450652122497559	-0.06456238776445389	-0.031522903591394424	0.009840581566095352	-0.01617586985230446	0.04271630197763443	0.06807486712932587	0.05123987793922424	0.046083733439445496	0.007678111083805561	0.015482700429856777	-0.0207014549523592	-0.026312677189707756	0.00494609447196126	0.02431345544755459	-0.04998484626412392	0.016384074464440346	-0.018081380054354668	-0.08616865426301956	0.001593353459611535	0.006430997047573328	-0.010949366725981236	-0.04497114196419716	-0.03336348012089729	0.0195928942412138	0.025403616949915886	-0.04359142482280731	-0.04315323755145073	-0.017172228544950485	-0.02477172017097473	0.05850265175104141	-0.06133861467242241	-0.02289268560707569	0.024965183809399605	0.004047156311571598	0.014011365361511707	-0.04481777548789978	0.03309893235564232	-0.03047335334122181	-0.09363558143377304	0.013399895280599594	-0.08158046752214432	-0.04048977419734001	-0.02408525161445141	-0.04290707781910896	-0.039035454392433167	0.1063041165471077	0.002108740620315075	-0.04306440055370331	-0.06910257041454315	-0.050154510885477066	0.004669404588639736	-0.056220848113298416	0.014572419226169586	0.009405143558979034	-0.015268894843757153	-0.020449323579669	0.019743027165532112	-0.036990463733673096	0.0016950143035501242	-0.004495490808039904	-0.016045475378632545	-0.03393949940800667	0.0730263888835907	-0.041568607091903687	-0.012389475479722023	0.023508204147219658	-0.048080746084451675	0.034521572291851044	0.10947485268115997	0.026432350277900696	-0.0016483261715620756	0.051414333283901215	0.036344122141599655	0.08207035064697266	0.04820965230464935	-0.04811152070760727	-0.0010647368617355824	-0.053124476224184036	-0.042112670838832855	0.03741168603301048	-0.04441526532173157	-0.0071638417430222034	0.007871802896261215	-0.009198080748319626	0.07858754694461823	0.0018432553624734282	-0.06123916059732437	-0.0011038134107366204	0.04378361254930496	-0.0073631019331514835	0.005517831537872553	0.018462548032402992	0.01781066693365574	-0.013610011897981167	-0.048999011516571045	-0.012451255694031715	-0.000565340043976903	0.0474250502884388	-0.02742667868733406	0.04746406152844429	-0.044022832065820694	-0.052000705152750015	0.007516671437770128	0.010636541992425919	0.1072036400437355	-0.07512623071670532	0.01093731727451086	0.06229376420378685	0.030201366171240807	-0.08490510284900665	0.03220602497458458	0.03537015616893768	-0.04626212641596794	-0.05512017756700516	0.021165557205677032	0.030089007690548897	-0.0822739228606224	-0.016230260953307152	-0.0240059494972229	-0.044769544154405594	-0.000573967001400888	-0.0008881185203790665	0.0012536405120044947	0.016529180109500885	-0.040397919714450836	-0.02643880434334278	-0.010321537032723427	-0.007631994783878326	0.005541746970266104	-0.06042347848415375	0.013499736785888672	-0.027548545971512794	0.04154319688677788	-0.033073123544454575	-0.03179386258125305	-0.04690714180469513	0.058896660804748535	-0.020357957109808922	-0.04092741012573242	-0.026860063895583153	-0.045514095574617386	-0.02196456491947174	-0.015886804088950157	0.03880408778786659	-0.07071369886398315	-0.02961939200758934	-0.06091468781232834	-0.045694734901189804	-0.060763947665691376	-0.06728774309158325	0.033164381980895996	-0.06705006211996078	0.021832145750522614	-0.034609511494636536	-0.017706451937556267	-0.019736282527446747	-0.004703224170953035	0.009534185752272606	0.0517144650220871	0.01775852032005787	-0.023006586357951164	0.05126773193478584	-0.007303810212761164	0.025769447907805443	0.030148057267069817	-0.05812489986419678	0.028225654736161232	0.02505417913198471	-0.06460069864988327	0.05345901846885681	-0.02460944838821888	0.0429680235683918	-0.014406566508114338	-0.02273632399737835	0.026223763823509216	-0.009775586426258087	-0.032389432191848755	-0.017759187147021294	0.0623810738325119	-0.04453667625784874	-0.034845877438783646	-0.0025795884430408478	-0.06022750586271286	-0.08430285006761551	-0.041072215884923935	-0.0006614048033952713	-0.015884073451161385	0.024677950888872147	-0.001313500921241939	0.03350932523608208	0.020008506253361702	-0.053315140306949615	-0.03906968981027603	-0.036518681794404984	-0.028527364134788513	0.04314452409744263	0.00039637950249016285	0.02405369281768799	-0.012577210552990437	-0.012674846686422825	-0.005433052312582731	0.021240955218672752	0.04128972813487053	-0.084792360663414	0.007406511344015598	-0.04743557423353195	-0.018733570352196693	0.0008279539179056883	-0.005299378652125597	0.005031993612647057	-0.08162207156419754	-0.022429833188652992	-0.02782152220606804	0.06810109317302704	-0.010903396643698215	-0.018824610859155655	0.047636471688747406	-0.006141536869108677	0.012624961324036121	0.03624798357486725	-0.051118236035108566	-0.05165097489953041	0.021517189219594002	-0.023842498660087585	0.03580017015337944	0.023955004289746284	0.03208579123020172	-0.03764775022864342	-0.06737446784973145	0.003043746342882514	0.00029288549558259547	-0.11091696470975876	-0.022794120013713837	0.013865034095942974	-0.0944344773888588	-0.08148195594549179	0.00709661515429616	-0.029564684256911278	0.07897934317588806	0.02567029744386673	0.014014732092618942	-0.060016412287950516	-0.009843869134783745	-0.04264847934246063	-0.04724959656596184	0.0371195450425148	-0.011324670165777206	-0.047174062579870224	-0.02976253628730774	0.0754079595208168	-0.06567277014255524	0.01716672070324421	0.04705816134810448	-0.03885031118988991	0.03812604397535324	0.03918435797095299	-0.04543051868677139	0.07927598804235458	0.029777389019727707	-0.018360422924160957	0.03771980106830597	-0.09918533265590668	0.08695243299007416	-0.04272164776921272	0.08222632110118866	0.024064211174845695	0.018571022897958755	0.07270203530788422	0.1071159765124321	-0.018568651750683784	-0.02200794406235218	-0.08666286617517471
+0.00232991692610085	-0.052666060626506805	-0.05610397085547447	0.051728058606386185	-0.07537893950939178	-0.04760459065437317	-0.0012704224791377783	0.06703782081604004	-0.007805871311575174	-0.058378055691719055	-0.029135551303625107	-0.01686975732445717	-0.02548329532146454	-0.014723556116223335	0.03343736752867699	0.015215353108942509	-0.04326201230287552	0.033916015177965164	-0.05671604350209236	-0.053174376487731934	0.012045071460306644	0.03296377882361412	-0.0546635240316391	0.03972778469324112	-0.0014277349691838026	0.003803203348070383	-0.020472068339586258	0.006932328455150127	0.00015911993978079408	0.04421908035874367	0.012662636116147041	-0.007100232411175966	0.05172240734100342	-0.01847543939948082	-0.07604966312646866	0.06366167962551117	-0.0219821035861969	0.10766977071762085	0.011266251094639301	-0.04320763051509857	-0.0425909049808979	-0.017482442781329155	-0.03146437555551529	-0.07472285628318787	0.04647659510374069	-0.003914559260010719	0.015696419402956963	-0.032149787992239	0.02460451051592827	0.07854900509119034	0.0784478411078453	0.02974032796919346	0.017895234748721123	-0.009907880797982216	-0.01960190199315548	0.019424233585596085	0.022146640345454216	-0.05087992176413536	-0.027487941086292267	0.04136931523680687	-0.08604633063077927	-0.024459386244416237	0.017384914681315422	0.03145280480384827	-0.01649809442460537	-0.00837007537484169	-0.008054260164499283	0.10093056410551071	0.029113169759511948	-0.05429316684603691	-0.03560726344585419	0.04552009701728821	-0.016675682738423347	-0.0380508229136467	0.04172079637646675	-0.022496741265058517	-0.013782442547380924	0.01539598312228918	-0.03262089192867279	-0.01515611819922924	0.010773804038763046	-0.03402382880449295	0.14998438954353333	-0.05532267689704895	-0.03854881599545479	-0.01335450541228056	-0.03265649080276489	-0.07563339173793793	0.057155542075634	-0.007558215409517288	-0.0048879277892410755	0.07814818620681763	-0.04421759769320488	0.04355093091726303	-0.016391148790717125	0.045613210648298264	0.003989026881754398	-0.13375040888786316	-0.046098493039608	-0.020697003230452538	-0.03182819485664368	-0.02526693418622017	0.05614917352795601	0.00725066801533103	0.02677970938384533	0.011963844299316406	-0.03390859439969063	-0.04966280236840248	0.012131692841649055	-0.0056449901312589645	-0.005871168803423643	0.08133455365896225	0.07525325566530228	0.02588913030922413	0.0620625764131546	0.00014133927470538765	-0.01509174332022667	-0.035803038626909256	-0.027080196887254715	0.007420226465910673	0.005444660782814026	0.02114323526620865	0.01722731813788414	-0.0706399455666542	-0.033194344490766525	0.01211947575211525	-0.09499882906675339	-0.030970359221100807	0.056673940271139145	-0.06842614710330963	0.11183479428291321	0.03748956322669983	-0.04721305891871452	0.018559740856289864	0.036158237606287	0.00883781909942627	-0.005288049578666687	-0.003656493965536356	-0.007108672987669706	-0.029787428677082062	-0.013665678910911083	0.07527156174182892	-0.02117776870727539	-0.04978002607822418	0.04566885903477669	0.04565758258104324	0.025563305243849754	-0.03479867801070213	-0.08872417360544205	0.03600403293967247	-0.0796804428100586	-0.07121673226356506	-0.012880933471024036	-0.07687105983495712	0.001739608938805759	0.04506360739469528	-0.014563673175871372	0.03370165824890137	-0.010998565703630447	-0.023861616849899292	-0.030272474512457848	-0.020850906148552895	-0.0052474853582680225	-0.0892561674118042	-0.031224537640810013	-0.01716267503798008	-0.03799749165773392	0.01827680505812168	-0.03976371884346008	-0.031258340924978256	-0.014624358154833317	0.014472628943622112	0.047468025237321854	0.08671239763498306	0.07088688760995865	0.02908948063850403	0.00616926234215498	-0.04872406646609306	-0.01771794818341732	-0.028723251074552536	0.025876764208078384	-0.02177872322499752	0.04360204562544823	-0.04536597803235054	-0.007812516763806343	-0.04203246533870697	0.04316418245434761	-0.02929273620247841	0.027625977993011475	0.00046342721907421947	-0.031871113926172256	-0.04340125992894173	0.042031317949295044	0.08452199399471283	-0.03402632102370262	0.040700893849134445	-0.009455409832298756	0.08678565174341202	-0.06060837581753731	-0.01984885334968567	-0.0499584935605526	0.0489577017724514	0.044064961373806	0.033039387315511703	0.009839272126555443	-0.07413624227046967	0.1015203669667244	0.015612161718308926	-0.003113530343398452	-0.04363527521491051	0.07654192298650742	0.059826791286468506	0.0024685021489858627	0.0640595555305481	0.04655780643224716	-0.006088270805776119	-0.02697049267590046	-0.0064439792186021805	0.02164377085864544	-0.060167402029037476	0.004172171466052532	-0.0015811799094080925	-0.07591583579778671	0.007601091172546148	-0.027317306026816368	-0.08062513917684555	-0.016938915476202965	0.03214277699589729	-0.008413733914494514	-0.016600508242845535	-0.009881246834993362	0.004066396038979292	-0.002254160586744547	-0.02647368051111698	-0.03274933993816376	-0.006749380379915237	-0.05815475061535835	0.07219968736171722	-0.018791604787111282	-0.057191863656044006	-0.0522720143198967	-0.011081891134381294	0.007218522485345602	-0.02014651522040367	0.02248920127749443	0.08909225463867188	0.057300470769405365	0.0288751982152462	-0.0019867499358952045	-0.01739654876291752	-0.07058564573526382	-0.023490354418754578	-0.005710902623832226	0.032770562916994095	-0.042564619332551956	-0.057407453656196594	-0.002745966427028179	-0.0879303365945816	-0.005976554471999407	0.004977460019290447	0.05760208144783974	-0.05370417982339859	-0.04273122921586037	-0.02307765558362007	-0.00928430538624525	-0.028746364638209343	-0.03855473920702934	-0.00264123291708529	-0.039252590388059616	0.0171204786747694	-0.03934735432267189	-0.010582552291452885	0.038958244025707245	-0.009498468600213528	0.0652301236987114	-0.0075006308034062386	0.03648693487048149	-0.011960567906498909	-0.08593983948230743	0.028311381116509438	-0.08239762485027313	-0.07069312036037445	-0.03875939920544624	-0.04626995697617531	-0.0503571480512619	0.09787485748529434	0.04117914289236069	-0.015794042497873306	-0.061977360397577286	-0.0291818268597126	-0.03890968859195709	-0.05449838936328888	-0.007773295044898987	-0.006019454915076494	-0.06895137578248978	-0.019883794710040092	0.013746997341513634	-0.03949594497680664	0.00011043144331779331	0.026775948703289032	-0.003176565980538726	-0.06647533923387527	0.07468704879283905	-0.0027357356157153845	-0.021536003798246384	-0.000702970486599952	-0.04909173771739006	0.02309480868279934	0.07556939125061035	0.02140848897397518	0.023102689534425735	0.058366965502500534	0.03459283709526062	0.060437727719545364	0.008054675534367561	0.022328585386276245	-0.01837567239999771	0.0028794072568416595	-0.004777648951858282	0.03966512903571129	-0.01678878627717495	-0.049621254205703735	0.025553448125720024	0.0008507908205501735	0.08236263692378998	-0.008753414265811443	-0.032650988548994064	-0.035064972937107086	0.02581130899488926	0.005287803243845701	0.054831601679325104	-0.01582511141896248	0.008303666487336159	-0.00017689455125946552	-0.07340719550848007	0.004965684376657009	-0.016840871423482895	0.016556458547711372	0.0021190340630710125	0.02868729829788208	-0.0023398424964398146	-0.054931674152612686	0.0031170982401818037	-0.009687499143183231	0.12430309504270554	-0.10406259447336197	0.005683046765625477	0.08675360679626465	0.008208056911826134	-0.07724394649267197	0.004371472634375095	0.014329114928841591	-0.04213275760412216	-0.07049652934074402	0.01971692405641079	0.06203705072402954	-0.07856553047895432	-0.0482330396771431	-0.04552586004137993	-0.020399319007992744	-0.019315898418426514	-0.02145991288125515	0.03135320171713829	0.038208965212106705	0.006340603344142437	-0.01556391455233097	0.004292172845453024	0.0169618371874094	0.03689580038189888	-0.04872659221291542	0.031448088586330414	0.010985483415424824	0.042690109461545944	-0.07513001561164856	-0.07319671660661697	-0.018206266686320305	0.049171581864356995	-0.02376502752304077	-0.03745028376579285	-0.046390242874622345	-0.0608021505177021	0.016552075743675232	0.06542275100946426	-0.007737381849437952	-0.037803392857313156	-0.03855093941092491	-0.027473801746964455	-0.046190377324819565	-0.08194311708211899	-0.06704247742891312	0.005542681086808443	-0.046303607523441315	0.009304240345954895	-0.0763605609536171	-0.016233744099736214	-0.005548229441046715	0.014894339255988598	0.04178876802325249	0.07093805074691772	0.01969882845878601	-0.018822574988007545	0.024575330317020416	-0.021774113178253174	0.04367872327566147	0.009269915521144867	-0.050975680351257324	0.020643843337893486	0.005859146360307932	-0.058690257370471954	0.06232728436589241	-0.04062686115503311	-0.016106389462947845	0.009815244935452938	-0.01636345498263836	0.0032846892718225718	0.0005799573264084756	0.021984217688441277	-0.04997383430600166	0.04992179572582245	-0.07469531148672104	-0.017909418791532516	0.042122144252061844	0.012128414586186409	-0.08206361532211304	-0.06344491243362427	-0.025274980813264847	-0.024077339097857475	0.07868854701519012	-0.012994421645998955	-0.0055389138869941235	0.013577505946159363	-0.06794555485248566	-0.06272304058074951	-0.04604281857609749	-0.012137548066675663	-0.0034015225246548653	0.034385766834020615	0.02638901397585869	0.018908552825450897	-0.004304641392081976	-0.058648090809583664	-0.00910747516900301	-0.011593673378229141	-0.06756366789340973	-0.017628973349928856	0.005569510627537966	-0.008734943345189095	0.026526810601353645	0.0047149998135864735	0.01283508725464344	-0.03632509335875511	-0.019025515764951706	-0.026606660336256027	0.06189696118235588	-0.02364915795624256	-0.009454498998820782	0.04648776724934578	-0.025009365752339363	-0.0038454020395874977	0.07631321996450424	-0.038175348192453384	-0.06241707503795624	-0.0064747948199510574	-0.013313661329448223	0.057097334414720535	0.02024688385426998	0.03689193353056908	-0.0741158202290535	-0.04757506772875786	-0.010377591475844383	-0.011122355237603188	-0.057472847402095795	-0.009006929583847523	0.05918852239847183	-0.0761636346578598	-0.03315762057900429	-0.0077695236541330814	-0.025391533970832825	0.07829927653074265	-0.005997935310006142	0.06801558285951614	-0.06485656648874283	-0.025043625384569168	-0.030519410967826843	-0.048575449734926224	0.01117794867604971	-0.014788009226322174	-0.064079150557518	-0.05668453127145767	0.09513436257839203	-0.03626716881990433	0.020627304911613464	0.08114100247621536	-0.059206411242485046	0.06804558634757996	0.022119347006082535	-0.003963733557611704	0.056148458272218704	0.028777429834008217	-0.03402956947684288	0.04828500747680664	-0.06913041323423386	0.06301453709602356	-0.032782554626464844	0.10908205807209015	0.012337525375187397	-0.005945115815848112	0.08064042031764984	0.09746148437261581	0.017690878361463547	0.01261792704463005	-0.0863945260643959
+0.02729630470275879	-0.022307289764285088	0.03168628364801407	0.0193279217928648	-0.06700780987739563	-0.061454176902770996	0.029881605878472328	0.029186328873038292	-0.013162392191588879	0.013390650041401386	0.007442769128829241	0.017840862274169922	-0.017556119710206985	0.015228195115923882	0.005904904566705227	-0.055984579026699066	-0.09366118907928467	-0.0035204177256673574	-0.07026004791259766	-0.0005393322207964957	0.0013903968501836061	0.045738499611616135	-0.11485190689563751	-0.01048972550779581	-0.047226130962371826	0.020504331216216087	-0.07934314012527466	-0.001151092816144228	-0.021330060437321663	-0.01719398982822895	0.049990516155958176	-0.013926231302320957	0.10245797783136368	-0.05561843141913414	-0.03947708010673523	0.022536342963576317	-0.04491506516933441	0.09669701009988785	0.07200460135936737	-0.05883624777197838	-0.06895427405834198	-0.00020280915487091988	0.018923617899417877	-0.07384543120861053	0.0020399854984134436	0.017963262274861336	-0.008551123552024364	-0.056017860770225525	0.06922884285449982	0.009732815437018871	0.10155240446329117	0.009175281971693039	-0.02270634099841118	-0.008216806687414646	-0.012300598435103893	-0.014937072061002254	-0.006376716308295727	-0.019114898517727852	-0.13676579296588898	0.022076480090618134	-0.11197193711996078	0.02188614197075367	-0.012331348843872547	0.03269584849476814	-0.06492963433265686	-0.056429244577884674	-0.0289433766156435	0.07071743905544281	0.05385691300034523	-0.0109562361612916	0.030010022222995758	0.09520868957042694	-0.05949564278125763	-0.05954108387231827	-0.011829421855509281	-0.050412628799676895	0.0003452898235991597	0.0042472584173083305	-0.015670282766222954	-0.005963668692857027	-0.021889816969633102	-0.005112707149237394	0.10151631385087967	-0.0679120197892189	-0.014298053458333015	-0.0024832547642290592	0.005868962500244379	-0.11239506304264069	0.05414546653628349	0.05598997697234154	0.004212598316371441	0.04736021161079407	-0.029648758471012115	0.035558778792619705	0.03130847588181496	0.061189692467451096	0.04071051627397537	-0.07645253092050552	-0.03176888823509216	-0.051116637885570526	-0.0051094233058393	0.04119597002863884	0.09558305889368057	0.018821267411112785	0.08351791650056839	-0.03065642900764942	-0.02342149429023266	0.026872172951698303	0.02993916906416416	0.015705227851867676	0.033851198852062225	0.08376773446798325	0.07139036804437637	0.022069556638598442	0.02367846667766571	-0.011687039397656918	0.045405253767967224	-0.03066728636622429	0.0025829675141721964	-0.007796894293278456	-0.0340002216398716	0.023854294791817665	-0.028913721442222595	-0.06872901320457458	-0.03697536885738373	0.049649953842163086	-0.06186294928193092	-0.05367426574230194	0.032497942447662354	-0.034923236817121506	0.09711981564760208	0.10613097250461578	-0.02016790769994259	0.02620074339210987	0.05105798318982124	-0.03163154050707817	-0.016705676913261414	0.041692592203617096	0.0035126334987580776	0.013959860429167747	-0.0010389704257249832	0.06742440909147263	-0.01250906940549612	-0.02602872997522354	-0.039293501526117325	0.03671436384320259	0.008640049956738949	-0.03063998743891716	-0.07638835161924362	0.057257842272520065	-0.0019169820006936789	-0.08547520637512207	-0.02593320794403553	-0.05341958627104759	0.02135845459997654	0.09373202174901962	0.00867463368922472	0.0418410524725914	-0.02315687946975231	0.005744838155806065	-0.03348597511649132	-0.05949933081865311	-0.027862511575222015	-0.08719044178724289	-0.040597300976514816	-0.03180226311087608	0.030506649985909462	0.008955420926213264	-0.01598954387009144	0.025447266176342964	-0.03393300250172615	0.011156939901411533	-0.03463328257203102	0.03732949495315552	0.03712252900004387	0.02113812230527401	-0.020379751920700073	-0.04286176711320877	0.05892622470855713	-0.03945986554026604	-0.007766352500766516	-0.05402813479304314	0.033727508038282394	-0.042022455483675	-0.009502565488219261	-0.0021550531964749098	0.05891021341085434	-0.01988336816430092	0.021859152242541313	0.04549679532647133	0.017502980306744576	-0.026816371828317642	-0.021408608183264732	0.0551387183368206	0.0031692108605057	0.0338052362203598	-0.01815159060060978	0.04014259949326515	-0.00708130095154047	-0.06181160733103752	-0.03645942732691765	0.04007158428430557	-0.01385833602398634	0.020104127004742622	0.01659344509243965	-0.012097149156033993	0.04115837439894676	0.03728887811303139	-0.006456358823925257	-0.0784454345703125	-0.00718710757791996	-0.0250743068754673	0.06678703427314758	-0.02955789305269718	0.061533503234386444	-0.0958184003829956	-0.0223553366959095	0.03867953270673752	0.04242129623889923	-0.011316782794892788	-0.0014076647348701954	0.01759263128042221	-0.04060287028551102	0.03457362949848175	-0.014278312213718891	-0.09427221864461899	0.013716292567551136	0.037199798971414566	-0.00016481606871820986	-0.01978999748826027	0.059292905032634735	0.024061674252152443	-0.059043169021606445	-0.010297120548784733	-0.018356410786509514	-0.043677423149347305	-0.07888864725828171	0.06885731220245361	0.013651569373905659	-0.03494976460933685	-0.031064437702298164	-0.0009883567690849304	0.015337915159761906	0.04604026675224304	-0.004556234460324049	-0.013705410063266754	0.10746337473392487	0.027775533497333527	-0.044747497886419296	0.011980993673205376	-0.0642714872956276	-0.06822793930768967	0.045694611966609955	0.05733507126569748	-0.050684135407209396	-0.00775660527870059	-0.011285937391221523	-0.04600459337234497	0.0038120439276099205	-0.008235509507358074	0.04999232664704323	0.017304636538028717	0.021140994504094124	-0.004194275010377169	0.06453673541545868	0.013342791236937046	-0.043020594865083694	0.0574871189892292	0.03946228697896004	0.04560801759362221	-0.02142324484884739	-0.028287677094340324	0.06079179421067238	0.028951872140169144	0.07161742448806763	-0.022661717608571053	0.05450883507728577	-0.02439011074602604	-0.08653037250041962	-0.03400072455406189	-0.12539039552211761	0.0017660509329289198	-0.008621389046311378	-0.05870158225297928	-0.07964549213647842	0.04457363113760948	0.013082130812108517	0.0036877731326967478	-0.042301442474126816	-0.019835883751511574	-0.029803793877363205	-0.05319015681743622	-0.026905862614512444	-0.0014686303911730647	-0.03645322471857071	-0.04125150665640831	0.02287883311510086	-0.09555875509977341	-0.04517638310790062	-0.009171481244266033	-0.04687061533331871	-0.0392049141228199	0.058281056582927704	-0.03349202871322632	0.00909087061882019	-0.009189916774630547	-0.11687131971120834	0.025231949985027313	0.09396270662546158	0.041037946939468384	0.08006712049245834	0.06761831045150757	0.01935405097901821	0.09742453694343567	0.05944890156388283	-0.07745398581027985	-0.026176849380135536	-0.0039062644354999065	-0.08653543144464493	0.03913618251681328	-0.03384912386536598	0.0460645854473114	-0.012558220885694027	0.01910741627216339	0.05998381972312927	-0.004267717711627483	-0.0463612861931324	-0.023922167718410492	0.01309056393802166	-0.051004741340875626	-0.00974008534103632	0.018519552424550056	-0.0038709102664142847	0.025538256391882896	-0.03418305888772011	-0.04053880274295807	-0.004592918325215578	0.0047722188755869865	-0.04357999190688133	0.03282729163765907	0.02973022125661373	-0.03477417305111885	0.021232297644019127	-0.05745486170053482	0.07377850264310837	-0.0546228364109993	0.03188079223036766	-0.027405602857470512	0.04099294915795326	-0.11737354844808578	-0.026404041796922684	0.051691263914108276	-0.011601820588111877	-0.04234938696026802	0.048262666910886765	0.019039468839764595	-0.03452945873141289	-0.004151888657361269	0.0019291144562885165	-0.044922877103090286	-0.02876407839357853	0.017923301085829735	0.057605765759944916	0.036037180572748184	0.01620631292462349	-0.058820389211177826	-0.018081778660416603	0.01738061010837555	0.01972164586186409	-0.017718825489282608	0.012465683743357658	-0.06846167147159576	0.04450163617730141	-0.025425603613257408	-0.0455210842192173	-0.016182219609618187	0.050385355949401855	0.04209262132644653	-0.02408609166741371	0.035779062658548355	-0.00593010988086462	-0.011990158818662167	0.005644422955811024	0.01580369472503662	0.009717066772282124	-0.015158374793827534	0.0010957519989460707	-0.025827761739492416	-0.08650687336921692	-0.011568738147616386	-0.01314414944499731	-0.034457359462976456	0.007240783888846636	-0.0035219520796090364	0.021363096311688423	0.03625413402915001	-0.03528296574950218	0.020876139402389526	0.029308006167411804	0.05417223647236824	-0.017743071541190147	-0.04153232276439667	0.008957249112427235	0.04466937109827995	-0.007638286333531141	-0.020191272720694542	0.014374264515936375	0.04475955292582512	-0.06136118620634079	-0.03183746337890625	-0.03386124223470688	-0.0034330340567976236	0.00809611938893795	-0.026396557688713074	-0.01969180814921856	0.018118441104888916	-0.014259546995162964	-6.95859343977645e-05	0.027899490669369698	-0.07715021073818207	-0.0010766125051304698	0.02906075306236744	-0.03512069210410118	-0.0895102396607399	-0.009609970264136791	-0.013348699547350407	-0.03632152080535889	0.050293441861867905	0.0037425747141242027	0.01514318585395813	0.041887592524290085	-0.037225428968667984	-0.024862907826900482	0.010838666930794716	0.009744320996105671	0.04352797195315361	-0.0387776680290699	0.051178522408008575	-0.0037610495928674936	-0.015811152756214142	-0.007128285709768534	0.006306752562522888	0.0462663471698761	-0.10002133250236511	-0.02894592471420765	-0.017888015136122704	0.03647549822926521	0.028745483607053757	0.0054670581594109535	-0.014746065251529217	-0.08283563703298569	-0.0162026509642601	0.03469763696193695	0.0324123352766037	-0.033922143280506134	-0.0031060436740517616	0.046587202697992325	-0.04736440256237984	-0.04166674241423607	-0.022145191207528114	-0.03042002022266388	-0.06350718438625336	0.0627521499991417	-0.050706155598163605	0.0028290036134421825	0.02922724559903145	0.044845547527074814	-0.005348813720047474	-0.08638131618499756	-0.04860415309667587	-0.0034421803429722786	-0.0634641945362091	-0.0019714070949703455	0.005920117255300283	-0.0818617194890976	-0.04176631569862366	0.0015471952501684427	-0.044454194605350494	0.04721923545002937	-0.004350697156041861	0.02893078699707985	0.007800104096531868	-0.003304435173049569	-0.01092492789030075	-0.009631899185478687	-0.001625623321160674	-0.007514327298849821	-0.05663478001952171	-0.03571523725986481	0.06324506551027298	-0.054151926189661026	0.04963879659771919	0.07007060945034027	-0.024947328492999077	0.0635470598936081	0.01157756894826889	-0.025952089577913284	0.007638982962816954	-0.013173221610486507	-0.02849859558045864	0.0524064339697361	-0.06211113929748535	0.0543137826025486	-0.05617306008934975	0.09975765645503998	0.018090706318616867	-0.03571803867816925	0.06730050593614578	0.11305558681488037	-0.022779101505875587	-0.013459122739732265	-0.024251148104667664
+0.07109864056110382	-0.039891861379146576	0.013921510428190231	0.0056035141460597515	-0.1004648357629776	-0.07937466353178024	0.036369241774082184	0.014789547771215439	0.01702265627682209	-0.037167053669691086	0.03283090144395828	0.006300012115389109	-0.025630133226513863	0.029714953154325485	0.03895149379968643	-0.006415692623704672	-0.053300999104976654	0.008967521600425243	-0.05455484613776207	0.008357450366020203	0.04445355758070946	-0.015441104769706726	-0.03852050006389618	0.00615082448348403	-0.04261143133044243	-0.005650926381349564	-0.06699945777654648	0.001673318212851882	-0.031321775168180466	-0.0013001355109736323	0.030990172177553177	-0.01732676289975643	0.056411370635032654	-0.039764031767845154	-0.05229390785098076	0.052915628999471664	-0.050408944487571716	0.11352525651454926	0.06189913675189018	-0.02733798511326313	-0.0399567075073719	-0.02070927619934082	-0.08854610472917557	-0.05017146095633507	0.04940193518996239	0.037838954478502274	-0.012983766384422779	-0.022199813276529312	0.06695570051670074	0.04775801673531532	0.06257214397192001	-0.03259431943297386	-0.09255862981081009	-0.017561450600624084	-0.015421739779412746	-0.01291427481919527	0.03984896466135979	-0.06890429556369781	-0.06184040382504463	0.06098640710115433	-0.13371874392032623	-0.005693121813237667	0.022433001548051834	0.05859300121665001	0.01999962329864502	-0.05464496091008186	0.008225705474615097	0.015214699320495129	0.009815521538257599	-0.06150227412581444	0.01695096679031849	0.09983783215284348	-0.02673247642815113	-0.06266780942678452	0.023683257400989532	-0.057388510555028915	0.02736487053334713	0.028092725202441216	-0.01452199462801218	0.017388606444001198	-0.02823404036462307	-0.031046004965901375	0.09450483322143555	-0.05535829812288284	-0.0030232639983296394	-0.027655787765979767	0.03273414075374603	-0.06334587186574936	0.041858404874801636	0.01888025552034378	-0.028868092224001884	0.026773622259497643	-0.08157874643802643	0.013521560467779636	-0.01562601886689663	0.0030879054684191942	-0.001808049390092492	-0.061442747712135315	-0.049920231103897095	-0.020373331382870674	0.007055379915982485	-0.0017202779417857528	0.02405133657157421	0.052172742784023285	0.06685556471347809	-0.0076652891002595425	0.010224073193967342	0.05447472631931305	0.026537830010056496	0.023151392117142677	0.008211501874029636	0.02423110231757164	0.1098908856511116	0.02557825855910778	0.04351245239377022	-0.04680614545941353	-0.007852387614548206	-0.06524781882762909	0.004213403910398483	0.0021165679208934307	0.0024039403069764376	0.014470364898443222	-0.02706502377986908	-0.06958393007516861	-0.052086640149354935	-0.006874719634652138	-0.10149244964122772	-0.050303928554058075	0.031183136627078056	-0.050416070967912674	0.09048232436180115	0.0072807250544428825	-0.08267389237880707	0.047912441194057465	0.05087299272418022	0.03646065294742584	-0.0022720571141690016	0.06045261025428772	-0.008096360601484776	-0.025385843589901924	0.07768598943948746	0.09276025742292404	-0.007251558359712362	-0.034306928515434265	0.022419681772589684	0.041770316660404205	0.02547088824212551	0.00929317157715559	-0.10955026000738144	0.04748200625181198	-0.06464067846536636	-0.04248657822608948	-0.017689190804958344	-0.08476465195417404	-0.013075597584247589	0.042633578181266785	-0.021397389471530914	0.012637239880859852	-0.068670354783535	-0.016314227133989334	-0.075662761926651	-0.027475804090499878	-0.035447776317596436	-0.05481546372175217	-0.05463536083698273	-0.040764495730400085	0.03571166843175888	0.04436490312218666	-0.05501070246100426	-0.04290771484375	-0.021784409880638123	-0.018560213968157768	0.02303389646112919	0.03312309458851814	0.08775188028812408	0.03817330300807953	-0.04337810352444649	-0.07385622709989548	0.04813842475414276	0.0010723196901381016	0.03916100040078163	-0.0432569794356823	0.05430988222360611	-0.03574015572667122	-0.0020695049315690994	-0.06298094987869263	0.03507959842681885	-0.0486750565469265	-0.004548535216599703	-0.011074263602495193	-0.034205470234155655	-0.05123564600944519	0.03231823444366455	0.07699388265609741	0.015050246380269527	0.024900468066334724	-0.05467629060149193	0.04588911682367325	-0.015119334682822227	-0.05123477429151535	-0.027630658820271492	0.05913933739066124	0.009906178340315819	0.06603877246379852	0.03949859365820885	-0.06168943643569946	0.07313321530818939	0.015441549941897392	0.035955239087343216	-0.05127326399087906	0.042296119034290314	0.01014968752861023	-0.013127157464623451	-0.03338833153247833	0.02857976034283638	-0.06878428906202316	0.001179317943751812	-0.009593927301466465	0.09254554659128189	0.01563950628042221	0.035388581454753876	0.039035141468048096	-0.05065280944108963	0.011187899857759476	-0.029334524646401405	-0.06483617424964905	-0.008171901106834412	0.06105900555849075	-0.07327619194984436	-0.023303812369704247	-0.02584882266819477	0.0027736506890505552	-0.04010850563645363	-0.02261548489332199	-0.015973756089806557	-0.011406686156988144	-0.06873387098312378	0.09835801273584366	-0.008598320186138153	-0.03265039995312691	-0.027485279366374016	-0.0022557037882506847	0.08136647939682007	-0.004166037309914827	0.033717259764671326	0.036281004548072815	0.02600356750190258	0.00018005003221333027	0.018452810123562813	-0.010194895789027214	-0.041391801089048386	-0.020602582022547722	-0.002355217933654785	0.04337962716817856	-0.08421924710273743	-0.01943039707839489	-0.03603560850024223	-0.03275139629840851	-0.029939724132418633	0.03695528954267502	0.022403592243790627	-0.00695172930136323	-0.0336725227534771	-0.023074310272932053	0.020823020488023758	0.0069366442039608955	0.0002239588211523369	0.014490471221506596	-0.0070314062759280205	0.0017651121597737074	-0.012072194367647171	-0.025168526917696	0.04909359663724899	0.003193422919139266	0.040458448231220245	-0.031130939722061157	0.020996645092964172	-0.0578109547495842	-0.07765096426010132	-0.009936073794960976	-0.09499689936637878	-0.020261457189917564	-0.02625170350074768	-0.05344262719154358	-0.015276394784450531	0.07823053002357483	0.04197930544614792	0.005823615472763777	-0.054210275411605835	-0.0027611299883574247	-0.01704835332930088	-0.06784547120332718	0.0055623105727136135	-0.010528742335736752	-0.04311521723866463	-0.043834980577230453	0.0055992561392486095	-0.047360874712467194	-0.028348250314593315	-0.05982533469796181	-0.04828622192144394	-0.07238662242889404	-0.006030588410794735	-0.023701263591647148	-0.0692562460899353	0.015524782240390778	-0.08022918552160263	0.03482499346137047	0.11877176910638809	0.06873562932014465	0.032975032925605774	0.007567614316940308	0.03514481335878372	0.07170750200748444	-0.015610338188707829	-0.018098851665854454	0.0024146202486008406	-0.03993228077888489	-0.04184763878583908	0.00465254345908761	-0.07458478212356567	-0.0030779866501688957	0.0030068133492022753	-0.03054712899029255	0.09666895121335983	-0.027402551844716072	-0.0438508577644825	0.016296209767460823	0.025614377111196518	-0.025410110130906105	0.0322260782122612	0.02531043067574501	0.005478230305016041	0.005684616509824991	-0.0012597896857187152	-0.009027890861034393	-0.014865508303046227	0.029507681727409363	-0.05412616953253746	0.018895220011472702	-0.015448343940079212	-0.04940253496170044	-0.012873207218945026	0.011959664523601532	0.09269753098487854	-0.012796686962246895	0.018453802913427353	0.037701357156038284	-0.019487053155899048	-0.08266984671354294	0.03737398982048035	0.003891510423272848	-0.008677140809595585	-0.007677408866584301	0.0012189678382128477	0.049773674458265305	-0.08291646093130112	-0.0018201104830950499	0.004145829938352108	-0.02028638869524002	-0.039851922541856766	0.027937550097703934	0.07312431931495667	0.041151344776153564	-0.030762266367673874	-0.024555647745728493	0.0052976966835558414	0.009690400213003159	0.02363666705787182	-0.029046090319752693	-0.012024159543216228	-0.02813507243990898	0.02949523739516735	-0.03925146162509918	-0.0533202663064003	-0.018689041957259178	0.09967296570539474	0.04124518111348152	-0.047175753861665726	0.0075604962185025215	-0.030666902661323547	-0.00030498605337925255	-0.023215746507048607	0.039973385632038116	0.006591673940420151	-0.024527380242943764	-0.023597704246640205	-0.006116431672126055	-0.13556960225105286	-0.01910257898271084	0.021956197917461395	-0.06975225359201431	-0.025469325482845306	-0.04462824761867523	0.0011552784126251936	-0.012770446948707104	-0.03213135153055191	-0.04092349484562874	0.02725357376039028	0.02220303565263748	0.015372122637927532	-0.004547007381916046	0.005210766103118658	0.05946529656648636	0.04381213337182999	-0.014466733671724796	-0.009404432959854603	-0.05007553473114967	-0.05839693546295166	0.03225415199995041	-0.042851269245147705	-0.011523124761879444	-0.004949634894728661	-0.005701588932424784	0.024885986000299454	-0.025228669866919518	0.04615212231874466	-0.005283523816615343	0.02698454260826111	-0.0709194466471672	-0.00273850467056036	0.0354878231883049	-0.01744353398680687	-0.11353736370801926	-0.026780175045132637	0.004035275429487228	-0.01711324043571949	0.022018160670995712	-0.02371666394174099	-0.00513734994456172	0.033491235226392746	-0.030989330261945724	-0.042545657604932785	-0.05710253119468689	-0.03714417293667793	0.06218796595931053	-0.04591646045446396	0.05068647116422653	0.014226214028894901	0.02596409246325493	-0.02733861468732357	0.025511613115668297	0.025612857192754745	-0.055639419704675674	-0.010167429223656654	-0.07641398906707764	0.035088974982500076	0.001657708897255361	-0.05470006912946701	0.04721839725971222	-0.02988232858479023	-0.05480649322271347	-0.026445114985108376	0.13944295048713684	-0.031107403337955475	-0.015732672065496445	0.05169518291950226	-0.03775329515337944	-0.0033324402756989002	0.034210171550512314	-0.05778658390045166	-0.07146166265010834	0.03413533791899681	-0.045468345284461975	-0.0024995324201881886	0.04407352954149246	0.04036315903067589	0.007468354422599077	-0.023772383108735085	-0.006063787266612053	0.028041820973157883	-0.06397499889135361	0.08011866360902786	-0.005021062213927507	-0.057244401425123215	-0.05021204054355621	-0.015127153135836124	-0.007731663528829813	0.0721055343747139	-0.015392561443150043	0.052257705479860306	0.00281925848685205	-0.014085828326642513	0.013444576412439346	-0.050539530813694	0.026531172916293144	0.03012891113758087	-0.030465302988886833	-0.0026543729472905397	0.06550604104995728	-0.055885471403598785	0.022532731294631958	0.055133845657110214	-0.06076428294181824	0.015049967914819717	0.04613347351551056	-0.049367308616638184	0.04535467550158501	0.01699322834610939	-0.08383367210626602	0.06477441638708115	-0.0438770093023777	0.04901363328099251	-0.06692925095558167	0.0952129140496254	-0.008506854996085167	-0.0375530980527401	0.09691556543111801	0.026162071153521538	-0.03914089500904083	-0.013481078669428825	-0.04667229950428009
+0.0163924191147089	0.015504357405006886	-0.021841175854206085	0.03514670208096504	-0.09905214607715607	-0.03828372061252594	0.009877878241240978	0.03209361806511879	0.023097140714526176	-0.013759586028754711	-0.02250763028860092	0.05316320061683655	0.018619157373905182	0.052000317722558975	0.007034593727439642	-0.04584010690450668	-0.032378263771533966	-0.0016960063949227333	-0.010249457322061062	-0.04893440380692482	0.008836592547595501	0.0021832173224538565	-0.0864623486995697	0.01874430663883686	0.011591563001275063	-0.0105411596596241	-0.05948318913578987	-0.013661115430295467	0.007336160633713007	0.01851443015038967	0.05206620693206787	-0.042970333248376846	0.06403214484453201	-0.045549940317869186	-0.09360771626234055	0.013272966258227825	-0.05277452617883682	0.1112145483493805	0.039571113884449005	-0.0252213291823864	-0.07802928239107132	-0.021325401961803436	-0.06216073036193848	-0.006208779755979776	-0.01587684452533722	-0.016493961215019226	-0.002169594168663025	-0.004246138501912355	0.030889853835105896	0.005156484432518482	0.0573074035346508	0.026320332661271095	-0.013918716460466385	-0.04121076315641403	-0.029135772958397865	0.008314894512295723	0.00508617889136076	-0.04025900363922119	-0.10416367650032043	0.028174221515655518	-0.07308089733123779	0.03867041692137718	0.008271295577287674	0.019124360755085945	-0.03220000118017197	-0.017772424966096878	-0.009120100177824497	0.08554481714963913	0.025353193283081055	-0.028632862493395805	0.034579094499349594	0.07167951762676239	-0.05096377432346344	-0.022293908521533012	0.02346966788172722	-0.03577364236116409	-0.023223215714097023	0.02118922583758831	0.005969969090074301	0.006350226234644651	-0.020133767277002335	-0.04730891063809395	0.09296057373285294	-0.026606569066643715	-0.05030020326375961	-0.04682055488228798	-0.005477620288729668	-0.06374432891607285	0.03455865755677223	0.005391019396483898	0.01206697802990675	0.0615193173289299	-0.07318118214607239	0.07071712613105774	0.0335620678961277	0.022276857867836952	0.012290320359170437	-0.1318059116601944	-0.0633702501654625	0.0039351144805550575	0.018050041049718857	-0.007190510164946318	0.06655306369066238	-0.04348739609122276	0.05915794521570206	0.05711115524172783	-0.06911463290452957	0.018639415502548218	0.02066667564213276	0.0022422331385314465	0.056542109698057175	0.06487210839986801	0.031431443989276886	0.06439298391342163	0.05348500981926918	0.01962653547525406	0.047338828444480896	-0.019635023549199104	-0.029023338109254837	-0.004011385142803192	-0.005985409487038851	0.059138257056474686	0.010337315499782562	-0.06278111040592194	-0.07042327523231506	0.01909411884844303	-0.10342379659414291	-0.019282594323158264	0.04910014942288399	-0.0042139156721532345	0.06783223897218704	0.08649974316358566	0.009883414022624493	-0.004868469201028347	0.03061544895172119	-0.015795910730957985	-0.011964602395892143	0.010324105620384216	0.022224625572562218	-0.026566294953227043	-0.04597402736544609	0.06931161880493164	0.027569791302084923	-0.06860616058111191	0.025543196126818657	0.05411861464381218	0.025883404538035393	-0.02603580430150032	-0.07233237475156784	-0.011203259229660034	-0.05374160781502724	-0.09192605316638947	0.001230575842782855	-0.06050465628504753	0.052524205297231674	0.06167428568005562	0.0463421531021595	0.046610984951257706	-0.009881015866994858	-0.009219386614859104	-0.0024517218116670847	0.004029216710478067	0.013793525286018848	-0.06779683381319046	0.013977402821183205	0.0022453381679952145	0.012969821691513062	0.04902103170752525	-0.055389154702425	0.04061521217226982	-0.00908447615802288	0.01507417019456625	0.03950616717338562	0.06132601201534271	0.003874382935464382	-0.03906070813536644	0.013639349490404129	-0.05831632390618324	0.03040650673210621	-0.012863016687333584	0.03184531256556511	-0.06983118504285812	0.02493884041905403	-0.05278961360454559	-0.015272227115929127	-0.0033602763433009386	0.04279720410704613	-0.016409408301115036	-0.018816977739334106	-0.02085956744849682	-0.059921570122241974	-0.06698343902826309	0.0297520961612463	0.06327259540557861	-0.021347176283597946	0.02223723568022251	0.01789579540491104	0.04007595404982567	0.021229075267910957	0.00594130577519536	-0.026301942765712738	0.0071020713075995445	0.009909163229167461	0.027630597352981567	0.00639726547524333	-0.06772693246603012	0.05826430022716522	-0.004871939774602652	0.02306249365210533	-0.0715545043349266	-0.018257271498441696	0.021720394492149353	-0.006018155254423618	0.046340301632881165	0.08642928302288055	-0.06427774578332901	0.0020868892315775156	0.025889141485095024	0.05785880982875824	-0.06414713710546494	-0.012483475729823112	0.0004887855029664934	-0.028793619945645332	-0.01492489967495203	-0.004573643673211336	-0.09613201767206192	-0.0057601043954491615	0.012553730048239231	-0.01831422932446003	-0.044105179607868195	-0.01060429960489273	0.023585952818393707	-0.00013395099085755646	-0.046945277601480484	-0.036711186170578	0.027212463319301605	-0.05477007478475571	0.05865975841879845	0.0038914098404347897	-0.06471434980630875	-0.02885712869465351	-0.02657882124185562	0.026376567780971527	0.01689256727695465	0.02928277477622032	0.07053635269403458	0.10731375962495804	0.018832437694072723	-0.03810423985123634	-0.0264400877058506	-0.053811971098184586	-0.04562435671687126	0.04484395682811737	0.10066524893045425	-0.06892229616641998	-0.02770608849823475	0.030887803062796593	-0.04745518043637276	0.001208509667776525	0.006513162981718779	0.036351583898067474	0.0013970605796203017	-0.04218006134033203	-0.037508927285671234	0.013921770267188549	0.023459726944565773	-0.005494407843798399	-0.021930493414402008	-0.019893864169716835	0.09145646542310715	-0.019670644775032997	-0.05967194586992264	0.018877753987908363	0.027957143262028694	0.022515591233968735	0.007624031975865364	0.046152565628290176	-0.03533293679356575	-0.07552231848239899	-0.05720454454421997	-0.04698699340224266	-0.04659028723835945	-0.03999460116028786	-0.015208428725600243	-0.05752541869878769	0.0981423407793045	-0.02184189297258854	-0.049262549728155136	-0.04683411493897438	-0.016164854168891907	-0.02291712909936905	-0.08444558829069138	-0.0021235814783722162	-0.015227245166897774	-0.08736173063516617	-0.03178894519805908	0.012269419617950916	-0.05303852632641792	0.0037200902588665485	0.05364112928509712	-0.007837893441319466	-0.06291835010051727	0.058115098625421524	-0.027306225150823593	-0.03304625675082207	-0.033937711268663406	-0.09320791810750961	0.013107167556881905	0.06034557521343231	0.0073597426526248455	0.03774385154247284	0.027909621596336365	0.06650619208812714	0.09894546866416931	0.02632530964910984	-0.0846552774310112	5.173138924874365e-05	-0.047797221690416336	-0.08455142378807068	0.05579031631350517	-0.045069701969623566	0.025837184861302376	-0.049081724137067795	0.014451070688664913	0.05473754554986954	0.021182937547564507	0.0069205318577587605	-0.008527996018528938	0.046349164098501205	0.013301030732691288	0.04264030233025551	0.01089703943580389	-0.07102563232183456	0.030332740396261215	-0.06969921290874481	-0.07106781750917435	-0.022450000047683716	-0.018479671329259872	0.02152620442211628	0.08510684221982956	0.018814750015735626	-0.07765582948923111	0.04216601699590683	-0.027898821979761124	0.08496467024087906	-0.10248959809541702	-0.016421543434262276	-0.017126087099313736	0.002896648831665516	-0.08460047096014023	-0.01847292110323906	0.0513455867767334	-0.04146977514028549	-0.059675462543964386	0.018113143742084503	0.0559694729745388	-0.01611567661166191	0.0013676413800567389	-0.008682994171977043	-0.040728744119405746	-0.02193397469818592	0.006910358089953661	-0.009583903476595879	0.044932711869478226	-0.007423616014420986	0.003485569264739752	0.017074957489967346	0.03969598188996315	0.017544539645314217	-0.010569782927632332	0.010987081564962864	-0.03137607127428055	0.12729622423648834	-0.015335983596742153	0.006427518557757139	-0.07411240041255951	0.01956481859087944	0.0025661769323050976	-0.030773501843214035	-0.04518134519457817	-0.0006792482454329729	0.00030449841869995	0.010319773107767105	0.01220722682774067	-0.053995802998542786	-0.017848096787929535	-0.04199686646461487	-0.025751400738954544	-0.06746623665094376	-0.03548230975866318	0.021783696487545967	-0.06876455992460251	-0.009502716362476349	-0.025657789781689644	-0.03874155506491661	0.023714637383818626	-0.04035314545035362	-0.0005233488627709448	0.04234883561730385	0.051259174942970276	-0.03142522647976875	0.05711457133293152	0.02714802511036396	0.046021316200494766	-0.027623863890767097	-0.047089241445064545	0.0716874971985817	0.0660187378525734	-0.04839594289660454	0.015927741304039955	-0.05265180021524429	0.01656017079949379	-0.0015685504768043756	0.0029578106477856636	-0.017112400382757187	0.016838226467370987	-0.017783192917704582	-0.021969804540276527	0.07510104030370712	-0.09312909096479416	-0.035079751163721085	0.057399503886699677	-0.04280950874090195	-0.06683758646249771	-0.004017223138362169	-0.01726372353732586	-0.06965866684913635	0.05777042359113693	0.021802350878715515	0.0411035418510437	0.054131750017404556	-0.07295027375221252	-0.04305878281593323	0.005628954619169235	-0.04610568284988403	0.011246238835155964	-0.032128944993019104	0.03440842777490616	0.02743568830192089	0.019286388531327248	0.026256728917360306	-0.0012819438707083464	0.030029963701963425	-0.08372452110052109	-0.029327819123864174	0.016544200479984283	-0.03917919844388962	-0.0074313548393547535	0.00037332408828660846	0.009559322148561478	-0.07449544221162796	0.006213771644979715	-0.043806858360767365	0.03398573026061058	-0.048889946192502975	-0.04719860851764679	0.029774757102131844	0.009084232151508331	0.004819559399038553	0.03600525110960007	-0.005123698618263006	-0.08705902844667435	0.034723009914159775	-0.04357104003429413	0.02169240452349186	0.01495947316288948	0.0022331250365823507	0.008921703323721886	-0.07702852785587311	-0.04327169060707092	-0.0008835683693177998	-0.08661625534296036	0.05551335960626602	-0.006492707412689924	-0.08665302395820618	-0.08366542309522629	-0.004172618500888348	-0.060975898057222366	0.06016715615987778	0.010063664056360722	0.052543945610523224	-0.016472557559609413	-0.015925008803606033	-0.004671542905271053	-0.021158603951334953	0.035485800355672836	-0.0338144525885582	-0.07570380717515945	-0.016835490241646767	0.08580116182565689	-0.0505145788192749	0.023337824270129204	0.03392105549573898	-0.03513206169009209	0.0446903258562088	-0.004986897576600313	-0.030142873525619507	0.03557105362415314	-0.027267038822174072	-0.03338833898305893	0.05390883982181549	-0.03332212567329407	0.08266844600439072	-0.019528206437826157	0.0851459875702858	0.0067195589654147625	-0.0021155080758035183	0.09656529128551483	0.07225791364908218	-0.009243404492735863	0.0013863258063793182	-0.050165243446826935
+0.06386351585388184	-0.013517242856323719	0.011548052541911602	-0.010036448948085308	-0.09201863408088684	-0.06221505627036095	-0.010545427910983562	0.03896331787109375	-0.002964561339467764	-0.022474845871329308	-0.046942539513111115	-0.031902916729450226	-0.02217293158173561	-0.0013041161000728607	0.02456737495958805	-0.04755796119570732	-0.0715700164437294	0.0006710565648972988	-0.04091935604810715	-0.043099530041217804	0.06074383854866028	0.008487800136208534	-0.07312308996915817	0.04699426889419556	0.0019454816356301308	0.018525920808315277	-0.06437656283378601	0.016687538474798203	-0.007762756198644638	-0.007161715999245644	0.06416495144367218	-0.0003760156105272472	0.07381661981344223	-0.06734035909175873	-0.03241317346692085	0.04047050327062607	-0.020076556131243706	0.10395514219999313	0.03338313475251198	-0.058630164712667465	-0.04753688722848892	0.0006944524357095361	-0.037526022642850876	-0.03029012493789196	0.02782222256064415	-0.014839835464954376	-0.024361345916986465	-0.029217230156064034	0.037271980196237564	0.027767585590481758	0.09678003191947937	-0.00042237568413838744	-0.02733718976378441	-0.024359701201319695	0.010310472920536995	0.027360815554857254	0.038875166326761246	-0.04572039842605591	-0.06369351595640182	0.018289033323526382	-0.10044333338737488	0.011830199509859085	-0.02834232710301876	-0.010934465564787388	-0.015759501606225967	-0.05349003151059151	0.009195372462272644	0.11362295597791672	0.027561504393815994	-0.018135322257876396	0.03269222378730774	0.08233895897865295	-0.08536795526742935	-0.05528866872191429	0.06780652701854706	-0.06536425650119781	0.005891269072890282	0.06079705059528351	-0.03419378399848938	0.015566965565085411	-0.030293600633740425	-0.030354438349604607	0.1292073279619217	-0.05035897344350815	-0.06292674690485	-0.04617979750037193	-0.003474149387329817	-0.055811695754528046	0.052245404571294785	0.04628117009997368	0.00039695893065072596	0.03855413943529129	-0.034720633178949356	0.05946904048323631	-0.027008377015590668	0.02182711847126484	0.031504783779382706	-0.11454008519649506	-0.04091796651482582	0.008487594313919544	-0.009160701185464859	-0.007814553566277027	0.05144674703478813	-0.016704151406884193	0.04029034450650215	0.036809973418712616	-0.03046533092856407	0.011280645616352558	0.031503546983003616	0.05153069645166397	0.03967621549963951	0.06506693363189697	0.083498015999794	0.04981941357254982	0.039061013609170914	0.012873024679720402	0.03635561838746071	-0.06619502604007721	-0.008266865275800228	0.028760278597474098	0.006859844084829092	0.035207655280828476	-0.039727117866277695	-0.021831786260008812	-0.031993553042411804	0.07335247844457626	-0.07556381076574326	-0.037851978093385696	0.06292831152677536	-0.026332708075642586	0.10187456011772156	0.057237450033426285	-0.03605562448501587	0.03384009748697281	0.07231537252664566	0.021399369463324547	0.02082463912665844	0.023553485050797462	-0.012378343380987644	-0.0828511118888855	-0.03953542932868004	0.017881318926811218	-0.008358779363334179	-0.07290259003639221	0.0385415181517601	0.055269986391067505	0.04706241562962532	-0.05619741603732109	-0.048759229481220245	-0.003169103991240263	-0.09038224071264267	-0.08182850480079651	0.004665670450776815	-0.09834538400173187	0.007117706350982189	0.04254961386322975	-0.0006462002638727427	0.03709552437067032	0.003238535486161709	-0.0035408097319304943	-0.06173977628350258	-0.0853656753897667	-0.03038080222904682	-0.0770384669303894	-0.024590549990534782	-0.023272186517715454	0.027521509677171707	0.03367919847369194	-0.051091764122247696	-0.02213415317237377	0.016355091705918312	-0.020419077947735786	-0.007843987084925175	0.06887359917163849	0.04061967879533768	0.007351992651820183	0.014293920248746872	-0.0067417072132229805	-0.017278021201491356	-0.030869169160723686	0.056142713874578476	-0.03276693448424339	0.037623316049575806	-0.06510904431343079	0.03597140312194824	-0.019387278705835342	0.006023773457854986	-0.04058786481618881	0.025352826341986656	0.010532185435295105	-0.025463515892624855	-0.054611049592494965	0.0018004109151661396	0.06814001500606537	-0.025439433753490448	0.057144612073898315	-0.02026216685771942	0.06754783540964127	-0.02923642471432686	-0.02687448263168335	-0.05389154329895973	0.05366982892155647	0.01585695706307888	0.04672577604651451	0.0022912505082786083	-0.0914846807718277	0.07357046753168106	0.025352012366056442	0.020277244970202446	-0.03787845000624657	0.0420309342443943	0.02392524480819702	0.02125551365315914	0.0473414845764637	0.052412308752536774	-0.01414701621979475	0.005492690950632095	-0.022765466943383217	0.057259369641542435	-0.048706766217947006	0.035864993929862976	0.02811441756784916	-0.07195102423429489	0.055870287120342255	-0.09305005520582199	-0.09516569972038269	-0.052775245159864426	0.011210029013454914	0.0009674258180893958	-0.04696568101644516	-0.01819062978029251	-0.0014975890517234802	-0.011483633890748024	-0.01683826744556427	0.01579437218606472	0.0015140925534069538	-0.08797042816877365	0.09471799433231354	0.01353074237704277	-0.0698605626821518	-0.055966489017009735	0.006731265224516392	0.03253276273608208	-0.004957782104611397	0.03388860076665878	0.025117604061961174	0.12235751003026962	0.009533324278891087	-0.001495335134677589	0.0004773528780788183	-0.039377979934215546	-0.06708064675331116	0.003039874602109194	0.0033838278613984585	-0.05119650438427925	-0.06182722747325897	0.03394080325961113	-0.056606922298669815	-0.007238740101456642	-0.02785377763211727	0.041199564933776855	0.008212634362280369	-0.03306833654642105	-0.02360694482922554	0.060011766850948334	-0.04538533091545105	-0.053497180342674255	0.02993224933743477	-0.015570295043289661	0.06077859178185463	-0.020360687747597694	0.004821970127522945	0.012237867340445518	-0.026358693838119507	0.024670448154211044	-0.04526706039905548	0.060184236615896225	-0.03645786643028259	-0.05640758201479912	-0.0249699205160141	-0.07158973067998886	-0.06485621631145477	-0.01244655903428793	-0.036381106823682785	0.004009817261248827	0.08598671108484268	0.01877758465707302	-0.033729519695043564	-0.04482167959213257	-0.03714602440595627	-0.03286905959248543	-0.03008061833679676	-0.02485671080648899	0.004719561897218227	-0.06084776669740677	-0.007038269191980362	0.051484718918800354	-0.045806583017110825	-0.02799304388463497	0.022614385932683945	-0.04802488908171654	-0.06587433815002441	0.029425032436847687	-0.018587905913591385	-0.020206307992339134	0.00046242278767749667	-0.04897918552160263	0.0215293001383543	0.09937760978937149	0.047770481556653976	0.030307916924357414	0.04235151782631874	0.011910012923181057	0.09620323777198792	-0.005069436505436897	-0.03328775614500046	0.005797126796096563	-0.061379775404930115	-0.06472337245941162	0.02172347530722618	-0.016369719058275223	-0.02066303789615631	-0.005835996940732002	-0.011179479770362377	0.05283892899751663	-0.03264224901795387	-0.04195784032344818	-0.04210781678557396	0.006163654383271933	-0.05310199409723282	0.005992391612380743	0.027525272220373154	0.02487560734152794	0.0005960442358627915	-0.04659998044371605	-0.030264539644122124	-0.06335289776325226	0.028597643598914146	-0.03124965727329254	0.011147580109536648	-0.030158162117004395	-0.0255126953125	-0.02253272384405136	0.011992336250841618	0.09822414815425873	-0.059070248156785965	0.030882729217410088	0.031832776963710785	0.03824257850646973	-0.09250298887491226	-0.002767378930002451	0.020526222884655	-0.03928237408399582	-0.013653572648763657	0.017933527007699013	0.037418972700834274	-0.07161380350589752	-0.014311963692307472	0.002251064172014594	-0.013298911973834038	-0.007161075249314308	0.031081819906830788	0.0235796719789505	0.044792063534259796	-0.028243426233530045	-0.021324096247553825	0.004866353701800108	0.004474682733416557	0.058511972427368164	-0.027541322633624077	0.046419598162174225	-0.03776058927178383	0.026373770087957382	-0.054890088737010956	-0.0811220034956932	-0.07538267225027084	0.027620509266853333	0.0013197564985603094	-0.007740786764770746	-0.026803817600011826	0.006008626893162727	0.010831781663000584	0.036818791180849075	0.04978802055120468	-0.050192877650260925	-0.01263335719704628	-0.033629655838012695	-0.052273403853178024	-0.0933178961277008	-0.04750116914510727	-0.011615773662924767	-0.07223803550004959	0.028098730370402336	-0.022096365690231323	-0.00727707939222455	-0.009684962220489979	-0.01352632138878107	0.020697830244898796	0.06572889536619186	0.02369491383433342	0.018565088510513306	0.04051786661148071	0.002828085096552968	0.030237900093197823	0.014689031057059765	-0.06382425129413605	0.012884719297289848	0.009486513212323189	-0.04411359876394272	0.05618191510438919	-0.01859395205974579	0.015879344195127487	-0.03037501871585846	-0.008603984490036964	0.03328053653240204	-0.013583456166088581	0.025086339563131332	-0.03171779215335846	0.05450347438454628	-0.05401553213596344	-0.04182281345129013	0.04627532884478569	-0.0316380076110363	-0.09573999047279358	-0.00980681274086237	-0.03553416207432747	-0.039134688675403595	0.042499661445617676	-0.03910524398088455	0.0133487144485116	0.026275532320141792	-0.04858734831213951	-0.06351078301668167	-0.045837923884391785	-0.013280161656439304	0.034196946769952774	0.020946191623806953	0.07676420360803604	0.02425437793135643	-0.009852562099695206	0.046259041875600815	-0.02237832359969616	0.013977236114442348	-0.1036546379327774	-0.0069959829561412334	-0.044339295476675034	0.0007553093018941581	0.030056817457079887	-0.008879275061190128	0.035056985914707184	-0.03867778927087784	0.0023915742058306932	-0.07067328691482544	0.08308558911085129	-0.016508828848600388	-0.004963873885571957	0.009000415913760662	-0.04957815259695053	-0.0016528437845408916	0.01701704040169716	-0.03201631084084511	-0.0630962923169136	0.006787396967411041	-0.03913723677396774	0.03509790822863579	0.02580122835934162	0.021992117166519165	0.00630527175962925	-0.06656522303819656	-0.010334552265703678	0.04160185530781746	-0.03328271210193634	0.018135162070393562	0.01543524581938982	-0.0451793409883976	-0.08119632303714752	-0.020258018746972084	-0.054394885897636414	0.05944833159446716	0.0026249645743519068	0.0018302323296666145	-0.022366445511579514	0.010757260955870152	0.004249316640198231	-0.04046432301402092	-0.010984563268721104	0.017894232645630836	-0.06968995183706284	-0.009802117012441158	0.0630815178155899	-0.013389728032052517	0.0334317609667778	0.08731476217508316	-0.04110968858003616	0.019438305869698524	0.03644557297229767	-0.03368762880563736	0.019816428422927856	-0.04419759288430214	-0.029575355350971222	0.04576454684138298	-0.06130098178982735	0.07311098277568817	-0.05815454572439194	0.12035328149795532	0.00014503912825603038	-0.05783138796687126	0.0800391361117363	0.052531588822603226	-0.005612569861114025	0.006322819273918867	-0.08574756234884262
+0.01811225526034832	0.01436321996152401	-0.015245934017002583	0.024783233180642128	-0.0871250107884407	-0.07442451268434525	-0.006941836327314377	-0.012653359211981297	0.0016333844978362322	-0.016987964510917664	-0.01966242305934429	-0.03637592867016792	-0.0049851564690470695	-0.005852080415934324	0.05414927005767822	-0.026654386892914772	-0.0713580995798111	0.022488493472337723	-0.03777741268277168	0.0021021924912929535	0.021483352407813072	0.0059067318215966225	-0.08573922514915466	0.04331960529088974	-0.0009643406956456602	0.006359177641570568	-0.05175115913152695	-0.004020117688924074	-0.0011764649534597993	0.004776878748089075	0.006602881010621786	0.0058600748889148235	0.05434824898838997	-0.05920858681201935	-0.06619291007518768	0.04753042757511139	-0.012653609737753868	0.10909900814294815	0.05527167394757271	-0.05231284350156784	-0.077939473092556	0.016705932095646858	-0.06871866434812546	-0.08405780792236328	0.012692010961472988	0.01874113827943802	0.0005358322523534298	-0.004179749172180891	0.039922188967466354	0.06128134950995445	0.08153027296066284	0.006387644913047552	-0.06327328085899353	-0.03470304608345032	-0.015729863196611404	-0.005486251786351204	0.04791633412241936	-0.04436185210943222	-0.061575137078762054	0.05135039985179901	-0.07682810723781586	0.01002668496221304	0.014694875106215477	-0.007592095527797937	-0.02294415980577469	-0.05258382856845856	-0.0011633331887423992	0.07908321171998978	-0.013762040995061398	-0.0017224325565621257	0.01839393563568592	0.09816404432058334	-0.06261453032493591	-0.0334307923913002	0.0507347546517849	-0.08423459529876709	-0.044092971831560135	0.0641191378235817	-0.00704950699582696	0.004882025066763163	-0.0031243544071912766	-0.03977520018815994	0.11511837691068649	-0.05442399904131889	-0.045339953154325485	-0.06746126711368561	0.001842114725150168	-0.038738932460546494	0.009931410662829876	0.0467761792242527	0.0075722383335232735	0.047276515513658524	-0.05289369449019432	0.047850027680397034	0.0024153280537575483	0.015694858506321907	0.04733049497008324	-0.10809726268053055	-0.04731714725494385	-0.005564793013036251	-0.018428582698106766	-0.02746276929974556	0.016148556023836136	-0.009562055580317974	0.021279551088809967	-0.004657065961509943	-0.047270677983760834	0.02640378288924694	0.019444623962044716	0.040630191564559937	0.04255107790231705	0.03102581948041916	0.08304805308580399	0.05983707308769226	0.0616595558822155	0.024896729737520218	0.04463830217719078	-0.05619543045759201	-0.015299231745302677	0.018216270953416824	0.0034632852766662836	0.019880885258316994	0.00861792080104351	-0.045088402926921844	-0.0013245947193354368	0.024345720186829567	-0.08619014918804169	-0.03989269956946373	-0.009470131248235703	-0.024892881512641907	0.10446763038635254	0.037924546748399734	-0.0390808992087841	-0.004686250351369381	0.031337443739175797	0.011945116333663464	0.022501297295093536	0.06095777451992035	-0.0341649129986763	-0.03975000977516174	-0.0045714364387094975	0.032005537301301956	-0.004564552567899227	-0.05438694730401039	0.05958733335137367	0.0517849363386631	0.037293002009391785	-0.03642091527581215	-0.09325382113456726	-0.016346443444490433	-0.09156045317649841	-0.05083085969090462	-0.007493007928133011	-0.09619981050491333	0.026044795289635658	0.033193353563547134	0.02204693853855133	0.05402173846960068	-0.02351950854063034	0.00032470605219714344	-0.029462046921253204	-0.03735898807644844	-0.0022137693595141172	-0.08873231709003448	-0.055481549352407455	-0.04650063440203667	0.015720024704933167	0.01652955263853073	-0.04909742996096611	-0.02984580770134926	-0.014014648273587227	0.0064643253572285175	0.0686892420053482	0.02897721901535988	0.04703211784362793	-0.017103955149650574	0.038239262998104095	-0.06586899608373642	-9.346973092760891e-05	-0.030796105042099953	0.04034320265054703	-0.056782789528369904	0.029330771416425705	-0.04418005049228668	0.014842112548649311	0.00826114509254694	0.0318613164126873	-0.02798609435558319	-0.016798745840787888	0.017867084592580795	-0.08661368489265442	-0.05856615677475929	0.06745482981204987	0.09139429032802582	-0.017761042341589928	0.020253540948033333	-0.032404109835624695	0.03163481503725052	-0.016168950125575066	-0.02378915250301361	-0.06886275857686996	0.01961669512093067	-0.020669614896178246	0.0606951080262661	-0.0038130437023937702	-0.09225770831108093	0.0567641481757164	0.002468427177518606	0.00200245575979352	-0.027689071372151375	0.024753917008638382	0.021658694371581078	-0.025080803781747818	-0.000277554034255445	0.08214025944471359	-0.006614387966692448	0.013010825961828232	0.012989452108740807	0.07450000196695328	-0.043231185525655746	0.032587021589279175	0.023753201588988304	-0.09727784246206284	-0.007270297966897488	-0.032181985676288605	-0.08386138826608658	-0.02095508947968483	0.05899465084075928	-0.03265818953514099	0.002972461748868227	-0.017398657277226448	0.00043411305523477495	-0.00843051727861166	-0.033475760370492935	-0.018402835354208946	0.017697390168905258	-0.08953576534986496	0.07455634325742722	0.011410333216190338	-0.07067377120256424	-0.03772401809692383	-0.006205025594681501	-0.0008933052886277437	-0.013850349932909012	0.035727083683013916	0.05465621501207352	0.05676400661468506	0.024403538554906845	0.028949789702892303	-0.02081192471086979	-0.05483186990022659	-0.05808072164654732	-0.006023293361067772	0.03550304099917412	-0.03927476704120636	-0.015533252619206905	-0.0004652466159313917	-0.04403802007436752	0.005488829221576452	0.03602871298789978	-0.0006704630795866251	-0.027801724150776863	-0.05200137197971344	-0.028163835406303406	0.0464191734790802	-0.033635322004556656	-0.03273795172572136	0.024607758969068527	0.00397129263728857	0.06406185775995255	-0.010819430463016033	-0.032365016639232635	0.019063353538513184	0.002706848783418536	0.03419872745871544	-0.005701197311282158	0.04954540729522705	0.0030799054075032473	-0.0639648586511612	-0.005864489357918501	-0.08180356025695801	-0.04900120943784714	-0.016210535541176796	-0.0189727321267128	-0.024297159165143967	0.0857650637626648	0.03788217157125473	-0.04240712895989418	-0.08820727467536926	-0.05768376961350441	-0.03714549541473389	-0.0711430087685585	-0.016056226566433907	-0.000798580062109977	-0.09544224292039871	-0.043834805488586426	0.04131721332669258	-0.014784141443669796	-0.018868720158934593	0.004771700594574213	-0.02517852559685707	-0.08142126351594925	0.03163280338048935	-0.012364271096885204	-0.031363409012556076	-0.008841251023113728	-0.04415789246559143	0.01381265465170145	0.07887651771306992	0.013349168002605438	0.04117308929562569	0.03106328658759594	0.017856109887361526	0.08591248095035553	-0.0036518306005746126	-0.05466156825423241	-0.038704294711351395	-0.08583555370569229	-0.09476787596940994	0.056405533105134964	-0.04076468199491501	0.004321172367781401	-0.02561948075890541	0.03239406645298004	0.06604659557342529	-0.027424445375800133	-0.031672872602939606	-0.023569602519273758	0.016651282086968422	-0.011896596290171146	0.010048246942460537	-0.0185257475823164	0.01923845149576664	-0.023093590512871742	-0.08332628011703491	-0.04459092393517494	-0.03188442438840866	-0.011962753720581532	0.0007855520234443247	0.06329400837421417	-0.020450333133339882	-0.045100901275873184	-0.018314698711037636	0.004625034984201193	0.14549510180950165	-0.06424620747566223	-0.009842540137469769	0.04836396500468254	0.04366469383239746	-0.04932111129164696	0.005322929006069899	0.043016958981752396	-0.06921271979808807	-0.005579530727118254	-0.017046649008989334	0.04246067255735397	-0.0772029459476471	-0.003432926954701543	0.01081366278231144	-0.01618550904095173	0.01723400317132473	-0.0037637650966644287	-0.006205441430211067	0.005459200590848923	-0.05221321061253548	-0.01758243329823017	0.012454899027943611	-0.0014582169242203236	0.0019390712259337306	-0.0418606661260128	0.011726620607078075	-0.02653481438755989	0.03473731875419617	-0.04299662634730339	0.006278288085013628	-0.06180979311466217	0.041360702365636826	-0.026205016300082207	-0.06407926976680756	-0.01066649705171585	0.0007498082704842091	-0.014477846212685108	0.025522636249661446	0.029282424598932266	-0.04270118474960327	-0.060690414160490036	-0.0741329938173294	-0.02438446693122387	-0.07823028415441513	-6.435432624130044e-06	0.008090894669294357	-0.0643426924943924	-0.0025611002929508686	-0.055119410157203674	-0.029733799397945404	-0.005601910408586264	-0.007512607146054506	-0.014454858377575874	0.04460103064775467	0.04550861567258835	-0.033874671906232834	0.04010886698961258	-0.0163542740046978	0.03365890309214592	0.010863704606890678	-0.06194955110549927	0.01534128189086914	0.03851458430290222	-0.05623582750558853	0.04350658506155014	-0.05258331820368767	0.004860926419496536	-0.00286897923797369	0.030009258538484573	-0.003059821669012308	-0.00420469231903553	-0.0006937627331353724	0.027314530685544014	0.030719468370079994	-0.0815516784787178	-0.06373010575771332	0.017702855169773102	-0.05171264708042145	-0.08952654898166656	-0.01437473390251398	0.011528503149747849	-0.00872164685279131	0.007807335816323757	-0.03823389858007431	0.0594102218747139	0.038237519562244415	-0.07079456746578217	-0.038453664630651474	-0.052599016577005386	-0.05147276446223259	0.05049896612763405	-0.03463244438171387	0.03074062243103981	0.005799942184239626	-0.003796881763264537	0.030200522392988205	0.034523457288742065	0.0001563847326906398	-0.0926036387681961	-0.03517068177461624	-0.029108986258506775	-0.002541779540479183	0.029086284339427948	-0.022421522065997124	0.00851534865796566	-0.028076959773898125	-0.025820419192314148	-0.02758936397731304	0.07367344945669174	-0.015772588551044464	-0.02444867230951786	0.08911962807178497	-0.01363449264317751	0.026646051555871964	0.040884848684072495	-0.05509649217128754	-0.0829591304063797	-0.0017040928360074759	-0.035248901695013046	0.06213560700416565	0.08291873335838318	0.020699912682175636	-0.008104672655463219	-0.04550088196992874	-0.003996662329882383	0.011151920072734356	-0.08076785504817963	0.012065831571817398	0.012075004167854786	-0.05444509908556938	-0.06824516505002975	0.010719561018049717	-0.049013346433639526	0.07126329094171524	0.03998664766550064	0.06972426176071167	-0.024408798664808273	-0.0011445338604971766	-0.002867547096684575	-0.011464042589068413	0.016497064381837845	0.023905819281935692	-0.06763454526662827	0.020488286390900612	0.1021299883723259	-0.05340178683400154	0.033695317804813385	0.050939712673425674	-0.035034626722335815	0.0379498153924942	0.027008652687072754	-0.04691430926322937	0.04663773626089096	-0.027022497728466988	-0.07021161168813705	0.04313516616821289	-0.07009775936603546	0.09830907732248306	-0.03731512278318405	0.10024766623973846	-0.0014052585465833545	-0.03668677434325218	0.0790773257613182	0.09019305557012558	-0.021718567237257957	0.03174709156155586	-0.051676809787750244
+0.08477882295846939	-0.05147663131356239	-0.031589217483997345	0.017220811918377876	-0.09696666151285172	-0.030500445514917374	-0.03180908039212227	0.07302964478731155	-0.06430578976869583	-0.06831353902816772	-0.07198217511177063	-0.026444677263498306	-0.0044242627918720245	-0.00974314846098423	0.033153802156448364	-0.01776854135096073	-0.04223193973302841	0.03660744056105614	-0.013380742631852627	-0.03798060864210129	0.012836575508117676	0.00865988526493311	-0.06629906594753265	0.048704102635383606	0.029091231524944305	-0.04596025496721268	-0.034623805433511734	0.04044328257441521	0.014110960997641087	0.030560865998268127	0.06550928950309753	-0.06262640655040741	0.012523975223302841	-0.05042671784758568	-0.06541979312896729	0.0230602715164423	-0.04425838589668274	0.11371952295303345	0.023341920226812363	-0.07106095552444458	-0.03611920028924942	0.06557304412126541	0.0009959116578102112	-0.06057589128613472	0.09309700131416321	0.015484113246202469	-0.04759249836206436	-0.06494222581386566	0.004017267841845751	0.04571795463562012	0.07119666785001755	0.028656501322984695	0.009645998477935791	-0.019407043233513832	-0.06131540611386299	0.01926456205546856	0.02893567830324173	-0.04427025839686394	-0.015431955456733704	0.033471524715423584	-0.04955669865012169	-0.008513012900948524	0.0511699803173542	0.04291563853621483	-0.0059378426522016525	-0.012645818293094635	0.021789943799376488	0.07785685360431671	0.012447324581444263	0.005666144657880068	-0.04289747402071953	0.06160111725330353	-0.039757076650857925	-0.036034196615219116	0.06731701642274857	-0.07337538152933121	-0.021666070446372032	0.029509449377655983	0.022753557190299034	-0.02540540136396885	0.02500176429748535	0.006087999325245619	0.07979033887386322	-0.03852299600839615	-0.037708498537540436	-0.021534541621804237	-0.012823382392525673	-0.031115557998418808	0.03004697524011135	0.023819521069526672	0.033830493688583374	0.012298393994569778	-0.019068503752350807	0.007121459115296602	-0.06855158507823944	-0.0053318338468670845	0.04358692094683647	-0.11216460913419724	-0.011226823553442955	-0.02233148366212845	0.005101753864437342	-0.029147397726774216	0.0159793421626091	-0.0018103388138115406	0.015792470425367355	0.02375108003616333	-0.058364029973745346	-0.0026099947281181812	0.0326676107943058	0.04027043282985687	0.009263355284929276	0.09530109912157059	0.07417872548103333	0.045363593846559525	0.11643953621387482	0.010281862691044807	-0.023360561579465866	-0.06771865487098694	-0.01995263062417507	0.024020981043577194	-0.026904812082648277	0.052372291684150696	-0.012399740517139435	-0.08145873993635178	0.016441836953163147	0.07029370963573456	-0.053659386932849884	-0.010645249858498573	0.032839011400938034	0.014157477766275406	0.06754091382026672	0.0037818478886038065	-0.02920413576066494	0.044046178460121155	0.054599449038505554	0.009006967768073082	0.021234184503555298	0.036800775676965714	0.010235975496470928	-0.07076074182987213	-0.02505054697394371	0.04783486947417259	0.03579713776707649	-0.03273509815335274	0.036455851048231125	-0.001731598749756813	0.05801265686750412	-0.00827493704855442	-0.05190034955739975	0.019519098103046417	-0.11068123579025269	-0.08664897084236145	0.007761890068650246	-0.08368706703186035	0.031040040776133537	0.012990212999284267	-0.0073798405937850475	-0.016965849325060844	0.007382087409496307	-0.0029491495806723833	-0.044563740491867065	-0.06134078651666641	0.01670321449637413	-0.0856458842754364	-0.019901324063539505	0.02020854502916336	-0.040820468217134476	0.02346055582165718	-0.041848357766866684	-0.035965967923402786	-0.004561746027320623	-0.012343324720859528	0.060388438403606415	0.08982232213020325	0.05649305507540703	0.029913906008005142	0.05465332791209221	0.00544395949691534	-0.023212909698486328	-0.013181455433368683	0.007607754319906235	-0.0671447217464447	0.0530690960586071	-0.095835380256176	0.036899734288454056	-0.03323345631361008	0.009210905991494656	-0.05087248235940933	0.05242593213915825	-0.02496945671737194	-0.03867330402135849	-0.06536218523979187	0.04906449839472771	0.053480785340070724	0.0015820114640519023	0.052238184958696365	0.004448494873940945	0.04026585817337036	-0.0065203323028981686	-0.03420564904808998	-0.036862779408693314	0.04483291134238243	0.01999642513692379	0.007256262004375458	0.00912733469158411	-0.07416656613349915	0.05639747157692909	0.03954595327377319	0.038080938160419464	-0.024358194321393967	0.022697916254401207	0.09233837574720383	0.004166215192526579	0.03761717677116394	0.06743141263723373	0.017139781266450882	9.658556518843397e-05	-0.009178519248962402	0.04008155316114426	-0.048673756420612335	0.011926364153623581	0.040995966643095016	-0.1033443734049797	0.06821723282337189	-0.024428948760032654	-0.10655563324689865	-0.01790435239672661	0.0024966730270534754	-0.01896454207599163	-0.07127169519662857	0.0174848772585392	0.014545006677508354	0.06699266284704208	-0.04905373603105545	-0.0007714001694694161	-0.000194090636796318	-0.037691131234169006	0.07378336042165756	0.012463517487049103	-0.07068321108818054	-0.025053873658180237	-0.018616102635860443	-0.001849244348704815	-0.009152359329164028	0.008435544557869434	0.03391961008310318	0.09375151991844177	0.004462351091206074	-0.052551355212926865	0.02635970339179039	-0.054963190108537674	-0.03331777825951576	-0.01983805000782013	0.04422222450375557	-0.023574385792016983	-0.07158484309911728	-0.0033847829326987267	-0.06751341372728348	-0.01842908374965191	-0.0060979328118264675	0.00107139116153121	-0.045509207993745804	-0.0576644092798233	-0.05881072208285332	0.045433126389980316	-0.02292618528008461	-0.047002822160720825	0.006322585046291351	-0.03618960827589035	0.03754733130335808	-0.015933973714709282	0.022945309057831764	0.048359014093875885	-0.047834720462560654	0.0015797956148162484	-0.049734558910131454	0.06814597547054291	-0.05145280808210373	-0.008731313049793243	-0.013505996204912663	-0.06389068812131882	-0.09058869630098343	0.0167926587164402	-0.05298776179552078	0.029741691425442696	0.11224217712879181	0.04849376529455185	-0.012513339519500732	-0.04289889708161354	-0.04336624965071678	-0.031665775924921036	-0.025775091722607613	-0.030235949903726578	0.042725756764411926	-0.0101117342710495	0.004269368946552277	0.018139054998755455	0.017264850437641144	-0.011318635195493698	0.011699721217155457	0.051252782344818115	-0.04515150934457779	0.05520376190543175	-0.02543441578745842	-0.0005481676780618727	0.056206103414297104	-0.030359284952282906	-0.005276048090308905	0.0872272253036499	0.07709117978811264	0.019335240125656128	0.03761960566043854	0.009527754969894886	0.13253344595432281	0.016547316685318947	-0.06839166581630707	-0.026141546666622162	-0.08046484738588333	-0.07898761332035065	0.03794988989830017	0.007547400426119566	-0.07969563454389572	-0.0018169437535107136	-0.023207921534776688	0.03826526924967766	-0.030836373567581177	-0.03473447263240814	-0.08435610681772232	-0.012975991703569889	-0.05321823060512543	0.0021092372480779886	-0.01683531515300274	0.0028353179804980755	-0.032351624220609665	-0.027478322386741638	0.0010501224314793944	-0.053464870899915695	0.03987351432442665	-0.0033089593052864075	0.042710158973932266	-0.03238370269536972	-0.024935297667980194	0.007731580175459385	0.04551279917359352	0.05640314891934395	-0.056875649839639664	0.03932468220591545	0.011421886272728443	0.06428195536136627	-0.01979459822177887	0.005334398243576288	-0.013414098881185055	-0.011111491359770298	0.006311553530395031	-0.039013467729091644	0.039201702922582626	-0.06759754568338394	-0.037404172122478485	-0.018081173300743103	-0.02590889111161232	0.014850434847176075	-0.024425972253084183	0.015479895286262035	0.08601247519254684	-0.02202208712697029	0.014998514205217361	0.03276263549923897	-0.004997348878532648	0.05281231924891472	-0.02114611491560936	0.032012201845645905	0.012360074557363987	0.027527406811714172	-0.025922203436493874	-0.04657851159572601	-0.04251648858189583	0.017532510682940483	-0.06234719604253769	0.018047228455543518	-0.015024501830339432	0.012877783738076687	0.02294236794114113	0.030343083664774895	0.024965010583400726	-0.041787438094615936	0.0020571653731167316	0.01428610272705555	-0.04736580327153206	-0.054102931171655655	-0.06396860629320145	0.022275300696492195	-0.04265056177973747	0.01270873099565506	-0.03142203390598297	-0.025362925603985786	0.0545499324798584	-0.0006470094085671008	0.04431639984250069	0.04100167006254196	0.024687524884939194	0.025840433314442635	0.03472399339079857	0.0038611795753240585	0.023086246103048325	0.02535453625023365	-0.08080518245697021	-0.02455195225775242	0.04171457886695862	-0.04683796688914299	0.02791070193052292	0.020091302692890167	0.05158304050564766	-0.033671196550130844	0.003548635635524988	0.04731546714901924	-0.06628675013780594	0.0016091042198240757	-0.0015179577749222517	0.059282079339027405	-0.029876485466957092	-0.09911464899778366	0.046182502061128616	-0.015065222047269344	-0.09343758225440979	-0.011186329647898674	-0.032256707549095154	0.008478185161948204	0.038526155054569244	-0.04439716786146164	0.01894998736679554	-0.003094722284004092	-0.03076944500207901	-0.048172127455472946	-0.06827787309885025	-0.014627411030232906	0.05968796834349632	0.03231088072061539	0.051673807203769684	0.03500612825155258	0.006850470323115587	0.025556044653058052	-0.050040796399116516	0.013957514427602291	-0.08643925189971924	-0.0014186094049364328	-0.042297784239053726	-0.009012806229293346	0.04532105103135109	0.026151547208428383	0.01766977645456791	-0.03545700013637543	-0.0419682152569294	-0.05687240883708	0.07060575485229492	-0.015874819830060005	0.010210680775344372	0.04809122532606125	-0.06667520850896835	-0.04637784883379936	0.02081787772476673	-0.0609867088496685	-0.07672267407178879	0.03139631450176239	0.005795768927782774	0.028943514451384544	0.029564175754785538	0.07144289463758469	-0.017647486180067062	-0.0874909907579422	0.048507362604141235	0.03685898333787918	-0.032128866761922836	0.0080117117613554	0.011435823515057564	-0.06934804469347	-0.07223228365182877	-0.023904813453555107	-0.07528316229581833	0.0810406506061554	-0.012201381847262383	-0.01297998521476984	-0.0718381404876709	0.028657682240009308	0.007663242053240538	-0.03105059452354908	0.009417231194674969	-0.023846657946705818	-0.0671100839972496	0.023188572376966476	0.10787063837051392	-0.02534729428589344	0.008711888454854488	0.0712047815322876	-0.03595653176307678	0.014938682317733765	0.010516555048525333	0.017165079712867737	0.0061000194400548935	-0.016608281061053276	0.0171352606266737	0.06472074240446091	-0.03871076926589012	0.0019706066232174635	-0.046315427869558334	0.04919397830963135	0.023317158222198486	-0.006609173957258463	0.06563710421323776	0.03480486571788788	-0.013762782327830791	0.008340306580066681	-0.09704115241765976
+0.02409682236611843	-0.03927113488316536	-0.00923890620470047	0.020646438002586365	-0.12082802504301071	-0.08187384903430939	0.0031540170311927795	0.05921350046992302	0.005904421675950289	-0.024521175771951675	0.011741505935788155	-0.0029065385460853577	0.018067672848701477	0.006326910573989153	0.008026343770325184	-0.002691534347832203	-0.09881909936666489	-0.01791827194392681	-0.05450887605547905	0.00678393617272377	0.04536604881286621	0.025314394384622574	-0.05219173803925514	0.01776169054210186	-0.012062436901032925	0.02594337798655033	-0.03255312889814377	-0.015051360242068768	-0.03878472372889519	-0.015739306807518005	0.007821646519005299	0.022708455100655556	0.02393670380115509	-0.04869020730257034	-0.06561606377363205	0.06742532551288605	-0.019126886501908302	0.13309791684150696	0.04102550446987152	-0.07946683466434479	-0.0634060651063919	0.04457356408238411	-0.04096229746937752	-0.05250426009297371	0.03354522958397865	-0.004554515704512596	0.006009076256304979	-0.019338790327310562	0.024432919919490814	0.05880031734704971	0.0656776875257492	0.010792272165417671	-0.03512183576822281	-0.02301204949617386	-0.0512649342417717	0.05110890418291092	-0.001278251176699996	-0.019795650616288185	-0.09667260199785233	0.037400126457214355	-0.05455100163817406	0.0009991246042773128	-0.002265236107632518	0.031159140169620514	0.0035234657116234303	-0.017102254554629326	-0.024119574576616287	0.07444299757480621	0.04904027283191681	-0.025242503732442856	0.00014271448890212923	0.0920424833893776	-0.03467496857047081	-0.03392931446433067	0.037763770669698715	-0.045260027050971985	-0.03504448011517525	0.037761569023132324	-0.029753996059298515	-0.043409351259469986	0.004492235369980335	-0.007063486147671938	0.11012982577085495	-0.09442810714244843	-0.0472915917634964	-0.01370446290820837	0.016848603263497353	-0.06215643510222435	0.03667508438229561	0.05722550302743912	-0.009611142799258232	0.06100030615925789	-0.06275639683008194	0.03394071385264397	-0.035897932946681976	0.025379745289683342	0.05178755894303322	-0.15018583834171295	-0.06611955165863037	-0.055326420813798904	-0.024709079414606094	-0.03454289212822914	0.04345787689089775	0.004360129591077566	0.04533209279179573	0.0018955715931952	-0.03058883547782898	0.006473904009908438	-0.01246345043182373	0.007708162534981966	0.02941794879734516	0.06464197486639023	0.1074838638305664	0.06506381183862686	0.07040050625801086	-0.02145855687558651	-0.003979227971285582	0.0004874843580182642	-0.02252340316772461	-0.002621132181957364	-0.01792551390826702	0.0011294276919215918	0.01725388504564762	-0.058582790195941925	-0.03134099021553993	0.021147675812244415	-0.09995631873607635	0.001671137404628098	0.022534074261784554	-0.07900459319353104	0.09482741355895996	0.025537967681884766	-0.04594022035598755	-0.002872783923521638	0.06288060545921326	0.011048543266952038	0.009121398441493511	0.04950801283121109	-0.03388111665844917	-0.006427018437534571	-0.003761971602216363	0.06787890195846558	-0.0002733458240982145	-0.03766385093331337	-0.020205583423376083	0.05257991701364517	0.042923782020807266	0.02034049481153488	-0.0780940055847168	0.05606135353446007	-0.08486912399530411	-0.08875168859958649	-0.03907177224755287	-0.10465468466281891	0.014211732894182205	0.09189269691705704	0.01566551811993122	0.07186046242713928	-0.021135501563549042	0.004488238133490086	-0.016360748559236526	-0.03966696187853813	-0.02381407842040062	-0.10965500771999359	-0.021073628216981888	-0.06493734568357468	-0.002234201179817319	-0.029162047430872917	0.009703392162919044	-0.025086669251322746	-0.020132064819335938	-0.00868822168558836	0.010693215765058994	0.03721053525805473	0.06979941576719284	-0.0009336060611531138	-0.01679055579006672	-0.04254999756813049	-0.005672600120306015	0.005028457846492529	0.01827724650502205	-0.03096850775182247	0.04613323137164116	-0.052877672016620636	-0.014362580142915249	-0.015777088701725006	0.02956007607281208	-0.02120319940149784	-0.01643422245979309	-0.014981104992330074	-0.019590483978390694	-0.06414257735013962	0.03375929966568947	0.08594922721385956	-0.0017815117025747895	0.060236793011426926	-0.015343069098889828	0.05580217391252518	-0.02241676300764084	-0.012310374528169632	-0.02308940328657627	0.04547383636236191	-0.03930250555276871	0.05044812336564064	0.01857866533100605	-0.03769029304385185	0.055601827800273895	-0.014849509112536907	0.0019656745716929436	-0.09007734060287476	0.06075430288910866	0.030620763078331947	0.03210851922631264	-0.006312545854598284	0.050161395221948624	-0.034719061106443405	-0.009879063814878464	0.013033795170485973	0.06804997473955154	-0.03387264534831047	0.021114714443683624	0.031646326184272766	-0.06540010869503021	0.012010617181658745	0.016583139076828957	-0.05505339428782463	-0.02933887019753456	0.05528918281197548	-0.013333264738321304	-0.023713843896985054	0.0023688292130827904	0.029307488352060318	-0.01574457436800003	-0.045836396515369415	-0.014504686929285526	0.06806584447622299	-0.07604345679283142	0.036701567471027374	-0.012158915400505066	-0.03134270757436752	-0.04818839207291603	0.0248799417167902	-0.0037954493891447783	0.016050878912210464	0.01862524263560772	0.056227777153253555	0.04287471994757652	0.0379958339035511	0.01519855484366417	-0.010088943876326084	-0.10059289634227753	-0.03260175883769989	-0.020146457478404045	0.060617610812187195	-0.031001446768641472	-0.046772550791502	-0.03885754197835922	-0.07282020151615143	-0.03717144951224327	0.02316495217382908	0.05652362108230591	-0.06054963916540146	-0.059691861271858215	0.011645904742181301	-0.006857875734567642	0.0011220581363886595	-0.04733394831418991	-0.02203316241502762	-0.0008082420099526644	0.0724230408668518	-0.0326881967484951	-0.00031022034818306565	0.039599403738975525	0.024646146222949028	0.07578690350055695	-0.005316061899065971	0.029728207737207413	-0.06117691472172737	-0.0992332398891449	-0.025687435641884804	-0.10182099044322968	-0.06442870199680328	-0.011081524193286896	-0.07728725671768188	-0.03525788336992264	0.09890624135732651	0.05763264372944832	-0.021226759999990463	-0.0851936936378479	-0.028118425980210304	-0.013170620426535606	-0.042639024555683136	-0.013015663251280785	0.029999010264873505	-0.08386211097240448	-0.0472603365778923	0.01574852503836155	-0.03278990089893341	-0.03912423551082611	0.022860651835799217	-0.03750087693333626	-0.015857670456171036	0.0607885904610157	-0.0008395329350605607	-0.01193808764219284	-0.004317926708608866	-0.10545360296964645	0.027770206332206726	0.09067592024803162	0.03998769447207451	0.07023272663354874	0.041005924344062805	0.01616467908024788	0.0579175129532814	0.03324536234140396	-0.02746759168803692	0.01027097087353468	-0.006936311721801758	-0.07627590000629425	0.021821223199367523	-0.05408097431063652	-0.028826646506786346	-0.036345966160297394	0.005192698910832405	0.08119082450866699	0.014810852706432343	-0.016013119369745255	-0.019628161564469337	0.03183848410844803	-0.02042410336434841	0.031131243333220482	-0.013320619240403175	-0.001380569883622229	0.023671351373195648	-0.008536485955119133	-0.007694001775234938	-0.02627333626151085	0.011799548752605915	0.01523652859032154	0.01797395385801792	0.030194872990250587	-0.029556086286902428	0.021123450249433517	-0.009738655760884285	0.0961441919207573	-0.06507102400064468	0.034280549734830856	0.058691758662462234	-0.006206201389431953	-0.06266677379608154	-0.010886961594223976	0.022449815645813942	-0.0046377358958125114	-0.02669929899275303	-0.016983550041913986	0.054842088371515274	-0.04986237734556198	-0.06829164177179337	-0.004005197435617447	-0.016006046906113625	0.020659254863858223	0.0003175224701408297	0.03684857115149498	0.029833553358912468	-0.013202936388552189	-0.023871440440416336	0.04323103278875351	-0.008512179367244244	0.04209277778863907	-0.06124558299779892	-0.006872083526104689	0.0341092124581337	0.06874468922615051	-0.029519207775592804	-0.047666117548942566	-0.040444351732730865	0.06849872320890427	-0.023245131596922874	-0.04464678838849068	-0.023313814774155617	-0.013340591453015804	0.028433457016944885	0.09788000583648682	-0.0033362761605530977	0.004994022194296122	-0.03864719718694687	-0.005605203565210104	-0.027688229456543922	-0.1025652289390564	-0.04932447895407677	0.024199357256293297	-0.07817855477333069	-0.01642310619354248	-0.09614221751689911	-0.006513824220746756	-0.0010677770478650928	0.0077234129421412945	0.011911527253687382	0.0034006424248218536	0.034195661544799805	-0.023427089676260948	0.008929453790187836	0.0024338476359844208	0.03722890466451645	-0.021401703357696533	-0.03806706517934799	0.01530606858432293	0.04797792434692383	-0.0309011097997427	0.02582523413002491	-0.026810267940163612	-0.0036913936492055655	-0.02214631997048855	0.004462049342691898	-0.018678925931453705	0.006798094604164362	-0.017558274790644646	-0.026373913511633873	0.015552069991827011	-0.07898356020450592	-0.007484241388738155	0.06617104262113571	-0.011213711462914944	-0.08889678865671158	-0.009715946391224861	-0.044198933988809586	-0.003971375059336424	0.0016740160062909126	-0.006570403929799795	0.002489627106115222	0.022516243159770966	-0.08342155814170837	-0.022647496312856674	-0.04123876616358757	-0.04651682823896408	0.0356435552239418	0.022361278533935547	0.06408049166202545	0.009649178013205528	-0.014286288991570473	-0.02126488834619522	0.021495306864380836	0.03517797961831093	-0.07045990973711014	0.0032943938858807087	-0.0554540753364563	0.002739910501986742	0.047552440315485	0.014927594922482967	0.03601989895105362	-0.024124182760715485	-0.03315598517656326	0.006092208903282881	0.0692448616027832	-0.017316101118922234	-0.009744392707943916	0.08045894652605057	-0.017282120883464813	-0.001758362865075469	0.0019406068604439497	-0.00702004786580801	-0.07861599326133728	0.04187886416912079	-0.04810402914881706	0.02883749082684517	0.06281983852386475	0.03350865840911865	0.00938376784324646	-0.0585399754345417	-0.04630451649427414	0.005389607045799494	-0.03931698575615883	0.025938929989933968	-0.0016268467297777534	-0.08071750402450562	-0.05461768060922623	-0.041367094963788986	-0.009511849842965603	0.05037807300686836	-0.06053505092859268	0.042080942541360855	-0.012145419605076313	0.012467524036765099	0.024845704436302185	-0.026813000440597534	0.009297197684645653	0.038501281291246414	-0.06516685336828232	-0.027052201330661774	0.07113206386566162	-0.0024385848082602024	0.03862840309739113	0.07453903555870056	-0.046623390167951584	0.07342114299535751	0.02559916116297245	-0.026185927912592888	0.012840189971029758	0.019834060221910477	-0.09016629308462143	0.03408734127879143	-0.03888516128063202	0.034837424755096436	-0.044811464846134186	0.10578015446662903	-0.02668815106153488	-0.03952542692422867	0.06078478321433067	0.07062935084104538	0.00839378871023655	0.004410881549119949	-0.03723379597067833
+0.0024152433034032583	-0.051598526537418365	-0.020341677591204643	0.02941076084971428	-0.09001290053129196	-0.04636429622769356	0.006704607978463173	0.0353923924267292	-0.024338414892554283	-0.07316779345273972	0.02483890764415264	-0.005181330256164074	-0.004584651906043291	0.016731882467865944	0.06201277673244476	-0.03796879202127457	-0.05512993037700653	0.014506553299725056	-0.04804178699851036	-0.01745886169373989	0.03225161135196686	-0.013250241056084633	-0.059762947261333466	0.02344532310962677	-0.010335824452340603	-0.007459643762558699	-0.04195224493741989	0.026334745809435844	0.01711377128958702	0.05013851076364517	0.05787583068013191	-0.028825463727116585	0.01835412159562111	-0.04540139436721802	-0.07610701024532318	0.05597872659564018	-0.07271870225667953	0.13410086929798126	0.04458712041378021	0.0066668083891272545	-0.0580146498978138	-0.016932785511016846	-0.04526017606258392	-0.04609619081020355	0.0694047287106514	-0.028811044991016388	0.015888679772615433	-0.04075143486261368	0.04041758179664612	0.04554333910346031	0.07166168838739395	0.010963644832372665	0.008929835632443428	0.009858960285782814	-0.026696285232901573	0.023770881816744804	0.008299463428556919	-0.03410933166742325	-0.06990831345319748	0.05381197854876518	-0.06921425461769104	-0.019608819857239723	0.007893295958638191	0.03268082067370415	0.01492222584784031	0.0037677998188883066	-0.023443061858415604	0.05092771723866463	-0.034311044961214066	-0.05317753925919533	-0.012799013406038284	0.09716363996267319	-0.051844604313373566	-0.09659090638160706	0.00862150639295578	-0.03800508752465248	0.016383955255150795	-0.0036985762417316437	-0.007972287014126778	0.0009071811218746006	-0.035280387848615646	-0.014333737082779408	0.11825745552778244	-0.04063401743769646	0.017132695764303207	-0.05018305405974388	-0.031006794422864914	-0.04297265037894249	0.0744311660528183	0.03857078030705452	0.048490967601537704	0.04797462001442909	-0.06026468425989151	0.007041111588478088	0.020405760034918785	-0.0024507197085767984	0.012886499986052513	-0.10470299422740936	-0.015500003471970558	-0.019151154905557632	-0.001892031286843121	-0.028520505875349045	0.08701933175325394	0.019093772396445274	0.06992379575967789	0.05979187786579132	-0.06309624761343002	0.03725193068385124	0.007196537218987942	0.02343316562473774	-0.0010242799762636423	0.1270027756690979	0.08212964981794357	0.049905404448509216	0.0440952330827713	-0.00838551577180624	-0.040444519370794296	-0.02569114789366722	-0.04083993658423424	-0.01429692842066288	-0.01628720760345459	0.034058962017297745	0.002949317917227745	-0.046779561787843704	-0.01615590788424015	0.02772284671664238	-0.12418323010206223	-0.03029368445277214	0.06079743430018425	-0.04957374557852745	0.08256831020116806	0.060162682086229324	0.00414870074018836	0.03481674566864967	0.07307562232017517	0.009039442986249924	0.01368478313088417	0.03292078152298927	-0.018247323110699654	-0.028530091047286987	-0.00885632075369358	0.059144336730241776	-0.022394025698304176	-0.038397643715143204	-0.018652919679880142	0.06488792598247528	0.009734869003295898	-0.008088329806923866	-0.09139052033424377	0.04672293737530708	-0.07051588594913483	-0.050411105155944824	-0.026679428294301033	-0.08423423767089844	0.03933297470211983	0.056885749101638794	0.030517391860485077	0.034963373094797134	0.009077224880456924	0.008904607966542244	-0.028025439009070396	-0.06759437173604965	0.004521896596997976	-0.07636476308107376	-0.025118447840213776	-0.022994069382548332	0.02076345682144165	-0.007563319522887468	-0.020945454016327858	-0.03944370895624161	-0.03641638532280922	0.00389590160921216	0.03708263114094734	0.02746627666056156	0.026602517813444138	0.021699022501707077	0.0008961282437667251	-0.07812120020389557	-0.0028332213405519724	-0.043998874723911285	0.020691707730293274	-0.04863663390278816	0.01223180815577507	-0.05577991157770157	-0.04514855891466141	-0.01703559048473835	0.01738731563091278	-0.017012707889080048	-0.032947927713394165	-0.017337117344141006	-0.05319114029407501	-0.0765228196978569	0.049776166677474976	0.07108935713768005	-0.04312018305063248	0.052265044301748276	-0.012257445603609085	0.05569717660546303	-0.04675700142979622	-0.05734381452202797	-0.04965966194868088	0.03112611174583435	0.01763015426695347	0.007390057668089867	0.004792782012373209	-0.0584104061126709	0.05339014530181885	-0.008763976395130157	0.02364494279026985	-0.06884889304637909	0.021770618855953217	0.03044806234538555	0.006528966594487429	0.007821284234523773	0.03545577451586723	-0.06980393081903458	-0.03547785431146622	0.03960691764950752	0.057943932712078094	-0.055401965975761414	0.006625821348279715	0.012864625081419945	-0.058982811868190765	0.00662462692707777	-0.00266398792155087	-0.08951672166585922	0.012499450705945492	0.04593953490257263	-0.06915323436260223	-0.03008018247783184	-0.022353215143084526	0.03993179649114609	-0.030931750312447548	-0.022448087111115456	0.008354204706847668	-0.0003569568507373333	-0.0641305223107338	0.07131300121545792	-0.02420358918607235	-0.06017397716641426	-0.08440146595239639	-0.008301680907607079	0.01143401674926281	0.007386265322566032	0.026780642569065094	0.031089913100004196	0.039620593190193176	0.029035966843366623	-0.03663237765431404	0.0016202089609578252	-0.05128197371959686	0.000947532185819	-0.023311540484428406	0.024068934842944145	-0.031461816281080246	-0.028653575107455254	0.0060404399409890175	-0.06538934260606766	-0.0030209522228688	0.001988384872674942	0.06296143680810928	-0.06366351246833801	-0.018272100016474724	0.026162708178162575	0.040359482169151306	-5.4556308896280825e-05	-0.046228110790252686	-0.0066730245016515255	-0.04113753139972687	0.038699667900800705	-0.06875268369913101	-0.025012679398059845	0.032218482345342636	0.01412905752658844	0.03633347153663635	-0.028592681512236595	0.04988813400268555	-0.04974715784192085	-0.10980242490768433	-0.043015167117118835	-0.08943185210227966	-0.043426983058452606	-0.009972860105335712	-0.022321980446577072	-0.030307164415717125	0.10307314246892929	0.04153468459844589	-0.017614932730793953	-0.05046776309609413	-0.06859473139047623	0.009250040166079998	-0.04242340847849846	0.01163178589195013	-0.02171904221177101	-0.05474215745925903	-0.0016370118828490376	0.044338028877973557	-0.054588980972766876	-0.000490803737193346	0.020637979730963707	-0.0062282150611281395	-0.01715610921382904	0.05086316540837288	-0.0851096585392952	-0.0065140146762132645	0.007797675672918558	-0.058436762541532516	0.06274332106113434	0.08364205062389374	0.057151440531015396	0.022817939519882202	0.023477772250771523	0.059264056384563446	0.07781502604484558	0.02713167853653431	-0.013753565028309822	-0.02726460061967373	-0.036574479192495346	-0.08449183404445648	0.0612771101295948	-0.05526015907526016	-0.0030269622802734375	-0.021614525467157364	-0.006934563163667917	0.11056549102067947	-0.031791672110557556	-0.018661221489310265	0.01781919226050377	0.03808970749378204	0.02520022913813591	0.026940137147903442	0.016887273639440536	-0.023532213643193245	0.025271983817219734	-0.03991324082016945	-0.0573546439409256	-0.057001743465662	0.04838593304157257	-0.028111319988965988	0.0668405219912529	-0.01209181360900402	-0.07234375923871994	0.02612447552382946	0.035089436918497086	0.11216896027326584	-0.043131642043590546	0.041174523532390594	0.017416521906852722	0.02117999643087387	-0.09207796305418015	0.003844404825940728	0.002243491355329752	-0.019306695088744164	-0.09473615139722824	0.028601987287402153	0.06469639390707016	-0.052589740604162216	-0.051846943795681	-0.009120188653469086	-0.04989984631538391	-0.014276373200118542	0.007374647539108992	-0.024121420457959175	0.03891729563474655	0.01627449505031109	-0.04271979257464409	0.025336941704154015	0.012121680192649364	0.010923866182565689	-0.0676739513874054	0.018465060740709305	-0.034134551882743835	0.0892089307308197	-0.01535206288099289	-0.038116659969091415	-0.04432644322514534	0.07663913816213608	-0.01872287690639496	-0.04970338195562363	-0.07991775125265121	-0.021719174459576607	-0.031051671132445335	0.00551238376647234	-0.02116973139345646	-0.06887656450271606	-0.038260865956544876	-0.011907009407877922	-0.04776949808001518	-0.0897725373506546	-0.05189045891165733	0.04010968655347824	-0.042395539581775665	-0.0012704383116215467	-0.05534682795405388	0.015695497393608093	0.00672850152477622	-0.03191903233528137	0.025611473247408867	0.030000532045960426	0.04953864961862564	-0.0304526649415493	0.06506740301847458	0.02042550966143608	0.014891746453940868	-0.020338932052254677	0.0023173398803919554	0.03694698214530945	0.007063515484333038	-0.046219274401664734	0.00799862015992403	-0.04594717547297478	0.04398641362786293	-0.03205404430627823	-0.05127756670117378	0.019567865878343582	0.0018578063463792205	-0.019146762788295746	-0.019139036536216736	0.04678874462842941	-0.08724895119667053	-0.004406415391713381	0.025737229734659195	-0.047684408724308014	-0.06887200474739075	-0.025965455919504166	-0.03325466439127922	-0.011362091638147831	0.037202708423137665	0.02421722188591957	0.030573558062314987	0.00022202108812052757	-0.03959594666957855	0.0027211373671889305	-0.021149249747395515	-0.030553147196769714	-0.008898596279323101	0.01352384127676487	0.02977159433066845	-0.013851947151124477	0.005969776771962643	0.016693875193595886	-0.002556405495852232	0.019153382629156113	-0.08288992941379547	-0.01804942823946476	0.004874459467828274	0.027944382280111313	0.009888160042464733	0.0031609120778739452	0.04874539375305176	-0.05664430558681488	-0.031807709485292435	-0.023642243817448616	0.06305817514657974	-0.0020856582559645176	0.02972574345767498	0.04564638063311577	-0.022236838936805725	-0.02242279052734375	-0.001423523877747357	-0.045694056898355484	-0.08409298211336136	0.005259301979094744	-0.030513854697346687	0.04594142735004425	0.0013365332270041108	0.03423861414194107	-0.007299080025404692	-0.09498661756515503	-0.000772496045101434	-0.03647209703922272	-0.05384818837046623	0.019374612718820572	0.02472922019660473	-0.06923572719097137	-0.05922911688685417	-0.016639981418848038	-0.03444001078605652	0.051356397569179535	-0.04306226596236229	0.04494452849030495	-0.0559530109167099	-0.020395100116729736	-0.005025805439800024	-0.04231370985507965	0.05674649775028229	-0.002366012427955866	-0.06913597136735916	-0.02696695737540722	0.08597631007432938	-0.04142962396144867	0.05527898669242859	0.05238964781165123	-0.03120425157248974	0.03033594600856304	0.031937167048454285	-0.013538660481572151	-0.00042509386548772454	0.007571278139948845	-0.01574038900434971	0.017193952575325966	-0.068793885409832	0.05482890456914902	-0.06723140925168991	0.10057739168405533	0.033443331718444824	0.029047805815935135	0.09172774106264114	0.06685910373926163	0.012454826384782791	0.02373158372938633	-0.0550382174551487
+0.03374823182821274	-0.009849214926362038	-0.01838904246687889	-0.004767043050378561	-0.07047703117132187	-0.09659206122159958	0.005408884957432747	0.024007366970181465	0.003662184812128544	-0.03162260353565216	0.0025456559378653765	-0.017178362235426903	-0.02091837301850319	0.008479473181068897	0.05045256018638611	-0.044946905225515366	-0.08819953352212906	-0.00022262039419729263	-0.0676877498626709	0.02201489545404911	0.0807451456785202	0.019004497677087784	-0.05967530235648155	0.044021621346473694	-0.015590240247547626	0.008366263471543789	-0.029864458367228508	0.010784635320305824	-0.02193382754921913	0.023257190361618996	0.08135409653186798	-0.026777006685733795	0.030895859003067017	-0.04752444848418236	-0.008199105970561504	0.040753331035375595	-0.034765779972076416	0.09945598244667053	0.06968502700328827	-0.045373935252428055	-0.06246805191040039	-0.007791673298925161	-0.046255480498075485	-0.050077877938747406	0.029780684038996696	-0.008523523807525635	-0.03482343629002571	-0.06071048602461815	0.04101685807108879	0.0525379553437233	0.07702566683292389	-0.04323944076895714	-0.04974820092320442	-0.012643350288271904	-0.017409585416316986	-0.008575472980737686	0.016198119148612022	-0.0837368443608284	-0.07420608401298523	0.054291803389787674	-0.06872817128896713	0.02384795807301998	0.010833833366632462	0.03775271773338318	0.015748916193842888	-0.0588432215154171	0.008045172318816185	0.04425841569900513	0.03606396168470383	-0.01969863474369049	0.002150761429220438	0.07831993699073792	-0.06164156645536423	-0.07040130347013474	0.03217775374650955	-0.045084744691848755	0.00942850299179554	0.005707323085516691	0.012852329760789871	0.02891848422586918	-0.0065571763552725315	-0.023425806313753128	0.11995559930801392	-0.05208177864551544	-0.024003198370337486	-0.06069251149892807	-0.017977839335799217	-0.049502111971378326	0.06150569021701813	0.02572389505803585	0.012125350534915924	0.062411852180957794	-0.04474237188696861	0.02865743637084961	-0.012310323305428028	0.013620356097817421	0.016662726178765297	-0.10144698619842529	-0.02294750139117241	-0.041033364832401276	-0.013370104134082794	0.012569203972816467	0.06373157352209091	0.04476596787571907	0.04702422022819519	0.03678063303232193	-0.0537128709256649	0.030900267884135246	0.029305247589945793	0.05010353773832321	0.01659778319299221	0.07303373515605927	0.11718572676181793	0.05522670969367027	0.055416982620954514	-0.047944895923137665	-0.0308474563062191	-0.028883256018161774	-0.029591618105769157	0.0035575299989432096	0.0004421996418386698	0.0280387494713068	-0.02810860238969326	-0.03900725394487381	-0.032961536198854446	0.0567658469080925	-0.13509789109230042	-0.05036679655313492	0.03256456181406975	-0.02108050510287285	0.09459718316793442	0.061612971127033234	-0.03611016646027565	0.005965165328234434	0.08915670961141586	0.016816969960927963	0.01314607448875904	0.03209442272782326	-0.04593232274055481	-0.030064577236771584	-0.0074400887824594975	0.06322021037340164	-0.022886047139763832	-0.0416039414703846	-0.007839075289666653	0.04649704322218895	0.002719687530770898	-0.01289647351950407	-0.0784495398402214	0.0218515545129776	-0.12400667369365692	-0.051591549068689346	0.0059493412263691425	-0.07569616287946701	0.002505784621462226	0.02280794270336628	0.00208477396517992	0.025326039642095566	-0.05075767636299133	-0.0177063699811697	-0.03937607258558273	-0.06077360734343529	-0.002587445080280304	-0.08358637243509293	-0.024866877123713493	-0.04257253557443619	0.028440430760383606	0.017262421548366547	-0.011701620183885098	-0.06268623471260071	-0.029363758862018585	-0.018345041200518608	0.00538282934576273	0.07250288873910904	0.031479835510253906	0.008615820668637753	-0.016820566728711128	-0.042835772037506104	0.01484985277056694	-0.03053487278521061	0.023629412055015564	-0.0522131472826004	0.03470872715115547	-0.0714244470000267	-0.02001473493874073	-0.02110874094069004	0.012224889360368252	-0.04033694043755531	-0.017464807257056236	0.001404523034580052	-0.05944161117076874	-0.08385848999023438	0.04529627785086632	0.08676789700984955	-0.025565525516867638	0.03968951106071472	-0.009940415620803833	0.06250318139791489	0.004510542843490839	-0.03304620087146759	-0.059927523136138916	0.036794230341911316	-0.0064322748221457005	0.046052951365709305	0.03331821784377098	-0.04807773232460022	0.08538354188203812	-0.0012005399912595749	0.018523499369621277	-0.042825572192668915	0.03840462490916252	0.04749251902103424	-0.012807748280465603	-0.0031556759495288134	0.04180072993040085	-0.046318233013153076	-0.021238358691334724	0.00986503716558218	0.06502141803503036	-0.05519071966409683	0.013172543607652187	0.03736623376607895	-0.04945793375372887	0.07108291238546371	0.007170100696384907	-0.08954699337482452	-0.0012737215729430318	0.014613574370741844	-0.03413635492324829	-0.042048245668411255	-0.0016227333107963204	0.009265457279980183	-0.0035175159573554993	-0.01254319678992033	0.017472008243203163	-0.004202166106551886	-0.12204737961292267	0.10462028533220291	-0.025721807032823563	-0.04945530742406845	-0.03847828134894371	0.0018783068517223	0.03450044244527817	0.0007735161925666034	0.018919385969638824	0.0492417998611927	0.07698109745979309	0.05069990083575249	0.0048402962274849415	0.0014965806622058153	-0.07742162048816681	-0.04503835365176201	-0.01423817127943039	0.009726039133965969	-0.03524508327245712	-0.0878804475069046	0.008710537105798721	-0.032040148973464966	-0.015496677719056606	-0.03187738358974457	0.06360946595668793	-0.03794190287590027	-0.036650992929935455	-0.03301163390278816	0.08088003098964691	0.0007313928217627108	-0.06279563158750534	0.05830277130007744	-0.01098523661494255	0.025045273825526237	-0.0018390485784038901	-0.05344495549798012	0.054332274943590164	0.010489740408957005	0.05662907660007477	0.012650774791836739	0.08406408876180649	-0.024543238803744316	-0.09508216381072998	-0.03845108672976494	-0.08164018392562866	-0.05731131136417389	0.006498540285974741	-0.021614938974380493	-0.02188984490931034	0.06484498083591461	0.03769439831376076	-0.02556215599179268	-0.04983152449131012	0.009162157773971558	-0.03562602400779724	-0.06843603402376175	-0.03413139656186104	-0.02452768385410309	-0.08125773072242737	-0.009670307859778404	0.0315985269844532	-0.07039439678192139	-0.047723352909088135	-0.0005775438621640205	-0.02617666684091091	-0.03325420990586281	0.010281114839017391	-0.057440608739852905	-0.035179294645786285	0.011389495804905891	-0.059809423983097076	0.03196719288825989	0.08364100009202957	0.05371416360139847	0.04557429626584053	0.028027569875121117	0.0534827895462513	0.07795622199773788	0.0034065728541463614	-0.020954255014657974	-0.031653307378292084	-0.03413764759898186	-0.07327066361904144	0.03284444659948349	-0.07459180802106857	-0.03972906246781349	0.011312112212181091	-0.0006255577318370342	0.08708078414201736	-0.03179335221648216	-0.010789375752210617	-0.0011951257474720478	0.026344921439886093	-0.012836366891860962	0.04816261678934097	0.0025998421479016542	-0.031167564913630486	0.022807244211435318	-0.03938543424010277	-0.05948267877101898	-0.06653537601232529	0.026333404704928398	-0.025495100766420364	0.043284062296152115	0.01605631411075592	-0.06571947783231735	-0.01423333678394556	0.019546952098608017	0.11662393808364868	-0.028322705999016762	0.018794920295476913	0.0262110885232687	-0.009658565744757652	-0.08267819136381149	-0.011643443256616592	-0.005940667353570461	0.002502750838175416	-0.047520481050014496	0.019808275625109673	0.06771326065063477	-0.09671281278133392	-0.03683052211999893	0.015483571216464043	-0.052029673010110855	0.00109470437746495	0.029405644163489342	0.006225649267435074	0.024991275742650032	0.01367011945694685	-0.04575305059552193	0.006797937676310539	0.01454868633300066	0.020889373496174812	-0.04531044140458107	0.013191749341785908	-0.032669126987457275	0.06903305649757385	-0.022113967686891556	-0.06079324707388878	-0.046581365168094635	0.0777050331234932	0.014177234843373299	-0.04195469990372658	-0.00788706261664629	0.026609579101204872	0.003937959671020508	0.04386017099022865	-0.013933381997048855	-0.056892093271017075	-0.019429095089435577	-0.007060235366225243	-0.019649825990200043	-0.09450791776180267	-0.0008918700041249394	-0.0005659920861944556	-0.06861122697591782	-0.026832010596990585	-0.027994856238365173	0.028073400259017944	0.007421312388032675	-0.046555694192647934	0.033145658671855927	0.025614019483327866	0.01818433403968811	-0.006771240849047899	0.01026075892150402	0.026049284264445305	0.0320105217397213	0.02128075622022152	-0.0020634783431887627	0.008577131666243076	0.004576219245791435	-0.05521281436085701	0.017232520505785942	-0.04853256791830063	0.028061386197805405	-0.0020854019094258547	-0.0005680601461790502	0.0077536399476230145	-0.022847715765237808	0.028702208772301674	-0.02939540147781372	0.02301103062927723	-0.07894288748502731	-0.0227985717356205	0.03139178827404976	-0.04612606391310692	-0.09643121808767319	0.011781116016209126	0.011236466467380524	-0.032176706939935684	0.030652372166514397	-0.006609715986996889	0.031107433140277863	-0.0047604115679860115	-0.06593165546655655	-0.02302604168653488	-0.037575721740722656	-0.014575554989278316	0.015382209792733192	-0.004026350565254688	0.06759467720985413	-0.014547398313879967	0.03178984299302101	-0.004488036502152681	-0.01927291415631771	-0.004919413942843676	-0.06965667754411697	-0.01337401382625103	-0.010520894080400467	0.005400775466114283	0.016189707443118095	0.016769472509622574	0.051856666803359985	-0.021615855395793915	-0.04412132129073143	-0.033411864191293716	0.10302643477916718	-0.05011468008160591	0.022488942369818687	0.012370274402201176	-0.03506580367684364	-0.011577052064239979	0.013338199816644192	-0.027184197679162025	-0.0932065099477768	-0.0006875087856315076	-0.03535744175314903	0.022713858634233475	0.031382687389850616	0.029589630663394928	-0.029067693278193474	-0.07626822590827942	-0.007416317239403725	0.009525591507554054	-0.006624209228903055	0.02508009597659111	-0.0019522710936143994	-0.056761134415864944	-0.036740947514772415	-0.03926696628332138	-0.05327979475259781	0.05680861696600914	-0.043855246156454086	0.034999992698431015	-0.017600953578948975	-0.008964404463768005	0.03297588601708412	-0.05034425109624863	0.007954097352921963	0.012531300075352192	-0.059411030262708664	-0.017914723604917526	0.0538894459605217	-0.015395339578390121	0.017319433391094208	0.07724767178297043	-0.014979733154177666	0.03608819842338562	0.04961187019944191	-0.020088588818907738	0.006613731849938631	-0.018932562321424484	-0.05021957680583	0.06486097723245621	-0.06127128377556801	0.058686841279268265	-0.07450395822525024	0.1131598949432373	0.009965798817574978	-0.023245809599757195	0.09411236643791199	0.04959908127784729	-0.003085987875238061	0.05502934381365776	-0.06525542587041855
+0.007527957204729319	-0.04629077762365341	0.014157194644212723	0.027112390846014023	-0.07806743681430817	-0.0535799078643322	-0.04566121846437454	0.037106338888406754	0.008407816290855408	-0.10554999113082886	-0.022060923278331757	-0.059050701558589935	-0.07051441818475723	0.014549312181770802	0.05474182218313217	-0.0013572911266237497	-0.030877944082021713	0.01919584348797798	-0.03516346216201782	0.0035573337227106094	-0.02001839689910412	-8.129641355480999e-05	-0.08355196565389633	0.0274319127202034	-0.02709612436592579	-0.034079812467098236	-0.031614482402801514	0.06326525658369064	-0.012932561337947845	0.07765530049800873	0.015893595293164253	-0.029914865270256996	0.038450371474027634	0.00422888295724988	-0.06346938014030457	0.014675489626824856	-0.07185251265764236	0.10258308798074722	0.047554630786180496	-0.013289354741573334	-0.015703234821558	0.011075273156166077	0.011685254983603954	-0.06975172460079193	0.04094797372817993	-0.03954634442925453	-0.007469259202480316	-0.012419501319527626	0.013708219863474369	0.08376599848270416	0.09468097239732742	0.0920918807387352	0.031223636120557785	-0.008275102823972702	0.0014177008997648954	0.006156960502266884	-0.012435167096555233	-0.05460379272699356	-0.005115504842251539	0.0648718923330307	-0.1026255339384079	-0.04567788913846016	0.0744505226612091	0.04983937740325928	0.0104491226375103	-0.001062045688740909	-0.027910709381103516	0.08097510784864426	-0.00947386585175991	-0.07121091336011887	-0.03756856918334961	0.045540452003479004	-0.033843059092760086	-0.08380112051963806	-0.006404475308954716	-0.01677042804658413	0.04808381199836731	-0.045652300119400024	0.007288317661732435	-0.004964527674019337	-0.009946189820766449	-0.022368988022208214	0.09528735280036926	-0.008508499711751938	0.04541395604610443	-0.03828223794698715	0.00027100875740870833	-0.10164192318916321	0.0503724031150341	0.011037648655474186	0.03985300287604332	0.028609247878193855	0.00835450179874897	-0.022265668958425522	-0.06217322126030922	0.029888007789850235	0.006797646172344685	-0.08056558668613434	0.05997805297374725	-0.046163029968738556	0.012613276019692421	0.01560155674815178	0.01884002424776554	0.035991039127111435	0.05377089977264404	0.055456843227148056	-0.02070211060345173	0.06193729490041733	0.072078175842762	-0.002132222056388855	0.0027336659841239452	0.07602720707654953	0.06918549537658691	0.06246774271130562	0.023188481107354164	0.0708155557513237	-0.03109501674771309	-0.03857649490237236	-0.0063301268965005875	-0.02106446400284767	-0.019003888592123985	0.04856860265135765	0.015390416607260704	-0.05164457857608795	0.052250899374485016	0.04305213689804077	-0.05949239432811737	-0.026798628270626068	-0.001597337075509131	0.006141524761915207	0.012074192054569721	0.04013315588235855	-0.0064651682041585445	0.02030634693801403	0.048593275249004364	0.008979398757219315	-0.03432577848434448	-0.012089851312339306	0.013760742731392384	-0.03482064977288246	0.02423960715532303	0.041026826947927475	-0.017158761620521545	-0.04406916722655296	0.02804073691368103	0.04195297136902809	0.014335667714476585	0.01707657054066658	-0.1353606879711151	0.04157654568552971	-0.049788396805524826	-0.03980404511094093	0.016266055405139923	-0.07011537998914719	-0.020878015086054802	0.058001868426799774	0.05028124898672104	-0.0024321735836565495	0.0011619605356827378	-0.013413934037089348	-0.01746279001235962	-0.04764605686068535	0.015049523673951626	-0.08902864903211594	-0.004934171214699745	0.0032095506321638823	-0.008656206540763378	0.04607846960425377	-0.09577686339616776	-0.042986880987882614	0.03423929587006569	0.03466319665312767	0.06153317168354988	0.0380181223154068	0.05233858898282051	0.009436081163585186	0.005098027642816305	-0.0780516043305397	-0.03891849145293236	-0.011784644797444344	0.014698314480483532	0.003841196186840534	0.028264958411455154	-0.09176662564277649	-0.0386800616979599	-0.026013115420937538	-0.03618840500712395	-0.0052063181065022945	0.0011319111799821258	-0.0071029760874807835	-0.012027622200548649	-0.03354083374142647	0.02567434497177601	0.02237280085682869	-0.016143782064318657	0.04998915642499924	0.009328646585345268	0.07191390544176102	-0.07527879625558853	-0.06950453668832779	-0.05208414047956467	0.03625998646020889	0.061310723423957825	0.0003240301157347858	0.018101729452610016	-0.08193497359752655	0.049438461661338806	0.03208890184760094	-0.023207271471619606	0.013334945775568485	0.06654314696788788	0.05106310173869133	0.04215899482369423	0.0049569327384233475	0.042174141854047775	-0.035912904888391495	0.013899499550461769	0.024088451638817787	0.04485553503036499	-0.08355553448200226	0.03120162896811962	0.005740229040384293	-0.05021824315190315	-0.0069380574859678745	-0.008413002826273441	-0.042930979281663895	0.03722039982676506	0.027290793135762215	-0.039912935346364975	-0.019735727459192276	0.058802492916584015	-0.013400578871369362	-0.02910791151225567	0.020746560767292976	0.01356775127351284	-0.032111428678035736	-0.05165056511759758	0.04512038826942444	-0.02707517147064209	-0.046828776597976685	-0.031230539083480835	-0.05656803771853447	-0.004583935718983412	-0.024873698130249977	0.025438101962208748	0.08079923689365387	0.04882651939988136	0.04920850694179535	-0.03047286719083786	-0.03762746974825859	-0.07075587660074234	-0.027761608362197876	0.005723233334720135	0.02702726423740387	0.004066369496285915	-0.02223975583910942	-0.017595594748854637	-0.11350838094949722	-0.029854534193873405	-0.0032707618083804846	0.01167366560548544	-0.0681060329079628	0.050849221646785736	-0.030925389379262924	0.01978498511016369	0.0014679390005767345	-0.05619070306420326	-0.0035503897815942764	-0.03520168364048004	0.03455796465277672	-0.06604563444852829	0.03346531093120575	0.03123667649924755	-0.02356758341193199	0.013810026459395885	-0.026142118498682976	0.025639008730649948	-0.033983681350946426	-0.059974074363708496	-0.004142393823713064	-0.11363010853528976	-0.06872223317623138	0.03850507736206055	-0.007333619985729456	-0.042930468916893005	0.076932892203331	0.060537949204444885	0.01438668929040432	-0.04677832871675491	-0.060109831392765045	-0.02680697664618492	0.002937550889328122	0.015128759667277336	-0.034962743520736694	0.004341214429587126	0.009416935034096241	-0.011142054572701454	-0.06605469435453415	0.014704110100865364	0.023519160225987434	-0.008054548874497414	-0.0555480495095253	0.03143790736794472	-0.04602234065532684	0.011663184501230717	0.04954764246940613	-0.02313903719186783	0.054356127977371216	0.0718977227807045	0.03524211794137955	-0.027200723066926003	0.031359847635030746	0.00016258633695542812	0.03733951598405838	0.017182467505335808	-0.035222191363573074	-0.048935920000076294	-0.03639506921172142	-0.027993207797408104	0.060251835733652115	-0.02209598384797573	0.031079154461622238	0.0365271158516407	-0.03375701978802681	0.06353619694709778	-0.023484420031309128	-0.05215631425380707	-0.007528679445385933	0.007723474409431219	0.00608306098729372	0.008596699684858322	-0.017527351155877113	0.008242422714829445	0.01835269294679165	-0.02217116393148899	0.0009575176518410444	-0.032367151230573654	0.06543219834566116	-0.045863304287195206	0.020111871883273125	-0.04205955192446709	-0.0409722775220871	0.010111656039953232	0.007882043719291687	0.09154952317476273	-0.06902167201042175	0.04651029035449028	0.038796309381723404	0.04475025460124016	-0.0805620551109314	-0.03827274590730667	-0.052695244550704956	-0.029076529666781425	-0.05570179969072342	0.017857829108834267	0.008869578130543232	-0.08604346960783005	-0.07943785190582275	-0.062081292271614075	0.019991038367152214	0.01088108029216528	0.027103211730718613	-0.005501044448465109	0.048213958740234375	-0.025402015075087547	-0.04758339375257492	-0.04804207384586334	-0.012227296829223633	0.03513828292489052	-0.04682053625583649	0.040171898901462555	0.04032331705093384	0.026152456179261208	-0.020460473373532295	-0.07675106823444366	-0.01672447845339775	-0.004467758350074291	-0.04701373726129532	0.029488231986761093	-0.007837854325771332	-0.05286502465605736	-0.0004834290302824229	-0.019752049818634987	-0.014320457354187965	-0.03312273323535919	-0.02752775140106678	0.04423922300338745	-0.0033425630535930395	-0.04287274181842804	-0.0593404695391655	0.018546728417277336	0.004125749226659536	0.03459079936146736	-0.048879168927669525	-0.003451958065852523	-0.017912084236741066	-0.03156210482120514	0.007944141514599323	0.07800520211458206	-0.006679045967757702	0.05908292159438133	0.008752410300076008	-0.02701238915324211	-0.02454150654375553	-0.00900996569544077	-0.07459054887294769	0.007063352502882481	-0.00890504289418459	-0.06033772602677345	0.0136268837377429	-0.05520947277545929	0.03533191978931427	0.007222767453640699	-0.03980347886681557	0.002989177592098713	-0.049878623336553574	-0.0448940172791481	-0.013331178575754166	0.05975118279457092	-0.018412787467241287	-0.003363539231941104	-0.026706604287028313	-0.01865861751139164	-0.07392167299985886	0.006153258960694075	-0.08151578158140182	-0.0395166240632534	0.0636075958609581	-0.008163311518728733	0.00032834798912517726	0.013286176137626171	-0.10318543761968613	0.007422528695315123	-0.034890010952949524	0.010217749513685703	-0.03832423686981201	0.04633185267448425	-0.025678955018520355	-0.038655322045087814	-0.0032567207235842943	-0.012920508161187172	-0.03516039624810219	0.035164639353752136	-0.04835304245352745	-0.03206567466259003	0.03849633038043976	0.03805079311132431	-0.0037973392754793167	0.04032345488667488	0.03197835758328438	-0.0911165252327919	-0.02624528482556343	-0.004303421825170517	0.04632492735981941	-0.0075396522879600525	0.0020934217609465122	0.02298160456120968	-0.10609301924705505	-0.055045656859874725	0.05717020481824875	-0.03150657191872597	-0.09029462188482285	-0.028652839362621307	-0.04512261971831322	0.04715840891003609	-0.017726996913552284	0.11241044849157333	-0.06789570301771164	-0.09586285799741745	0.045435234904289246	-0.038644470274448395	-0.03819054737687111	-0.04749705269932747	0.05045902356505394	-0.055665865540504456	0.004032392054796219	-0.01238351408392191	-0.0443548709154129	0.05061636492609978	-0.022740764543414116	0.009384325705468655	-0.0619877353310585	-0.0030058615375310183	-0.023488730192184448	-0.05706053599715233	0.05644000694155693	-0.010965371504426003	-0.04835130646824837	-0.04971451312303543	0.11636865139007568	-0.05013502016663551	0.08391546458005905	0.0741686299443245	-0.0007493544835597277	0.01565816067159176	0.0626024603843689	-0.004543791990727186	-0.015841035172343254	0.01178319938480854	-0.0059619019739329815	0.020225999876856804	-0.018125424161553383	-0.016654787585139275	-0.026406381279230118	0.15431764721870422	-0.004809173755347729	-0.01007327064871788	0.031774718314409256	0.06217164918780327	0.05451059341430664	0.0380418561398983	-0.09614741802215576
+0.0360177680850029	-0.017501486465334892	-0.018589720129966736	0.00897746067494154	-0.10384707152843475	-0.05284261330962181	0.015084948390722275	0.052723076194524765	-0.023086177185177803	-0.032335713505744934	-0.0017844309331849217	-0.040962301194667816	0.007321903947740793	-0.0005980815039947629	0.04690077155828476	-0.024644313380122185	-0.055090323090553284	-0.007192893419414759	-0.06412012130022049	-0.02270498126745224	0.048938266932964325	0.003432523226365447	-0.06460089981555939	0.07476536929607391	0.007625933270901442	0.01987222395837307	-0.04914886876940727	0.020505154505372047	-0.020754840224981308	0.02136864699423313	0.023428073152899742	-0.01347313541918993	0.010205967351794243	-0.0342460535466671	-0.04994023218750954	0.06699362397193909	-0.04787397384643555	0.12850748002529144	0.030799686908721924	-0.052726440131664276	-0.04566072300076485	0.025491835549473763	-0.02919761650264263	-0.0468963086605072	0.0385788269340992	0.004004652611911297	0.01798359863460064	-0.023779183626174927	0.04280718043446541	0.045615218579769135	0.07315265387296677	0.016163419932127	-0.005049468018114567	-0.018142178654670715	0.0030364112462848425	0.036109112203121185	0.01478327251970768	-0.06410294771194458	-0.05598641559481621	0.04724341258406639	-0.06638462841510773	-0.016991859301924706	0.013586561195552349	0.0006800959235988557	0.01810511201620102	-0.05439549311995506	-0.006807070691138506	0.08668073266744614	0.02772401086986065	-0.0014956906670704484	-0.006086273584514856	0.09498655796051025	-0.08196758478879929	-0.0715804249048233	0.054395727813243866	-0.045181434601545334	-0.023525448516011238	0.0365147702395916	-0.0005946805467829108	-0.012789163738489151	-0.023339755833148956	-0.018531812354922295	0.10471170395612717	-0.0461418479681015	-0.061486195772886276	-0.029022572562098503	-0.006770746316760778	-0.07181933522224426	0.07244757562875748	0.014706621877849102	-0.006356450263410807	0.06280311942100525	-0.08467237651348114	0.04314931482076645	0.0024118029978126287	0.005636504851281643	0.029249735176563263	-0.14239045977592468	-0.012736482545733452	-0.0370851494371891	-0.03339628502726555	-0.028127441182732582	0.03380030766129494	0.005315053276717663	0.03268491104245186	0.04190622642636299	-0.0583777092397213	0.022539544850587845	0.0073369406163692474	0.041043948382139206	-0.005367635283619165	0.07056643813848495	0.11567793041467667	0.03980182856321335	0.04774501547217369	-0.015926972031593323	-0.010212447494268417	-0.039066705852746964	-0.06859377026557922	-0.0270252525806427	-0.0034927011001855135	0.03458039462566376	0.005324604921042919	-0.06247445568442345	-0.011111615225672722	0.05967783182859421	-0.11644995957612991	-0.009336644783616066	0.057127695530653	-0.03884349763393402	0.09227265417575836	0.046770013868808746	-0.03299012407660484	0.022859813645482063	0.08406902104616165	0.002185765653848648	0.016123207286000252	0.04786432161927223	-0.025645337998867035	-0.028677120804786682	-0.005286863073706627	0.028657246381044388	0.024854352697730064	-0.08124008029699326	-0.004911328665912151	0.06758137792348862	0.04328750818967819	-0.022561591118574142	-0.08860320597887039	0.030544379726052284	-0.09478887170553207	-0.10166464745998383	-0.010647742077708244	-0.07887455075979233	-0.019074589014053345	0.04352822154760361	0.013058842159807682	0.030605381354689598	-0.006639539264142513	-0.013154065236449242	-0.04901369288563728	-0.04964533448219299	-0.01717301271855831	-0.08354110270738602	-0.03824843838810921	-0.04076787456870079	0.007523455657064915	-0.0007482026121579111	-0.021426808089017868	-0.05424867197871208	0.012339320965111256	0.005281174089759588	0.03228059038519859	0.06585396081209183	0.056026898324489594	0.04200013726949692	0.013999602757394314	-0.015308124013245106	-0.0035108320880681276	-0.029915058985352516	0.010965111665427685	-0.03720395639538765	0.055729299783706665	-0.05932380631566048	-0.0052939532324671745	-0.015314063057303429	0.03464368358254433	-0.02549069933593273	-0.0034776737447828054	-0.027941374108195305	-0.026795638725161552	-0.0733119398355484	0.0354408323764801	0.07024873793125153	-0.04479118809103966	0.04618941247463226	-0.020241765305399895	0.03658691793680191	-0.05702434107661247	-0.03481997922062874	-0.05331294611096382	0.06520325690507889	0.017267070710659027	0.031150491908192635	0.015468215569853783	-0.0670299306511879	0.05641772598028183	-0.0067858025431632996	0.00048315449384972453	-0.07828626781702042	0.05057008937001228	0.038590993732213974	0.004789209924638271	0.01215352863073349	0.05718786641955376	-0.015260891988873482	0.005655737593770027	-0.012297454290091991	0.0490601509809494	-0.06045476719737053	0.02880106121301651	0.004128747619688511	-0.09466829895973206	0.041588038206100464	-0.02104913629591465	-0.07906881719827652	-0.01782502979040146	0.058700136840343475	-0.016460750252008438	-0.04762207344174385	-0.03049623593688011	0.023253308609128	-0.013647738844156265	-0.03746596723794937	0.016658788546919823	0.008622782304883003	-0.08903691917657852	0.07855664938688278	0.007203369401395321	-0.06092572212219238	-0.0551019087433815	-0.0011676521971821785	-0.003471829229965806	-0.020522121340036392	0.04466383159160614	0.046425994485616684	0.0794203132390976	0.028803987428545952	0.012110373005270958	-0.00023047100694384426	-0.06320879608392715	-0.011868848465383053	-0.0259189885109663	0.02396652102470398	-0.031788621097803116	-0.04195821285247803	0.002560574794188142	-0.06343037635087967	-0.04315851256251335	-0.009384802542626858	0.06018460541963577	-0.07219003885984421	-0.02848951332271099	-0.01867716573178768	0.015562086366117	-0.03989505395293236	-0.060912713408470154	0.025010861456394196	-0.006918608210980892	0.05360276252031326	-0.023053854703903198	-0.013564911670982838	0.024653758853673935	0.01374702900648117	0.03006632626056671	-0.03053491935133934	0.037083700299263	-0.034154012799263	-0.09435156732797623	-0.022860638797283173	-0.07910323143005371	-0.08600844442844391	-0.0003483612963464111	-0.04274122044444084	-0.021342210471630096	0.11414102464914322	0.04262441396713257	-0.029489457607269287	-0.09599442780017853	-0.030419902876019478	-0.007726926822215319	-0.03160633146762848	-0.0033374284394085407	0.0162576362490654	-0.04870374873280525	-0.02354142814874649	0.022849740460515022	-0.04320968687534332	-0.02568010240793228	-0.01647929660975933	-0.009943142533302307	-0.0556923933327198	0.04788991063833237	-0.051065631210803986	0.0009447986958548427	0.015126031823456287	-0.05461592599749565	0.028461633250117302	0.1096709668636322	0.07464565336704254	0.03816276788711548	0.011122867465019226	0.021140988916158676	0.09072286635637283	0.02064490132033825	-0.027772322297096252	-0.016233647242188454	-0.017106903716921806	-0.0753864124417305	0.02191176638007164	-0.06775388866662979	-0.01686370000243187	-0.0026293303817510605	-0.022304410114884377	0.06995578110218048	-0.0005870986497029662	-0.05860317498445511	-0.01724209450185299	0.02694365754723549	-0.007167331874370575	0.02236703224480152	0.0077938358299434185	0.0017106980085372925	-0.0015166206285357475	-0.025312112644314766	-0.042589254677295685	-0.03823816776275635	0.04740167781710625	-0.019449586048722267	0.06082242727279663	-0.016279732808470726	-0.045901715755462646	-0.001325750257819891	0.015240373089909554	0.10734911262989044	-0.0576719231903553	0.03267015889286995	0.05999685823917389	0.01161341555416584	-0.08937148749828339	0.005741337314248085	0.026094911620020866	-0.021867668256163597	-0.06281094253063202	0.012962836772203445	0.05393587425351143	-0.07560157775878906	-0.06129245087504387	-0.0002651461982168257	-0.04196086898446083	0.0058112600818276405	0.010191359557211399	0.009483715519309044	0.01810966432094574	-0.037926629185676575	-0.010273612104356289	0.0179294366389513	0.00770768616348505	0.04927879199385643	-0.056334126740694046	0.021847469732165337	0.00853603146970272	0.07888476550579071	-0.05943036824464798	-0.060390546917915344	-0.043342821300029755	0.06324571371078491	-0.023755885660648346	-0.028872091323137283	-0.05043890327215195	0.0031526603270322084	-0.0066031599417328835	0.04182508587837219	0.0009049827349372208	-0.03347286209464073	-0.014725706540048122	-0.0019075333839282393	-0.04092847555875778	-0.07990521937608719	-0.04647580906748772	0.005806626286357641	-0.05355684459209442	-0.0033745793625712395	-0.02762431837618351	-0.03323018178343773	-0.013312947936356068	-0.031387653201818466	0.012599530629813671	0.04965910315513611	0.04765614867210388	-0.018695851787924767	0.057685963809490204	0.026128772646188736	0.034540265798568726	0.0017822330119088292	-0.029102984815835953	0.03702479600906372	0.03273341432213783	-0.04952683672308922	0.04348559305071831	-0.039381373673677444	0.03547339513897896	-0.025654859840869904	-0.004571007564663887	0.011116326786577702	-0.017233306542038918	-0.0008447387954220176	-0.051230289041996	0.0657845064997673	-0.07176331430673599	-0.013464671559631824	0.023176908493041992	-0.04515697434544563	-0.08442748337984085	-0.0017242669127881527	-0.03771938756108284	0.005397345870733261	0.021934237331151962	-0.011716333217918873	0.02772797830402851	0.02481723017990589	-0.07344117015600204	-0.03338135778903961	-0.05224163830280304	-0.0003616378817241639	0.03141099959611893	0.007834045216441154	0.060859564691782	0.02281174249947071	0.007399799767881632	-0.018781006336212158	-0.005884918384253979	0.023225510492920876	-0.07807058840990067	0.014050763100385666	-0.03388393670320511	0.00357890035957098	0.046837784349918365	0.010716649703681469	0.031922824680805206	-0.04648200049996376	-0.04041638225317001	-0.011762259528040886	0.05821479484438896	-0.015541858971118927	-0.0005219478625804186	0.05163933336734772	-0.021823817864060402	-0.02150959149003029	-0.017270686104893684	-0.04463348537683487	-0.05750399827957153	0.01546564046293497	-0.0036985022015869617	0.037607431411743164	0.020166240632534027	0.044679924845695496	-0.010301857255399227	-0.08846580237150192	-0.021669819951057434	8.396206249017268e-05	-0.05442998930811882	-2.5988760171458125e-05	0.04395744204521179	-0.06974691897630692	-0.044860292226076126	-0.016837434843182564	-0.009243771433830261	0.040775496512651443	-0.04722485691308975	0.032535117119550705	-0.03249988332390785	-0.010779554024338722	-0.025099586695432663	-0.05562569946050644	0.01113115530461073	0.014959406107664108	-0.05910106375813484	-0.03329293802380562	0.08389557152986526	-0.024062050506472588	0.025558900088071823	0.08128780126571655	-0.02763666957616806	0.014007742516696453	0.036607883870601654	-0.013210523873567581	0.03168278932571411	0.0002603818429633975	-0.0314372256398201	0.05756666138768196	-0.07707177102565765	0.08816082030534744	-0.06957177817821503	0.09505100548267365	-0.009709821082651615	-0.029999865218997	0.09862165153026581	0.08785635232925415	0.019561758264899254	-0.004042539279907942	-0.06134100258350372
+0.017391089349985123	-0.07236850261688232	-0.029133113101124763	0.004034343175590038	-0.011146907694637775	-0.010871384292840958	-0.03913997858762741	0.025075646117329597	0.022033637389540672	-0.05794697254896164	0.007348920684307814	-0.011807471513748169	-0.018984369933605194	0.001054131891578436	0.06339031457901001	-0.002041653962805867	-0.04156113415956497	0.0004577330546453595	-0.018761616200208664	-0.040534138679504395	0.033100683242082596	0.031938765197992325	-0.010002264752984047	0.07292921096086502	-0.025133971124887466	-0.012745055370032787	-0.05241809040307999	0.03344610705971718	-0.01761375553905964	0.09610501676797867	0.04111459106206894	-0.04748016223311424	0.06639457494020462	-0.051990728825330734	0.031170429661870003	0.06799068301916122	0.0004184358404017985	0.11003492772579193	0.03712102398276329	-0.02936423569917679	0.016033561900258064	-0.05302039161324501	-0.027351560071110725	-0.08493519574403763	0.013805589638650417	-0.036827001720666885	-0.04072720929980278	-0.042056892067193985	-0.025235667824745178	0.0697178766131401	0.06943639367818832	-0.017682354897260666	0.004378991667181253	0.031362421810626984	-0.021367911249399185	-0.015871914103627205	0.037913721054792404	-0.02255304530262947	0.012685684487223625	0.049206264317035675	-0.019974958151578903	-0.029466629028320312	0.005362980533391237	0.023624153807759285	-0.009558162651956081	-0.024237027391791344	0.07871290296316147	0.021017711609601974	0.036509208381175995	0.024360505864024162	-0.014295578934252262	0.029995553195476532	-0.046052977442741394	-0.06751048564910889	0.011811326257884502	-0.02744743973016739	-0.005562060512602329	-0.0008065444999374449	0.029318267479538918	0.08267842233181	0.021149426698684692	-0.007624612655490637	0.10137678682804108	-0.02163309045135975	0.025037776678800583	-0.06367091089487076	0.00410770857706666	-0.0003991343837697059	0.022944247350096703	-0.0464683473110199	0.040273282676935196	0.026977337896823883	-0.004719729069620371	0.024829380214214325	-0.0072811925783753395	0.004799763206392527	0.048747580498456955	-0.0607200488448143	-0.00412656320258975	0.008683182299137115	-0.023139018565416336	-0.008523539640009403	0.04131370037794113	0.04988319054245949	0.047507621347904205	0.04695919156074524	-0.03794151172041893	0.022442109882831573	0.07466725260019302	0.03102867677807808	-0.009898191317915916	0.10765354335308075	0.018545858561992645	-0.05533306673169136	0.0631052628159523	-0.010076256468892097	0.027499333024024963	-0.053846415132284164	-0.0003559356555342674	0.02095276303589344	-0.01713341660797596	0.043378911912441254	-0.08552271872758865	-0.051816098392009735	0.02415795437991619	0.09428860992193222	-0.10183984786272049	-0.04137009382247925	-0.003373117884621024	-0.005823858082294464	0.056329116225242615	0.040488407015800476	0.008189522661268711	0.011129590682685375	0.01674923300743103	0.014670153148472309	-0.02051675133407116	0.057879719883203506	0.03834197670221329	-0.043330948799848557	-0.027190862223505974	0.025507232174277306	0.023842565715312958	0.009401299990713596	0.01693057268857956	0.03767283633351326	-0.0345071516931057	-0.004047449678182602	-0.07974598556756973	-0.013538074679672718	-0.07768572866916656	-0.049214523285627365	0.010427230037748814	-0.07930969446897507	-0.01246982254087925	-0.03031817078590393	-0.0275777168571949	0.04635898396372795	-0.0010418773163110018	-0.010804861783981323	-0.0712147057056427	-0.08606895804405212	0.04102480411529541	-0.029478570446372032	-0.01946762017905712	0.005938331130892038	-0.010282689705491066	-0.012566209770739079	-5.341163614502875e-06	-0.014899414964020252	0.026819346472620964	-0.044263724237680435	0.03468383476138115	0.06147554889321327	0.04791916161775589	0.03754210099577904	0.029737211763858795	-0.039719920605421066	-0.024708278477191925	-0.06531763821840286	-0.024107692763209343	-0.0414327047765255	-0.016411742195487022	-0.06381505727767944	-0.008655409328639507	-0.017887569963932037	0.012796983122825623	0.027078786864876747	-0.013324759900569916	-0.03290565311908722	-0.018149763345718384	-0.07198603451251984	0.05910789594054222	0.020066911354660988	-0.025241928175091743	0.026805898174643517	0.056936465203762054	0.0924372598528862	-0.06389418989419937	-0.018514322116971016	-0.03218519315123558	0.05356517806649208	0.08350574225187302	-0.010083375498652458	-0.008777827955782413	0.006137877237051725	0.08311277627944946	0.019000310450792313	0.0280012134462595	-0.016789522022008896	-0.0010857817251235247	0.039621345698833466	-0.04447384178638458	0.03924505040049553	0.05174492299556732	-0.018183767795562744	0.011616325937211514	0.0005134412203915417	0.02804097905755043	-0.051812078803777695	-0.04249460622668266	0.0040465896017849445	-0.08245214074850082	0.008264284580945969	-0.03042781539261341	-0.09344765543937683	-0.0240365918725729	-0.019024940207600594	-0.0448136180639267	-0.04425737261772156	0.019043369218707085	0.028386875987052917	0.0007671010098420084	-0.0002842852263711393	0.027612291276454926	-0.020796101540327072	-0.027449658140540123	0.07716871052980423	-0.07503340393304825	-0.07511325180530548	-0.037661392241716385	0.023426935076713562	0.05709362030029297	-0.011512243188917637	0.049971580505371094	0.05198175460100174	0.10144983232021332	0.029431380331516266	-0.03727549687027931	-0.0024076078552752733	-0.018283000215888023	-0.05562936142086983	-0.028691638261079788	-0.006701316684484482	0.015460051596164703	-0.0926043763756752	0.009611577726900578	-0.08891943842172623	-0.011007853783667088	-0.006306157913058996	0.06422274559736252	-0.06895365566015244	0.023581618443131447	-0.07220892608165741	0.018807847052812576	-0.030320726335048676	-0.00929123256355524	0.09603565186262131	-0.051877710968256	-0.02875998057425022	-0.013462257571518421	0.03353026509284973	0.011938229203224182	0.0022781435400247574	-0.029961762949824333	-0.05920211598277092	0.0806395411491394	-0.03299444913864136	-0.0355999581515789	-0.07872053235769272	-0.05328941345214844	-0.01973484456539154	0.022274842485785484	-0.015291493386030197	0.004852754529565573	0.03160387650132179	0.048578616231679916	-0.0025764552410691977	-0.0444277748465538	-0.08831246197223663	-0.0330890454351902	-0.026486525312066078	0.033636171370744705	0.00032118873787112534	-0.043743960559368134	0.01261901669204235	0.05026649311184883	-0.016334019601345062	0.0028386549092829227	0.02093438245356083	0.06591331213712692	-0.04575167968869209	0.01867060363292694	0.0007062636432237923	-0.03987659513950348	0.01769634336233139	-0.04413643479347229	0.0024302820675075054	0.07147352397441864	0.0642816424369812	0.03218044713139534	0.03766715154051781	0.07466082274913788	0.06000436842441559	-0.02073964662849903	0.030728109180927277	-0.039765648543834686	0.003176485886797309	-0.05759180337190628	-0.0006765493308193982	-0.011595507152378559	-0.01511829998344183	-0.0003368762554600835	-0.02955998294055462	0.02004661038517952	-0.007521146442741156	-0.04434105381369591	-0.019022472202777863	0.06083805859088898	0.0063103982247412205	0.102169930934906	-0.006681237369775772	-0.00010743207531049848	-0.026390377432107925	-0.04657035693526268	-0.0324176549911499	-0.07782577723264694	0.06292818486690521	-0.04383833706378937	0.03413276746869087	0.04938824102282524	-0.08417948335409164	-0.04299139976501465	0.02341640554368496	0.0990975946187973	-0.11126884073019028	-0.04342883080244064	0.017431458458304405	0.009592225775122643	-0.12326157838106155	-0.022726843133568764	-0.058766353875398636	-0.08210389316082001	-0.029706444591283798	-0.0016771244117990136	0.07866670191287994	-0.02315344288945198	-0.03702060878276825	-0.03518761321902275	-0.0018470922950655222	-0.027197593823075294	0.09857963025569916	-0.03008456528186798	0.04635680839419365	-0.031359825283288956	-0.053370967507362366	-0.030770529061555862	0.03716877102851868	0.023919206112623215	-0.02672138810157776	0.04891146719455719	0.010718175210058689	-0.030638542026281357	-0.031233521178364754	-0.0382239893078804	-0.05313275754451752	0.03025730326771736	-0.05253008008003235	0.013464338146150112	-0.031086256727576256	0.01094089262187481	0.0051889983005821705	0.02689169906079769	0.007809728384017944	-0.003193949582055211	-0.0599537119269371	0.0071222176775336266	-0.03741896525025368	-0.06623853743076324	-0.021598005667328835	0.06298152357339859	-0.01132325641810894	0.034419141709804535	-0.04023236781358719	-0.012878845445811749	-0.038642387837171555	0.014973665587604046	0.037842754274606705	0.08991982787847519	0.03678976744413376	-0.00865102093666792	0.07762952893972397	0.040027033537626266	-0.0014654963742941618	0.02928534522652626	-0.0394190177321434	0.031015878543257713	-0.07681643217802048	-0.05492749437689781	0.017753560096025467	0.0028005577623844147	-0.012744267471134663	-0.01829097978770733	-0.047731395810842514	0.014332921244204044	-0.028639230877161026	0.023228177800774574	-0.022769011557102203	0.04772626608610153	-0.0652805045247078	0.01461787335574627	-0.06357599794864655	-0.009309682995080948	-0.038890738040208817	-0.05425209924578667	0.016191666945815086	-0.00956649612635374	0.06374584883451462	-0.06378760188817978	-0.02863648720085621	0.028009066358208656	0.014549524523317814	-0.02164069563150406	-0.01462332159280777	0.0381314791738987	-0.03404790908098221	0.04406262934207916	0.026583733037114143	-0.053020644932985306	0.02137569524347782	-0.008266815915703773	-0.08574312180280685	0.01605343259871006	0.008454798720777035	-0.03645322844386101	0.023769469931721687	-0.025989912450313568	0.016132209450006485	0.02087823674082756	0.058085888624191284	-0.04941389337182045	-0.044387225061655045	-0.06450017541646957	0.07725168764591217	5.171334487386048e-05	0.039328597486019135	-0.027499409392476082	-0.07130315899848938	-0.06560719013214111	0.041439689695835114	-0.06557630747556686	-0.08449059724807739	-0.036455411463975906	0.012284691445529461	0.03997563198208809	-0.07124683260917664	0.038737703114748	-0.04368370398879051	-0.11251597106456757	0.0009271473973058164	0.010468469001352787	-0.060515522956848145	-0.012142819352447987	0.02514684945344925	-0.04741685837507248	0.02958144247531891	0.00020569012849591672	-0.06376394629478455	0.052639078348875046	-0.0024910306092351675	0.004597426392138004	-0.054304592311382294	-0.00921280961483717	-0.06839007139205933	-0.05242844671010971	0.025689266622066498	-0.01277344860136509	-0.05950256437063217	-0.023933518677949905	0.03274249657988548	-0.01077121403068304	0.05577772855758667	0.06484328210353851	-0.005884153768420219	0.045117028057575226	0.08855968713760376	0.02486303634941578	-0.007892580702900887	-0.05580325797200203	-0.006470310967415571	0.05926370620727539	0.0007018879405222833	0.055460698902606964	-0.07179520279169083	0.14617964625358582	0.009752044454216957	0.000247763266088441	0.025420330464839935	0.04228382930159569	0.09485530853271484	0.030637865886092186	-0.09431017935276031
+0.016014158725738525	0.00028746455791406333	-0.011281787417829037	0.04019075259566307	-0.05755728483200073	-0.08876074850559235	-0.0343991182744503	0.02838469296693802	0.048257261514663696	-0.06056607887148857	-0.049346521496772766	-0.036911364644765854	0.015535086393356323	0.017829250544309616	0.022225037217140198	-0.005575484596192837	-0.01763768494129181	0.07555537670850754	-0.04400958865880966	-0.0028782787267118692	0.019424276426434517	-0.005206432193517685	-0.07633333653211594	0.04063892737030983	0.008801347576081753	-0.006203181110322475	-0.04301629588007927	0.014816704206168652	0.038336414843797684	0.02798069454729557	0.008254953660070896	0.004113023169338703	0.06955748796463013	-0.07528343051671982	-0.030973291024565697	0.027376020327210426	-0.035757265985012054	0.08382490277290344	0.01706783100962639	-0.001957198139280081	-0.0796145498752594	-0.01588842272758484	-0.07413569837808609	-0.10283306986093521	-0.017265411093831062	-0.0010232353815808892	0.012521776370704174	0.0169505812227726	0.023577740415930748	0.08200651407241821	0.061469417065382004	-0.004853472113609314	-0.033847156912088394	-0.016651898622512817	0.004427876323461533	0.0015788626624271274	0.028194723650813103	-0.050482720136642456	-0.015202552080154419	-0.015276824124157429	-0.10164901614189148	0.0008047952433116734	-0.005131760612130165	0.03996974974870682	-0.048084910959005356	-0.046927742660045624	-0.006415290758013725	0.09541601687669754	0.008622987195849419	-0.054571278393268585	-0.002046389039605856	0.0617188923060894	-0.0990941971540451	-0.032481011003255844	0.03862573951482773	-0.08389513194561005	-0.012144693173468113	0.039790552109479904	-0.011885893531143665	-0.030945641919970512	0.013885736465454102	-0.04772656410932541	0.14963330328464508	-0.056786466389894485	-0.037756115198135376	-0.03063197061419487	0.018740201368927956	-0.05450093373656273	0.01433042623102665	0.009719813242554665	0.007486357819288969	0.03905957192182541	-0.0011493187630549073	0.03384320065379143	0.01001475565135479	0.059258583933115005	0.021993208676576614	-0.13630928099155426	-0.03425512835383415	0.017755797132849693	-0.02395520731806755	0.0037878197617828846	0.030094105750322342	-0.0013438669266179204	0.017430825158953667	-0.0009750425233505666	-0.046332381665706635	-0.009698957204818726	0.04391110688447952	0.037795111536979675	0.037033144384622574	0.058475423604249954	0.05432119220495224	0.04199710488319397	0.018387602642178535	0.034805718809366226	0.056806113570928574	-0.021379487589001656	-0.031447894871234894	0.02814674936234951	0.028254127129912376	0.03353237360715866	-0.026619670912623405	-0.06205934286117554	1.4443833606492262e-05	0.032294224947690964	-0.044752027839422226	-0.061592165380716324	-0.03461028262972832	-0.03469567745923996	0.10584398359060287	0.04628469794988632	-0.05603429302573204	-0.017789378762245178	0.06698178499937057	0.01755046844482422	-0.010157901793718338	-0.0025212247855961323	0.027138113975524902	-0.05325531214475632	-0.015065666288137436	0.05679956078529358	-0.035734377801418304	-0.05265140160918236	0.10123541951179504	0.07824963331222534	0.03540187329053879	-0.030977828428149223	-0.08894780278205872	-0.025087669491767883	-0.04414646327495575	-0.0587351992726326	0.027493759989738464	-0.056083254516124725	0.025723829865455627	0.027630651369690895	0.013048121705651283	0.04548303037881851	0.000328687863657251	-0.01473317015916109	-0.06517630815505981	-0.01869366504251957	0.001865863217972219	-0.04633062705397606	-0.04523522034287453	-0.002914497861638665	0.008517444133758545	0.024231215938925743	-0.0966130867600441	-0.04082769528031349	-0.014790087938308716	0.004836191423237324	0.07141578942537308	0.05335269495844841	0.06033260375261307	0.0016904874937608838	-0.018030669540166855	-0.04880508407950401	-0.03446162864565849	-0.018870055675506592	0.06113608554005623	-0.062083590775728226	0.00419215764850378	-0.018502049148082733	0.047291532158851624	0.03307008370757103	-0.007880520075559616	-0.024260830134153366	0.009453168138861656	0.00922700297087431	-0.05953037366271019	0.0021777839865535498	0.0302837323397398	0.05454288795590401	-0.012038864195346832	0.04660145193338394	-0.06236953288316727	0.05045346915721893	-0.021326374262571335	-0.04918547719717026	-0.06125123053789139	0.017925715073943138	0.024343393743038177	0.04207481071352959	0.017702562734484673	-0.10366307944059372	0.044377539306879044	0.04604155197739601	0.01749272830784321	-0.032724738121032715	0.04032711312174797	0.023220058530569077	0.0005196649581193924	0.045281872153282166	0.06412751972675323	0.0015144463395699859	0.005642717704176903	0.011279379948973656	0.026962434872984886	-0.056711386889219284	0.045788105577230453	0.035346124321222305	-0.1049150675535202	0.010729066096246243	-0.07095865905284882	-0.04859135299921036	-0.04395846277475357	0.031274307519197464	-0.03314487263560295	-0.03006952442228794	-0.02757163718342781	-0.017636308446526527	0.004195824731141329	-0.05038563907146454	-0.022309184074401855	0.024742934852838516	-0.06379261612892151	0.07020903378725052	0.03876015171408653	-0.05721323937177658	-0.0520164892077446	0.007517784833908081	-0.003403055714443326	-0.023147251456975937	0.0005831983289681375	0.055188048630952835	0.06969349086284637	0.0050953966565430164	-0.005205890163779259	-0.0030638917814940214	-0.0675201490521431	-0.07876331359148026	0.017509514465928078	0.03601396456360817	-0.03142964094877243	-0.01913009211421013	0.026023300364613533	-0.08571802824735641	-0.02863568440079689	0.02055935561656952	-0.00940193422138691	-0.014864838682115078	0.006092715542763472	-0.037247080355882645	0.02292201854288578	-0.07917580008506775	-0.03078153170645237	0.015161216259002686	-0.017274508252739906	0.02457544021308422	-0.02809598296880722	0.0025011685211211443	0.052256979048252106	-0.02799450047314167	0.015493045561015606	-0.010247213765978813	0.05406421422958374	-0.01926778443157673	-0.030626559630036354	0.004863660782575607	-0.0887693464756012	-0.07239367812871933	-0.02474268339574337	0.03401821479201317	0.0006913641700521111	0.08451515436172485	0.06217900663614273	-0.031411103904247284	-0.08175334334373474	-0.07211965322494507	-0.06499996781349182	-0.058790918439626694	0.010523724369704723	-0.019650021567940712	-0.05962206795811653	-0.047050025314092636	0.02332920953631401	-0.03984036669135094	0.03944272920489311	0.014011112041771412	-0.018233109265565872	-0.08765383809804916	0.040848445147275925	0.007042271085083485	-0.03227679058909416	0.005843803286552429	-0.026103464886546135	-0.007952772080898285	0.05906621366739273	0.017717696726322174	-0.008947265334427357	0.0588073693215847	0.011729576624929905	0.05023995786905289	-0.016886232420802116	-0.008497527800500393	-0.024546366184949875	-0.03354921191930771	-0.04168597608804703	0.04099404811859131	-0.016537949442863464	0.006853253114968538	0.012561502866446972	0.013000360690057278	0.020157523453235626	-0.017050594091415405	-0.0595899261534214	-0.014309876598417759	0.015791289508342743	-0.027202190831303596	-0.006708658300340176	-0.003463959787040949	0.04897928982973099	-0.04675036668777466	-0.09784263372421265	-0.011732115410268307	-0.020283292979002	-0.0003274663758929819	-0.02422880195081234	0.04239927604794502	-0.037330206483602524	-0.04257172346115112	-0.04902699217200279	-0.006100524682551622	0.15889999270439148	-0.048003263771533966	-0.0034886652138084173	0.09743278473615646	0.05785133317112923	-0.08197097480297089	0.015236612409353256	0.017196349799633026	-0.05571504682302475	-0.03658125177025795	-0.0028249265160411596	0.0008011744939722121	-0.05451693385839462	-0.02209877036511898	-0.010094075463712215	0.006020100321620703	0.0054865311831235886	0.022790269926190376	0.0029257703572511673	0.04280921071767807	0.008117474615573883	-0.021870551630854607	0.00039322773227468133	0.007379789836704731	0.02464844286441803	-0.027228884398937225	0.011490508913993835	-0.019848845899105072	0.028035065159201622	-0.04457258805632591	-0.038863588124513626	-0.05026855692267418	-0.004722292069345713	-0.014823341742157936	-0.06998301297426224	-0.02250811830163002	-0.040216583758592606	-0.0046227131970226765	0.03518908470869064	0.051284562796354294	-0.022977249696850777	-0.06629852950572968	-0.08299326151609421	-0.020010778680443764	-0.07785077393054962	-0.030155489221215248	-0.016708889976143837	-0.015347293578088284	0.015905797481536865	-0.03356422856450081	-0.04476447403430939	-0.009066473692655563	0.02052413858473301	0.017443044111132622	0.07613100111484528	-0.008160781115293503	-0.008297830820083618	0.03601214662194252	-0.04538068547844887	0.05402567610144615	0.04229035601019859	-0.07489953190088272	-0.02789095602929592	0.03888136148452759	-0.05875711515545845	0.022563891485333443	-0.06986821442842484	-0.020937573164701462	-0.008030897006392479	-0.00921671837568283	-0.004322481341660023	0.026810461655259132	0.05414669215679169	0.00739418575540185	0.04534107819199562	-0.06411837786436081	-0.032053180038928986	0.005669572856277227	-0.03320957347750664	-0.04977107420563698	-0.0501977764070034	-0.02612541988492012	-0.014215386472642422	0.012784354388713837	-0.06801874190568924	0.04047619178891182	0.012244450859725475	-0.07674135267734528	-0.049262817949056625	-0.010904901660978794	0.009216541424393654	0.04209364205598831	-0.015991929918527603	-0.012651926837861538	0.02116820588707924	-0.013688266277313232	0.050612855702638626	-0.0029409569688141346	0.013803720474243164	-0.052736420184373856	-0.011887893080711365	0.008254754357039928	0.021675482392311096	0.007348012179136276	-0.010184659622609615	0.049815136939287186	-0.0694541409611702	-0.000836591818369925	-0.017290500923991203	0.0599660724401474	0.03835207223892212	0.004679628182202578	0.03858723118901253	-0.06429196149110794	-0.00015249676653183997	0.05991262570023537	-0.025314636528491974	-0.03964121267199516	-0.022849326953291893	-0.04286429286003113	0.06994348764419556	0.06245175376534462	0.04150905832648277	-0.043600358068943024	-0.025027094408869743	0.03315885365009308	0.003458977909758687	-0.07117035984992981	-0.020360078662633896	0.07222945243120193	-0.021087709814310074	-0.03201180696487427	-0.00794264953583479	-0.06361328065395355	0.06834284961223602	0.051987383514642715	0.07303395122289658	-0.05045843496918678	-0.021165844053030014	-0.01969054713845253	-0.024524444714188576	0.026180973276495934	0.031155144795775414	-0.044003624469041824	0.021039636805653572	0.11029761284589767	-0.019868213683366776	0.024268757551908493	0.06949511915445328	-0.025323335081338882	0.06294113397598267	0.004498956725001335	-0.025445643812417984	0.04699530079960823	-0.03683273866772652	-0.06691418588161469	0.05200710520148277	-0.09720218181610107	0.09578794240951538	-0.07517040520906448	0.12649063766002655	0.0438699871301651	-0.047352664172649384	0.08296441286802292	0.09420859068632126	-0.009385467506945133	0.028833750635385513	-0.03383974730968475
+0.021422510966658592	-0.0550529919564724	-0.05748765170574188	0.030675942078232765	-0.1081475168466568	-0.051962610334157944	-0.0006688110297545791	0.06972968578338623	-0.03695622831583023	-0.030995117500424385	0.0028635631315410137	-0.002291735028848052	0.006244214251637459	0.006162364035844803	0.06090482696890831	-0.014356556348502636	-0.05415193736553192	-0.014226850122213364	-0.04340757802128792	-0.02762022614479065	0.025495078414678574	0.020714567974209785	-0.05421607941389084	0.06179439648985863	-0.0019709826447069645	-0.025325268507003784	-0.049633339047431946	-0.002143515506759286	-0.007809212431311607	0.021253677085042	0.006664425600320101	-0.020271161571145058	-0.001049352460540831	-0.026198619976639748	-0.08276693522930145	0.06264965981245041	-0.02437273971736431	0.11623544245958328	0.036872029304504395	-0.02962767519056797	-0.03278575837612152	0.06793174892663956	-0.041279036551713943	-0.05794225260615349	0.04900982230901718	0.013634201139211655	0.023782305419445038	-0.021664360538125038	0.02016034722328186	0.056655220687389374	0.039090029895305634	0.03949896618723869	0.011382735334336758	-0.034411925822496414	-0.04225149750709534	0.01991063915193081	0.013054810464382172	-0.04936361685395241	-0.04213147982954979	0.024820411577820778	-0.06345132738351822	0.0065593537874519825	0.020093325525522232	-0.006397376302629709	0.04950246214866638	-0.018908901140093803	-0.00018025771714746952	0.0636979341506958	0.01460278406739235	-0.010104265995323658	-0.009153367020189762	0.08648853003978729	-0.06206906586885452	-0.04173117130994797	0.07914043962955475	-0.03659434989094734	-0.016736986115574837	0.026732107624411583	-0.01909283548593521	-0.024632301181554794	-4.4204902224009857e-05	0.005597355309873819	0.07797255367040634	-0.04192104563117027	-0.0340244434773922	-0.040126167237758636	0.014428460039198399	-0.04276927560567856	0.026728585362434387	-0.017335519194602966	0.03333374857902527	0.051403868943452835	-0.08410795778036118	0.011245441623032093	-0.03647952526807785	0.0028256357181817293	0.04500442370772362	-0.1493431031703949	-0.032817207276821136	-0.0088896369561553	-0.01707860268652439	-0.04477511718869209	0.037557151168584824	-0.03781522810459137	0.0064919753931462765	0.02977716736495495	-0.06824195384979248	0.01108496729284525	0.00572925666347146	0.010335422120988369	0.039733074605464935	0.08171755075454712	0.0715106874704361	0.052747953683137894	0.07532597333192825	0.007794011849910021	-0.013278106227517128	-0.030922239646315575	-0.043103884905576706	0.007722815964370966	-0.039396610110998154	0.037226494401693344	0.027212167158722878	-0.07355117797851562	0.0048818811774253845	0.04497779160737991	-0.10824115574359894	0.00971826072782278	0.047293487936258316	-0.05238697677850723	0.07798992097377777	-0.022255733609199524	-0.01048386748880148	0.036845430731773376	0.052040815353393555	0.020806126296520233	0.00785176083445549	0.06361687928438187	0.004743373021483421	-0.04545813053846359	-0.03593289107084274	0.08237328380346298	0.05244478955864906	-0.02902045287191868	0.03898567333817482	0.07609286904335022	0.005501882638782263	0.023168349638581276	-0.055144499987363815	0.032827261835336685	-0.10533680766820908	-0.08968748897314072	-0.025347895920276642	-0.09445478767156601	0.02567281201481819	0.05340193212032318	0.03524622321128845	0.043276168406009674	-0.005342933349311352	-0.02356715314090252	-0.04234077036380768	-0.04668466001749039	-0.009366592392325401	-0.0669093057513237	-0.011851675808429718	0.0001598959934199229	-0.027251306921243668	0.006793084088712931	-0.0255744531750679	-0.04813934490084648	0.012890081852674484	-0.026146819815039635	0.06199017912149429	0.059365782886743546	0.04397619515657425	0.024949965998530388	0.037608854472637177	-0.03759836405515671	-0.02484733611345291	-0.0035596792586147785	0.011776522733271122	-0.09342007339000702	0.07196934521198273	-0.0708824098110199	-0.02531711384654045	-0.021898530423641205	0.01682855375111103	-0.03592435270547867	-0.02128576673567295	-0.07113369554281235	-0.05082407593727112	-0.09462733566761017	0.07463777810335159	0.08237268775701523	-0.019882971420884132	0.045786052942276	0.015142761170864105	0.04250967130064964	-0.040640607476234436	0.018121911212801933	-0.07488083094358444	0.04283495992422104	0.01260688528418541	0.001228826236911118	0.014979206956923008	-0.06455066055059433	0.06318545341491699	-0.03281419351696968	0.025459466502070427	-0.05447400361299515	0.044015463441610336	0.07806629687547684	-0.019515449181199074	0.02277362160384655	0.054747458547353745	0.01859070174396038	0.020783064886927605	-0.03205327317118645	0.0661444216966629	-0.09043905884027481	0.03588339313864708	0.016755061224102974	-0.07574049383401871	0.008352421224117279	0.0280515905469656	-0.09040085971355438	-0.03055315464735031	0.024363910779356956	-0.0042299311608076096	-0.026835866272449493	-0.030601056292653084	0.058230265974998474	0.026371562853455544	-0.045537319034338	-2.4964399926830083e-05	0.03507908806204796	-0.030375337228178978	0.06320563703775406	-0.011321952566504478	-0.063058041036129	-0.03255802020430565	-0.016887616366147995	-0.0025955846067517996	-0.01005888544023037	0.03802458941936493	0.049099795520305634	0.053161561489105225	0.010590695776045322	0.00041019104537554085	-0.01914319582283497	-0.047589417546987534	-0.0024238633923232555	-0.050506483763456345	0.052136749029159546	-0.005920872092247009	-0.07526843994855881	-0.01126689650118351	-0.09573225677013397	-0.02225046418607235	-0.008043351583182812	0.024635527282953262	-0.07286537438631058	-0.05557767301797867	-0.05658663064241409	-0.024155309423804283	-0.014044827781617641	-0.043914761394262314	-0.03383537381887436	-0.04689402878284454	0.029552122578024864	-0.029306113719940186	0.02546699158847332	-0.00580219691619277	0.0024463003501296043	-0.00438923854380846	-0.058387383818626404	0.002578956540673971	-0.05069457367062569	-0.06190265715122223	-0.004949917551130056	-0.02608215995132923	-0.08826396614313126	0.011167059652507305	-0.04148875176906586	-0.0064078159630298615	0.136824831366539	0.068803571164608	-0.04394160583615303	-0.07408571988344193	-0.04098594933748245	-0.02376960963010788	-0.018944423645734787	0.022462183609604836	0.0491812564432621	-0.06646289676427841	-0.025406673550605774	0.00944715365767479	-0.0116152074187994	-0.026012664660811424	0.04069839417934418	0.03545350953936577	-0.027436641976237297	0.042892806231975555	-0.008058033883571625	-0.009862934239208698	-0.004894243087619543	-0.0575522743165493	0.021599052473902702	0.07462812960147858	0.06497428566217422	0.03874491900205612	0.009832861833274364	0.0241928081959486	0.07760131359100342	0.017705855891108513	-0.027303915470838547	0.031654175370931625	-0.05192270129919052	-0.0729399248957634	0.03041202388703823	-0.03224285691976547	-0.008628725074231625	-0.01306048035621643	-0.03889333829283714	0.08637972176074982	-0.015460141934454441	-0.01033973228186369	-0.0339636504650116	0.03146820515394211	0.016270790249109268	0.033124424517154694	0.00030958568095229566	-0.006400616839528084	0.01077161356806755	-0.0491013266146183	-0.027578109875321388	-0.08442066609859467	0.04217144474387169	0.018287092447280884	0.06100374460220337	0.019616015255451202	-0.04695653170347214	0.009877546690404415	0.04490174725651741	0.11474086344242096	-0.07850880920886993	0.01382281631231308	0.020580900833010674	0.029015274718403816	-0.0638454407453537	-0.010694751515984535	-0.022949617356061935	-0.04548291862010956	-0.01755491830408573	-0.0031771075446158648	0.06423844397068024	-0.06957557052373886	-0.0680118128657341	-0.009778155013918877	-0.007981632836163044	0.027869127690792084	-0.005705157294869423	-0.028713952749967575	0.07560079544782639	-0.02887396328151226	0.024475371465086937	0.03993896394968033	0.024381952360272408	0.033618297427892685	-0.03925957530736923	0.03971704840660095	0.044279299676418304	0.0806477889418602	-0.05541243404150009	-0.06580021977424622	-0.06876589357852936	0.028925927355885506	-0.07425619661808014	0.0018618647009134293	-0.09103485196828842	-0.012040071189403534	-0.008074789308011532	0.04903574287891388	-0.007923582568764687	-0.03917498141527176	0.017837487161159515	-0.0024718979839235544	-0.051112592220306396	-0.054479971528053284	-0.0467841736972332	0.04743228480219841	-0.035523246973752975	0.01582629606127739	-0.08868302404880524	-0.031691353768110275	0.020704397931694984	-0.014411038719117641	-0.019264351576566696	0.04391834884881973	0.05595902353525162	0.0020407498814165592	0.0772339403629303	0.020363159477710724	0.04237522557377815	-0.0018891310319304466	-0.05702332407236099	0.03129098564386368	0.0517130047082901	-0.05324457213282585	0.024142351001501083	-0.019677376374602318	-0.0042067947797477245	-0.028992928564548492	-0.0037107663229107857	-0.003556217532604933	-0.021832983940839767	-0.009897255338728428	-0.044261399656534195	0.0563170351088047	-0.05066310241818428	-0.055321067571640015	0.02851511351764202	-0.02436959743499756	-0.08366063237190247	-0.011782608926296234	-0.05119078978896141	0.02716490440070629	0.042689915746450424	-0.023283476009964943	0.030568907037377357	0.04378729686141014	-0.038384344428777695	-0.02057967148721218	-0.04238106682896614	-0.033979322761297226	-0.01997312158346176	0.02407681755721569	0.042572882026433945	0.009589120745658875	0.008482815697789192	0.003371648257598281	-0.03564072772860527	0.03246859088540077	-0.03894539922475815	0.01779172569513321	-0.04743977263569832	-0.009588993154466152	0.0442771352827549	0.006515792105346918	0.025049693882465363	-0.036858223378658295	-0.039816126227378845	-0.03506327420473099	0.07130356878042221	0.005193757358938456	0.008008674718439579	0.06973974406719208	-0.03534878045320511	-0.019362639635801315	0.011887933127582073	-0.020047355443239212	-0.07261184602975845	0.01997062750160694	-0.047723669558763504	0.013410954736173153	0.006883344613015652	0.07592032849788666	0.03366050869226456	-0.10622511804103851	-0.00639102840796113	0.0022598805371671915	-0.03204891458153725	0.03580255061388016	0.0235137902200222	-0.04305080696940422	-0.05260546877980232	-0.05976405367255211	-0.05108095332980156	0.07975856214761734	-0.03194529935717583	0.015468155965209007	-0.028270773589611053	0.011775759980082512	-0.023211106657981873	-0.02849847450852394	0.0027464344166219234	0.0006980725447647274	-0.08326385915279388	-0.01102125272154808	0.11168107390403748	0.006200622767210007	0.020257052034139633	0.0778311938047409	-0.028553510084748268	0.019502747803926468	0.0092169139534235	0.028224501758813858	0.028673751279711723	0.019573651254177094	-0.015189861878752708	0.04415537416934967	-0.004703656770288944	0.053504809737205505	-0.031001055613160133	0.06973496824502945	-0.016483742743730545	-0.03248264268040657	0.0844637006521225	0.05703556910157204	0.0218605138361454	-0.008406519889831543	-0.04451366141438484
+0.07001019269227982	0.02073092944920063	0.04951867088675499	0.0435447096824646	-0.0877954363822937	-4.630191688193008e-05	-0.033378101885318756	0.025140579789876938	0.017703501507639885	-0.056371670216321945	-0.012233600951731205	0.006128712557256222	-0.007846747525036335	0.04584398493170738	0.01076239999383688	-0.052536219358444214	-0.03709133341908455	-0.010122749954462051	-0.03130410239100456	-0.05107438564300537	0.06863640248775482	0.023263124749064445	-0.019128531217575073	0.048429787158966064	0.00807253923267126	0.034163668751716614	-0.021179627627134323	0.03255666792392731	0.0026230132207274437	0.02692207135260105	0.0783727765083313	-0.02591310627758503	0.04164772853255272	-0.06333955377340317	-0.023936543613672256	0.07156882435083389	-0.022910544648766518	0.10544567555189133	0.047190889716148376	-0.041444629430770874	-0.016901563853025436	-0.05853797495365143	-0.020299987867474556	-0.03066927008330822	0.0011556947138160467	-0.03257374465465546	-0.01542599219828844	-0.021713275462388992	0.03875584527850151	0.015284700319170952	0.09841246157884598	-0.020131099969148636	-0.014860355295240879	-0.002462080679833889	0.03199954330921173	0.021359851583838463	0.05551537498831749	-0.05507199093699455	-0.019689852371811867	0.029346277937293053	-0.06562753021717072	-0.027857810258865356	-0.0014383114175871015	0.021252674981951714	-0.008340529166162014	-0.012953788042068481	0.022406913340091705	0.08989210426807404	0.06024423614144325	-0.03250424936413765	0.03398941084742546	0.09619239717721939	-0.05361705645918846	-0.0446176677942276	0.03452133759856224	-0.0631527528166771	-0.028244420886039734	0.006095597054809332	-0.029456287622451782	0.03252473101019859	-0.0019250878831371665	-0.0726596862077713	0.12030471861362457	-0.032637663185596466	-0.057829566299915314	-0.02562968246638775	-0.015381038188934326	-0.0944155603647232	0.07410536706447601	0.02761959843337536	8.083914872258902e-05	0.08114873617887497	-0.06535423547029495	0.0591525174677372	0.0014468426816165447	0.013336134143173695	0.009801882319152355	-0.09508954733610153	-0.03711225092411041	-0.005411315243691206	0.0039634560234844685	0.0007599038654007018	0.05374902859330177	-0.004960190039128065	0.0691400021314621	0.06804128736257553	-0.033780381083488464	-0.013060040771961212	0.007056606933474541	0.048718083649873734	0.025599408894777298	0.08239751309156418	0.0796201303601265	0.04437575116753578	0.040282249450683594	-0.039094045758247375	0.014113088138401508	-0.06411516666412354	-0.03692435473203659	-0.024959173053503036	0.005217124707996845	0.08937707543373108	-0.054880425333976746	-0.0680876076221466	-0.05798282474279404	0.032830335199832916	-0.08770793676376343	-0.018616486340761185	0.081988126039505	-0.007527782581746578	0.09401296824216843	0.0979067012667656	-0.04564175382256508	0.038774218410253525	0.039131660014390945	0.03258480504155159	-0.045145630836486816	0.023124724626541138	0.015417653135955334	-0.014998949132859707	-0.010640880092978477	0.024783940985798836	0.03267575055360794	-0.08767817169427872	0.0020488672889769077	0.06547293812036514	0.047071851789951324	-0.04768913611769676	-0.04964897409081459	-0.006640673149377108	-0.03058088943362236	-0.06374172121286392	-0.0012668982381001115	-0.07379823178052902	-0.006658121477812529	0.053843580186367035	-0.006360307335853577	0.018592897802591324	-0.028765903785824776	-0.007070299703627825	-0.04810263589024544	-0.0505046620965004	0.013004745356738567	-0.050739776343107224	-0.03183282911777496	-0.025833860039711	0.020592862740159035	-0.005536474287509918	-0.055716320872306824	-0.014022842980921268	-0.0051384116522967815	-0.010637065395712852	0.02025221474468708	0.0909147784113884	0.07441196590662003	-0.002428551437333226	-0.0139023931697011	-0.030669407919049263	0.02265225350856781	-0.012379647232592106	0.01388738676905632	-0.02709096111357212	-0.007720389869064093	-0.09539337456226349	-0.036337342113256454	-0.01903335005044937	0.040910329669713974	0.018514469265937805	0.01678168587386608	-0.0005798320053145289	-0.014407160691916943	-0.023928219452500343	-0.0008006105781532824	0.04363628104329109	-0.04833237826824188	0.046574730426073074	-0.028544791042804718	0.04860313609242439	-0.01383395865559578	-0.042795516550540924	-0.05889524146914482	0.059500306844711304	0.02791820839047432	0.07463206350803375	0.043927207589149475	-0.055441081523895264	0.07239369302988052	0.021994883194565773	0.016162490472197533	-0.084864541888237	0.002901956671848893	0.022948062047362328	0.029506200924515724	0.07703576982021332	0.04013822227716446	-0.046182066202163696	0.0006793210050091147	-0.025053558871150017	0.027886999770998955	-0.037044160068035126	-0.023921621963381767	-0.02034929394721985	-0.0525660365819931	0.03704813867807388	-0.054184310138225555	-0.03646109625697136	-0.05551697686314583	0.014669683761894703	0.01700684428215027	-0.06327606737613678	0.020559318363666534	0.01578911766409874	-0.01773494854569435	-0.0033820548560470343	0.004970420151948929	-0.04389409348368645	-0.09675136208534241	0.10732413828372955	-0.004312790464609861	-0.055573392659425735	-0.07681219279766083	0.018488137051463127	0.07047991454601288	-0.007115129381418228	0.04415673017501831	0.04676007106900215	0.10083724558353424	0.008843568153679371	-0.018610099330544472	-0.010202860459685326	-0.05389471352100372	-0.08335267007350922	-0.009872091002762318	0.020569195970892906	-0.028836270794272423	-0.0657384991645813	0.030762584879994392	-0.04812401533126831	-0.011054491624236107	-0.016296422109007835	0.07115022093057632	-0.006825351621955633	0.007910205982625484	-0.04586559906601906	0.03433523327112198	-0.040094468742609024	-0.030841771513223648	0.03533395007252693	-0.009848127141594887	0.0599801279604435	0.00713053485378623	0.0011444245465099812	0.05832218751311302	0.01358742918819189	0.017776088789105415	0.0017380531644448638	0.07082781195640564	-0.03231554105877876	-0.043347712606191635	-0.056976567953825	-0.06127199903130531	-0.04507160559296608	-0.03918416053056717	-0.030482454225420952	-0.04771449789404869	0.06147187203168869	-0.034533753991127014	-0.029653146862983704	-0.023294778540730476	0.02047008089721203	-0.013049621134996414	-0.05193978175520897	-0.024731513112783432	-0.015147806145250797	-0.06946107000112534	-0.008879744447767735	-0.0025715394876897335	-0.058549363166093826	-0.038670022040605545	-0.021791616454720497	-0.030746612697839737	-0.06634875386953354	0.030930623412132263	-0.039280474185943604	-0.02158447355031967	-0.03289686143398285	-0.057447679340839386	0.04732668772339821	0.08490706980228424	0.05607319250702858	0.054155416786670685	0.005479640793055296	0.03805020824074745	0.10758082568645477	0.005166903603821993	-0.009960098192095757	-0.004840961657464504	-0.027163008227944374	-0.061967041343450546	0.06761827319860458	-0.015446275472640991	-0.018506960943341255	-0.010307395830750465	0.01339714229106903	0.07985647767782211	0.013860299251973629	-0.017859363928437233	-0.010845785029232502	0.049721576273441315	-0.018642492592334747	0.06265132129192352	-0.02370927482843399	-0.02141224406659603	-0.02460574358701706	-0.03173074871301651	-0.05714818090200424	-0.02618863806128502	0.035606808960437775	-0.003847203217446804	0.028594154864549637	-0.0058981929905712605	-0.09667974710464478	-0.003543621627613902	-0.0024684749078005552	0.08245693147182465	-0.061450716108083725	0.0019319005077704787	0.021291112527251244	-0.012766098603606224	-0.11745928972959518	-0.012293104082345963	0.0032346092630177736	-0.027371946722269058	-0.07246984541416168	0.012765040621161461	0.051692843437194824	-0.04611653834581375	0.014143131673336029	-0.038445789366960526	-0.033121030777692795	-0.0015899288700893521	0.03704853355884552	0.017458874732255936	0.01955401338636875	0.01721535623073578	-0.05529046058654785	-0.02441224455833435	0.010673834010958672	0.013487241230905056	-0.013914840295910835	0.017360679805278778	-0.0582580529153347	0.04630882665514946	-0.052261918783187866	-0.03241931274533272	-0.04433942586183548	0.0426311194896698	0.019411059096455574	-0.01979941688477993	-0.005725822411477566	-0.027819914743304253	0.01541843917220831	0.02310839667916298	0.014652262441813946	-0.04043901711702347	-0.036460548639297485	-0.038655154407024384	-0.016835426911711693	-0.09762604534626007	-0.046827320009469986	0.02491679973900318	-0.0648408904671669	-0.00582156190648675	0.01445968821644783	-0.027038760483264923	-0.02146960236132145	-0.016637727618217468	0.048497673124074936	0.03282397985458374	0.04124842584133148	-0.026977282017469406	0.054774899035692215	0.0243654977530241	0.04059480503201485	0.017665620893239975	-0.04142966493964195	0.02203219383955002	-0.0006449326756410301	-0.029716964811086655	0.08649803698062897	-0.03858492895960808	0.009014111943542957	-0.017057253047823906	0.020372804254293442	0.04229217395186424	-0.03786764293909073	0.005551768466830254	-0.04222188889980316	0.07997269183397293	-0.09863072633743286	-0.01444185059517622	0.02776418998837471	-0.035494882613420486	-0.10988646000623703	-0.03190317004919052	-0.017735911533236504	-0.05684411898255348	0.04586643725633621	0.018809933215379715	-0.009929893538355827	0.032531075179576874	-0.02275135926902294	-0.0305615346878767	-0.01682818867266178	0.003232005052268505	0.022263817489147186	0.0009482651948928833	0.05449346825480461	0.030159901827573776	0.00435050530359149	0.031201552599668503	-0.005147032905369997	0.0078192800283432	-0.09264937043190002	-0.04225592315196991	0.03454795852303505	-0.04186885431408882	0.011461443267762661	0.016091905534267426	0.029872089624404907	-0.05613326653838158	-0.026059450581669807	-0.06652974337339401	0.08381867408752441	-0.02963985688984394	0.01667526364326477	-0.003903500270098448	-0.03955954685807228	0.0017614893149584532	0.01913530007004738	-0.05344109982252121	-0.08186837285757065	-0.001994645455852151	0.014846092090010643	-0.01736883819103241	-0.04939787834882736	0.0016400458989664912	-0.01785394549369812	-0.06062019616365433	-0.010377476923167706	0.03056829608976841	-0.06177274137735367	0.06884416192770004	0.005319757387042046	-0.05559045076370239	-0.06282197684049606	0.020868666470050812	-0.037022341042757034	-0.001518167438916862	-0.00999288447201252	0.028172681108117104	0.015420042909681797	0.002910647541284561	-0.01457193959504366	-0.08876549452543259	0.01948718912899494	0.013218902982771397	-0.010151445865631104	-0.04636650159955025	0.038927700370550156	-0.03868972882628441	0.008262296207249165	0.0502561554312706	-0.04136774688959122	0.014209281653165817	0.0703776627779007	-0.0364110991358757	0.037255704402923584	-0.02534787729382515	-0.011168130673468113	0.09784508496522903	-0.061722997575998306	0.10151232033967972	-0.06654902547597885	0.11269641667604446	0.00015499370056204498	0.008686328306794167	0.0749347060918808	0.04763968288898468	0.03981151059269905	0.028130751103162766	-0.06825636327266693
+0.0052576567977666855	-0.07613712549209595	-0.05286562442779541	0.029947573319077492	-0.022109203040599823	0.015472331084311008	-0.04549441486597061	0.05092169716954231	0.001870330423116684	-0.07378283888101578	-0.0445273332297802	-0.038499727845191956	-0.00873119942843914	-0.06483149528503418	0.03510460630059242	-0.0002425403072265908	-0.03594746068120003	0.024905933067202568	-0.0013642089907079935	-0.013415457680821419	-0.051589637994766235	0.04079369083046913	-0.06860809773206711	0.04051896929740906	-0.010444387793540955	-0.021399518474936485	-0.032897625118494034	0.06344487518072128	-0.05525152385234833	0.11620967090129852	0.01832105591893196	-0.02814028412103653	0.0363553985953331	-0.02510695531964302	-0.034863248467445374	0.008014929480850697	-0.0112586859613657	0.1019793376326561	0.02284013293683529	-0.052394721657037735	0.033193789422512054	0.027147667482495308	0.03493958339095116	-0.09465266019105911	0.010452081449329853	0.005246882326900959	-0.022178173065185547	-0.03403325751423836	-0.040325459092855453	0.024399712681770325	0.08154696971178055	0.04900101572275162	0.03260922059416771	-0.0035339619498699903	-0.05662102252244949	0.021416891366243362	0.024301715195178986	-0.024958422407507896	0.006582376081496477	0.060116734355688095	-0.07787199318408966	-0.0036815600469708443	0.012965808622539043	-0.0006540770991705358	-0.0632609948515892	0.014801961369812489	0.032684244215488434	0.04164895415306091	0.007747539319097996	-0.013409575447440147	-0.021239958703517914	0.003557246644049883	-0.03152874484658241	-0.0639042928814888	0.0030551650561392307	-0.039057523012161255	-0.032091766595840454	-0.01622115820646286	0.015934081748127937	-0.003242113860324025	0.009932826273143291	-0.005688686389476061	0.11001715064048767	-0.02478357031941414	0.01716178096830845	-0.046837806701660156	-0.022133290767669678	-0.07443065196275711	-0.005984103307127953	-0.016214502975344658	0.023733850568532944	0.03460576385259628	0.044300973415374756	-0.0013740604044869542	-0.004084601067006588	0.0076736630871891975	0.045783109962940216	-0.09508790075778961	0.004896675236523151	0.017649823799729347	-0.046645477414131165	0.03962128236889839	0.030122194439172745	0.06627218425273895	0.03611213341355324	0.020250583067536354	-0.05049370601773262	0.034235723316669464	0.0706721842288971	0.03311154246330261	-0.004623775836080313	0.08761536329984665	-0.013188586570322514	-0.0025103692896664143	0.05311563238501549	0.06824346631765366	0.025105927139520645	0.005385381169617176	0.0188885610550642	0.003861122764647007	-0.030144967138767242	0.031873419880867004	0.0023564628791064024	-0.08382362127304077	0.0861826241016388	0.0869852602481842	-0.03561416268348694	-0.010523885488510132	-0.04757491499185562	-0.031711217015981674	0.014492797665297985	0.03586573526263237	0.0005483654094859958	0.007885217666625977	-0.003046089317649603	0.022692710161209106	-0.030645091086626053	0.011562179774045944	0.03583421930670738	-0.011021541431546211	-0.03502633050084114	-0.02777440845966339	-0.017643097788095474	0.006535160820931196	0.05140950158238411	-0.010410303249955177	-0.028310110792517662	-0.02322949469089508	-0.0631822720170021	0.0012610543053597212	-0.03423212468624115	-0.06721276789903641	0.035944677889347076	-0.045724425464868546	0.019370093941688538	0.008879746310412884	0.06113695725798607	0.025648653507232666	0.008642354048788548	0.032280970364809036	-0.022938668727874756	-0.035690441727638245	0.0013620088575407863	-0.08799390494823456	-0.004461492877453566	0.021703241392970085	-0.021650904789566994	-0.016592616215348244	-0.04484011232852936	0.008550071157515049	0.023662105202674866	0.08309097588062286	0.05733362212777138	0.06251879036426544	0.07653914391994476	0.03761710599064827	0.10811006277799606	-0.049036819487810135	-0.04574686288833618	-0.05666927620768547	-0.018310189247131348	0.03145184740424156	0.00536948349326849	-0.05294749513268471	-0.0021393252536654472	0.028543010354042053	0.015774767845869064	0.040381576865911484	0.05652240663766861	-0.016480926424264908	0.019603077322244644	-0.07006947696208954	0.05390918627381325	-0.016193188726902008	0.022094247862696648	0.03359146788716316	0.07159440964460373	0.1071038469672203	-0.07227689772844315	-0.03151770308613777	-0.03912859037518501	0.02792252041399479	0.07155871391296387	-0.025424744933843613	-0.0027493725065141916	0.02798294462263584	0.030416293069720268	0.05091862380504608	0.009668069891631603	0.023855134844779968	0.04215334355831146	0.04759155213832855	-0.010862733237445354	0.02987564727663994	0.08188841491937637	0.0016627191798761487	-0.0149808619171381	0.005871793255209923	0.016938334330916405	-0.09778165817260742	0.0034115223679691553	-0.014742526225745678	-0.10067138820886612	-0.017840733751654625	-0.03585338220000267	-0.06262688338756561	0.02377542108297348	0.01584026776254177	-0.009891065768897533	0.021578963845968246	0.04194989800453186	0.018276890739798546	-0.006812056060880423	-0.006118444260209799	-1.2180538533357321e-06	-0.01559180486947298	-0.04919131472706795	0.015187055803835392	-0.05246256664395332	-0.06946663558483124	-0.014251711778342724	0.0045079076662659645	-0.011844446882605553	-0.042553845793008804	0.034012243151664734	0.05785522609949112	0.07819853723049164	0.07331746071577072	-0.05576068162918091	0.01253417693078518	-0.04071158915758133	-0.030672302469611168	0.009649758227169514	-2.1830390323884785e-05	0.07132252305746078	-0.021935461089015007	0.029449140653014183	-0.12586653232574463	-0.019921718165278435	0.008902770467102528	0.0013493752339854836	-0.05498289689421654	0.016472499817609787	-0.04621347412467003	0.0034804672468453646	-0.03193194046616554	-0.042638134211301804	0.052354566752910614	-0.03170497715473175	0.0038785834331065416	-0.09676375240087509	0.032840218394994736	0.02633051946759224	-0.03782449662685394	-0.015161309391260147	-0.03345787525177002	0.056473907083272934	-0.04014826565980911	-0.02966124564409256	-0.04486674442887306	-0.07749297469854355	0.013852884992957115	0.036255501210689545	-0.02516373246908188	-0.05948875471949577	0.038622863590717316	0.05560975894331932	-0.0076685706153512	-0.06887004524469376	-0.08835357427597046	-0.04798886179924011	-0.015922896564006805	-0.007861156947910786	0.014839393086731434	0.02029252238571644	-0.014795647002756596	0.06469225138425827	-0.008009783923625946	0.0019000702304765582	0.029637254774570465	0.04723135754466057	-0.06502734869718552	0.04553787037730217	-0.03741629794239998	-0.028279997408390045	0.0683649331331253	-0.0423198863863945	-0.016353638842701912	0.05829467251896858	0.013495122082531452	0.02401193417608738	0.03507968783378601	-0.005987205076962709	0.02889031544327736	-0.016053980216383934	-0.03694586083292961	-0.07650968432426453	-0.0019846633076667786	-0.0042740823701024055	0.033544111996889114	0.042314331978559494	0.0010580800008028746	0.019201135262846947	-0.006388513371348381	0.032433949410915375	-0.004795457702130079	-0.02843322418630123	-0.06983891874551773	0.023449353873729706	-0.03915714845061302	0.026595400646328926	-0.049387332051992416	0.06593576818704605	-0.017155084758996964	-0.022876527160406113	0.00354572432115674	-0.0032698549330234528	0.03321808949112892	-0.017259487882256508	0.04103761911392212	-0.00016667452291585505	-0.052529800683259964	0.00919615849852562	-0.011336583644151688	0.04723484441637993	-0.1348819136619568	0.033106349408626556	0.04707273840904236	0.031315091997385025	-0.05321976915001869	-0.042703039944171906	0.03081422485411167	-0.08429743349552155	-0.04135436192154884	-0.0072932192124426365	-0.006949235685169697	-0.01750204712152481	-0.0610571913421154	-0.06347571313381195	0.002862293040379882	0.047496259212493896	0.05145619437098503	-0.024894243106245995	0.0069066197611391544	-0.04449750483036041	-0.023558612912893295	-0.054319724440574646	-0.010112432762980461	0.015727007761597633	-0.032552026212215424	0.042301807552576065	0.004224928095936775	-0.031117571517825127	-0.04484592378139496	-0.0332183837890625	-0.04796848073601723	-0.004453405272215605	-0.07484441995620728	0.04415459558367729	-0.018093761056661606	-0.033820610493421555	-0.027665235102176666	0.009120805189013481	-0.008221321739256382	-0.05200362950563431	-0.05177690088748932	0.042397547513246536	-0.05938924849033356	0.014333013445138931	-0.07028762251138687	0.04533347859978676	0.03201822564005852	0.026422658935189247	-0.06112493574619293	0.012134767137467861	0.027870312333106995	0.04547855257987976	0.02993556670844555	0.10694184899330139	0.028480377048254013	0.008666452951729298	0.058728285133838654	-0.0358593687415123	-0.006832147482782602	0.017617687582969666	-0.0868496522307396	0.0028710991609841585	0.0017077070660889149	-0.040178265422582626	-0.00376279023475945	-0.011903820559382439	0.0041284989565610886	0.047123029828071594	-0.04953710734844208	-0.010459617711603642	-0.03154545649886131	-0.01935477927327156	-0.001977712381631136	0.06330350786447525	-0.02679736167192459	-0.01734466478228569	-0.06038153916597366	-0.0499357171356678	-0.024693289771676064	-0.032218463718891144	-0.06560255587100983	-0.021190276369452477	0.03051486611366272	-0.04762038215994835	0.009965023025870323	0.018427710980176926	-0.04822789132595062	-0.006924993824213743	-0.0014625481562688947	0.03978518024086952	-0.03133803978562355	0.07733454555273056	0.008676773868501186	-0.03609717637300491	-0.052227333188056946	-0.05027983337640762	-0.045027900487184525	0.03280104324221611	-0.020810566842556	-0.0219907034188509	0.039990976452827454	0.002519212430343032	-0.011012205854058266	0.03393256664276123	-0.004520836286246777	-0.10168273001909256	-0.027503490447998047	-0.008069029077887535	0.009135555475950241	-0.011689217761158943	0.018159231171011925	0.040395766496658325	-0.08076915889978409	-0.07705149054527283	0.040219780057668686	-0.03913430497050285	-0.06005830317735672	-0.04966143146157265	-0.04376358166337013	0.06772483140230179	0.00011683613411150873	0.027402320876717567	-0.0920010656118393	-0.08998668938875198	0.008577643893659115	0.019295040518045425	-0.056794796139001846	-0.09053361415863037	0.06336406618356705	-0.06390584260225296	0.011398314498364925	0.04065246507525444	-0.032476089894771576	0.036546606570482254	0.03262440487742424	-0.0003299604868516326	-0.10011683404445648	0.01069497037678957	-0.08226257562637329	-0.012623115442693233	0.049610503017902374	-0.06920488178730011	-0.0913209319114685	-0.045293502509593964	0.1040506437420845	-0.04153968393802643	0.04742204025387764	0.05083328112959862	0.005556495860219002	0.0482272133231163	0.0547664500772953	0.009892419911921024	-0.05472522974014282	-0.033422645181417465	0.038140129297971725	0.02718384563922882	-0.05736662819981575	0.02551768161356449	-0.02861807495355606	0.13716979324817657	0.0064418562687933445	0.018693167716264725	0.026761764660477638	0.10627563297748566	0.09911558032035828	0.0010778221767395735	-0.1123940721154213
+0.007622762583196163	-0.01186227798461914	-0.048389896750450134	0.016242122277617455	-0.09057731181383133	-0.045728668570518494	0.01812325417995453	0.04178345203399658	-0.009833849966526031	-0.03760542720556259	-0.06099085137248039	-0.01249491423368454	-0.005615763366222382	0.000557890220079571	0.023443570360541344	-0.04505867883563042	-0.06425070017576218	-0.003402463626116514	-0.01320955716073513	-0.02261340245604515	0.0328821986913681	-0.0228094682097435	-0.09364652633666992	0.042369548231363297	0.026984477415680885	0.014056434854865074	-0.044723737984895706	0.026849789544939995	0.010054760612547398	0.06490331888198853	0.045341551303863525	-0.03371879458427429	0.02891983836889267	-0.07953150570392609	-0.07389715313911438	0.03162630647420883	-0.059538472443819046	0.11475920677185059	0.05399507284164429	-0.022142205387353897	-0.09424703568220139	0.017098134383559227	-0.06706569343805313	-0.05795782804489136	0.058846615254879	0.009953436441719532	-0.011565688997507095	-0.028240451589226723	0.0657808855175972	0.03080190159380436	0.06305132806301117	-0.022612247616052628	-0.02613730914890766	-0.02729794569313526	-0.03965948522090912	0.029192481189966202	0.02255869098007679	-0.013335954397916794	-0.05397667735815048	0.032235678285360336	-0.08961828052997589	-0.01576286181807518	0.012267068959772587	0.010207069106400013	-0.04784717038273811	-0.0183314960449934	-0.015963271260261536	0.07120854407548904	-0.04564734175801277	-0.03391510248184204	-0.027773970738053322	0.07948867231607437	-0.05272624269127846	-0.04988621920347214	0.02204025350511074	-0.059564098715782166	-0.01524171233177185	0.02297919988632202	0.010683841072022915	0.0007224431610666215	-0.03148922696709633	-0.04111943766474724	0.15623337030410767	-0.0545758381485939	-0.04485708847641945	-0.06352975219488144	-0.048090964555740356	-0.06437137722969055	0.05200483277440071	0.029585545882582664	0.03242306783795357	0.03987503796815872	-0.026599720120429993	0.01630461774766445	0.036113541573286057	0.017117954790592194	0.04285021498799324	-0.11301112174987793	-0.020144615322351456	0.010254067368805408	0.005507024936378002	-0.029723692685365677	0.07292094826698303	0.0025840646121650934	0.04811311140656471	0.015851326286792755	-0.08231271058320999	0.03800696134567261	0.03332158550620079	0.011782919988036156	0.02993508242070675	0.09875264018774033	0.08724414557218552	0.0314561165869236	0.07548430562019348	-0.004120886791497469	-0.0011298502795398235	-0.039585571736097336	-0.015919096767902374	-0.011309630237519741	-0.0033086028415709734	0.044226884841918945	0.0253470279276371	-0.06677763909101486	0.01320213358849287	0.014347931370139122	-0.0994110181927681	-0.016548098996281624	0.015699634328484535	-0.03455105423927307	0.07806763052940369	0.06358534842729568	-0.01793922856450081	0.02616262063384056	0.05876320227980614	0.007573943585157394	0.04552626982331276	0.06854690611362457	-0.0029554469510912895	-0.025751814246177673	-0.022650010883808136	0.03188827261328697	-0.033594369888305664	-0.04447232931852341	0.05268983542919159	0.05511069297790527	0.0350518561899662	-0.025183701887726784	-0.08213722705841064	0.02566472440958023	-0.07959679514169693	-0.034582749009132385	0.016857296228408813	-0.07921850681304932	0.027423745021224022	0.05475333705544472	0.03605089709162712	0.032231077551841736	0.0017806078540161252	0.011511631309986115	-0.027240576222538948	-0.053505223244428635	-0.0033731809817254543	-0.08766833692789078	-0.032533831894397736	-0.004071840550750494	0.04952908307313919	-0.0014290355611592531	-0.048305314034223557	-0.008427632041275501	0.0005655907443724573	0.009128819219768047	0.07827814668416977	0.0449611134827137	0.042984530329704285	0.0007702010334469378	0.014246232807636261	-0.0895579382777214	0.007062658667564392	-0.05283013731241226	0.03516877815127373	-0.04953659698367119	0.011778546497225761	-0.04455286264419556	-0.021852735430002213	-0.00021406922314781696	0.026863737031817436	-0.03522178903222084	0.006265268195420504	-0.01242695190012455	-0.04708580672740936	-0.0699584111571312	0.04503604769706726	0.053742263466119766	-0.013832788914442062	0.029787177219986916	-0.000652085174806416	0.0536326989531517	-0.03328646346926689	-0.057869426906108856	-0.08628960698843002	0.0328119695186615	-0.0030888812616467476	0.03697993978857994	0.03938361629843712	-0.056364577263593674	0.07427123934030533	-0.013470087200403214	0.03240307793021202	-0.07325531542301178	0.03309515863656998	0.02555782161653042	0.010329288430511951	0.005603688769042492	0.10641767084598541	-0.03237716108560562	-0.03201162815093994	0.013652076944708824	0.07688819617033005	-0.06671137362718582	0.005659009329974651	0.01988712139427662	-0.09270115941762924	-0.007888834923505783	-0.035890743136405945	-0.08709722757339478	0.0027002166025340557	0.047275010496377945	-0.03755614534020424	-0.009009843692183495	-0.02014896273612976	0.03557225316762924	-0.044119201600551605	-0.007341613527387381	-0.005701833870261908	0.014553755521774292	-0.0583852156996727	0.048145096749067307	-0.033104851841926575	-0.090960793197155	-0.059258975088596344	0.017073851078748703	0.010463072918355465	-0.0059547568671405315	0.031875982880592346	0.028303401544690132	0.06019430235028267	0.007187425158917904	-0.02848033607006073	0.003836285090073943	-0.05264347791671753	-0.022842887789011	-0.007313104346394539	0.038338661193847656	-0.02790950797498226	-0.03542110323905945	0.007808617781847715	-0.054092392325401306	0.008469761349260807	0.0069497074000537395	0.029488900676369667	-0.010262769646942616	-0.02413523942232132	0.009660501033067703	0.055005013942718506	-0.01734703592956066	-0.030515627935528755	0.01459855679422617	-0.03793195262551308	0.03443154692649841	-0.05173530802130699	-0.02288951352238655	0.00497058080509305	0.03256368264555931	0.01085730828344822	-0.04667748883366585	0.08747788518667221	-0.0010469792177900672	-0.06618168950080872	-0.05304546281695366	-0.07493387162685394	-0.03566105291247368	0.0071707963943481445	-0.016406051814556122	-0.031739749014377594	0.08111265301704407	0.04734235629439354	-0.032128289341926575	-0.0877472534775734	-0.056120242923498154	-0.018194176256656647	-0.05021383985877037	-0.03511842340230942	-0.00030644750222563744	-0.07480368763208389	-0.029446057975292206	0.04874372482299805	-0.03433811664581299	-0.01082572340965271	0.034054480493068695	-0.010757694952189922	-0.056819792836904526	0.03360878676176071	-0.04493536427617073	-0.04266887158155441	0.04213768243789673	-0.06441891938447952	0.028554769232869148	0.09824029356241226	0.05962982773780823	0.012853330932557583	0.00936164241284132	0.020959511399269104	0.0678047463297844	-0.029165932908654213	-0.04699823632836342	-0.043729349970817566	-0.07109767943620682	-0.09070589393377304	0.04619862139225006	-0.04113705828785896	0.021061519160866737	-0.04637971892952919	-0.013173562474548817	0.059935130178928375	-0.015309843234717846	-0.015471259132027626	-0.0613107904791832	-0.020207684487104416	-0.024832766503095627	0.023094285279512405	0.007672542240470648	0.013057106174528599	-0.010314248502254486	-0.0797174796462059	-0.0826897844672203	-0.05141192302107811	0.04190793260931969	0.005715458653867245	0.028730494901537895	-0.007664516568183899	-0.03401780128479004	0.034785930067300797	0.018400127068161964	0.07900088280439377	-0.05156220495700836	0.031943194568157196	0.009928015992045403	0.030156876891851425	-0.08013895899057388	-0.007904665544629097	0.04073458909988403	-0.04377661272883415	-0.0835176333785057	-0.011579721234738827	0.026341186836361885	-0.027873104438185692	-0.008947905153036118	-0.008436627686023712	-0.0663173571228981	-0.0016956903273239732	0.010358739644289017	0.013396361842751503	0.04892678186297417	0.025132592767477036	-0.035895757377147675	0.03319800645112991	-0.01597786881029606	0.005561238154768944	-0.023215120658278465	0.022556515410542488	-0.014522513374686241	0.06263332068920135	-0.03130973502993584	-0.03323887661099434	-0.07683727145195007	0.05165832117199898	-0.01587093248963356	-0.041614875197410583	-0.0484028160572052	-0.015180004760622978	-0.03973410278558731	0.0254453606903553	0.013287404552102089	-0.06206464394927025	-0.03152158483862877	-0.028609484434127808	-0.06273026764392853	-0.11984780430793762	-0.010111583396792412	0.04532023146748543	-0.03784370422363281	0.02578040212392807	-0.04658621922135353	0.011968297883868217	0.03500119224190712	0.006195711437612772	0.005875921808183193	0.05265402793884277	0.044086623936891556	-0.008385022170841694	0.03340162709355354	-0.022795839235186577	0.06282036006450653	-0.004371548071503639	-0.020794957876205444	0.00020949955796822906	0.041346803307533264	-0.056482359766960144	0.036053139716386795	-0.06035734340548515	0.02267313189804554	-0.045114222913980484	-0.03045942634344101	0.017558638006448746	0.0033873296342790127	0.0192694254219532	-0.0057427361607551575	0.039015401154756546	-0.06510291248559952	-0.041294876486063004	0.026791758835315704	-0.007166269700974226	-0.09595021605491638	-0.026389943435788155	-0.010255744680762291	-0.004856760613620281	0.03834706172347069	-0.017602339386940002	0.04797651246190071	0.036414265632629395	-0.06022091209888458	-0.024403857067227364	-0.012258722446858883	-0.01105960551649332	0.028221651911735535	-0.008125360123813152	0.053459532558918	0.04215336591005325	-0.0017582322470843792	0.022149674594402313	-0.0031602096278220415	0.01945195160806179	-0.06825274974107742	-0.009079703129827976	0.016045093536376953	-0.005755094811320305	0.013309825211763382	0.01243111677467823	0.0016889431281015277	-0.024555684998631477	-0.02666349895298481	-0.04789295792579651	0.05639883875846863	0.008543103002011776	0.010420747101306915	0.05770835652947426	-0.05406152084469795	0.01564209721982479	-0.0017143858131021261	-0.044346243143081665	-0.06309524178504944	-0.014668263494968414	-0.04723956063389778	0.04919624701142311	0.04824279621243477	0.0074641224928200245	0.01930210366845131	-0.06307323276996613	-0.006989858113229275	-0.03393227979540825	-0.0679253414273262	0.025906555354595184	0.03427533432841301	-0.023150766268372536	-0.03966506943106651	-0.01972641423344612	-0.052064694464206696	0.07312972098588943	0.015871355310082436	0.04124903306365013	-0.03719530999660492	-0.0015320002567023039	0.009848077781498432	-0.002311576856300235	0.03654048219323158	-0.043469563126564026	-0.06230132654309273	0.007198597304522991	0.11048194766044617	-0.035493217408657074	0.05865523964166641	0.04107002913951874	-0.034619469195604324	0.04131540656089783	0.07017966359853745	-0.033297766000032425	0.004854269325733185	0.007382718846201897	-0.031057216227054596	0.05444285646080971	-0.07305523008108139	0.0997360572218895	-0.06875885277986526	0.10417938977479935	0.041481856256723404	-0.010656861588358879	0.09023672342300415	0.07110164314508438	0.0007039016927592456	0.016856590285897255	-0.06062009930610657
+0.07659488171339035	-0.016653241589665413	-0.009515197016298771	0.019966529682278633	-0.029444996267557144	-0.02763104811310768	-0.024137631058692932	0.023371411487460136	-0.020460255444049835	-0.028049714863300323	-0.05634574219584465	0.017738306894898415	-0.031197132542729378	-0.00628995755687356	0.008769788779318333	-0.06318335235118866	-0.03600165992975235	0.024341566488146782	-0.025439077988266945	-0.005827158223837614	0.012957504950463772	0.03216376155614853	-0.07598385959863663	0.05172628536820412	-0.010311364196240902	0.02251945622265339	-0.017478596419095993	0.026844942942261696	0.012570641003549099	0.07073766738176346	0.08604256808757782	-0.04390822723507881	0.06469796597957611	-0.039487965404987335	-0.01783241704106331	-0.00011084076686529443	-0.02180968038737774	0.08109249919652939	0.0407700389623642	0.0297735296189785	-0.07681329548358917	0.011410965584218502	-0.002518518827855587	-0.04574785754084587	-0.004007034935057163	-0.0038349370006471872	-0.005915018264204264	-0.0087802829220891	0.02744932845234871	0.03850795328617096	0.07356379181146622	0.010859090834856033	-0.0020518770907074213	-0.011296406388282776	0.011359036900103092	0.010093394666910172	0.025895213708281517	-0.017637433484196663	-0.03695928677916527	0.0062902867794036865	-0.09126096218824387	0.03965660557150841	-0.005582035053521395	-0.03820553421974182	-0.015163537114858627	-0.031626276671886444	-0.0012857962865382433	0.08927860110998154	0.03277824819087982	-0.027863740921020508	0.03735865280032158	0.09354954957962036	-0.11026535928249359	-0.03847290202975273	0.023099638521671295	-0.06292658299207687	0.0021580990869551897	-0.004279166925698519	-0.017806999385356903	-0.008032652549445629	-0.03217964619398117	-0.03207240626215935	0.14882326126098633	-0.03527817875146866	-0.05145202949643135	-0.061290256679058075	-0.02885439805686474	-0.08401843160390854	0.040434665977954865	0.004696091171354055	0.05586261674761772	0.012066764757037163	0.002276742598041892	-0.0010800242889672518	0.01954430341720581	0.001516717835329473	0.04161852225661278	-0.07527519762516022	-0.024630945175886154	0.02082078345119953	0.006648124661296606	0.04172847419977188	0.07242954522371292	-0.004519029054790735	0.05937650427222252	0.027702834457159042	-0.059772975742816925	0.012811893597245216	0.06480738520622253	0.02412039041519165	0.06241351366043091	0.0927496924996376	0.023858780041337013	0.02434457279741764	0.0616922453045845	0.026849981397390366	0.06493895500898361	-0.04695350304245949	0.01562713086605072	0.0015975218266248703	-0.020584050565958023	0.10121230036020279	-0.013659457676112652	-0.08584713935852051	-0.02160126343369484	0.06911216676235199	-0.09161629527807236	-0.05937745049595833	0.04278893768787384	0.0010039082262665033	0.06527845561504364	0.08081116527318954	-0.009503630921244621	0.022332843393087387	0.0004554148472379893	-0.04326940327882767	-0.06338287889957428	0.016071252524852753	0.06822645664215088	-0.029195962473750114	-0.015541641972959042	0.035321034491062164	-0.02348516695201397	-0.032628364861011505	0.05252647399902344	0.04308193176984787	0.016757074743509293	-0.06693567335605621	-0.018540991470217705	0.009058997966349125	-0.011133217252790928	-0.07093778997659683	0.027124376967549324	0.0002496128436177969	-0.0005417281645350158	0.03103179670870304	0.0202884953469038	0.024996526539325714	0.004172779619693756	-0.03922796994447708	-0.06555027514696121	-0.04843509942293167	-0.052369654178619385	-0.03829502686858177	0.017951039597392082	0.06101618707180023	0.032616615295410156	0.011847937479615211	-0.08397236466407776	-0.00031908031087368727	-0.001224127016030252	-0.001243418431840837	-0.006671213079243898	0.08338135480880737	0.08736652135848999	0.012371774762868881	0.010864187963306904	-0.0353412851691246	0.013331061229109764	-0.039629124104976654	0.003946412820369005	-0.06251021474599838	0.014927896670997143	-0.06452350318431854	0.012447908520698547	0.01628369651734829	0.02092781662940979	-0.0036369478330016136	0.0387297086417675	0.019991612061858177	0.013991069048643112	-0.043879181146621704	0.01288446132093668	0.02947380021214485	-0.02518603950738907	0.0259019136428833	0.0008419987861998379	0.09955378621816635	-0.0036831172183156013	-0.01080586388707161	-0.08238046616315842	0.018869781866669655	0.014726505614817142	0.018239092081785202	0.0833548977971077	-0.0389888770878315	0.05834386125206947	0.02294745482504368	0.0009954397100955248	-0.045098379254341125	-0.018476935103535652	-0.027506045997142792	0.03816961497068405	0.053673308342695236	0.06962525844573975	-0.024214427918195724	-0.002426039194688201	-0.004579984582960606	0.032645080238580704	-0.05240073427557945	-0.05331147089600563	-0.05601676553487778	-0.06454862654209137	0.010743589140474796	-0.060808438807725906	-0.07400156557559967	-0.00990543607622385	0.013467366807162762	0.027867531403899193	-0.0207695122808218	0.05915272608399391	0.03882689028978348	-0.009460913017392159	-0.030515532940626144	0.05087204650044441	-0.06620205193758011	-0.08320307731628418	0.09152721613645554	0.009638821706175804	-0.09267558157444	-0.024638043716549873	-0.011945991776883602	0.07282032072544098	0.007544545456767082	0.03976089879870415	0.018793292343616486	0.11384358257055283	0.02845471166074276	-0.07742024958133698	0.01752607338130474	-0.017532451078295708	-0.04978790134191513	0.03353158384561539	0.028277579694986343	-0.027368027716875076	-0.08336768299341202	0.04686380922794342	-0.04372924193739891	0.02716519683599472	-0.03126230835914612	0.01093218382447958	-0.008206326514482498	0.021012919023633003	-0.05768159031867981	0.025471985340118408	-0.05336305871605873	-0.03014296293258667	0.0623861625790596	-0.027741091325879097	0.018555134534835815	-0.004343737848103046	0.02975195273756981	0.026475246995687485	0.0007268565241247416	-0.03520883619785309	-0.05236493796110153	0.07580889761447906	0.009028499945998192	-0.049282174557447433	-0.06793247163295746	-0.04586239531636238	-0.04339988902211189	-0.00037311171763576567	0.0015601753257215023	-0.04618728533387184	-0.003708883421495557	0.021148288622498512	-0.0635049119591713	-0.04958353191614151	-0.022682199254631996	-0.09405714273452759	-0.04839204251766205	-0.060434553772211075	-0.0047358316369354725	-0.011587275192141533	-0.03643594682216644	-0.0007501380168832839	-0.048737816512584686	-0.009305170737206936	0.0117505406960845	0.03796873986721039	-0.07519584149122238	0.013074264861643314	-0.006482498720288277	-0.010547783225774765	-0.003135460428893566	-0.06285730004310608	0.04470411315560341	0.060107629746198654	0.11084142327308655	0.05608392134308815	0.033050622791051865	0.008462799713015556	0.11435235291719437	0.0059860688634216785	-0.02204705774784088	-0.041556764394044876	-0.03984026238322258	-0.051794275641441345	0.041481863707304	0.03620488941669464	0.014115589670836926	-0.0042716022580862045	0.03151369094848633	-0.003162963083013892	0.025911379605531693	-0.026818983256816864	-0.049664996564388275	0.003495306009426713	-0.03997127711772919	0.0076833185739815235	-0.01904924400150776	0.021503742784261703	-0.002838743617758155	-0.05586715415120125	-0.06246841326355934	-0.05895451083779335	0.03110790252685547	-0.01727309077978134	0.06616171449422836	-0.0057883718982338905	-0.03131755068898201	-0.01792675070464611	-0.009860684163868427	0.07898235321044922	-0.04031136631965637	0.005296902731060982	-0.008387596346437931	0.09328842163085938	-0.1197429969906807	-0.04160111024975777	0.020670156925916672	-0.027335505932569504	-0.05501140281558037	0.0015013966476544738	0.00252153049223125	-0.0028725245501846075	-0.006963103078305721	-0.025447862222790718	-0.013834407553076744	-0.029298443347215652	0.014307442121207714	-0.007585771381855011	0.03935519978404045	0.016298668459057808	-0.022128846496343613	-0.02190983109176159	0.0013960967771708965	0.03484990447759628	0.028125405311584473	-0.007994663901627064	-0.031785108149051666	-0.007280872203409672	-0.06697862595319748	-0.049769531935453415	-0.04886150732636452	0.004917920101433992	-0.012009845115244389	0.04056067019701004	-0.021680770441889763	-0.021262364462018013	-0.05221175402402878	-0.040433574467897415	0.007938647642731667	-0.042694251984357834	0.030490968376398087	0.001132420846261084	-0.06696062535047531	-0.06912141293287277	-0.02529432624578476	-0.020554397255182266	0.0568048469722271	0.032436221837997437	0.008252163417637348	0.018657781183719635	0.05920269340276718	0.022053014487028122	0.05794805288314819	0.09390299022197723	0.019944466650485992	0.056746602058410645	0.0442863367497921	0.02453000657260418	0.056719426065683365	0.019064784049987793	-0.10200712084770203	-0.04854746162891388	-6.78299184073694e-05	-0.07643312960863113	0.024262642487883568	-0.020802780985832214	-0.021256398409605026	-0.019855134189128876	-0.008407256565988064	0.004177609458565712	-0.043803658336400986	0.03978800028562546	-0.032674260437488556	0.07675710320472717	-0.06013698875904083	-0.026827866211533546	0.044025395065546036	-0.048685088753700256	-0.05243034288287163	-0.01908918283879757	-0.0356951579451561	-0.01140524446964264	0.09046493470668793	-0.055389758199453354	0.059055134654045105	0.05145984888076782	-0.005130371078848839	-0.09395381808280945	0.001319297356531024	0.01072610355913639	0.03287535533308983	0.008353066630661488	0.058723922818899155	0.033055126667022705	0.004724172409623861	0.01151791587471962	-0.06325343251228333	0.01588078774511814	-0.07854294776916504	-0.006008717231452465	0.01783360168337822	-0.010370545089244843	0.004645174369215965	0.014304065145552158	-0.01500697247684002	-0.0337287075817585	0.032367393374443054	-0.05378289520740509	0.0652962476015091	0.029976148158311844	0.03558490797877312	-0.024724053218960762	-0.0789552554488182	-0.0129683967679739	0.015927106142044067	-0.01881914958357811	-0.0659119263291359	-0.02006080187857151	-0.03511402755975723	-0.007573251146823168	-0.0411192812025547	0.04712245240807533	-0.005577682517468929	-0.06799650192260742	0.0137947341427207	0.033490099012851715	-0.012720387428998947	0.03315748646855354	0.017660558223724365	-0.06530500948429108	-0.037312161177396774	0.01457244623452425	-0.11295437067747116	0.030305372551083565	0.019157685339450836	0.01789567992091179	0.03379266709089279	0.04225776717066765	-0.005859348922967911	-0.005128361284732819	0.026721438392996788	-0.06138622760772705	-0.029222724959254265	-0.018289513885974884	0.07072258740663528	-0.011375836096704006	0.04032403230667114	0.040477652102708817	-0.0029520150274038315	0.0684448853135109	0.051564574241638184	-0.022951869294047356	0.030429603531956673	-0.022571468725800514	0.026516983285546303	0.06471537798643112	-0.04455706104636192	0.08282428979873657	-0.06415487080812454	0.11269624531269073	0.019335471093654633	-0.03312429413199425	0.10487652570009232	0.04968012496829033	0.04231899976730347	0.04956279695034027	-0.08282210677862167
+0.007695118896663189	-0.015249108895659447	-0.01830366440117359	0.02066694386303425	-0.06221196800470352	-0.08310182392597198	-0.03650195524096489	0.04381965473294258	0.022663472220301628	-0.05528617650270462	-0.09890701621770859	-0.009024792350828648	0.018076959997415543	0.008795592933893204	0.009032727219164371	-0.005967484321445227	-0.04278774932026863	-0.02518828958272934	0.010473096743226051	-0.021265903487801552	0.053764376789331436	-0.010421743616461754	-0.07581926882266998	0.11632890999317169	0.06785711646080017	0.007484519854187965	-0.029553115367889404	0.03418378159403801	-0.0014233400579541922	0.08339928090572357	0.00560862198472023	-0.026767175644636154	0.010319871827960014	-0.07087189704179764	-0.029180210083723068	-0.011116430163383484	-0.050165336579084396	0.06457854807376862	-0.002015245845541358	-0.0004662878636736423	-0.058813516050577164	0.06614629179239273	-0.03549348935484886	-0.036581046879291534	0.002963224658742547	0.03151967003941536	0.0033712899312376976	-0.0025265912991017103	0.0029207118786871433	0.06591503322124481	0.055277567356824875	-0.02053346112370491	-0.01841837726533413	-0.04805156588554382	-0.002932955976575613	0.02416948974132538	0.020555125549435616	-0.038363922387361526	-0.010333430953323841	0.015594104304909706	-0.08860311657190323	0.010829119011759758	0.01814981922507286	-0.015172889456152916	-0.016928227618336678	0.028906134888529778	-0.017004048451781273	0.0928085520863533	-0.0027626841329038143	-0.0466325581073761	0.029525531455874443	0.056005533784627914	-0.06099976599216461	0.025442413985729218	0.03138667717576027	-0.007031707093119621	0.018126122653484344	0.05168287083506584	0.015768438577651978	0.014600844122469425	0.04156091809272766	-0.046206556260585785	0.14007405936717987	-0.02354390174150467	-0.0462220124900341	-0.07224830985069275	-0.02157999947667122	-0.04850103333592415	0.04063098505139351	-0.014232263900339603	0.04775378480553627	0.07350104302167892	0.02830907702445984	0.02067520096898079	-0.00690861139446497	0.003302238881587982	0.015340041369199753	-0.13350677490234375	0.0066913035698235035	0.03680093213915825	-0.022869689390063286	-0.0054169087670743465	0.034408170729875565	0.006841745227575302	0.017580443993210793	0.054733481258153915	-0.059934861958026886	0.022238116711378098	0.04480481147766113	0.018193941563367844	0.03798803314566612	0.056453779339790344	0.06066045165061951	0.07218415290117264	0.033521972596645355	0.00781602319329977	-0.0007459766929969192	0.013311665505170822	-0.027907337993383408	0.006889416836202145	0.022836659103631973	0.07980441302061081	0.026902474462985992	-0.10564447194337845	0.060371141880750656	0.06505493819713593	-0.07282193005084991	-0.0008737085736356676	0.0298045352101326	-0.02505612000823021	0.06360034644603729	0.02079942636191845	-0.010465482249855995	0.030872944742441177	0.039899393916130066	0.019801339134573936	0.001889730803668499	0.019434813410043716	0.013413512147963047	-0.04844304546713829	-0.05220452696084976	0.02107159048318863	-0.015531341545283794	-0.07263003289699554	0.09510432928800583	0.053470347076654434	-0.008288449607789516	-0.007991475984454155	-0.026093242689967155	-0.03739258646965027	-0.08310370147228241	-0.07049664855003357	0.033838994801044464	-0.007599609903991222	-0.029893971979618073	0.013941100798547268	0.04040740057826042	0.02320605143904686	-0.03535143658518791	-0.002647834364324808	-0.039798177778720856	-0.03361904248595238	-0.01989208161830902	-0.05492820590734482	0.02851785533130169	0.025206511840224266	0.029488809406757355	0.014221874997019768	-0.0840194821357727	-0.04518833011388779	0.003845117287710309	-0.008506087586283684	0.05068839341402054	0.05756190046668053	0.05495176836848259	-0.014805212616920471	0.06904201209545135	-0.04170402139425278	-0.03672826290130615	-0.03472775220870972	0.04822394624352455	-0.041338369250297546	0.030906064435839653	-0.035036850720644	-0.03627700358629227	0.011042024940252304	-0.0315905436873436	-0.04312682896852493	0.008236410096287727	-0.02119678072631359	-0.016931919381022453	-0.045503582805395126	0.06867532432079315	0.04627739638090134	-0.015858637169003487	0.0667467713356018	0.02980782277882099	0.0806422010064125	-0.04605909436941147	0.002098392229527235	-0.06892700493335724	0.032118991017341614	0.022882677614688873	0.04573912173509598	0.04898744449019432	-0.050130218267440796	0.06535281985998154	0.02272612974047661	0.02895914390683174	-0.048501648008823395	0.07938642054796219	0.06189841777086258	-0.00506633659824729	0.08297685533761978	0.10600659251213074	0.010736802592873573	-0.01617417111992836	-0.026416772976517677	0.01612081751227379	-0.09899097681045532	-0.011031880043447018	0.0019915769807994366	-0.08878224343061447	-0.016961131244897842	-0.06370715796947479	-0.03213629126548767	-0.02396898716688156	0.031584590673446655	0.006075582467019558	-0.00742684118449688	-0.014878969639539719	0.013876316137611866	-0.017798706889152527	-0.030446037650108337	0.009413636289536953	0.04142715409398079	-0.07208779454231262	0.05788348615169525	-0.02900676429271698	-0.054527316242456436	-0.024934224784374237	0.042910899966955185	0.0017687389627099037	0.006832893006503582	0.026390420272946358	0.0652172863483429	0.07208085805177689	0.06983089447021484	-0.01596122793853283	-0.010821115225553513	-0.04039796441793442	-0.007977467030286789	-0.020431432873010635	0.016093246638774872	-0.003903459757566452	-0.038120802491903305	0.008011750876903534	-0.0767892450094223	-0.04799564927816391	-0.013824043795466423	-0.02192322537302971	-0.03915548697113991	-0.027338173240423203	-0.033432506024837494	-2.275057340739295e-05	-0.02250896953046322	-0.05645111948251724	0.02554965391755104	-0.03250114619731903	0.059397440403699875	-0.06024269759654999	-0.01688525639474392	0.026018451899290085	0.018956344574689865	-0.0300150066614151	-0.05781880021095276	0.07883904129266739	0.007313065230846405	-0.03825818747282028	-0.04456866532564163	-0.02644072286784649	-0.07243724912405014	0.0029003878589719534	-0.0016977620543912053	0.018666179850697517	0.06203839182853699	0.04331906512379646	-0.08598165214061737	-0.08011370897293091	-0.03804967179894447	-0.0566910095512867	-0.027806105092167854	-0.01757928542792797	-0.0019405524944886565	-0.019425854086875916	-0.03470567241311073	0.024545904248952866	-0.035373810678720474	-0.013735220767557621	0.07542572170495987	0.014010966755449772	-0.07411430031061172	0.057090092450380325	-0.005472863558679819	-0.03806506097316742	0.07333452999591827	-0.04870358109474182	-0.004176890477538109	0.0825713723897934	0.03372006490826607	-0.02721625193953514	0.04960428550839424	0.026952749118208885	0.06601151078939438	-0.04550442472100258	-0.026642287150025368	-0.04193064570426941	-0.01583774760365486	-0.03763303905725479	0.048096850514411926	-0.017165644094347954	0.010599803179502487	-0.005187573377043009	-0.04084097594022751	0.014843332581222057	0.013301264494657516	-0.01480670552700758	-0.053220562636852264	-0.026775602251291275	-0.02825126424431801	0.016460327431559563	-0.007314009126275778	0.024177124723792076	-0.002949106739833951	-0.05295125022530556	-0.08136031031608582	-0.08489109575748444	0.048248715698719025	0.006881681736558676	0.0470237210392952	-0.015891442075371742	0.015598786063492298	0.0009579582838341594	-0.005801467224955559	0.08283068984746933	-0.05883883312344551	0.019910836592316628	0.06705434620380402	0.02807031199336052	-0.06323231756687164	-0.0031105109956115484	0.0029488704167306423	-0.040850166231393814	-0.05223064497113228	-0.06585405021905899	0.008466405794024467	-0.03894737735390663	-0.05629584565758705	0.006356190424412489	-0.021587718278169632	0.03143695369362831	0.037746362388134	0.009262165986001492	0.06591306626796722	0.0209698174148798	-0.015450682491064072	-0.008667881600558758	-0.043701279908418655	0.010991181246936321	0.0043566967360675335	0.0266443882137537	0.03762451559305191	0.04508820176124573	-0.0462176650762558	-0.03003920055925846	-0.05057266727089882	0.00299269612878561	-0.07815936207771301	0.014136457815766335	-0.01762569323182106	-0.010919412598013878	-0.03321824595332146	0.04701601713895798	0.022646119818091393	-0.08439955115318298	0.02060486190021038	-0.009822921827435493	-0.06433294713497162	-0.05086815357208252	-0.05388212949037552	0.006472935900092125	-0.008979244157671928	0.06168794259428978	-0.0610971562564373	0.04816051572561264	0.0227032583206892	0.05136861279606819	0.018499862402677536	0.06667309254407883	0.002247752621769905	0.02226639725267887	0.06416382640600204	-0.023644186556339264	0.03443846479058266	0.001979278167709708	-0.09446713328361511	0.011229821480810642	0.04797094315290451	-0.039739541709423065	0.04857512190937996	-0.065461166203022	-0.02168111875653267	-0.009466839022934437	-0.03628723323345184	0.018649214878678322	0.006754752714186907	0.02672220952808857	-0.04290981590747833	0.04986521601676941	-0.0012078247964382172	-0.06654369086027145	0.02494962140917778	-0.01639902964234352	-0.057459067553281784	-0.015535760670900345	-0.07053905725479126	-0.017152003943920135	0.02499794028699398	-0.03207670524716377	0.03925210237503052	0.052629776298999786	-0.06440813094377518	-0.04210244119167328	0.03468574211001396	0.01850525662302971	0.011834945529699326	0.06053217500448227	0.05936747044324875	0.05499652400612831	-0.006438703741878271	0.013316601514816284	-0.08206917345523834	0.033287521451711655	-0.026035035029053688	0.0494619682431221	0.042626332491636276	-0.012779876589775085	0.0023837469052523375	0.041950490325689316	0.02786015160381794	-0.061064496636390686	-0.01653786189854145	-0.04020165279507637	0.025476524606347084	0.038649603724479675	0.04910515621304512	0.03387606143951416	-0.09521672129631042	-0.02163035236299038	0.00485756853595376	0.009608428925275803	-0.1024647206068039	-0.07958201318979263	-0.089146688580513	0.06765151768922806	-0.017399923875927925	-0.012642916291952133	-0.025489799678325653	-0.06355533003807068	0.01471687387675047	0.0051574306562542915	0.015939563512802124	-0.012930994853377342	0.03627016022801399	-0.02087709680199623	-0.018964486196637154	-0.025221476331353188	-0.05923239141702652	0.048247821629047394	0.061242662370204926	0.003484632121399045	-0.02427743375301361	0.005788327660411596	0.038432858884334564	-0.01741761900484562	-0.0025966449175029993	-0.039005178958177567	-0.05568685382604599	0.0004605894209817052	0.12499044090509415	0.006250078324228525	0.03885064646601677	0.0675269216299057	-0.03380928561091423	0.05479275435209274	0.03618486970663071	-0.006105201784521341	0.016296451911330223	-0.01680886745452881	-0.03045428730547428	0.044218629598617554	-0.048079971224069595	0.06660179048776627	-0.06105472892522812	0.10776179283857346	0.022586125880479813	-0.05266823247075081	0.08615606278181076	0.04761093482375145	0.06894231587648392	0.0448094867169857	-0.1113998219370842
+0.04456571489572525	-0.04280107468366623	0.012884859926998615	0.03721040487289429	-0.09032414108514786	-0.06757975369691849	-0.02417636848986149	0.047173406928777695	0.02549842931330204	-0.03366176411509514	-0.04503367468714714	0.01308651827275753	0.0034699784591794014	0.021483801305294037	0.03609025850892067	-0.012782384641468525	-0.06678863614797592	-0.006709604989737272	-0.0380549430847168	-0.042951006442308426	0.048164401203393936	0.008104074746370316	-0.047934792935848236	0.03397274762392044	-0.0017602737061679363	0.03216126561164856	-0.057769134640693665	0.015976998955011368	-0.0023454902693629265	-0.012444023974239826	0.031948693096637726	-0.018714476376771927	0.043750349432229996	-0.07094036042690277	-0.0554724857211113	0.0472085066139698	-0.05512148141860962	0.10385560244321823	-0.010135360062122345	-0.035252828150987625	-0.040691860020160675	0.01429953146725893	-0.03559498116374016	-0.04714395850896835	0.03689343109726906	0.020895270630717278	0.01821797527372837	-0.02147337794303894	0.0050271362997591496	0.02398975007236004	0.09870212525129318	0.01889994367957115	-0.00984193291515112	-0.03241899609565735	-0.022680798545479774	0.014218287542462349	0.06151101738214493	-0.06410501152276993	-0.0774712860584259	0.05988883227109909	-0.11272192746400833	-0.0310694370418787	-0.00498688779771328	0.02715284749865532	-0.03736315295100212	-0.005207008682191372	-0.009997187182307243	0.089752696454525	0.014160462655127048	-0.06585540622472763	0.03998008742928505	0.07177504152059555	-0.025218132883310318	-0.047732848674058914	0.04259505122900009	-0.054261673241853714	-0.0015142867341637611	0.05879642069339752	-0.029693977907299995	0.023202991113066673	0.0021813693456351757	-0.053222957998514175	0.11659510433673859	-0.048307646065950394	-0.07291935384273529	-0.022181401029229164	-0.021501773968338966	-0.04705541953444481	0.03590910881757736	0.04915273189544678	0.0025958262849599123	0.08255600929260254	-0.03914818912744522	0.03358171880245209	-0.017415383830666542	0.04026448354125023	0.014523208141326904	-0.10801625996828079	-0.04275898262858391	-0.020247753709554672	-0.023691367357969284	-0.021603573113679886	0.05492191016674042	0.027200069278478622	0.06428581476211548	0.024258378893136978	-0.02757013775408268	-0.0037460089661180973	0.008640242740511894	0.036411989480257034	0.024066688492894173	0.054694488644599915	0.08692280948162079	0.07214149832725525	0.06390685588121414	-0.022654376924037933	-0.006415865384042263	-0.048562757670879364	-0.03519101068377495	-0.0012466859770938754	0.02273525297641754	0.03403352573513985	-0.016146624460816383	-0.08282814919948578	-0.014049249701201916	0.011847307905554771	-0.1041467934846878	-0.021799886599183083	0.0698348879814148	-0.05527491122484207	0.10900936275720596	0.05844797566533089	-0.024437056854367256	0.03195221349596977	0.04489785432815552	0.04753905162215233	0.0025240564718842506	0.018450988456606865	-0.005271087400615215	-0.038285031914711	-0.011270289309322834	0.03668827563524246	0.0044095986522734165	-0.07497721165418625	0.04567708447575569	0.030163411051034927	0.0252489373087883	-0.024754894897341728	-0.0758129134774208	0.0038331463001668453	-0.08448899537324905	-0.05845235660672188	-0.019510099664330482	-0.07005055248737335	-0.005334849003702402	0.0368184931576252	0.011607727035880089	0.02772289700806141	-0.042791057378053665	-0.0061648450791835785	-0.02964179217815399	-0.036934930831193924	-0.011314397677779198	-0.0952586904168129	-0.01980563998222351	-0.022037947550415993	0.0004897357430309057	0.013283106498420238	-0.04985370859503746	-0.026490896940231323	-0.0292061734944582	-0.011192641220986843	0.038864098489284515	0.04863780736923218	0.07171536237001419	-0.02083984762430191	0.01697532832622528	-0.05762888118624687	-0.0009442470618523657	-0.028420107439160347	0.014362700283527374	-0.0033389653544873	0.016612665727734566	-0.06936715543270111	-0.015025305561721325	-0.025508377701044083	0.006283225025981665	-0.05671761557459831	0.02364633046090603	0.003807940986007452	-0.040207117795944214	-0.03433580324053764	0.04220733419060707	0.09258125722408295	-0.03506232053041458	0.06614380329847336	-0.008662457577884197	0.04558815434575081	-0.03590778633952141	-0.05403084680438042	-0.03464515879750252	0.047627922147512436	-0.003020199714228511	0.05002396181225777	-0.02864139899611473	-0.07816115021705627	0.08917304873466492	0.03136022761464119	0.020079229027032852	-0.05033867806196213	0.03584395721554756	0.0647425428032875	0.0005689869867637753	0.045292217284440994	0.089324951171875	-0.03540702164173126	-0.030685869976878166	-0.0027080185245722532	0.03229746222496033	-0.044559404253959656	0.006604698020964861	0.005549609661102295	-0.06804604083299637	0.0013792620738968253	-0.06063701957464218	-0.07321116328239441	-0.036639925092458725	0.028789900243282318	-0.002355853095650673	-0.020829003304243088	-0.013547415845096111	-0.0011737041641026735	0.004246972035616636	-0.030181141570210457	-0.026656320318579674	-0.001059853588230908	-0.08599672466516495	0.08429274708032608	0.016721265390515327	-0.05293390154838562	-0.0641060322523117	0.005852960515767336	0.038469210267066956	-0.011729903519153595	0.008838671259582043	0.06166112422943115	0.08501281589269638	0.042500659823417664	-0.0011501566041260958	-0.005350783932954073	-0.03616257384419441	-0.034694720059633255	0.018659772351384163	0.02337760105729103	-0.045344140380620956	0.003910573665052652	-0.00671676080673933	-0.05613851547241211	-0.03119063936173916	0.015974445268511772	0.042738281190395355	-0.02298191376030445	-0.06802301853895187	-0.009265759959816933	0.03210636228322983	-0.014998546801507473	-0.049182552844285965	0.013631278648972511	-0.019655775278806686	0.08971034735441208	-0.05290687829256058	-0.023918574675917625	0.057610075920820236	-0.02017912268638611	0.05785372480750084	-0.03824451565742493	0.060609906911849976	-0.010662706568837166	-0.07443810999393463	0.01793672703206539	-0.08296838402748108	-0.047437481582164764	-0.02619612216949463	-0.04957643896341324	-0.0413835272192955	0.11082325130701065	0.01607401669025421	-0.02534591406583786	-0.04922560229897499	-0.013160773552954197	0.013552119955420494	-0.050607264041900635	0.008280781097710133	0.004714410752058029	-0.05926769599318504	-0.007553673814982176	0.033228687942028046	-0.049369171261787415	-0.03204471245408058	0.014663554728031158	-0.05939927697181702	-0.07251769304275513	0.056901440024375916	-0.03645354136824608	-0.041217971593141556	-0.006652260199189186	-0.05540713295340538	0.017078692093491554	0.10867588222026825	0.011375321075320244	-0.002887115115299821	0.03782830759882927	0.008871178142726421	0.10213827341794968	-0.0031225141137838364	-0.01297633908689022	-0.002288482617586851	-0.035967569798231125	-0.03223805129528046	0.05339362844824791	-0.04545263573527336	-0.022929031401872635	-0.0010305976029485464	-0.010240313597023487	0.07029718905687332	-0.018744012340903282	-0.0493183396756649	-0.007439128123223782	0.0075164479203522205	0.00629328191280365	0.029057059437036514	0.024653734639286995	0.009710685350000858	-0.018635956570506096	-0.04226340726017952	-0.03554719686508179	-0.021573426201939583	0.048170287162065506	-0.0178100373595953	0.03495010361075401	-0.042020201683044434	-0.06547993421554565	0.01378935668617487	0.01609133370220661	0.10496348887681961	-0.08401893079280853	0.026098482310771942	0.06166721507906914	0.005115744192153215	-0.06975442171096802	0.01832536607980728	0.032528508454561234	-0.028982341289520264	-0.050428956747055054	-0.0002457122609484941	0.029340412467718124	-0.09034762531518936	-0.013985256664454937	-0.0072523970156908035	-0.028145968914031982	0.02518513612449169	0.018795715644955635	0.03634914010763168	0.02537786215543747	-0.01955275982618332	-0.023515496402978897	-0.007490736898034811	0.015964947640895844	0.054806895554065704	-0.05378226563334465	0.028529634699225426	-0.021947110071778297	0.05830695852637291	-0.04073374718427658	-0.04118233919143677	-0.021828971803188324	0.06889332085847855	0.0038211264181882143	-0.020391423255205154	-0.008699213154613972	-0.05429501086473465	0.01338203065097332	0.0469144806265831	0.016837958246469498	-0.06523444503545761	-0.036777570843696594	-0.04287419468164444	-0.0700664222240448	-0.0967705249786377	-0.0875021368265152	0.021611038595438004	-0.08443839102983475	0.018078071996569633	-0.022960128262639046	0.014474250376224518	-0.016171960160136223	-0.006306625436991453	0.03799887374043465	0.04682105779647827	0.03172525763511658	-0.03215161710977554	0.02981904149055481	-0.019958119839429855	0.0441894493997097	0.0007856978918425739	-0.035935044288635254	0.04054226353764534	0.021678250283002853	-0.05210652947425842	0.06249447166919708	-0.03875730559229851	0.017696760594844818	0.007408809382468462	-0.022815918549895287	0.02850143425166607	0.0189354307949543	-0.000883964414242655	-0.057613518089056015	0.05241138115525246	-0.07704662531614304	-0.02315242402255535	0.06813499331474304	-0.009390223771333694	-0.10389284789562225	-0.05075512081384659	-0.02009398490190506	-0.05072043836116791	0.007724684663116932	0.01167223323136568	-0.008595935069024563	0.016425518319010735	-0.04140348359942436	-0.018241988494992256	-0.018481409177184105	-0.030858980491757393	0.034458644688129425	0.020826423540711403	0.07561614364385605	0.01482533197849989	0.019052674993872643	0.00603401567786932	0.018535299226641655	0.010924463160336018	-0.09677497297525406	-0.005265233106911182	-0.02084272727370262	-0.016985837370157242	-0.024569282308220863	-0.0037087860982865095	0.014363251626491547	-0.08810286968946457	-0.019511966034770012	-0.038129180669784546	0.08893249183893204	-0.014023037627339363	0.006596607621759176	0.06470490992069244	-0.0301730427891016	-0.0035655912943184376	0.004642808809876442	-0.034080520272254944	-0.10530303418636322	0.0019590328447520733	-0.041785188019275665	0.0476081445813179	0.011789184994995594	-0.012863299809396267	-0.029755139723420143	-0.06509966403245926	0.01077362708747387	0.018384061753749847	-0.04462631046772003	0.01779264397919178	0.030669663101434708	-0.05711802840232849	-0.046453773975372314	0.011166665703058243	-0.04105769097805023	0.07225674390792847	0.04067403823137283	0.027441062033176422	-0.030262311920523643	-0.014574935659766197	0.001396744279190898	-0.036692872643470764	0.00032625204767100513	0.012202970683574677	-0.061160147190093994	-0.02234330028295517	0.06582819670438766	-0.062288012355566025	0.019809089601039886	0.07966780662536621	-0.07148314267396927	0.04591873288154602	0.03485531732439995	-0.03691870719194412	0.03571777790784836	-0.013451897539198399	-0.04150327295064926	0.045629389584064484	-0.07955861836671829	0.04693511873483658	-0.056148216128349304	0.08840511739253998	0.011437524110078812	-0.021206505596637726	0.06521215289831161	0.07225438207387924	-0.012517288327217102	-0.002676533069461584	-0.09025372564792633
+0.03814154118299484	-0.009570969268679619	0.016360612586140633	0.0653124526143074	-0.08504900336265564	0.0028062674682587385	-0.01568644866347313	0.02147921919822693	-0.00908341258764267	-0.05360604450106621	-0.03350098058581352	0.0015326776774600148	-0.005356467328965664	0.026192590594291687	0.004377234261482954	-0.039582259953022	-0.04128463938832283	0.024905800819396973	-0.005988017655909061	-0.06648002564907074	0.02005801908671856	0.014315206557512283	-0.061570826917886734	0.07668360322713852	0.03564465790987015	-0.026398561894893646	-0.04876841604709625	0.026626834645867348	-0.017344120889902115	0.05249334126710892	0.07846155017614365	-0.0337090790271759	0.041780974715948105	-0.04784009978175163	-0.05843288451433182	0.036946676671504974	-0.04090120270848274	0.10027607530355453	0.011483879759907722	-0.035106271505355835	-0.02615572325885296	0.011306372471153736	-0.024864304810762405	-0.018716003745794296	0.04156298190355301	0.006255322135984898	-0.011037764139473438	-0.024790404364466667	0.039725687354803085	0.024945847690105438	0.11479537189006805	0.01880771666765213	0.0004255683161318302	-0.013410636223852634	-0.008397229015827179	0.025068357586860657	0.031810905784368515	-0.04798179119825363	-0.04595066234469414	0.034990739077329636	-0.05692346766591072	0.0052565885707736015	0.045599181205034256	-0.010671171359717846	-0.006195310037583113	0.015062176622450352	-0.007323186378926039	0.1100861057639122	0.03314661234617233	-0.002662600949406624	0.031250543892383575	0.11937778443098068	-0.06761551648378372	-0.024702617898583412	0.07562839984893799	-0.040021494030952454	-0.02510092593729496	0.05232451111078262	0.0005228767986409366	0.024394551292061806	0.018717922270298004	-0.04513990879058838	0.09821709245443344	-0.017645295709371567	-0.029976142570376396	-0.02738581784069538	0.015018193051218987	-0.06250558793544769	0.08030159771442413	0.03606388345360756	0.02265460230410099	0.06810074299573898	-0.04428559169173241	0.07446812838315964	0.005643668584525585	-0.01977011375129223	0.020458664745092392	-0.1118881031870842	-0.03422342985868454	0.029206672683358192	0.03907289355993271	-0.014798475429415703	0.04629218205809593	-0.03317447379231453	0.05713335797190666	0.08866056799888611	-0.05893568694591522	0.005847809370607138	0.027514787390828133	0.045327626168727875	0.042103901505470276	0.09570809453725815	0.05233069881796837	0.049068909138441086	0.04524632543325424	0.007302529644221067	0.01116033922880888	-0.0508410818874836	-0.03894859179854393	0.02517661266028881	-0.00889698788523674	0.073958620429039	-0.019362619146704674	-0.0673426166176796	-0.029858315363526344	0.056102994829416275	-0.07976945489645004	-0.006447905674576759	0.08656270802021027	-0.0007842464838176966	0.1047823578119278	0.03960240259766579	-0.010993188247084618	0.03671809658408165	0.04496418684720993	0.015588534064590931	-0.017463522031903267	0.016266075894236565	-0.003168083494529128	-0.024225996807217598	-0.035870932042598724	0.06055748462677002	0.03582033887505531	-0.06864690780639648	0.04348491132259369	0.035503193736076355	0.04103102535009384	-0.014712655916810036	-0.046646811068058014	-0.026485631242394447	-0.07316508889198303	-0.11537981778383255	0.0003425499307923019	-0.059141047298908234	-0.001219981350004673	0.03163221478462219	0.021944843232631683	0.034145619720220566	-0.01439749076962471	-0.01584009826183319	-0.040218111127614975	-0.0287636611610651	-0.02581869065761566	-0.06020472198724747	-0.0014053834602236748	-0.012796764262020588	-0.012816229835152626	0.025031520053744316	-0.05574522167444229	-0.011687332764267921	-0.009850765578448772	0.004275238607078791	0.03159989416599274	0.09158921241760254	0.05271663889288902	0.023088017478585243	0.05543937161564827	-0.015437014400959015	-0.020253002643585205	-0.029942912980914116	0.015448281541466713	-0.04377180337905884	0.04005158692598343	-0.0548659972846508	0.0024955689441412687	-0.010568033903837204	0.06236021965742111	-0.01704511046409607	0.004440175835043192	-0.012612293474376202	-0.029939739033579826	-0.06648174673318863	0.060054097324609756	0.056233812123537064	-0.02690991759300232	0.017940357327461243	-0.0036452459171414375	0.05201982706785202	0.004323218483477831	0.006677682977169752	-0.03437311574816704	0.04666052386164665	0.03160058706998825	0.041556913405656815	0.033799588680267334	-0.08714141696691513	0.05786360055208206	0.030314195901155472	0.04054313898086548	-0.05729813128709793	-0.01165307778865099	0.021425429731607437	0.008910414762794971	0.06775441020727158	0.0532139427959919	-0.00383859034627676	-0.004860344342887402	0.006789010018110275	0.05450740084052086	-0.05608029663562775	-0.006405417807400227	-0.01067098043859005	-0.07239468395709991	0.034934982657432556	-0.04647164046764374	-0.08033990859985352	-0.03404498100280762	0.014652655459940434	-0.00461728498339653	-0.044247932732105255	0.021508261561393738	0.017225582152605057	-0.001440125168301165	-0.03390246257185936	0.012484163045883179	0.001401467015966773	-0.08964137732982635	0.09931360185146332	-0.015483060851693153	-0.0371590293943882	-0.05449787899851799	6.788038444938138e-05	0.03059302270412445	0.019312016665935516	0.060067933052778244	0.05056364834308624	0.10581015795469284	0.042961783707141876	-0.019311103969812393	-0.0040769390761852264	-0.033431798219680786	-0.049536820501089096	-0.0059964912943542	0.054898906499147415	-0.03709941729903221	-0.044160060584545135	0.01706918142735958	-0.057636551558971405	0.007641507778316736	0.01574341207742691	0.010010397993028164	-0.023393960669636726	-0.016016049310564995	-0.03765976056456566	0.02240721322596073	-0.020980732515454292	-0.03424646705389023	0.026769021525979042	-0.010884680785238743	0.08015235513448715	-0.029952703043818474	-0.03966348245739937	0.019921479746699333	0.007915936410427094	0.01944047212600708	-0.03694804757833481	0.052500490099191666	-0.04425126686692238	-0.0830916240811348	-0.06495723873376846	-0.04152783378958702	-0.07265657186508179	-0.032192960381507874	-0.04241030290722847	-0.020918045192956924	0.07884898036718369	-0.03184325993061066	-0.05866970494389534	-0.06174148991703987	-0.034264225512742996	-0.028506217524409294	-0.05791560187935829	-0.027544405311346054	-0.026125237345695496	-0.04350023344159126	-0.0073159909807145596	0.016619300469756126	-0.02562890388071537	-0.021880099549889565	0.01639801822602749	0.03231209143996239	-0.04324186593294144	0.0855935737490654	-0.017724737524986267	0.007006796542555094	0.004477906506508589	-0.06434778869152069	0.03142411261796951	0.08549748361110687	0.03969523683190346	0.04479517787694931	0.04600640386343002	0.06572484970092773	0.14112211763858795	0.04478507116436958	-0.08136068284511566	-0.030615555122494698	-0.03791096806526184	-0.08224444836378098	0.05830718204379082	-0.0037370764184743166	-0.0387854129076004	-0.001123238354921341	0.0003352173662278801	0.06798747181892395	0.005911203566938639	0.006369705777615309	-0.050117094069719315	0.0635475441813469	-0.014501683413982391	0.011102489195764065	-0.020188583061099052	-0.022473415359854698	0.011144546791911125	-0.033264052122831345	-0.03218165785074234	-0.026781708002090454	0.033238597214221954	0.002660604892298579	0.06435386091470718	-0.008252836763858795	-0.051075100898742676	0.02552039362490177	-0.01023249514400959	0.07768838852643967	-0.07728979736566544	0.0035987989977002144	0.04016020521521568	0.018943509086966515	-0.0818757563829422	0.012633652426302433	0.019639378413558006	-0.04688330367207527	-0.0303159411996603	-0.009194386191666126	0.05863122642040253	-0.03543493151664734	-0.016657967120409012	-0.017833765596151352	-0.04471295699477196	-0.02189686894416809	0.009581337682902813	0.00412283930927515	0.04855705425143242	-0.020993180572986603	-0.009052740409970284	-0.008710114285349846	0.0008329750271514058	0.0002856987703125924	-0.0052539287135005	0.012667021714150906	-0.03325848653912544	0.059475984424352646	-0.031795304268598557	-0.009365905076265335	-0.061748214066028595	0.02257436141371727	-0.04325902834534645	-0.0035036648623645306	0.00047956188791431487	0.009616452269256115	0.005470550153404474	-0.008581692352890968	0.03011240065097809	-0.08639024198055267	0.009631858207285404	-0.03089110367000103	-0.04276329278945923	-0.05685829743742943	-0.04934677109122276	-0.0064826649613678455	-0.060371506959199905	-0.003909071907401085	-0.044115275144577026	-0.018808698281645775	0.010844841599464417	-0.0058026500046253204	0.019643288105726242	0.0636981725692749	0.043182846158742905	-0.014981416054069996	0.06498301029205322	0.027103058993816376	0.00714780343696475	0.009375694207847118	-0.08886592835187912	0.041045427322387695	0.039096202701330185	-0.02487611584365368	0.039164260029792786	-0.02156797982752323	0.012924489565193653	0.0020550857298076153	0.010744278319180012	0.02155878394842148	-0.05665534734725952	-0.029212776571512222	-0.02058376558125019	0.08035149425268173	-0.05390946567058563	-0.05885415896773338	0.039109546691179276	-0.05906597897410393	-0.0748358815908432	0.0010253473883494735	-0.037298791110515594	-0.04103655368089676	0.05255551263689995	-0.017927205190062523	0.003645076649263501	0.039853885769844055	-0.04790276661515236	-0.060213059186935425	-0.02663755975663662	-0.040015146136283875	0.026174409314990044	0.02274058572947979	0.07239656895399094	0.012509343214333057	-0.02831449545919895	-0.005525761749595404	-0.05289776250720024	0.018709981814026833	-0.09993652999401093	-0.01492441724985838	-0.009016248397529125	-0.04228208214044571	0.027142981067299843	0.012858682312071323	0.007833052426576614	-0.04351761192083359	-0.02491065114736557	-0.04312895983457565	0.06165997311472893	-0.04845809563994408	-0.014859302900731564	0.01848369464278221	-0.009326349943876266	-0.006507900543510914	0.04294602572917938	-0.0599655918776989	-0.08944691717624664	0.004409798886626959	-0.009566280990839005	0.033552784472703934	-0.0026393141597509384	0.0022575613111257553	-0.04153111204504967	-0.07142934948205948	-0.032785505056381226	0.025115173310041428	-0.07071049511432648	0.00798869039863348	-0.01521546021103859	-0.11799164116382599	-0.09875506907701492	0.01676834374666214	-0.06285041570663452	0.06854167580604553	0.011751266196370125	0.0350584052503109	-0.01154239010065794	-0.008898487314581871	0.001781677477993071	-0.05081009492278099	0.017975283786654472	-0.025156261399388313	-0.06842862814664841	-0.010456169955432415	0.07406865805387497	-0.04890991002321243	0.021853769198060036	0.05811724066734314	-0.029797285795211792	0.03435000032186508	0.025098450481891632	-0.03634835034608841	0.04016386345028877	-0.02057570219039917	0.013713558204472065	0.05879592522978783	-0.060344744473695755	0.10272102802991867	-0.041766390204429626	0.08576136082410812	0.00033850647741928697	0.015621310099959373	0.09468993544578552	0.08245278894901276	0.002524347510188818	0.0138644864782691	-0.10536101460456848
+0.0063360086642205715	-0.04588404670357704	0.0035813148133456707	0.010137775912880898	-0.03229938820004463	-0.04843016341328621	0.010614916682243347	0.05758492648601532	-0.010730125941336155	-0.054111186414957047	-0.04468289762735367	0.01827920973300934	-0.004794674925506115	-0.009186582639813423	0.03010598011314869	-0.020753173157572746	-0.08589862287044525	0.005562040489166975	-0.06409070640802383	-0.04844209924340248	-0.0001291237713303417	0.03200152888894081	-0.07385800033807755	0.053220901638269424	-0.0038729754742234945	0.021445291116833687	-0.040419697761535645	-0.00839283037930727	-0.004544977564364672	0.03405337408185005	0.07496605068445206	-0.0048957946710288525	0.09965254366397858	-0.05774233117699623	-0.014584182761609554	0.05730907991528511	-0.0421997532248497	0.09960157424211502	-0.0030927557963877916	-0.0017331806011497974	-0.03756109997630119	-0.013979384675621986	-0.004897655453532934	-0.09360207617282867	0.03149741142988205	0.008498492650687695	0.010977781377732754	-0.005203909706324339	0.01484439056366682	0.02249811217188835	0.08003493398427963	0.01913522742688656	-0.0056768013164401054	-0.020779700949788094	0.002344014821574092	0.02831059880554676	0.029787300154566765	-0.02666832134127617	-0.04916749894618988	0.04493486136198044	-0.11834453791379929	-0.018374953418970108	0.0349603146314621	0.01360921561717987	-0.05095798522233963	-0.026127014309167862	-0.043209899216890335	0.08030693978071213	0.01594708301126957	-0.04480808228254318	0.034215353429317474	0.04937877506017685	-0.04392637312412262	-0.05891580134630203	0.022868609055876732	-0.028013860806822777	0.012553988955914974	0.02301657199859619	-0.008271905593574047	9.95215714283404e-07	0.012996086850762367	-0.00994325615465641	0.13015194237232208	-0.04340946674346924	-0.02766043320298195	-0.05338439717888832	-0.0021223078947514296	-0.0521734282374382	0.0875818207859993	-0.0016969518037512898	0.0018588474486023188	0.07154388725757599	-0.004300575703382492	0.0096970209851861	0.02491837739944458	-0.007086471654474735	-0.036174673587083817	-0.09441898763179779	-0.03655330836772919	-0.017925893887877464	0.023246798664331436	0.022614382207393646	0.07887395471334457	0.0346289686858654	0.08387879282236099	0.018405187875032425	-0.019180817529559135	-0.008672703057527542	0.06900098919868469	-0.007194871082901955	-0.0028332388028502464	0.1324446201324463	0.039179906249046326	0.018526531755924225	0.07351237535476685	0.01409967616200447	0.008070698007941246	-0.03339514508843422	-0.017799293622374535	-0.025433752685785294	-0.0197683684527874	0.011472330428659916	-0.004545228555798531	-0.10009535402059555	-0.023517580702900887	0.052947673946619034	-0.096282459795475	-0.05671367421746254	0.07531652599573135	-0.040409285575151443	0.13157188892364502	0.051356762647628784	-0.017902851104736328	0.012785914354026318	0.04213697463274002	0.004858794156461954	-0.013406246900558472	0.03368286415934563	0.007110700011253357	-0.048700522631406784	0.008722422644495964	0.05042262002825737	-0.04089238494634628	-0.04145284369587898	0.04070786014199257	0.02229803055524826	0.002778479363769293	-0.026175476610660553	-0.05391761288046837	0.032251227647066116	-0.03286963701248169	-0.08308226615190506	0.003723741276189685	-0.042723577469587326	-0.018170567229390144	0.04473155364394188	0.004070354625582695	0.006313594989478588	-0.021126504987478256	-0.031009873375296593	-0.07861090451478958	-0.04063298925757408	-0.03260648623108864	-0.09406979382038116	-0.01604476012289524	-0.02524176798760891	-0.00838465616106987	0.053582992404699326	-0.06189287081360817	-0.033967114984989166	-0.020701786503195763	-0.04052479937672615	-0.009049911051988602	0.06444519758224487	0.06131729111075401	0.0305312592536211	0.0015876920660957694	-0.08553384989500046	0.005582032259553671	-0.042383741587400436	-0.015995141118764877	-0.018528969958424568	0.053668312728405	-0.048385828733444214	0.01285625621676445	-0.01882331632077694	0.038881201297044754	-0.029612472280859947	0.008819693699479103	0.005139519926160574	0.002722217468544841	-0.0213053897023201	0.04131070151925087	0.04772159457206726	0.007748793810606003	0.01967497169971466	0.01889585517346859	0.05732692778110504	-0.019916333258152008	-0.0595681369304657	-0.0749494731426239	0.01894538663327694	0.04497426375746727	0.02386784926056862	0.013633589260280132	-0.05683332681655884	0.08196300268173218	0.046711329370737076	0.01202522311359644	-0.0669313445687294	0.007452042773365974	0.013493292033672333	-0.004978733602911234	0.04427896812558174	0.07292146980762482	-0.046545177698135376	-0.02623756416141987	0.02727014385163784	0.0486518070101738	-0.059538740664720535	-0.05371712148189545	0.0203692764043808	-0.08127731829881668	0.013128210790455341	-0.031290046870708466	-0.09640421718358994	-0.024279162287712097	0.04700395092368126	-0.02051982283592224	-0.0014279752504080534	-0.0019421973265707493	0.018507780507206917	-0.03308994695544243	-0.0037876551505178213	0.006420179270207882	-0.0546516552567482	-0.07612449675798416	0.0844963937997818	-0.005207989364862442	-0.09539792686700821	-0.06071535497903824	-0.00957474671304226	0.063721664249897	-0.000680334575008601	0.04322757199406624	0.01787395589053631	0.07544244080781937	0.007370282895863056	-0.04537339136004448	0.00039959378773346543	0.0016319442074745893	-0.03425952047109604	0.001632718020118773	0.046348873525857925	-0.047103673219680786	-0.04323795437812805	0.003170030191540718	-0.03251692280173302	-0.002307058311998844	0.020792324095964432	0.05283157899975777	-0.04549615457653999	0.0009786232840269804	-0.009569753892719746	0.01701834797859192	-0.04910881817340851	0.001521724509075284	0.07316333055496216	0.00403145607560873	0.048124250024557114	-0.047537077218294144	-0.03683245927095413	0.025643115863204002	-0.022028539329767227	0.020486481487751007	-0.08024869859218597	0.07588571310043335	0.010164258070290089	-0.07982891798019409	0.002886870875954628	-0.08709888160228729	-0.029019340872764587	-0.012055978178977966	-0.055694736540317535	-0.05136662349104881	0.07170717418193817	0.04156864061951637	-0.06307723373174667	-0.04120107740163803	-0.07165438681840897	-0.02334456890821457	-0.02340231090784073	-0.013562061823904514	-0.02818358689546585	-0.014220129698514938	0.006912252400070429	0.06566233187913895	-0.0815749242901802	-0.008118260651826859	0.04290115833282471	0.009569953195750713	-0.061959926038980484	0.054333969950675964	-0.031481802463531494	-0.03494939208030701	0.0008488198509439826	-0.08336671441793442	0.010927441529929638	0.07906197011470795	0.052298158407211304	0.019868383184075356	0.026244832202792168	0.018699700012803078	0.10537455976009369	-0.009221368469297886	-0.0007054490852169693	-0.025488948449492455	-0.016686376184225082	-0.042289849370718	0.018807796761393547	-0.02433170937001705	-0.0034452409017831087	-0.012453820556402206	-0.002115362323820591	0.04663525149226189	0.00816368032246828	-0.02549450658261776	-0.005948187317699194	0.024671845138072968	-0.01285470463335514	0.029063081368803978	0.014755289070308208	0.03937895596027374	-0.027978109195828438	-0.056331366300582886	-0.060312669724226	-0.005986394360661507	0.06493045389652252	-0.045571718364953995	0.049129270017147064	-0.039117030799388885	-0.0700112134218216	-0.005441657733172178	-0.014392819255590439	0.10470504313707352	-0.05825686827301979	0.028495991602540016	0.055851880460977554	0.05533452332019806	-0.10074679553508759	0.025451362133026123	0.032929953187704086	-0.04297637939453125	-0.07064330577850342	-0.0036015049554407597	0.032506320625543594	-0.06271008402109146	-0.03440137207508087	-0.0032794165890663862	-0.04251673445105553	-0.01915016770362854	0.011790742166340351	0.03266233950853348	0.03211185336112976	0.018276143819093704	-0.01594356819987297	-0.00887216441333294	0.03498198837041855	0.026025116443634033	-0.005893899593502283	0.019327964633703232	-0.04134596139192581	0.01898541860282421	-0.06510000675916672	-0.03578713536262512	-0.017767952755093575	0.06512250006198883	-0.026261013001203537	0.021059881895780563	0.00868956372141838	-0.06687739491462708	-0.024317895993590355	-0.007124393712729216	0.00015769335732329637	-0.04526222124695778	-0.017803212627768517	-0.0053010368719697	-0.09394470602273941	-0.09454480558633804	-0.05507604405283928	-0.0032351433765143156	-0.02215658128261566	0.018981000408530235	-0.056574881076812744	0.05838588997721672	0.01871495693922043	0.015890995040535927	0.043871697038412094	0.058876510709524155	0.051280997693538666	-0.025915872305631638	0.008235425688326359	-1.2474439245124813e-05	0.05495425686240196	0.0015974028501659632	-0.02372630499303341	0.012254086323082447	0.005050839856266975	-0.04638290777802467	0.035905249416828156	-0.023232053965330124	-0.008920992724597454	0.004779214505106211	-0.059560567140579224	-0.028052954003214836	0.021517788991332054	-0.003920688759535551	-0.03976847976446152	0.03458629176020622	-0.04470541700720787	-0.021321743726730347	0.01298603881150484	-0.037064824253320694	-0.07716728746891022	-0.07144249230623245	-0.03854648396372795	-0.02423054911196232	0.05690527707338333	-0.010880542919039726	0.018814045935869217	0.029201658442616463	-0.013948274776339531	-0.05164049193263054	0.01938125118613243	-0.015302005223929882	0.012582873925566673	-0.010491108521819115	0.06088249757885933	0.006208411883562803	0.01709246262907982	-0.02687733620405197	-0.04800666123628616	0.02721300721168518	-0.10695330798625946	0.01991099864244461	-0.003920838236808777	-0.018044384196400642	0.005406090058386326	-0.0022682063281536102	0.006884058006107807	-0.05954339727759361	-0.028095291927456856	0.0007755234837532043	0.05642523244023323	-0.0015592797426506877	0.04421478882431984	0.005688730161637068	-0.06255948543548584	-0.05077674239873886	0.02912338264286518	-0.06398744881153107	-0.08247311413288116	0.015300964936614037	-0.04621647298336029	0.04071726277470589	-0.009372576139867306	0.024351155385375023	-0.03473115339875221	-0.1041780635714531	0.00352310948073864	-0.03696923702955246	-0.042480695992708206	-0.02501615881919861	0.04571231082081795	-0.08466576039791107	-0.007142636459320784	0.002152590546756983	-0.033301856368780136	0.05081268399953842	0.018927069380879402	0.05880136415362358	-0.056729741394519806	-0.02732023037970066	-0.025627847760915756	0.010360137559473515	0.010921022854745388	-0.02477327734231949	-0.0758289098739624	-0.017477484419941902	0.07621892541646957	-0.0780983418226242	0.031094590201973915	0.07769153267145157	-0.05180880054831505	0.049440693110227585	0.0166492722928524	-0.03874073550105095	0.03740684315562248	-0.0012018228881061077	-0.024451028555631638	0.028944268822669983	-0.09187979996204376	0.06193016096949577	-0.0726478174328804	0.08798597007989883	0.008996214717626572	-0.006636673118919134	0.07952486723661423	0.09844500571489334	0.010699850507080555	0.02531672641634941	-0.07571949809789658
+0.018160002306103706	-0.04438316076993942	0.02162603661417961	0.03304839879274368	-0.0633116066455841	-0.04567323997616768	-0.011225955560803413	0.024945443496108055	0.02304336428642273	-0.04055148735642433	-0.019552480429410934	0.00036880761035718024	0.0221006590873003	0.008822635747492313	0.02309631183743477	-0.0442434586584568	-0.09740233421325684	-0.007839483208954334	-0.03424842655658722	-0.04192443564534187	0.01426237914711237	0.024242941290140152	-0.06376681476831436	0.051800381392240524	-0.01081742625683546	0.025005962699651718	-0.057832516729831696	-0.009451557882130146	-0.02352837100625038	0.007252341136336327	0.06625449657440186	0.021793123334646225	0.07338345795869827	-0.053219422698020935	-0.04989156499505043	0.05391893535852432	-0.049191512167453766	0.11354588717222214	-0.0010387429501861334	-0.006204092409461737	-0.045378729701042175	0.00789021234959364	-0.0011649824446067214	-0.055103156715631485	0.023799143731594086	0.033487431704998016	0.009113533422350883	-0.003059150418266654	-0.0005122077418491244	0.0052543082274496555	0.11373771727085114	0.027079302817583084	-0.009609549306333065	-0.00696994923055172	-0.013239205814898014	0.02386488951742649	0.07086148113012314	-0.05563950911164284	-0.07376694679260254	0.04256632924079895	-0.11991958320140839	-0.010049440898001194	0.005215638317167759	-0.01844147779047489	-0.03457977622747421	-0.005521762650460005	-0.05812539532780647	0.07650687545537949	-0.0034370608627796173	-0.03240589052438736	0.062434084713459015	0.07578243315219879	-0.05278792604804039	-0.056701332330703735	0.05068043991923332	-0.0429392084479332	-0.013195862993597984	0.05141926556825638	-0.05894365534186363	0.009340658783912659	0.006100716535001993	-0.01852833852171898	0.11772207915782928	-0.05364035442471504	-0.03236517682671547	-0.03802148997783661	0.0027354201301932335	-0.07008615881204605	0.05324403569102287	0.042029883712530136	0.015001141466200352	0.08232352882623672	-0.010956831276416779	0.026729900389909744	0.026990504935383797	0.005287819541990757	-0.003281094366684556	-0.10226116329431534	-0.039876677095890045	0.00929388590157032	0.014487603679299355	0.015170956961810589	0.07844117283821106	0.01061304472386837	0.06872659921646118	0.03352298215031624	-0.047588977962732315	0.00930332113057375	0.016507543623447418	0.0270848348736763	0.028712857514619827	0.10751733928918839	0.0350833423435688	0.042361900210380554	0.04449520260095596	0.02416035905480385	0.008575213141739368	-0.02320217527449131	-0.024426722899079323	0.0015514749102294445	-0.018060415983200073	0.034845687448978424	-0.014710115268826485	-0.08941928297281265	-0.008452726528048515	0.027454769238829613	-0.0940118357539177	-0.0318346731364727	0.0788998007774353	-0.059039272367954254	0.11516285687685013	0.05375276133418083	-0.015178467147052288	0.04691405966877937	0.038422394543886185	0.04553930461406708	-0.013522607274353504	0.012620652094483376	-0.019652895629405975	-0.03635842725634575	-0.017932727932929993	0.04653124511241913	-0.021371055394411087	-0.05626829341053963	0.05743829160928726	0.04006216675043106	0.016585558652877808	-0.005577308591455221	-0.056009307503700256	0.0029192776419222355	-0.04056962579488754	-0.07364390790462494	-0.023190973326563835	-0.041657738387584686	0.0031611688900738955	0.06482724100351334	0.012850533239543438	0.05855615809559822	-0.0004804116324521601	-0.03467180207371712	-0.04799994081258774	-0.030874326825141907	-0.04736853390932083	-0.0861099511384964	-0.005055747460573912	-0.04167531803250313	0.0013864848297089338	0.013312917202711105	-0.05887800082564354	-0.019885318353772163	-0.014674689620733261	-0.00841225404292345	0.014857127331197262	0.04321048781275749	0.0751304104924202	0.005719679873436689	0.03051302582025528	-0.07785294950008392	-0.03171660006046295	-0.04819982498884201	0.01262571383267641	-0.004687514156103134	0.015715304762125015	-0.06624310463666916	-0.004207467660307884	0.013084170408546925	0.04630348086357117	-0.020805032923817635	0.00361838866956532	-0.0037653683684766293	-0.010128233581781387	-0.03400382399559021	0.06832532584667206	0.05317750200629234	-0.018170956522226334	0.023180168122053146	0.004483254626393318	0.053669147193431854	-0.02321706898510456	-0.04671108350157738	-0.04988463595509529	0.038702573627233505	0.024657657369971275	0.044119153171777725	0.005144889000803232	-0.04351241514086723	0.07203067094087601	0.01035124808549881	0.04033278301358223	-0.045422766357660294	0.0226146150380373	0.019419558346271515	-0.006846239790320396	0.04871128126978874	0.0948561504483223	-0.04453802853822708	-0.009366362355649471	-0.0018112335819751024	0.07390035688877106	-0.06820749491453171	0.0030197908636182547	-0.012279649265110493	-0.06732605397701263	-0.001968192867934704	-0.04884113371372223	-0.10129087418317795	-0.03358421102166176	0.05236397683620453	-0.017052212730050087	0.006962534971535206	0.0010852316627278924	0.039527539163827896	-0.034336913377046585	-0.0005776535836048424	0.003109040204435587	-0.023138364776968956	-0.09882368892431259	0.07686750590801239	0.004129139706492424	-0.06642331928014755	-0.059562016278505325	0.014383061788976192	0.02951217256486416	0.0034059537574648857	0.019300630316138268	0.03421899303793907	0.07194683700799942	0.044270437210798264	-0.028344690799713135	0.026820719242095947	-0.01795017533004284	-0.048700593411922455	0.012807660736143589	0.026450050994753838	-0.016378067433834076	-0.005299432668834925	-0.0026282507460564375	-0.06793762743473053	-0.005122884642332792	0.02681908756494522	0.015181608498096466	-0.035992518067359924	-0.013888797722756863	-0.0045389048755168915	0.007153104059398174	-0.047317299991846085	-0.03105000965297222	0.029590796679258347	-0.013777805492281914	0.07025083154439926	-0.04757336527109146	-0.01683547906577587	0.021505391225218773	-0.002383751329034567	0.0448954775929451	-0.05392927676439285	0.06912074238061905	-0.0007986063719727099	-0.08958438038825989	-0.002697926014661789	-0.07926047593355179	-0.026381848379969597	-0.014312317594885826	-0.06435927003622055	-0.06205777823925018	0.07940015941858292	0.02539091557264328	-0.07422944903373718	-0.06700300425291061	-0.06301769614219666	0.003851122921332717	-0.01825009100139141	-0.005937632638961077	-0.012790984474122524	-0.027312036603689194	-0.03154507279396057	0.05956360325217247	-0.0641157329082489	-0.01943686604499817	0.03542745113372803	-0.025696076452732086	-0.04044988378882408	0.050177961587905884	-0.051495663821697235	-0.01951676979660988	-0.00791177898645401	-0.08178936690092087	0.04336647689342499	0.10678871721029282	0.026079261675477028	0.0029833249282091856	0.027832897379994392	0.018277935683727264	0.10538524389266968	0.006533649750053883	-0.030372869223356247	-0.0007393888663500547	-0.042455192655324936	-0.02904483862221241	0.056148454546928406	-0.023646391928195953	0.004709769506007433	-0.008546493947505951	-0.02112530544400215	0.06119178235530853	0.007627487648278475	-0.023463930934667587	-0.00887197908014059	0.03315570205450058	-0.02100295014679432	0.008200418204069138	0.005415173247456551	0.042461976408958435	-0.0022660703398287296	-0.05159673094749451	-0.041587945073843	-0.029767686501145363	0.04264085367321968	-0.023500626906752586	0.0394446924328804	-0.05004288628697395	-0.06445184350013733	0.02015472762286663	-0.017849907279014587	0.12328694015741348	-0.08199124783277512	0.023135846480727196	0.06292116641998291	0.03950527682900429	-0.07979916036128998	0.0037426301278173923	0.04002958536148071	-0.06596512347459793	-0.045342396944761276	0.0006905227201059461	0.013934226706624031	-0.06089784577488899	-0.03747137263417244	-0.01612750068306923	-0.02161986008286476	0.028393689543008804	0.012300419621169567	0.014209273271262646	0.026949141174554825	-0.00472413981333375	-0.029343247413635254	-0.013025019317865372	0.03206050023436546	0.023537227883934975	-0.048330437391996384	0.031376443803310394	-0.040502700954675674	0.022074183449149132	-0.04399888962507248	-0.039990343153476715	-0.04438687860965729	0.056736718863248825	-0.03807377070188522	-0.002414529910311103	-0.01205772627145052	-0.05033199116587639	-0.040751997381448746	0.00021427539468277246	0.005249951500445604	-0.10251869261264801	-0.029280750080943108	-0.045504987239837646	-0.08298816531896591	-0.0782279446721077	-0.0763806626200676	0.025852449238300323	-0.04561888426542282	0.0368562750518322	-0.045844193547964096	0.04548623040318489	-0.003834484377875924	0.016579901799559593	0.04235519841313362	0.061595045030117035	0.03306453302502632	-0.023807145655155182	0.04099397733807564	-0.0013736223336309195	0.03270149976015091	0.012940456159412861	-0.04499775543808937	0.023273207247257233	0.0391479916870594	-0.045455463230609894	0.03465374931693077	-0.033320311456918716	0.007527428213506937	0.017477018758654594	-0.044316262006759644	-0.00277160806581378	0.006608591880649328	-0.02875979244709015	-0.048532504588365555	0.037288058549165726	-0.04343917965888977	-0.03970881924033165	0.02381828986108303	-0.06869944930076599	-0.08365955948829651	-0.05590261146426201	-0.03303247317671776	-0.03240552917122841	0.014999326318502426	0.014921886846423149	0.018490372225642204	0.033671386539936066	-0.02677745744585991	-0.03988360986113548	0.006292018108069897	-0.012692105956375599	0.015367915853857994	0.01286628469824791	0.05094682797789574	-0.002762262476608157	-0.02617485262453556	-0.0018980230670422316	-0.019444361329078674	0.03652666509151459	-0.09792692214250565	0.00988958403468132	-0.021811101585626602	-0.029124442487955093	-0.007193193305283785	-0.011852112598717213	0.007075559347867966	-0.1018330305814743	-0.00496507715433836	-0.02709786966443062	0.05580325052142143	-0.01800910010933876	0.046733468770980835	0.02611599490046501	-0.0352434366941452	-0.006369466427713633	0.02292286977171898	-0.050228580832481384	-0.08060889691114426	0.00966362003237009	-0.06282636523246765	0.03445581719279289	-0.003840677672997117	-0.014665782451629639	-0.013935573399066925	-0.10045887529850006	-0.004339553881436586	0.006592311430722475	-0.05862674489617348	-0.02265978790819645	0.02496262826025486	-0.08504455536603928	-0.044533856213092804	0.020876675844192505	-0.03897857666015625	0.06586996465921402	0.02347627468407154	0.04209586977958679	-0.044169701635837555	-0.017697473987936974	-0.022333981469273567	-0.020454254001379013	0.007011704612523317	-0.017116297036409378	-0.049924518913030624	-0.0328950472176075	0.06843893229961395	-0.05535246059298515	0.01599912904202938	0.10954786837100983	-0.049749042838811874	0.0540761835873127	0.03325529024004936	-0.0592392235994339	0.013248122297227383	0.0037847531493753195	-0.033851366490125656	0.02796586975455284	-0.09901952743530273	0.08436445146799088	-0.061614152044057846	0.10663950443267822	0.02475757710635662	-0.0030645388178527355	0.0653766468167305	0.09916501492261887	0.0007941495859995484	0.006199215538799763	-0.07606981694698334
+0.040559072047472	0.029359452426433563	0.026420990005135536	0.044961586594581604	-0.12967616319656372	-0.01718338020145893	-0.023004142567515373	0.006264727097004652	0.00956945400685072	-0.05658486858010292	-0.03035537339746952	-0.00958959013223648	-0.014003251679241657	0.00447334349155426	0.013882441446185112	-0.021485481411218643	-0.04446283355355263	-0.022917140275239944	-0.02448245882987976	-0.05937032774090767	0.049612466245889664	-0.015394727699458599	-0.030103245750069618	0.07598485052585602	0.022306673228740692	0.022303685545921326	-0.02888980321586132	0.02752426639199257	-0.03278760984539986	0.058245353400707245	0.05662441626191139	-0.01084944512695074	0.027789736166596413	-0.06986521184444427	-0.041986919939517975	0.06581893563270569	0.00582679221406579	0.10245434939861298	0.031193656846880913	-0.061629585921764374	-0.02066841907799244	-0.012434428557753563	-0.04582921415567398	-0.0584380067884922	0.029853595420718193	-0.03241709619760513	-0.017167525365948677	-0.018481748178601265	0.06123572215437889	0.03442222997546196	0.0726589560508728	0.004715976305305958	-0.04391952604055405	-0.029156481847167015	0.015594415366649628	0.018299270421266556	0.045857202261686325	-0.0364762619137764	-0.007018044590950012	0.07605762779712677	-0.05368746444582939	-0.035340242087841034	0.04298890382051468	0.022630229592323303	0.013806243427097797	0.006665326654911041	0.03195502609014511	0.08180754631757736	0.030496343970298767	-0.018389081582427025	0.014865797944366932	0.10785365849733353	-0.03373914584517479	-0.003341419156640768	0.03878018632531166	-0.06047416478395462	-0.05304820090532303	0.03301287814974785	0.020228659734129906	0.047130897641181946	0.010434728115797043	-0.0884234756231308	0.1190313771367073	-0.03419192507863045	-0.05133421719074249	-0.04491039738059044	-0.027085773646831512	-0.04335586354136467	0.07658329606056213	0.004718543495982885	0.001847908366471529	0.06825416535139084	-0.07445485889911652	0.05132194235920906	-0.02580261044204235	0.01148791890591383	0.0420488603413105	-0.10615485906600952	-0.0018778889207169414	0.008381465449929237	-0.005235207732766867	-0.06267762184143066	-0.009851966053247452	-0.007412594743072987	0.05737239494919777	0.0713677927851677	-0.03290082514286041	-0.0124515350908041	0.03002392314374447	0.029293056577444077	0.03992479294538498	0.0712393969297409	0.09702875465154648	0.050144970417022705	0.0685688853263855	-0.01597747579216957	-0.0008478713571093976	-0.09875201433897018	-0.03321616351604462	-0.0012324800482019782	-0.00047087352140806615	0.06549961864948273	-0.02082534320652485	-0.058126576244831085	-0.009419402107596397	0.020034629851579666	-0.09325569123029709	-0.002478963928297162	0.04629144072532654	-0.009862998500466347	0.09606310725212097	0.036854088306427	-0.013008096255362034	0.030317112803459167	0.03284939005970955	0.03380861133337021	-0.026544539257884026	0.040394268929958344	0.0032367282547056675	-0.034231264144182205	-0.00199811439961195	0.02765515260398388	0.04130752757191658	-0.06410102546215057	0.010706481523811817	0.04201497882604599	0.03659193962812424	-0.010825383476912975	-0.07163617759943008	0.017364077270030975	-0.09402782469987869	-0.047118596732616425	0.021130017936229706	-0.09042102843523026	-0.02426288090646267	0.051196563988924026	0.007913632318377495	0.003282858757302165	-0.048739466816186905	-0.010684550739824772	-0.015099192038178444	-0.0315735824406147	0.028834236785769463	-0.07293228805065155	-0.050966039299964905	-0.02547024004161358	0.0053007956594228745	0.010386507026851177	-0.032724812626838684	-0.031652409583330154	-0.004695245996117592	-0.022666562348604202	0.05984467267990112	0.08481425046920776	0.04237666726112366	-0.011877873912453651	0.01695975847542286	-0.0474531389772892	0.024973338469862938	-0.03963318094611168	0.007478291168808937	-0.04381635785102844	0.054417531937360764	-0.06506077945232391	-0.039027001708745956	-0.03205146640539169	0.03036232478916645	0.009608034044504166	0.009674867615103722	-0.0020405345130711794	-0.06522069871425629	-0.039250973612070084	0.028267502784729004	0.062071602791547775	-0.05372102931141853	0.04294877499341965	0.03063163161277771	0.03148260712623596	-0.00020737263548653573	-0.03644053265452385	-0.07400524616241455	0.04496511071920395	0.012082620523869991	0.08176897466182709	0.04847754165530205	-0.09160394966602325	0.07423947006464005	0.018269063904881477	-0.008352993056178093	-0.08125222474336624	0.010546484962105751	0.06302124261856079	0.0282953679561615	0.05295727029442787	0.07260558754205704	-0.004224909003823996	0.025417141616344452	-0.01777230016887188	0.04003181681036949	-0.02868608385324478	-0.020622069016098976	-0.004221121780574322	-0.08450424671173096	0.00036637153243646026	-0.04449262097477913	-0.05039170756936073	-0.040444422513246536	0.02428736537694931	-0.00344643322750926	-0.05191149562597275	0.0287060234695673	0.027236979454755783	-0.020406514406204224	-0.013461735099554062	-0.0069204168394207954	0.0013912434224039316	-0.06741484254598618	0.12279056757688522	-0.03835073485970497	-0.0919010192155838	-0.04046979919075966	0.03520800918340683	0.048195842653512955	-0.0026955767534673214	0.056088656187057495	0.05926775559782982	0.1003689393401146	0.024550478905439377	0.00519862025976181	-0.05993492901325226	-0.05863146856427193	-0.07437407225370407	0.0031013423576951027	0.05255098640918732	-0.048201579600572586	-0.062427740544080734	-0.013960649259388447	-0.06346631795167923	0.0005029636668041348	0.02629833109676838	0.08176832646131516	-0.03393709287047386	-0.027268854901194572	-0.04311716929078102	0.03170786425471306	-0.016389217227697372	-0.04847142472863197	0.056420281529426575	-0.013499008491635323	0.04281581938266754	0.0167671050876379	-0.0021312993485480547	0.0349748320877552	0.02450108528137207	0.008362283930182457	-0.02017511986196041	0.06313904374837875	-0.011812927201390266	-0.04024067521095276	-0.03839140385389328	-0.05912284180521965	-0.07178445905447006	-0.029887866228818893	-0.021613918244838715	-0.025508739054203033	0.0645141676068306	0.0004884476074948907	-0.029194768518209457	-0.059099845588207245	0.0017516089137643576	-0.020339112728834152	-0.053972143679857254	-0.027476567775011063	0.012929074466228485	-0.07724001258611679	0.011601077392697334	-0.0026182993315160275	-0.023872168734669685	-0.028972720727324486	0.01308735366910696	-0.0071032424457371235	-0.0841664969921112	0.046753399074077606	-0.013307158835232258	-0.031226804479956627	0.007844367995858192	-0.04591822251677513	0.028973443433642387	0.10420890897512436	0.046342648565769196	0.052355341613292694	-0.0008105050073936582	0.04507330432534218	0.07800032198429108	-0.01626596413552761	-0.016452578827738762	-0.02891775779426098	-0.04119400307536125	-0.10614434629678726	0.043276309967041016	-0.037126678973436356	-0.04143514111638069	-0.023327210918068886	0.029019787907600403	0.06367850303649902	-0.004344350192695856	-0.01994611695408821	-0.04011981561779976	0.03740987926721573	0.017200084403157234	0.08350811153650284	-0.012148335576057434	-0.023128274828195572	-0.04073226824402809	-0.04279031977057457	-0.041103679686784744	-0.042441584169864655	0.060806892812252045	0.03216392546892166	0.039656978100538254	-0.0011447085998952389	-0.10371128469705582	-0.016165917739272118	0.0028436966240406036	0.07532209903001785	-0.0927468091249466	0.004334548953920603	0.024790987372398376	0.002383151091635227	-0.08522196114063263	-0.0050169373862445354	0.007882339879870415	-0.025493355467915535	-0.05073922872543335	-0.026516709476709366	0.08274897933006287	-0.058928556740283966	0.01065849605947733	-0.012043054215610027	-0.02739272266626358	-0.0248741302639246	0.033348068594932556	0.008713935501873493	-0.007522521074861288	-0.006675899028778076	-0.02297596074640751	-0.016702616587281227	-0.01442356500774622	-0.004510139115154743	0.022111911326646805	0.03378208726644516	0.001141148735769093	0.04091069474816322	-0.027558650821447372	0.0030982745811343193	-0.05790716037154198	0.08026387542486191	-0.02280331403017044	-0.008617016486823559	0.00872220192104578	-0.022927338257431984	0.03413328900933266	0.04944003373384476	0.004611993208527565	-0.029505761340260506	-0.02744082547724247	-0.013645629398524761	-0.0349801629781723	-0.09652619063854218	-0.005608018953353167	0.022816408425569534	-0.07924287021160126	0.0012215657625347376	-0.019244534894824028	-0.023133723065257072	-0.02715039998292923	-0.010077170096337795	0.01997348479926586	0.015352332964539528	0.02843342535197735	-0.013723689131438732	0.03563076630234718	0.004961451981216669	0.05828933045268059	-0.002362509025260806	-0.07696746289730072	0.039413947612047195	0.011787934228777885	-0.053900063037872314	0.10395956039428711	-0.03569591045379639	-0.0238786768168211	-0.029649456962943077	0.031157510355114937	0.02660258300602436	-0.01625188998878002	-0.007896889932453632	-0.02184957079589367	0.04684334993362427	-0.057997431606054306	-0.027204496785998344	0.039302557706832886	0.010560838505625725	-0.10039402544498444	-0.020311161875724792	0.004126439802348614	-0.03528209030628204	0.05405062809586525	-0.0017852116143330932	0.009116699919104576	0.029625901952385902	-0.04554262384772301	-0.01402478851377964	-0.04096433147788048	-0.03267235308885574	0.03975662589073181	-0.016550913453102112	0.0822969451546669	0.02081865631043911	0.030095279216766357	0.0020371584687381983	0.004601731430739164	0.021921509876847267	-0.06914639472961426	-0.007672163657844067	0.007435710169374943	-0.03532779961824417	0.019760901108384132	0.015278075821697712	0.025397764518857002	-0.02409381978213787	-0.07227781414985657	-0.039564188569784164	0.07373800128698349	-0.031470149755477905	0.002946163062006235	0.038598597049713135	-0.03292563557624817	0.012090012431144714	0.017001187428832054	-0.060497548431158066	-0.09101029485464096	-0.007051547989249229	0.0019542390946298838	0.006238865200430155	-0.014972120523452759	0.0238172709941864	-0.019962865859270096	-0.0463840626180172	-0.020573172718286514	0.02595589868724346	-0.07054400444030762	0.04353407025337219	0.010975033976137638	-0.040383677929639816	-0.05048954114317894	-0.013754229061305523	-0.051067739725112915	0.053342416882514954	0.03127666935324669	0.04211805760860443	-0.0012184083461761475	0.01022443175315857	0.021503938362002373	-0.0558951161801815	0.010535093955695629	0.014669408090412617	-0.03372953459620476	-0.00969762820750475	0.06404370069503784	-0.04147619381546974	0.05829896777868271	0.020122544839978218	-0.04736544191837311	0.03495783731341362	0.08097676932811737	-0.03485378995537758	0.05855799838900566	-0.00442875362932682	-0.03785090520977974	0.0823606550693512	-0.04211683198809624	0.08299509435892105	-0.024323003366589546	0.07328858971595764	-0.029574520885944366	-0.029302870854735374	0.06648021191358566	0.07576710730791092	0.041580475866794586	0.030617695301771164	-0.06804560124874115
+0.051195234060287476	-9.534317359793931e-05	0.0234138872474432	0.004832443781197071	-0.0708998367190361	-0.03862697631120682	-0.017044968903064728	-0.005872041452676058	-0.009168781340122223	-0.02955748699605465	-0.03268854692578316	-0.01593208871781826	-0.024570001289248466	-0.003315662033855915	0.042258623987436295	-0.05839764326810837	-0.07017126679420471	0.0034138585906475782	-0.026665931567549706	0.001620912691578269	0.06203632801771164	0.016542010009288788	-0.06289529800415039	0.052018217742443085	0.019686734303832054	0.054159145802259445	-0.046440888196229935	0.02350737527012825	-0.007122907321900129	0.02929246798157692	0.058756329119205475	-0.014325655065476894	0.028720665723085403	-0.06725676357746124	-0.03860803693532944	0.03387684375047684	-0.027555342763662338	0.09944048523902893	0.06487780809402466	-0.0361044742166996	-0.04516657069325447	-0.011721174232661724	-0.05531986430287361	-0.04090264439582825	0.017616111785173416	-0.025127701461315155	-0.022072141990065575	-0.04824939742684364	0.026511386036872864	0.03326800838112831	0.09694556146860123	-0.0653836727142334	-0.053980328142642975	0.0037464580964297056	0.008433980867266655	0.00900701992213726	0.022639676928520203	-0.05463616922497749	-0.07326433062553406	0.032627806067466736	-0.06937643140554428	0.023680897429585457	-0.026319729164242744	0.022422965615987778	-0.018838489428162575	-0.03775862976908684	0.01098195556551218	0.07201938331127167	0.0011993340449407697	-0.02179337479174137	0.038719892501831055	0.10169047862291336	-0.076702781021595	-0.0438234843313694	0.010590833611786366	-0.051529496908187866	-0.0018653504084795713	0.05129116773605347	0.015889210626482964	0.0005561994621530175	0.020013045519590378	-0.040254514664411545	0.12193021178245544	-0.05702782794833183	-0.02324213646352291	-0.043752286583185196	-0.05394250527024269	-0.06712480634450912	0.05942070484161377	0.053827494382858276	0.041850924491882324	0.05727221071720123	-0.03692852705717087	0.06166806071996689	0.02212619222700596	0.008900510147213936	0.02583250403404236	-0.08976296335458755	-0.0303757693618536	-0.011549768969416618	-0.01750335283577442	0.0008331186254508793	0.055926188826560974	0.017270484939217567	0.0719105675816536	0.0349690318107605	-0.048383764922618866	0.025313133373856544	0.008334832265973091	0.05340955778956413	0.030951108783483505	0.06369910389184952	0.1147710457444191	0.07031209021806717	0.045853517949581146	-0.00798750575631857	-5.3775558626512066e-05	-0.04067061468958855	-0.013664561323821545	0.014845585450530052	0.01815539225935936	0.0459209606051445	-0.010747639462351799	-0.0587150938808918	-0.014426223933696747	0.04994751885533333	-0.08682248741388321	-0.05074909329414368	0.037330374121665955	-0.026240434497594833	0.10726121068000793	0.0885898545384407	-0.05277574807405472	0.022931499406695366	0.04563411697745323	0.005540108773857355	0.02129771187901497	0.01808067224919796	-0.02889220044016838	-0.035637423396110535	-0.03321586549282074	0.007526238914579153	-0.02398042008280754	-0.056797102093696594	-0.01656181365251541	0.01931311935186386	0.040504466742277145	-0.04710947349667549	-0.07386000454425812	-0.02740957774221897	-0.09249026328325272	-0.05500316619873047	0.000778819783590734	-0.08045051246881485	0.03622492402791977	0.05136464908719063	0.008997485972940922	0.03949020057916641	-0.021953146904706955	0.02026316709816456	-0.045532677322626114	-0.06452231854200363	-0.014212356880307198	-0.09453463554382324	-0.031824346631765366	-0.025486212223768234	0.040140822529792786	-0.0032357536256313324	-0.03298494219779968	-0.023938192054629326	-0.05597040429711342	-0.013553436845541	0.0030586221255362034	0.07057879120111465	0.06511490046977997	0.013335762545466423	0.012698030099272728	-0.02682914212346077	0.01116101909428835	-0.03207593783736229	0.04324422776699066	-0.03906799852848053	0.015901487320661545	-0.045498501509428024	-0.019024252891540527	-0.02664175257086754	0.033214420080184937	-0.010127703659236431	0.034601837396621704	0.038752444088459015	-0.0359576940536499	-0.06023339182138443	0.03382721170783043	0.07379461079835892	-0.026567718014121056	0.05497241020202637	-0.02776368521153927	0.07776373624801636	-0.004308743868023157	-0.04647436738014221	-0.0402127169072628	0.012522894889116287	0.007621871307492256	0.07172097265720367	0.01821896620094776	-0.050829172134399414	0.08201397210359573	0.04445848613977432	0.041478149592876434	-0.0688529908657074	0.012179543264210224	0.007102507632225752	0.016712015494704247	0.024579519405961037	0.06301256269216537	-0.08284907042980194	-0.019735466688871384	0.017628662288188934	0.020948922261595726	-0.03929391875863075	-0.014804989099502563	0.014501763507723808	-0.049835510551929474	0.04517409950494766	-0.060895368456840515	-0.07729607075452805	-0.01813853159546852	0.034953903406858444	-0.017559150233864784	-0.0504702664911747	-0.0006590809789486229	0.027284039184451103	-0.03968707472085953	-0.0084232734516263	0.014307260513305664	-0.017753954976797104	-0.12220577150583267	0.08431871980428696	-0.02062164805829525	-0.0381009466946125	-0.08727042376995087	0.0272818673402071	0.007990709505975246	0.04789229854941368	0.023752786219120026	0.029409201815724373	0.09835182130336761	0.021841268986463547	-0.02759603038430214	0.02433602139353752	-0.045076657086610794	-0.03924990072846413	0.013520878739655018	-0.009382659569382668	-0.05363063886761665	-0.05298927426338196	0.0013259166153147817	-0.03308068588376045	-0.026569172739982605	-0.016910500824451447	0.03730609640479088	-0.005926340818405151	-0.027417726814746857	-0.003756917780265212	0.06664139777421951	0.007412477396428585	-0.03726997226476669	0.04712170735001564	-0.01588299125432968	0.05407178774476051	-0.026905275881290436	-0.04615022987127304	0.051834940910339355	-0.004157882183790207	0.029876304790377617	0.008390158414840698	0.08792967349290848	-0.026355737820267677	-0.06839371472597122	-0.06338972598314285	-0.09159255772829056	-0.017540255561470985	-0.023946233093738556	-0.004368322901427746	-0.013860443606972694	0.06712783873081207	0.009764645248651505	-0.03857539966702461	-0.022639933973550797	-0.0162303876131773	-0.030327728018164635	-0.07331746816635132	-0.04457459971308708	0.00045905981096439064	-0.06353721767663956	-0.020482953637838364	0.052766211330890656	-0.05387694016098976	-0.060418661683797836	-0.006704403553158045	-0.05092398449778557	-0.05504542961716652	0.01625748910009861	-0.027625443413853645	-0.03067687153816223	0.0191348809748888	-0.09283257275819778	0.051501598209142685	0.09377386420965195	0.041865382343530655	0.04921487346291542	0.043356187641620636	0.056891847401857376	0.12453367561101913	0.002953391522169113	-0.04149581119418144	-0.04847170040011406	-0.03751835972070694	-0.09106296300888062	0.07505407184362411	-0.02821611426770687	0.005441498942673206	-0.022533798590302467	0.029634051024913788	0.0633033961057663	-0.016176771372556686	-0.015932170674204826	-0.013712935149669647	-0.008088942617177963	-0.039181243628263474	0.0077444748021662235	-0.0140024209395051	-0.003925458062440157	0.0028436509892344475	-0.0308974776417017	-0.08341453969478607	-0.04149936884641647	0.022571224719285965	-0.018350176513195038	0.05583605542778969	-0.01866999827325344	-0.04383888468146324	-0.0045426529832184315	-0.0028292012866586447	0.09753666073083878	-0.04099901020526886	0.04402348771691322	0.008508128114044666	0.005528576206415892	-0.09650657325983047	-0.0056289746426045895	0.05367988348007202	-0.04011425003409386	-0.06487017124891281	-0.0075265103951096535	0.05699780583381653	-0.04664429649710655	-0.051018036901950836	0.02181215025484562	-0.04810727387666702	0.013064432889223099	0.009268504567444324	0.03257003054022789	0.032570723444223404	0.013326223939657211	-0.040259264409542084	0.005949509330093861	-0.02360917255282402	0.02260122075676918	-0.025183793157339096	-0.0006713143666274846	-0.07139046490192413	0.05712234973907471	-0.0344502292573452	-0.016507510095834732	-0.04342154785990715	0.0618862621486187	0.009733638726174831	-0.00921613909304142	-0.007654895540326834	0.035349417477846146	-0.03340251371264458	0.03678112477064133	0.03275993838906288	-0.051858723163604736	-0.04385775327682495	-0.03591064736247063	-0.016757532954216003	-0.0842781588435173	-0.01942340098321438	-0.006820949725806713	-0.06116271764039993	0.016642628237605095	-0.011930580250918865	0.0076173292472958565	0.01859075203537941	-0.035998277366161346	0.03216414526104927	0.023852260783314705	0.028456613421440125	-0.045369308441877365	0.03270591422915459	0.013955480419099331	0.025371624156832695	0.021710291504859924	-0.03894650936126709	0.008090046234428883	0.026164056733250618	-0.005800994578748941	0.035935524851083755	-0.022434387356042862	0.01486038789153099	-0.004069593269377947	-0.033231113106012344	0.0351618155837059	-0.007198838982731104	0.018888305872678757	-0.029232138767838478	0.05656641721725464	-0.10910862684249878	-0.049903690814971924	0.025679226964712143	-0.05142837390303612	-0.09392666071653366	-0.015842201188206673	-0.01753951609134674	-0.041386935859918594	0.007502484600991011	-0.01122655626386404	0.03535918891429901	0.04181242361664772	-0.0333063043653965	-0.027584290131926537	-0.013190530240535736	0.004194164182990789	0.049136653542518616	0.009328995831310749	0.05487691983580589	0.0031715130899101496	0.004513983149081469	0.025898803025484085	-0.002104097045958042	-0.016100086271762848	-0.10104082524776459	-0.028985584154725075	-0.0065530454739928246	-0.0033713211305439472	0.03667965158820152	0.021582886576652527	0.026177195832133293	-0.04906287044286728	-0.04058214649558067	-0.05863606557250023	0.062418922781944275	-0.00876438245177269	0.034469760954380035	0.03210674598813057	-0.043492384254932404	-0.014259301126003265	-0.011758445762097836	-0.025309721007943153	-0.08942017704248428	-0.019572250545024872	-0.01577743887901306	0.04278695583343506	0.009708252735435963	-0.007797537837177515	-0.02047439105808735	-0.06157657876610756	0.002265925519168377	0.016575343906879425	-0.04919847846031189	0.003774218028411269	0.013645371422171593	-0.0618203841149807	-0.09098653495311737	0.02073926292359829	-0.04605801776051521	0.00429279962554574	0.0102170929312706	0.05003614351153374	-0.01083637960255146	-0.0026845233514904976	0.042954131960868835	-0.0420210026204586	0.018695367500185966	0.014350298792123795	-0.05308759957551956	-0.007860776036977768	0.08385670930147171	-0.02675769291818142	0.026898089796304703	0.061428628861904144	-0.02458873763680458	0.01338482927531004	0.039664097130298615	-0.04830317944288254	0.0019073071889579296	-0.03749707713723183	-0.03188638761639595	0.06507157534360886	-0.08131679892539978	0.08603401482105255	-0.08100372552871704	0.12420496344566345	0.0024533187970519066	-0.028710482642054558	0.09985293447971344	0.057758983224630356	-0.009286819957196712	0.049430519342422485	-0.06576363742351532
+0.0103229358792305	-0.0637633427977562	-0.03039880096912384	0.02175459824502468	-0.07347515225410461	-0.034708425402641296	0.01576472632586956	0.04312460869550705	-0.017934361472725868	-0.03657704219222069	-0.014870055951178074	0.0176489669829607	0.017634274438023567	0.009602160193026066	0.07121029496192932	-0.04792235791683197	-0.02284579910337925	0.02757120132446289	-0.004898485727608204	-0.029539668932557106	0.02874797023832798	0.02687077596783638	-0.039119280874729156	0.015811851248145103	0.002997227245941758	-0.0006051271921023726	-0.047502707690000534	0.02115226536989212	0.03603813797235489	0.05865985527634621	0.05427106097340584	-0.0501735582947731	0.03051079995930195	-0.05127715319395065	-0.05150153115391731	0.013940030708909035	-0.0739593431353569	0.11330533772706985	0.031422100961208344	0.002130302134901285	-0.040760237723588943	-0.022668160498142242	-0.043069273233413696	-0.024189220741391182	0.033751651644706726	0.0035559674724936485	0.03151950240135193	-0.03385264426469803	0.01918148621916771	0.026506133377552032	0.06796160340309143	0.020073706284165382	-0.0021407909225672483	-0.02663520909845829	-0.011481260880827904	-0.008071173913776875	0.039773449301719666	-0.03694745525717735	-0.08442742377519608	0.057471856474876404	-0.09642840176820755	0.0048064920119941235	-0.0018173236167058349	0.0014687004731968045	-0.00865689106285572	-0.03066319040954113	-0.00788026861846447	0.049870070070028305	-0.03488535434007645	-0.05544760823249817	0.023761607706546783	0.11002058535814285	-0.0672854334115982	-0.10156586766242981	0.03468570485711098	-0.03249070793390274	0.0414147712290287	-0.0013378000585362315	-0.018970133736729622	-0.016177982091903687	-0.044741105288267136	-0.05106624588370323	0.12439866364002228	-0.06011905148625374	-0.04377822205424309	-0.05783670395612717	-0.04056605324149132	-0.05290253460407257	0.05426781624555588	0.018372338265180588	0.04866056889295578	0.025115156546235085	-0.06248647719621658	0.032157186418771744	0.02887054905295372	-0.011552291922271252	0.007753422483801842	-0.10063369572162628	-0.05856391787528992	0.0034580272622406483	-0.001042168471030891	-0.034685492515563965	0.10902155935764313	0.023115362972021103	0.04020879417657852	0.04681774601340294	-0.05785062164068222	0.041014663875103	0.0272737517952919	0.06636755168437958	-0.013991900719702244	0.09196475148200989	0.0606609508395195	0.03500036522746086	0.039158597588539124	-0.014896860346198082	-0.02657170221209526	-0.004470108076930046	-0.00793484691530466	0.01818177103996277	0.022736039012670517	0.03255414590239525	0.013327759690582752	-0.08381810784339905	-0.03494703024625778	0.06162096932530403	-0.11933478713035583	-0.019354404881596565	0.06654580682516098	-0.05326883867383003	0.11511876434087753	0.04950821399688721	-0.017642714083194733	0.05538579076528549	0.0373893566429615	0.022405065596103668	-0.011396643705666065	0.0482570081949234	0.012030643410980701	-0.053250402212142944	-0.04974714666604996	0.06263132393360138	0.00974731333553791	-0.05177074670791626	0.02814570814371109	0.06634197384119034	0.039282526820898056	-0.05573685094714165	-0.06597205251455307	0.014092940837144852	-0.05873291194438934	-0.05819317325949669	0.0048492541536688805	-0.05596001073718071	0.02064453437924385	0.0030357511714100838	0.012562090530991554	0.01573977991938591	0.011702090501785278	0.010894397273659706	-0.06636234372854233	-0.0665593147277832	-0.041081126779317856	-0.043069954961538315	0.0028155033942312002	0.019840480759739876	0.03162943944334984	-0.013303183019161224	-0.058369431644678116	-0.03293200209736824	-0.007558773271739483	0.010983902961015701	0.055909715592861176	0.05131247639656067	0.02612430416047573	0.00036857055965811014	0.021126117557287216	-0.061292920261621475	0.013730693608522415	-0.033847205340862274	0.01894400082528591	-0.03791190683841705	0.031228741630911827	-0.04242986440658569	-0.022778773680329323	-0.023137416690587997	0.05646743252873421	-0.04380663111805916	-0.012810304760932922	-0.0467359684407711	-0.019611630588769913	-0.07496535778045654	0.04614639654755592	0.08273416012525558	-0.039774566888809204	0.02837921679019928	-0.017488976940512657	0.05767448991537094	-0.053655918687582016	-0.029184352606534958	-0.03942707180976868	0.03305942565202713	0.021289266645908356	-0.004400522913783789	-0.031730905175209045	-0.05873565003275871	0.07613421976566315	-0.011057324707508087	0.024070732295513153	-0.07808437198400497	0.017372163012623787	0.030411547049880028	-0.027482660487294197	0.03321251645684242	0.05733206123113632	-0.06738356500864029	-0.0066120498813688755	-0.005662681069225073	0.06937959790229797	-0.03636074811220169	0.006613416597247124	-0.010121003724634647	-0.07350454479455948	-0.01701164059340954	-0.03820059448480606	-0.09163209050893784	-0.00022654529311694205	0.008668825030326843	-0.022025225684046745	-0.028881553560495377	-0.02910800464451313	0.03599175810813904	0.011849820613861084	-0.0019212758634239435	-0.015088208951056004	0.008835679851472378	-0.05469244718551636	0.07123579829931259	-0.02057790569961071	-0.0608670637011528	-0.09042411297559738	-0.03728248551487923	0.032974742352962494	-0.0059643639251589775	0.03335879370570183	0.041964732110500336	0.056526411324739456	0.019006915390491486	-0.029630789533257484	0.011308066546916962	-0.00941522978246212	-0.014103543013334274	-0.0158144049346447	0.0245676152408123	-0.068457692861557	-0.01999380998313427	0.04512004554271698	-0.07488592714071274	0.00770080229267478	-0.02945523150265217	0.02513837441802025	-0.04031367227435112	-0.03007698617875576	0.0033309562131762505	0.028046773746609688	0.0008917562081478536	-0.03727949783205986	0.0017277506412938237	-0.042056191712617874	0.0536653995513916	-0.023038920015096664	-0.004756756126880646	0.031889013946056366	-0.0018771460745483637	0.0047308956272900105	-0.05207386612892151	0.040161605924367905	-0.029698964208364487	-0.0894346833229065	-0.029131103307008743	-0.03700758144259453	-0.06321515142917633	-0.011129429563879967	-0.03505466878414154	-0.035626184195280075	0.08272350579500198	0.012223144061863422	-0.04840238019824028	-0.05299578979611397	-0.05221133679151535	0.003933190368115902	-0.06249217316508293	-0.01560031808912754	-0.01066172868013382	-0.07471232116222382	-0.017050396651029587	0.036323826760053635	-0.06679089367389679	-0.00841693114489317	0.019669098779559135	0.009716515429317951	-0.0580572709441185	0.052791446447372437	-0.056641098111867905	-0.015214876271784306	-0.017422962933778763	-0.05783599615097046	0.07442969083786011	0.054319217801094055	0.06757573783397675	0.03451533615589142	0.032685860991477966	0.03157952055335045	0.10696973651647568	0.0014634744729846716	-0.011702430434525013	-0.01609853282570839	-0.060525357723236084	-0.06177370622754097	0.026548122987151146	-0.025093192234635353	-0.00857187993824482	-0.011472772806882858	-0.00687024462968111	0.07847382128238678	-0.01862543635070324	-0.003916142508387566	-0.013623890466988087	0.04525485262274742	0.005956234410405159	0.01787152700126171	0.01563999056816101	0.0020108905155211687	0.0012844945304095745	-0.0574462004005909	-0.055803146213293076	-0.038132041692733765	0.04859865829348564	-0.041555918753147125	0.07959631085395813	-0.020421328023076057	-0.05438033118844032	-0.007066854741424322	0.0567435547709465	0.11101996153593063	-0.04102773219347	0.022681577131152153	0.013255671598017216	0.01650855876505375	-0.09192757308483124	0.021667513996362686	0.009676886722445488	-0.03651942312717438	-0.05702444911003113	0.024725910276174545	0.0550554096698761	-0.03514575958251953	-0.0166157279163599	-0.006350143812596798	-0.04367382079362869	-0.009354153648018837	0.009712493978440762	-0.02359897643327713	0.06222490593791008	-0.002408728003501892	-0.01129270251840353	0.0019638643134385347	0.013756804168224335	0.018956691026687622	-0.014702889136970043	0.005175394006073475	-0.03629340976476669	0.05841308459639549	-0.050794634968042374	-0.036497559398412704	-0.06173533573746681	0.06055820733308792	-0.014451054856181145	-0.015670310705900192	-0.07320927083492279	-0.016093900427222252	-0.020969128236174583	0.011386360973119736	-0.009952913969755173	-0.05961419641971588	3.5147444577887654e-05	-0.031339749693870544	-0.0688750147819519	-0.09076032787561417	-0.07145819813013077	0.03020183928310871	-0.03970472142100334	-0.0028577474877238274	-0.04073617234826088	0.04268333688378334	0.0011444471310824156	-0.024994822219014168	0.027117561548948288	0.06101367995142937	0.06602832674980164	-0.01720322109758854	0.07949027419090271	0.03670782968401909	0.06770011782646179	-0.01744261011481285	-0.03949505463242531	0.041431255638599396	0.01721338927745819	-0.06299155950546265	0.012309334240853786	-0.053958311676979065	0.024997742846608162	-0.01801355741918087	-0.014889584854245186	0.034992050379514694	-0.021563485264778137	0.01510248426347971	-0.023621154949069023	0.09562716633081436	-0.09858205914497375	-0.026950106024742126	0.0358869843184948	-0.02668054960668087	-0.05253983661532402	-0.027462095022201538	-0.04176853224635124	-0.027536503970623016	0.06265504658222198	-0.02765979990363121	0.02889464609324932	0.026660019531846046	-0.01210172288119793	-0.027027811855077744	0.009693286381661892	-0.02992159128189087	0.015849819406867027	0.029204368591308594	0.04064657911658287	0.004950310569256544	0.009152555838227272	0.0057593476958572865	-0.03679328039288521	0.0297609381377697	-0.1049894243478775	0.0071860128082334995	-0.033868253231048584	0.017747169360518456	-0.019827432930469513	-0.026986485347151756	0.05387385934591293	-0.06669043004512787	0.011535889469087124	-0.053652647882699966	0.07942979037761688	0.011124550364911556	0.0024243448860943317	0.02750215493142605	-0.026721693575382233	-0.0440995953977108	0.02307329699397087	-0.05492116138339043	-0.08478417992591858	-0.016250334680080414	-0.018607888370752335	0.053675439208745956	0.0014753816649317741	0.011433624662458897	-0.0027661104686558247	-0.06436638534069061	-0.006382015999406576	0.009897996671497822	-0.045845020562410355	0.06033489108085632	0.050070688128471375	-0.05823158100247383	-0.05580953508615494	-0.0006712783360853791	-0.042892612516880035	0.04580402001738548	0.013858476653695107	0.027218136936426163	-0.04981018230319023	0.008199325762689114	-0.01288444921374321	-0.03052394464612007	0.06419142335653305	-0.03185421973466873	-0.07219960540533066	-0.03699161112308502	0.09771829098463058	-0.054416362196207047	0.030392654240131378	0.038550667464733124	-0.05678873881697655	0.02993156388401985	0.029801001772284508	0.003048718674108386	0.02547028288245201	-0.016999317333102226	0.012304422445595264	0.045201051980257034	-0.09229506552219391	0.09065061062574387	-0.06253571063280106	0.08495880663394928	0.037239111959934235	-0.0027237292379140854	0.10364769399166107	0.05022367462515831	0.02212373912334442	-0.019789455458521843	-0.09854093939065933
+-0.02155582793056965	-0.10892313718795776	-0.09719283133745193	0.014348096214234829	-0.048857610672712326	-0.02441515401005745	-0.03475668281316757	0.08466614037752151	0.00722124520689249	-0.052742041647434235	-0.07324305176734924	0.016029441729187965	0.021425293758511543	-0.01111945603042841	0.06297433376312256	0.0489133782684803	-0.0032460426446050406	0.015327798202633858	-0.00039688509423285723	0.0032082130201160908	-0.008716040290892124	0.021194608882069588	-0.032665666192770004	0.069074347615242	0.05609358847141266	-0.027304455637931824	-0.0665009543299675	0.05604520067572594	0.017657095566391945	0.12083697319030762	0.013795587234199047	-0.05964650958776474	0.008993356488645077	-0.06875669956207275	-0.00938168540596962	0.019195031374692917	-0.04490601643919945	0.04910207539796829	0.037221167236566544	-0.01610076054930687	0.0008115165983326733	-0.003790101734921336	-0.02130897529423237	-0.1027950793504715	0.042344748973846436	0.022720081731677055	0.007843036204576492	-0.06218283623456955	0.01475694589316845	0.08423811942338943	0.06341596692800522	-0.004937989171594381	0.030641645193099976	-0.0011389459250494838	-0.07410414516925812	-0.004412524867802858	-0.012093564495444298	-0.002518853172659874	-0.0033146687783300877	0.03141361102461815	-0.05158615857362747	-0.01427252683788538	-0.013950338587164879	0.06330739706754684	-0.07430938631296158	0.025623716413974762	0.039856333285570145	0.02180839702486992	0.013256717473268509	-0.024462487548589706	-0.0509517565369606	0.010240348987281322	-0.025168294087052345	-0.030095534399151802	-0.03685114532709122	-0.0013285550521686673	0.007068169768899679	-0.033987898379564285	0.040292881429195404	0.018794838339090347	0.03578004240989685	-0.03401266410946846	0.09547001868486404	-0.04433650150895119	0.019058020785450935	-0.05807069316506386	-0.03878812864422798	-0.003787057939916849	-0.00353874359279871	-0.018048705533146858	0.06553617119789124	0.022624246776103973	0.06321342289447784	-0.008264313451945782	0.0010183625854551792	0.031256817281246185	0.03775971755385399	-0.10490166395902634	0.006462215445935726	0.022204015403985977	-0.04666408523917198	0.005346123594790697	0.09093966335058212	0.04625293239951134	0.03501535952091217	0.00219981512054801	-0.07305645942687988	0.01411706767976284	0.07146237790584564	0.028169376775622368	-0.027343714609742165	0.06866689026355743	0.03370589017868042	-0.023910805583000183	0.05134066939353943	-0.023992231115698814	-0.020342521369457245	-0.01265809591859579	-0.03454377129673958	0.008428347297012806	0.016476135700941086	0.030752090737223625	-0.0025834059342741966	-0.08424554020166397	0.09207265824079514	0.07612039148807526	-0.07126544415950775	-0.004550733137875795	-0.05154997110366821	-0.05236608162522316	0.054429955780506134	0.017453480511903763	-0.02546059712767601	-0.0026881778612732887	0.020274067297577858	0.014857595786452293	0.009291755966842175	0.05633866414427757	0.07534393668174744	-0.021222775802016258	-0.053184956312179565	0.0021727196872234344	0.006540771573781967	-0.001649054465815425	0.07415034621953964	0.015186944045126438	-0.03100530244410038	-0.003589148400351405	-0.05536485090851784	-0.014464001171290874	-0.06465906649827957	-0.0337640717625618	0.0707797259092331	-0.022961098700761795	0.0038935833144932985	-0.04054209962487221	0.04717179387807846	0.02062837779521942	0.003142050700262189	0.03311821445822716	-0.043114908039569855	-0.06769496202468872	0.03099590167403221	-0.05569155514240265	0.035108812153339386	0.02185928449034691	-0.0066139791160821915	-0.041869550943374634	-0.007454914506524801	-0.0417710617184639	-0.024493861943483353	0.024600619450211525	0.08140585571527481	0.05452389270067215	0.047220803797245026	-0.0036413881462067366	0.048194196075201035	-0.048243340104818344	-0.030177922919392586	-0.03761766850948334	-0.06504001468420029	-0.02527366578578949	-0.023679425939917564	-0.032115545123815536	-0.03126823529601097	-0.01461570244282484	0.011253985576331615	-0.029488444328308105	-0.021332083269953728	-0.05010904371738434	-0.00244692200794816	-0.0662660077214241	0.06998191028833389	0.02313331514596939	-0.015221355482935905	0.04909912496805191	0.06498033553361893	0.05854189023375511	-0.057862360030412674	-0.028950724750757217	-0.02802029252052307	0.06290784478187561	0.048076849430799484	-0.034764185547828674	-0.00015323952538892627	-0.009508865885436535	0.06452420353889465	0.02899913303554058	3.769049726543017e-05	-0.0025315294042229652	0.008298574015498161	0.09501780569553375	-0.0376490019261837	0.014930234290659428	0.08635783195495605	0.016027553007006645	-0.030415698885917664	-0.0009076563292182982	0.021124981343746185	-0.09717151522636414	-0.012795777060091496	0.03288552537560463	-0.08458848297595978	-0.02287822961807251	-0.017782000824809074	-0.06333263963460922	0.010631844401359558	-0.01212973240762949	-0.012295002117753029	0.009913334622979164	0.024847760796546936	-0.023536302149295807	0.04080072045326233	0.011621641926467419	-0.011834368109703064	0.027933651581406593	-0.005296215880662203	0.008969375863671303	-0.07223989814519882	-0.05740363150835037	-0.045035991817712784	0.024596739560365677	0.026088014245033264	-0.024373330175876617	0.030232837423682213	0.053568825125694275	0.042762529104948044	0.05946983024477959	-0.044495683163404465	0.0118452999740839	-0.03572811186313629	-0.009925078600645065	-0.027727996930480003	0.01681063324213028	0.04359786584973335	-0.048295650631189346	0.043312057852745056	-0.1297065168619156	-0.023788079619407654	0.006291656289249659	0.011771639809012413	-0.0760011151432991	0.0004873104626312852	-0.05089684575796127	0.04710303246974945	-0.023337623104453087	-0.06038377806544304	0.05304955318570137	-0.06491178274154663	0.003693914972245693	-0.10556188225746155	0.0027291749138385057	0.027866508811712265	0.014957443810999393	-0.02966313622891903	-0.06137428805232048	0.05748255178332329	-0.027188459411263466	-0.033936988562345505	-0.04009391739964485	-0.062818743288517	-0.043077509850263596	0.04193900153040886	0.012537362985312939	0.013132347725331783	0.02730097621679306	0.0556597076356411	0.013274682685732841	-0.09002794325351715	-0.08013039082288742	-0.02266295626759529	0.011674825102090836	0.022506171837449074	-0.004521778319031	-0.044268183410167694	0.013834151439368725	0.03485081344842911	-0.019836286082863808	-0.016849858686327934	0.07153098285198212	0.06368836760520935	-0.05641932785511017	0.0637214407324791	-0.01614386774599552	-0.014040306210517883	0.05198423191905022	-0.03937812149524689	-0.0249931700527668	0.04490983486175537	0.0042030117474496365	-0.009200354106724262	0.03802075982093811	-0.007158481515944004	0.011647703126072884	-0.05010184645652771	0.006190430372953415	-0.08295721560716629	-0.0009419788839295506	-0.03828173875808716	-0.001428609131835401	-0.0017630046932026744	-0.029539890587329865	-0.0010885423980653286	-0.025413691997528076	-0.0014862417010590434	0.01923297345638275	-0.05240616947412491	-0.07181377708911896	-0.021119127050042152	0.006642957683652639	0.052352309226989746	-0.013012506067752838	0.01754860207438469	-0.046809036284685135	-0.033962421119213104	-0.025105632841587067	-0.031183164566755295	0.08278077095746994	-0.00635130750015378	0.03098207525908947	0.04008616879582405	-0.04610002413392067	4.510653161560185e-05	0.03077412024140358	0.08553940802812576	-0.08950937539339066	0.05360749736428261	0.027221279218792915	0.016633857041597366	-0.03865880146622658	0.016866231337189674	-0.028474101796746254	-0.06350003182888031	-0.058374036103487015	0.017144467681646347	0.03178602457046509	-0.016054341569542885	-0.06156003102660179	-0.03491508960723877	-0.04150966554880142	0.02881639264523983	0.04084780067205429	-0.007791560608893633	0.056242115795612335	0.03059709072113037	-0.03436943143606186	-0.002034480683505535	-0.00736232241615653	-0.004484022501856089	-0.017029069364070892	0.026745349168777466	0.033610764890909195	0.016063986346125603	-0.03435882553458214	-0.01279979757964611	-0.025814877822995186	-0.01537738461047411	-0.04537615180015564	0.022002460435032845	-0.02113085426390171	-0.028868377208709717	0.021869387477636337	0.06427162885665894	0.013210914097726345	-0.013824678026139736	-0.05717260017991066	0.0007731958758085966	-0.04502294585108757	-0.07491424679756165	-0.03866272792220116	0.07184335589408875	-0.00402786023914814	0.02441183291375637	-0.06028837710618973	0.024702349677681923	-0.0038169941399246454	0.034328509122133255	0.008918995037674904	0.06342259794473648	0.0474579744040966	0.005685173440724611	0.02718774974346161	-0.014176164753735065	0.018058495596051216	-0.04370807856321335	-0.05803332477807999	0.049720823764801025	0.008189140819013119	-0.07814624160528183	-0.05202699825167656	-0.027916444465517998	-0.03198549523949623	0.004391237627714872	-0.07385403662919998	-0.001996678300201893	0.004870522767305374	0.047281671315431595	-0.006065855268388987	0.06609756499528885	-0.06203686445951462	0.003547580912709236	-0.03448382392525673	0.0006896647391840816	-0.00197295262478292	-0.025951268151402473	-0.030498672276735306	0.017167074605822563	0.05954025685787201	-0.05716937407851219	-0.03410228714346886	0.030268818140029907	-0.015476260334253311	0.02763727307319641	0.01966794580221176	0.05344945937395096	-0.016250625252723694	0.10277074575424194	0.011479981243610382	-0.04982873424887657	-0.01677669957280159	-0.017977168783545494	-0.08636806160211563	0.024227524176239967	0.033794060349464417	-0.02119399420917034	0.05182693153619766	0.02856343239545822	-0.027093982324004173	0.07377517968416214	0.052931882441043854	-0.04885201156139374	-0.03302033618092537	-0.010402029380202293	0.04564729332923889	0.05668816342949867	0.022356703877449036	0.04588320851325989	-0.08776848763227463	-0.08820599317550659	0.02365753799676895	-0.08296096324920654	-0.10715295374393463	-0.07559701055288315	-0.015127617865800858	0.07380062341690063	-0.0008931433549150825	0.008991575799882412	-0.06404092907905579	-0.05353829264640808	0.03694995865225792	-0.016245130449533463	0.011620540171861649	-0.03985568508505821	0.07954144477844238	-0.0037127812393009663	0.03358282148838043	-0.05813930928707123	-0.07551216334104538	0.07635170966386795	0.050912316888570786	-0.017882639542222023	-0.1013258695602417	0.010787324979901314	-0.033580292016267776	-0.003051314502954483	0.0294945165514946	-0.04982268065214157	-0.06952881067991257	0.01892007514834404	0.07403737306594849	-0.019472237676382065	0.05036948621273041	0.03485477343201637	-0.00452227471396327	0.06533138453960419	0.05589669942855835	0.05115264281630516	-0.03429132327437401	-0.03359058126807213	0.011231614276766777	0.06088711693882942	-0.04440579190850258	0.005688958335667849	-0.046108413487672806	0.1088845282793045	0.050913743674755096	-0.006096613127738237	0.045005857944488525	0.061364106833934784	0.08973974734544754	0.011576392687857151	-0.10804413259029388
+0.039049938321113586	-0.010520060546696186	0.00677670631557703	0.03055630624294281	-0.06992195546627045	-0.026375742629170418	-0.01840498298406601	0.028887037187814713	0.016552584245800972	-0.017562756314873695	-0.022443780675530434	0.055720824748277664	0.005864748265594244	0.03003019094467163	0.02471262402832508	-0.03025229275226593	-0.05736527591943741	0.0008422867977060378	-0.04495399072766304	-0.04974254220724106	0.04777906462550163	0.018311232328414917	-0.06672804802656174	0.05225307494401932	-0.006431052461266518	0.033924948424100876	-0.059219762682914734	0.010811839252710342	-0.018567631021142006	-0.0031116497702896595	0.04272130876779556	-0.0451338030397892	0.07391856610774994	-0.05669547989964485	-0.05805448442697525	0.055371612310409546	-0.03427024558186531	0.10135187208652496	0.034459229558706284	-0.01071563083678484	-0.0446099229156971	-0.0017797834007069468	-0.05595951899886131	-0.03476925566792488	0.01343222614377737	0.028960175812244415	0.015689317137002945	-0.016386542469263077	0.028898343443870544	0.00823522824794054	0.06295561790466309	0.0219741091132164	-0.023042067885398865	-0.02677389420568943	-0.009940915741026402	0.019329532980918884	0.06587298959493637	-0.07191285490989685	-0.09083620458841324	0.013635657727718353	-0.08747537434101105	0.02246306650340557	-0.021362701430916786	-0.011512430384755135	-0.027845213189721107	-0.012853067368268967	0.003675664309412241	0.0849875882267952	0.03439163789153099	-0.027703773230314255	0.06552057713270187	0.08451686799526215	-0.06599631905555725	-0.03155333548784256	0.045795418322086334	-0.041436873376369476	-0.03894871100783348	0.06258902698755264	-0.04459436982870102	0.013247648254036903	-0.022805584594607353	-0.04324159026145935	0.11301344633102417	-0.03689365088939667	-0.07666156440973282	-0.04342745989561081	-0.004103521816432476	-0.055209867656230927	0.021786170080304146	0.020059261471033096	-0.0023813804145902395	0.07324997335672379	-0.07004997134208679	0.05647371709346771	0.03337237238883972	0.0271354578435421	0.012395891360938549	-0.11267728358507156	-0.06176559627056122	-0.005033453460782766	-0.0207610372453928	0.004200451076030731	0.05885125696659088	-0.00879837479442358	0.06019824370741844	0.023641496896743774	-0.042827676981687546	0.007135435938835144	-0.0034178488422185183	0.029124611988663673	0.04189510643482208	0.05487450212240219	0.04188718646764755	0.0637814924120903	0.07269832491874695	-0.022031020373106003	0.03781655430793762	-0.0344751700758934	-0.027237366884946823	-0.02527850680053234	-0.008341914974153042	0.04706306755542755	-0.011266450397670269	-0.07325402647256851	-0.04644951969385147	0.034557074308395386	-0.12654396891593933	-0.026115164160728455	0.06113767996430397	-0.06530485302209854	0.10138902813196182	0.07893563061952591	-0.027169203385710716	0.001985268434509635	0.027252601459622383	0.0014473097398877144	-0.006867827847599983	0.031106507405638695	-0.0073149846866726875	-0.03232352435588837	-0.017920782789587975	0.03769218176603317	0.008949785493314266	-0.0704108253121376	0.04732629284262657	0.023875461891293526	0.013617086224257946	-0.046748049557209015	-0.06353804469108582	-0.016492288559675217	-0.06799577176570892	-0.09608197957277298	-0.0225455891340971	-0.05786663293838501	0.01730271242558956	0.05175360292196274	0.036437876522541046	0.047166045755147934	-0.022551683709025383	-0.007447421085089445	-0.04674781486392021	-0.010268653742969036	-0.029596349224448204	-0.05944293737411499	-0.02028561197221279	-0.004060032777488232	0.020326558500528336	0.022450149059295654	-0.03584258258342743	0.0074118576012551785	-0.03492271900177002	0.010933348909020424	0.009370708838105202	0.04785938560962677	0.07173683494329453	-0.010077529586851597	0.02069794200360775	-0.056709107011556625	0.0201866552233696	-0.020889198407530785	0.01605920121073723	-0.055999502539634705	0.014803574420511723	-0.03760095313191414	-0.003460436360910535	-0.01926930621266365	0.02032923884689808	-0.01992577314376831	0.020548414438962936	-0.008975442498922348	-0.0415881872177124	-0.06629130244255066	0.03199728950858116	0.0853474959731102	-0.048458509147167206	0.014530304819345474	0.004947454668581486	0.04191999509930611	-0.02263139933347702	0.004638053011149168	-0.046269021928310394	0.03700949251651764	-0.010439230129122734	0.0484343096613884	-0.00497311819344759	-0.052714116871356964	0.04578520357608795	0.0004473365843296051	0.017942553386092186	-0.06212116405367851	0.012696354649960995	0.021061982959508896	-0.008082728832960129	0.03784659132361412	0.07972952723503113	-0.05436507984995842	-0.03727899119257927	0.0014339896151795983	0.049125537276268005	-0.07282847166061401	-0.02638309821486473	-0.02505902759730816	-0.06379136443138123	0.0004012583813164383	-0.04150475934147835	-0.08684933930635452	-0.025906458497047424	0.024700148031115532	0.005645579658448696	-0.025101101025938988	-0.02406446263194084	0.017204415053129196	0.011696030385792255	-0.04703035205602646	0.01053872611373663	-0.014756498858332634	-0.07328765094280243	0.08944246172904968	0.02056899480521679	-0.04021313041448593	-0.04709678888320923	0.01624736562371254	0.07309132069349289	0.001301842276006937	0.019507119432091713	0.03736471012234688	0.11516430974006653	0.034782830625772476	-0.025707203894853592	-0.01175431627780199	-0.023079531267285347	-0.04629216343164444	0.02356093004345894	0.055634915828704834	-0.015862194821238518	-0.010884844698011875	0.035461246967315674	-0.020502110943198204	-0.010013533756136894	0.031667161732912064	0.04560353606939316	-0.005011405795812607	-0.062088120728731155	-0.03690048307180405	-0.00012742597027681768	-0.009398438967764378	-0.010168151929974556	0.016353171318769455	-0.015999840572476387	0.08798999339342117	-0.04480631276965141	-0.04978349432349205	0.0336904302239418	-0.007790848612785339	0.061115562915802	-0.02984415553510189	0.05336647853255272	-0.018260251730680466	-0.07700027525424957	-0.048287324607372284	-0.053309086710214615	-0.049227144569158554	-0.031997136771678925	-0.028667502105236053	-0.054901838302612305	0.09951473772525787	-0.0022242621053010225	-0.052252113819122314	-0.047787174582481384	-0.0011954614892601967	-0.012601161375641823	-0.07427675276994705	0.006436639465391636	-0.010415672324597836	-0.07144548743963242	-0.03811097890138626	0.032403454184532166	-0.039058998227119446	-0.024617614224553108	0.004340179264545441	-0.03306170552968979	-0.0871020257472992	0.04809783399105072	-0.016085796058177948	-0.04512915387749672	-0.03622085601091385	-0.08053415268659592	0.007028120569884777	0.0902276262640953	0.019437436014413834	0.0520448200404644	0.015364306978881359	0.04629155620932579	0.1270359605550766	-0.0029807952232658863	-0.02674953266978264	-0.005601528566330671	-0.022765330970287323	-0.05971815437078476	0.07087145000696182	-0.03619217500090599	0.0038321982137858868	-0.030444294214248657	0.0035561828408390284	0.06118633225560188	0.005805913358926773	-0.004638704005628824	-0.01654377207159996	0.04221508279442787	0.0153500996530056	0.02823527157306671	-0.011999948881566525	-0.013627718202769756	0.006447677966207266	-0.05355337634682655	-0.05946236476302147	-0.021968703716993332	0.007633553817868233	0.003939343616366386	0.0716179758310318	-0.018738003447651863	-0.08294730633497238	0.03653572499752045	0.0030017502140253782	0.08686722069978714	-0.07878956943750381	0.012635068036615849	0.02920248918235302	-0.004520157817751169	-0.08705690503120422	-0.009197600185871124	0.0724242553114891	-0.04005905240774155	-0.04576290026307106	0.027410589158535004	0.041022174060344696	-0.04791097715497017	-0.008159073069691658	0.0013002165360376239	-0.04016430675983429	-0.0075978366658091545	0.016618914902210236	0.034194786101579666	0.017966682091355324	-0.026518037542700768	-0.0016278130933642387	-0.0013294010423123837	0.05113896355032921	0.04421656206250191	-0.02675320953130722	0.02142752893269062	-0.05081642046570778	0.09134816378355026	-0.07689189910888672	-0.012272942811250687	-0.04390385374426842	0.06679591536521912	0.0010569416917860508	-0.02570868283510208	-0.026952054351568222	-0.021127844229340553	-0.02081381529569626	0.02018989622592926	0.017387473955750465	-0.06470900028944016	-0.016699694097042084	-0.04115896299481392	-0.0813264548778534	-0.06591660529375076	-0.04619991406798363	-0.005286051891744137	-0.04872960224747658	0.0067872293293476105	-0.015594340860843658	-0.027017051354050636	0.030209919437766075	-0.008693077601492405	0.011091387830674648	0.043360717594623566	0.07555653154850006	-0.029807457700371742	0.051992207765579224	0.00866768416017294	0.0335586816072464	0.0027937095146626234	-0.025864718481898308	0.04493106156587601	0.0323038287460804	-0.05808185040950775	0.041548777371644974	-0.050810880959033966	-0.00231536733917892	0.023228703066706657	0.00805877335369587	0.008420889265835285	0.008839497342705727	0.013954680413007736	-0.060985017567873	0.07828862965106964	-0.10417966544628143	-0.029191264882683754	0.065442755818367	-0.05218510702252388	-0.09919242560863495	-0.04090184345841408	-0.019623344764113426	-0.04378626123070717	0.024258216843008995	0.003924836870282888	0.014993439428508282	0.05835201218724251	-0.017791347578167915	-0.05991272255778313	-0.0004109530127607286	-0.03580823540687561	0.022287806496024132	-0.003562078345566988	0.08932134509086609	0.036118507385253906	0.009078217670321465	0.019752250984311104	0.025586582720279694	-0.00618643406778574	-0.09954171627759933	-0.023147979751229286	-0.010731297545135021	-0.032204851508140564	-0.022562721744179726	-0.021943556144833565	0.0086549436673522	-0.07755036652088165	0.0021775253117084503	-0.038434382528066635	0.08573342114686966	-0.03756134584546089	-0.01898231916129589	0.06389636546373367	0.005423887632787228	0.0004417717573232949	0.00512684416025877	-0.04189171642065048	-0.10401353240013123	0.0028375699184834957	-0.048487111926078796	0.03671002388000488	0.01365902554243803	-0.04187490791082382	0.0051012602634727955	-0.0567469522356987	-0.02193157933652401	0.01714921183884144	-0.053176864981651306	0.042255088686943054	0.002663025166839361	-0.07694046199321747	-0.08366209268569946	0.017852941527962685	-0.04943238943815231	0.050557516515254974	0.01966649480164051	0.05370701476931572	0.002889188239350915	-0.0159298162907362	-0.023211734369397163	-0.023363137617707253	0.016401268541812897	0.015638284385204315	-0.08768026530742645	-0.013430199585855007	0.06103385239839554	-0.05321070924401283	0.02018047869205475	0.06537270545959473	-0.042731110006570816	0.04022038355469704	0.017411354929208755	-0.04773847758769989	0.039371833205223083	-0.033433787524700165	-0.013581731356680393	0.05083516612648964	-0.048476800322532654	0.09118534624576569	-0.05186301842331886	0.08120516687631607	0.0010299764107912779	0.0024018699768930674	0.09911730885505676	0.07499252259731293	-0.006787450052797794	0.0009399127447977662	-0.06444691866636276
+0.02617594599723816	-0.059284139424562454	-0.013513937592506409	0.01385246217250824	-0.041644394397735596	-0.03746204078197479	-0.0012548092054203153	0.03330449387431145	-0.007767481729388237	-0.08709339797496796	-0.03942441567778587	0.00610875990241766	-0.011582301929593086	-0.004380622413009405	0.0221817959100008	-0.040981706231832504	-0.09595680236816406	0.03145163133740425	-0.06189322844147682	-0.04458236321806908	-0.018464716151356697	0.03719313442707062	-0.0502164363861084	0.004058824852108955	-0.010854601860046387	0.022494859993457794	-0.03370608016848564	-0.03380792960524559	0.009748592041432858	0.023966079577803612	0.060855522751808167	0.00712087657302618	0.09586122632026672	-0.04583939164876938	-0.03828820586204529	0.0788668841123581	-0.015183165669441223	0.12246783822774887	0.0001696911349426955	-0.024208195507526398	-0.04263404756784439	-0.05088416486978531	-0.04659675806760788	-0.11706709116697311	0.014575495384633541	-0.02082182839512825	-0.02018015831708908	-0.022530337795615196	0.011281016282737255	0.01762823387980461	0.07579080760478973	-0.011356745846569538	-0.04646812006831169	0.006331625860184431	-0.023924654349684715	0.031410880386829376	0.024655967950820923	-0.008092952892184258	-0.016106873750686646	0.02905024215579033	-0.10663242638111115	-0.012622050009667873	0.01591290533542633	0.04586280509829521	-0.035266388207674026	-0.014004775322973728	-0.012652380391955376	0.05741487070918083	0.011643181554973125	-0.04721628129482269	-0.020671537145972252	0.05309522524476051	-0.001927281846292317	-0.05436611920595169	0.030897438526153564	-0.07457906752824783	-0.0168099794536829	-0.0013117010239511728	-0.015160806477069855	0.0005483520799316466	0.02898823842406273	-0.01939002051949501	0.17492146790027618	-0.05182325839996338	0.0025745162274688482	-0.021299736574292183	-0.011562714353203773	-0.05848631262779236	0.05489259958267212	0.021762117743492126	0.021433888003230095	0.06080297753214836	0.010571075603365898	0.020499680191278458	-0.000368486566003412	0.0004247325996402651	-0.0018151885597035289	-0.07756741344928741	-0.0603918619453907	-0.02513553388416767	0.031184403225779533	-0.00023788590624462813	0.08636423200368881	0.04322049394249916	0.07132723182439804	-0.013866513036191463	-0.0019664745777845383	-0.042027007788419724	0.03398308530449867	-0.02095087803900242	-0.0034685132559388876	0.1420385241508484	0.03771733120083809	0.011318515054881573	0.06498017907142639	-0.0019728741608560085	0.02084621787071228	-0.05593142285943031	0.010688282549381256	0.01151458639651537	-0.030587850138545036	0.003941329661756754	-0.0412890687584877	-0.0705232247710228	-0.04831090196967125	0.009605610743165016	-0.06087711453437805	-0.0722428634762764	0.04133821651339531	-0.04643760249018669	0.12944701313972473	0.06025267392396927	-0.04960848391056061	0.029180454090237617	0.015934543684124947	0.002825939329341054	-0.003956761676818132	0.02063066139817238	-0.0021126887295395136	-0.030114425346255302	0.005994026083499193	0.08800411224365234	-0.05067601054906845	0.007588074076920748	0.050881367176771164	0.0022261179983615875	0.00011330545385135338	-0.036666810512542725	-0.0661706030368805	0.04286899045109749	-0.02188093028962612	-0.06469707936048508	-0.007655719760805368	-0.0657942071557045	0.02729514054954052	0.04650673642754555	-0.0036071508657187223	0.017681747674942017	-0.030760517343878746	-0.020840512588620186	-0.05363607406616211	-0.043918441981077194	-0.015858860686421394	-0.11104224622249603	-0.04307122528553009	-0.012052672915160656	-0.0226428359746933	0.023860104382038116	-0.06227374076843262	-0.008752367459237576	-0.02948625199496746	-0.02938799373805523	0.007314164657145739	0.06744711101055145	0.0653446689248085	0.01810930110514164	-0.011990507133305073	-0.09062623977661133	-0.01274788472801447	-0.014338672161102295	0.04865790903568268	-0.0317123644053936	0.022116081789135933	-0.02994568459689617	0.028225162997841835	-0.0009371127234771848	0.053780823945999146	-0.009086723439395428	0.03436524048447609	0.028467804193496704	-0.0188378244638443	-0.016009505838155746	0.013553024269640446	0.03919592499732971	0.013528659008443356	0.035565413534641266	-0.0058579351752996445	0.10153797268867493	-0.0021145562641322613	-0.05218963325023651	-0.06871578097343445	0.018744293600320816	0.01901969313621521	0.0484001487493515	0.01984189823269844	-0.052312497049570084	0.08561141788959503	0.06309977173805237	0.059487007558345795	-0.039578262716531754	0.01721295714378357	-0.007292557507753372	0.003579117124900222	0.05669577047228813	0.03838867321610451	-0.05279611423611641	-0.043904948979616165	0.04056483507156372	0.03384102135896683	-0.01418273150920868	-0.05812807381153107	0.02588501200079918	-0.08982108533382416	-0.0019312077201902866	-0.03668580576777458	-0.07549083977937698	-0.05031733214855194	0.038223352283239365	-0.031328968703746796	-0.022458581253886223	0.007242057006806135	0.029365768656134605	-0.05773022025823593	-0.021571243181824684	-0.013868158683180809	-0.06218409910798073	-0.06661707907915115	0.07446838915348053	-0.0131660345941782	-0.10051336884498596	-0.04253176599740982	-0.00736403651535511	0.03647572547197342	0.010909166187047958	0.006260438822209835	0.04164349287748337	0.06403465569019318	-0.000280911335721612	-0.060311369597911835	0.004893118049949408	-0.005670534446835518	-0.05437846854329109	0.0025635147467255592	0.03148016706109047	-0.038880180567502975	-0.055275775492191315	-0.009889993816614151	-0.04559502750635147	0.02328607253730297	0.026243383064866066	0.05820085108280182	-0.011206068098545074	-0.009933619759976864	-0.007996384985744953	0.031474582850933075	-0.02933911792933941	0.031433023512363434	0.05646747350692749	-0.013707374222576618	0.006378677673637867	-0.02841155044734478	-0.01943371258676052	0.03837306424975395	-0.03608102351427078	0.052398763597011566	-0.030628016218543053	0.09023530036211014	-0.024117713794112206	-0.04743049293756485	0.011893975548446178	-0.09166927635669708	-0.009258463978767395	-0.06338826566934586	-0.05450039356946945	-0.061190083622932434	0.051898665726184845	0.007677612826228142	-0.029263468459248543	0.006618475075811148	-0.056885022670030594	-0.042884524911642075	-0.06975457072257996	-0.011661576107144356	-0.027492234483361244	-0.03755797818303108	-0.012517343275249004	0.04941888153553009	-0.05657381936907768	-0.007637417409569025	0.036138247698545456	0.00104748725425452	-0.05788769945502281	0.06874288618564606	0.002413600916042924	-0.05194953829050064	-0.027271302416920662	-0.07499171793460846	0.005015052389353514	0.07008958607912064	0.03345441445708275	0.02497936598956585	0.05835188925266266	0.050689246505498886	0.07652748376131058	-0.00035839449265040457	0.001777969067916274	-0.042055413126945496	-0.01534195151180029	-0.04790901392698288	0.06494700908660889	0.0016387492651119828	-0.018569612875580788	-0.002665127394720912	0.02900834195315838	0.049754638224840164	-0.028588782995939255	-0.020998893305659294	-0.025286689400672913	0.025388838723301888	-0.03441803157329559	0.047465622425079346	-0.025614041835069656	0.05110274627804756	-0.03503866493701935	-0.07108396291732788	-0.04433120787143707	-0.0029087592847645283	0.015228637494146824	-0.02008690871298313	0.024126840755343437	-0.010282964445650578	-0.06574199348688126	-0.029024258255958557	-0.05155505985021591	0.1064523234963417	-0.06229007989168167	-0.005325361620634794	0.051121942698955536	0.050531480461359024	-0.09260492771863937	0.017610590904951096	0.010210514068603516	-0.032647304236888885	-0.06913387030363083	-0.019445408135652542	0.04370689392089844	-0.0634855180978775	-0.024652376770973206	-0.03608701378107071	-0.009998812340199947	-0.04444320872426033	-0.005121275782585144	0.043536413460969925	0.04556706175208092	0.0411190502345562	-0.051294244825839996	0.03293268755078316	0.017971515655517578	0.00012189299013698474	-0.021353907883167267	0.015537849627435207	-0.07231318950653076	-0.005934863816946745	-0.04477155953645706	-0.042757902294397354	-0.037901680916547775	0.059486836194992065	-0.0040466925129294395	-0.03401258587837219	0.0224838238209486	-0.07123477011919022	-0.023813258856534958	0.001965874806046486	0.02119600586593151	-0.017183350399136543	-0.06526859849691391	-0.04101237282156944	-0.05379725247621536	-0.07717496901750565	-0.020736221224069595	0.00010107896378031	-0.0303118247538805	0.015414360910654068	-0.06061931326985359	0.02285841293632984	0.04202431067824364	0.04874639958143234	0.05265241116285324	0.034362342208623886	0.013213098049163818	-0.05084078758955002	0.017702436074614525	-0.026485338807106018	0.058471038937568665	0.03048243746161461	-0.02352663315832615	-0.03721678629517555	-0.027697110548615456	-0.055298738181591034	0.06333580613136292	-0.024785658344626427	-0.025819512084126472	0.019777001813054085	-0.03357529267668724	-0.011097663082182407	0.008669373579323292	0.03128533437848091	-0.0007066132966428995	-0.004785584751516581	-0.08063339442014694	-0.05449191853404045	0.02434299699962139	-0.026559874415397644	-0.08131001889705658	-0.08398435264825821	-0.007950469851493835	-0.04190237075090408	0.04712405800819397	-0.015501754358410835	-0.0039863381534814835	0.03505486249923706	-0.014982804656028748	-0.061503082513809204	0.00026943295961245894	-0.036036401987075806	0.031860142946243286	-0.007442675530910492	0.044801849871873856	0.009586857631802559	0.009937966242432594	-0.012104754336178303	-0.0030079353600740433	0.008662021718919277	-0.09131666272878647	-0.005007796920835972	0.011629268527030945	-0.022227758541703224	0.010965824127197266	-0.019041020423173904	0.01196768507361412	-0.038007017225027084	-0.017111197113990784	-0.002657151548191905	0.05523360148072243	-0.0015972459223121405	0.029917249456048012	0.021974243223667145	-0.05582798272371292	0.003488309681415558	0.05979905277490616	-0.05423951894044876	-0.05237947404384613	0.039802584797143936	-0.054849326610565186	0.0405365526676178	0.002275611739605665	0.019970964640378952	-0.046752654016017914	-0.057490695267915726	0.004431550391018391	-0.030666979029774666	-0.07068387418985367	-0.0018126857466995716	0.03418170288205147	-0.06833091378211975	-0.05089392140507698	0.027424810454249382	-0.060578882694244385	0.05191052332520485	0.028301142156124115	0.08764113485813141	-0.04575475677847862	-0.017136866226792336	-0.017229799181222916	0.021030696108937263	0.018933475017547607	-0.014638237655162811	-0.06771539896726608	-0.019279202446341515	0.08530296385288239	-0.08068607747554779	0.007990878075361252	0.08105242252349854	-0.039440203458070755	0.06619320809841156	0.016711296513676643	-0.050959937274456024	0.04951313138008118	0.018108291551470757	-0.047294966876506805	0.03422222286462784	-0.08723592013120651	0.04680834710597992	-0.06839585304260254	0.1006668359041214	0.021096011623740196	0.010704798623919487	0.06425774842500687	0.09353281557559967	-0.005730758886784315	0.053121425211429596	-0.05317942053079605
+0.04364706948399544	-0.004543746821582317	0.006001701578497887	0.0643640086054802	-0.08264203369617462	-0.0019346828339621425	-0.010112057439982891	0.02494926191866398	-0.034648120403289795	-0.06266329437494278	-0.039309605956077576	-0.01848786510527134	-0.02186001092195511	0.011725559830665588	0.03412417322397232	-0.009728772565722466	-0.03447394445538521	0.04525604099035263	-0.015480623580515385	-0.055161748081445694	0.026451153680682182	0.006954318843781948	-0.05681391432881355	0.0692460685968399	0.040920618921518326	-0.014137640595436096	-0.04613715410232544	0.01717553846538067	-0.007398552261292934	0.05357113108038902	0.04350360482931137	-0.0386185459792614	0.01626765914261341	-0.04646963253617287	-0.04113549366593361	0.04394618049263954	-0.040518514811992645	0.07539879530668259	0.022623473778367043	-0.02400725521147251	-0.03538655489683151	0.03186286985874176	-0.07159237563610077	-0.04828548803925514	0.07626621425151825	0.027612382546067238	-2.2375412299879827e-05	-0.04060760885477066	0.07028030604124069	0.04636896774172783	0.07557211816310883	-0.006490389816462994	-0.027570946142077446	-0.02601524442434311	-0.016259929165244102	0.016480542719364166	0.053145892918109894	-0.07662466913461685	-0.032697781920433044	0.05307146534323692	-0.05311926454305649	-0.026951083913445473	0.07044052332639694	0.012383210472762585	-0.007377874571830034	0.0018913467647507787	0.0003650236176326871	0.0924997627735138	0.007934234105050564	-0.03716884180903435	-0.0016324351308867335	0.11915342509746552	-0.051772188395261765	-0.025060003623366356	0.07913384586572647	-0.047698263078927994	-0.027543531730771065	0.06761495023965836	0.03193287178874016	0.005000345874577761	0.020735811442136765	-0.06872919201850891	0.08486553281545639	-0.021217690780758858	-0.0429961197078228	-0.025160886347293854	-0.0004875453014392406	-0.03166805952787399	0.0824180543422699	0.030060017481446266	0.009122423827648163	0.05216822400689125	-0.06823814660310745	0.04717171937227249	0.006554230581969023	-0.024003122001886368	0.00503090163692832	-0.10389126092195511	-0.0432855598628521	0.01004004292190075	0.03065309301018715	-0.03357382491230965	0.03133057430386543	-0.004740152508020401	0.055543240159749985	0.03671516850590706	-0.050969962030649185	-0.010180703364312649	0.015206203795969486	0.02371395193040371	0.018215825781226158	0.07036597281694412	0.09695736318826675	0.05196310952305794	0.062467847019433975	-0.027762336656451225	-0.02899388037621975	-0.05808497220277786	-0.04562599956989288	0.008387785404920578	0.0015848056646063924	0.04329387843608856	0.006275160703808069	-0.0627540573477745	-0.01040829997509718	0.03251304104924202	-0.09557779878377914	-0.011154844425618649	0.080838643014431	-0.01023106835782528	0.12425779551267624	0.002763571683317423	-0.04036778211593628	0.018748072907328606	0.0707608014345169	0.025594033300876617	0.013718762435019016	0.04576918110251427	-0.018948597833514214	-0.006881729234009981	0.006817597430199385	0.06909379363059998	0.011500481516122818	-0.08031393587589264	0.053046416491270065	0.018841657787561417	0.03751606121659279	-0.011649097315967083	-0.06366728246212006	-0.0022053366992622614	-0.09152866899967194	-0.0856257826089859	0.0024888948537409306	-0.08440425992012024	-0.010303858667612076	0.026258083060383797	0.025052564218640327	0.011635988019406796	-0.03892909362912178	-0.013673102483153343	-0.07406919449567795	-0.02401808649301529	-0.03759293258190155	-0.06582945585250854	-0.04605318605899811	-0.01897037960588932	-0.006064690183848143	0.04410463571548462	-0.04260827600955963	-0.04335834085941315	-0.03091754950582981	0.00048680478357709944	0.06789183616638184	0.08171902596950531	0.06769304722547531	0.026305140927433968	0.02439720556139946	-0.04194588214159012	0.006291532423347235	-0.01727473922073841	0.010130534879863262	-0.04507940262556076	0.06514298915863037	-0.026656320318579674	-0.0011260187020525336	-0.03397997096180916	0.06062225252389908	-0.04719182476401329	0.0020451494492590427	-0.016247212886810303	-0.05777253583073616	-0.058271050453186035	0.08405593782663345	0.09540355205535889	-0.028794722631573677	-0.0013981375377625227	-0.04440232738852501	0.022212157025933266	0.006968380883336067	-0.015228867530822754	-0.05615774914622307	0.046090226620435715	0.012427149340510368	0.06420700252056122	0.023812158033251762	-0.10659588128328323	0.05149466171860695	0.03193832188844681	0.028764434158802032	-0.06179125979542732	-0.006760191638022661	0.020098445937037468	-0.006196102127432823	0.03737853839993477	0.049261126667261124	0.010921787470579147	-0.01742309331893921	-0.0001539995864732191	0.06572403013706207	-0.0391475185751915	0.001761624007485807	0.018203657120466232	-0.09288757294416428	0.040149349719285965	-0.017139000818133354	-0.06100039184093475	-0.021048061549663544	0.02428356744349003	-0.016478003934025764	-0.011361205019056797	-0.00392046058550477	0.014523845165967941	0.0014368618139997125	-0.03666641563177109	0.015256816521286964	-0.010674197226762772	-0.07840762287378311	0.0956786498427391	0.007985567674040794	-0.029238233342766762	-0.07298088818788528	-0.00471156882122159	0.042308639734983444	-0.00047164407442323864	0.053622614592313766	0.036068905144929886	0.060321222990751266	0.016782622784376144	0.024663155898451805	-0.02261166088283062	-0.015572096221148968	-0.023815952241420746	-0.033506717532873154	0.0445883572101593	-0.04890440031886101	-0.03620404005050659	-0.00045458655222319067	-0.01723330095410347	-0.006921781226992607	0.03416728228330612	0.007368252146989107	-0.028925582766532898	-0.03765295445919037	-0.01661085896193981	0.02677009254693985	-0.01676175184547901	-0.016544677317142487	0.01727568358182907	-0.018209781497716904	0.05712579935789108	-0.037714675068855286	-0.06070369482040405	0.04030502215027809	-0.0023812223225831985	0.022297877818346024	-0.03930296748876572	0.037471406161785126	-0.02955751121044159	-0.09203717857599258	-0.03404593467712402	-0.0446673221886158	-0.06312575936317444	-0.025148916989564896	-0.01562848873436451	-0.020054472610354424	0.1022886261343956	-0.005528068169951439	-0.06238778680562973	-0.060789283365011215	-0.0004239295667503029	-0.03253263235092163	-0.07656712085008621	-0.02090943604707718	-0.02303469367325306	-0.04559234529733658	0.0013454321306198835	0.0025104384403675795	-0.017291586846113205	-0.03597937524318695	-0.01881670579314232	0.012836877256631851	-0.06354796141386032	0.07099233567714691	-0.0017098167445510626	-0.04041653126478195	0.017205843701958656	-0.03785725682973862	0.024203112348914146	0.08136828988790512	0.04669024050235748	0.018341904506087303	0.012115394696593285	0.057944487780332565	0.123841293156147	0.022109737619757652	-0.061337221413850784	-0.044638726860284805	-0.04838700219988823	-0.07701169699430466	0.04943087697029114	-0.03565724566578865	-0.043067049235105515	-0.0035019267816096544	-0.011403657495975494	0.08698523044586182	-0.00984126701951027	0.004140703473240137	-0.040925946086645126	0.03413187712430954	0.01989961601793766	0.0046888357028365135	-0.02346196584403515	-0.023723827674984932	-0.020509690046310425	-0.04239269345998764	-0.030044080689549446	-0.008834986947476864	0.05084219574928284	0.0020828901324421167	0.06192454695701599	-0.03333663567900658	-0.05396758392453194	0.037006061524152756	0.029875101521611214	0.06194094195961952	-0.024267779663205147	0.023352378979325294	0.04680413752794266	-0.013943624682724476	-0.056471843272447586	0.029329106211662292	0.03369166702032089	-0.0280403271317482	-0.037968508899211884	-0.015276479534804821	0.04789096489548683	-0.05751578137278557	-0.013174387626349926	-0.00869294535368681	-0.09237842261791229	-0.02367623895406723	-0.018009977415204048	0.028603002429008484	0.03806926682591438	-0.017339814454317093	0.0048257093876600266	0.012372714467346668	-0.00014935937360860407	-0.023610224947333336	0.014330718666315079	-0.009656183421611786	-0.014266501180827618	0.06786909699440002	-0.05765223503112793	-0.008929228410124779	-0.04238727316260338	0.0701930895447731	-0.0340900644659996	-0.021756872534751892	0.006941423285752535	-0.015209045261144638	-0.0088625093922019	0.0006617368781007826	0.023650676012039185	-0.059624601155519485	0.008362919092178345	-0.04151978716254234	-0.05238278955221176	-0.07806351035833359	-0.01626538671553135	-0.008428666740655899	-0.051923830062150955	-0.025156410411000252	-0.048478107899427414	-0.019626734778285027	0.02501438744366169	-0.014416246674954891	-0.0026630160864442587	0.021000316366553307	0.056527577340602875	-0.04048280790448189	0.025704126805067062	-0.012390349991619587	0.04286682978272438	0.028665946796536446	-0.05396777763962746	0.01377327274531126	0.03102114237844944	-0.029940150678157806	0.05025457218289375	-0.03709767386317253	0.012453492730855942	-0.00927884504199028	0.017393354326486588	0.023230355232954025	-0.05136195570230484	-0.011091278865933418	-0.03486401215195656	0.08479612320661545	-0.05927002429962158	-0.06940702348947525	0.03556835278868675	-0.029643939808011055	-0.10501424968242645	-0.010542768985033035	-0.006148075684905052	-0.0018005089368671179	0.021688874810934067	-0.03728700801730156	0.019372621551156044	0.04223180562257767	-0.045279860496520996	-0.052778009325265884	-0.042381104081869125	-0.04624726623296738	0.04448084905743599	0.015496761538088322	0.07424148172140121	0.0227176733314991	0.009355619549751282	-0.02253592573106289	-0.008786540478467941	-0.016043683513998985	-0.10839507728815079	0.004500273615121841	-0.028042912483215332	-0.03221576288342476	0.020070839673280716	0.0026593348011374474	-0.018259167671203613	-0.006748005747795105	-0.05741436034440994	-0.01656389608979225	0.0998002290725708	-0.031317777931690216	-0.030339693650603294	0.06009172275662422	0.012633680365979671	-0.002856313483789563	0.0472363643348217	-0.08102703094482422	-0.08235885947942734	-0.004504956305027008	0.006284995470196009	0.03624632582068443	0.03961164876818657	0.010817921720445156	-0.05343947932124138	-0.02609327808022499	-0.016327515244483948	-0.010715687647461891	-0.0732026919722557	0.01397229079157114	0.0053750802762806416	-0.08016970753669739	-0.09454187750816345	-0.005996074993163347	-0.06103616580367088	0.06144213303923607	0.02726113796234131	0.0604301281273365	-0.002104279352352023	-0.0007714347448199987	0.014810326509177685	-0.03395051136612892	0.02184978500008583	0.00318825407885015	-0.0812428742647171	0.013772952370345592	0.08600067347288132	-0.07442931085824966	0.010849998332560062	0.03449664264917374	-0.050409119576215744	0.012509766966104507	0.033135730773210526	-0.030617505311965942	0.0759182795882225	0.00810237880796194	0.0023273348342627287	0.07576286792755127	-0.07322637736797333	0.112892746925354	-0.04781365394592285	0.04820182919502258	-0.005635960027575493	0.0186723992228508	0.10735506564378738	0.07017607241868973	-0.04148348420858383	-0.0038723705802112818	-0.08456627279520035
+0.008356054313480854	-0.04196074604988098	-0.03233652561903	0.03972102329134941	-0.08032242953777313	-0.04298015311360359	0.027466999366879463	0.02040228620171547	-0.030663486570119858	-0.03550662100315094	-0.017344031482934952	-0.02353384904563427	-0.03457571566104889	-0.0013099695788696408	0.04749802127480507	-0.003856606548652053	-0.053079795092344284	0.031082920730113983	-0.05117464438080788	0.015879347920417786	0.06224600970745087	0.008029562421143055	-0.0778999999165535	0.03820670023560524	-0.015859246253967285	-0.013393365778028965	-0.015035895630717278	0.01941419392824173	-0.024167034775018692	0.032740648835897446	0.04632452875375748	-0.01048828661441803	0.0028498226311057806	-0.0293149184435606	-0.04898221418261528	0.03143619745969772	-0.004139465745538473	0.10087347775697708	0.0512237474322319	-0.02333902195096016	-0.05046067386865616	0.02853228896856308	-0.0580781064927578	-0.06493232399225235	0.04904140904545784	-0.03017784096300602	0.011540865525603294	-0.054196033626794815	0.053520578891038895	0.09556872397661209	0.06245897710323334	0.004505526274442673	-0.024333130568265915	-0.013675092719495296	-0.024425998330116272	-0.00955510325729847	0.018292833119630814	-0.06506754457950592	-0.03567749261856079	0.029255397617816925	-0.042311593890190125	0.027469869703054428	0.03211864456534386	0.0029247989878058434	0.024015866219997406	-0.006167972926050425	0.01604897528886795	0.044966354966163635	0.022437559440732002	-0.03842870891094208	-0.050046492367982864	0.08674938231706619	-0.0782361701130867	-0.04619520902633667	0.04934338107705116	0.004331097472459078	0.017017649486660957	0.0018245907267555594	-0.013739151880145073	-0.03816515952348709	-0.019245166331529617	-0.036095019429922104	0.12572160363197327	-0.07597789913415909	-0.013820385560393333	-0.048267971724271774	-0.010340888053178787	-0.06300566345453262	0.03438272699713707	0.009749354794621468	0.022776193916797638	0.03532926365733147	-0.060193002223968506	0.032393500208854675	-0.010386297479271889	0.02001425065100193	0.03109225258231163	-0.16239012777805328	-0.030410006642341614	-0.0022041993215680122	0.007589429616928101	-0.047641821205616	0.05381082370877266	-0.021354591473937035	0.009436174295842648	0.036799900233745575	-0.0726926252245903	-0.011699086986482143	-0.007499221246689558	0.03011004626750946	0.007873164489865303	0.08187726885080338	0.08630107343196869	0.05112956091761589	0.0356854647397995	-0.028715159744024277	-0.03806346282362938	-0.02929738163948059	-0.011546849273145199	0.03322238475084305	-0.021458374336361885	0.02535741962492466	0.022299490869045258	-0.041744958609342575	-0.022019410505890846	0.028053583577275276	-0.109824538230896	-0.03110177256166935	0.023342687636613846	-0.043646443635225296	0.09418781101703644	0.0036973843816667795	-0.030299784615635872	-0.0017579650739207864	0.06500154733657837	0.008388482965528965	0.01995973475277424	0.021026303991675377	-0.017405852675437927	-0.043695054948329926	0.007026645354926586	0.09484447538852692	-0.002905917586758733	-0.002603172557428479	0.00798164214938879	0.0736883133649826	0.02577611245214939	-0.010142912156879902	-0.07884696871042252	0.060317981988191605	-0.12327639013528824	-0.06805620342493057	-0.018893394619226456	-0.10392493009567261	0.031026754528284073	0.04683970287442207	0.009912506677210331	0.0794309452176094	-0.005550045985728502	-0.003339234506711364	-0.04108850657939911	-0.06296403706073761	-0.029615456238389015	-0.03347036987543106	-0.031062614172697067	0.013106961734592915	-0.004214534070342779	-0.027095915749669075	0.015684714540839195	-0.07283204793930054	-0.005568660795688629	0.015451375395059586	0.049392346292734146	0.06479410082101822	0.045100703835487366	0.013903003185987473	-0.008410727605223656	-0.04003937542438507	-0.02961764670908451	-0.02339790016412735	0.006133876740932465	-0.07052641361951828	0.05643898621201515	-0.06572278589010239	-0.048563696444034576	-0.03464122116565704	0.02313288301229477	-0.0325784869492054	-0.008681765757501125	-0.03777433559298515	-0.05836028233170509	-0.05903495103120804	0.06749509274959564	0.10401035845279694	-0.04942522943019867	0.04386436566710472	-0.01002439484000206	0.07905980944633484	-0.031008120626211166	-0.01211579330265522	-0.07307939231395721	0.04424220323562622	0.0034713896457105875	0.020897379145026207	0.04583761468529701	-0.07390329986810684	0.056570712476968765	-0.00667983666062355	-0.007001599762588739	-0.057287152856588364	0.05399460345506668	0.029639817774295807	0.024897165596485138	0.012202078476548195	0.026408255100250244	-0.021572239696979523	-0.012032500468194485	-0.009630372747778893	0.04941117390990257	-0.02303784154355526	0.0541006438434124	0.01591031439602375	-0.060163937509059906	0.05082909017801285	0.011801605112850666	-0.08255146443843842	0.004393311217427254	-0.030453545972704887	-0.04204791784286499	-0.0201646089553833	0.014188091270625591	0.06005816161632538	-0.013219435699284077	-0.05109216645359993	0.016518276184797287	0.04180654510855675	-0.03970775008201599	0.08533290028572083	-0.04752105847001076	-0.04347999393939972	-0.03899729251861572	-0.0023923288099467754	-0.006000827997922897	0.03326420113444328	-0.0031864535994827747	0.03808669000864029	0.07940149307250977	0.022523226216435432	-0.010363810695707798	-0.007996984757483006	-0.10304742306470871	-0.03928225487470627	-0.024263033643364906	0.03438066691160202	-0.038478970527648926	-0.09352486580610275	-0.03605683892965317	-0.07203713059425354	-0.007805143017321825	-0.005146321840584278	0.058234427124261856	-0.020908959209918976	-0.014097378589212894	-0.01712760701775551	0.015456912107765675	0.01187962107360363	-0.08505947887897491	-0.01932201161980629	-0.040307823568582535	-0.012256504036486149	0.0039017738308757544	-0.006617880892008543	0.007548104505985975	0.00013071176363155246	0.045001715421676636	-0.014163458719849586	0.007631705608218908	-0.0686485692858696	-0.10005510598421097	-0.022062363103032112	-0.06286422163248062	-0.09901406615972519	-0.016370505094528198	-0.005220678169280291	-0.04180724546313286	0.07151836901903152	0.047851670533418655	-0.03987615555524826	-0.06317146867513657	-0.011630344204604626	-0.062418822199106216	-0.0432155504822731	-0.0245850570499897	-0.00029794263537041843	-0.0722745954990387	-0.028931116685271263	-0.0007504832465201616	-0.04565907642245293	-0.019803734496235847	0.005960254464298487	0.01563095860183239	-0.02544070966541767	0.059667203575372696	-0.012706908397376537	-0.0030315713956952095	0.023444050922989845	-0.06323760002851486	0.07611794769763947	0.06533637642860413	0.05742665380239487	0.07105959206819534	0.04467283934354782	0.032812412828207016	0.053905099630355835	0.0095641715452075	-0.006672232411801815	-0.022624855861067772	-0.02831108868122101	-0.09417471289634705	0.020147671923041344	-0.01802944764494896	-0.037445198744535446	0.0005785079556517303	-0.0023890859447419643	0.0922972708940506	-0.019425157457590103	0.005032464861869812	-0.04591577127575874	0.009003830142319202	0.0146440165117383	0.007959427312016487	-0.013995762914419174	-0.031211398541927338	0.013032186776399612	-0.07801847159862518	-0.0030595636926591396	-0.05317047983407974	0.007826999761164188	-0.0013994502369314432	0.04642863571643829	-0.00011701836774591357	-0.04435989633202553	0.006965268403291702	0.02971990406513214	0.10201217234134674	-0.0408230721950531	0.027304233983159065	0.04088805243372917	-0.014345974661409855	-0.08440522104501724	-0.05140828341245651	0.00764191010966897	0.003869937267154455	-0.04478204995393753	0.0027220628689974546	0.07557254284620285	-0.028799640014767647	-0.04818614572286606	-0.010543733835220337	-0.013767626136541367	-0.016727421432733536	0.029798325151205063	-0.011667516082525253	0.05136048048734665	0.006159707438200712	0.02234814316034317	0.010056741535663605	0.022249272093176842	0.02140321582555771	-0.01548693049699068	0.018631327897310257	0.03499374911189079	0.07462731003761292	0.0005801619845442474	-0.10605722665786743	-0.07612477242946625	0.04307571426033974	-0.042346708476543427	-0.03924443945288658	-0.07130210846662521	0.00948962289839983	0.011678005568683147	0.06800933182239532	-0.03852856159210205	-0.027838973328471184	0.021810157224535942	0.012622479349374771	-0.04281534627079964	-0.10326405614614487	-0.0133281284943223	0.010782569646835327	-0.02659345045685768	-0.016376150771975517	-0.06622987985610962	-0.01291610486805439	0.028302883729338646	-0.04548487812280655	0.016995426267385483	0.035771071910858154	0.013712681829929352	0.011275200173258781	0.0259519275277853	-0.008450684137642384	0.0478738471865654	-0.011331208981573582	-0.05149875581264496	0.004105179104954004	0.018644895404577255	-0.04236544668674469	0.021422915160655975	-0.06752143800258636	-0.026013018563389778	-0.009540464729070663	0.008830904960632324	-0.001098789507523179	-0.025545451790094376	-0.0002193853142671287	-0.060623154044151306	0.051582831889390945	-0.07056499272584915	-0.042269863188266754	0.04216984659433365	-0.014125942252576351	-0.07440473884344101	0.027487177401781082	-0.024814805015921593	0.0111723393201828	0.08015670627355576	-0.026458198204636574	0.050725847482681274	-0.012018518522381783	-0.08160116523504257	-0.02580563724040985	-0.07721813023090363	-0.013682974502444267	0.003906433470547199	0.02518470212817192	0.0627596378326416	0.012528256513178349	-0.03130001947283745	-0.03264298662543297	-0.03845240920782089	0.0027152844704687595	-0.05341724678874016	-2.1296300474205054e-05	-0.03837186470627785	0.05440841615200043	0.047618117183446884	-0.01966770365834236	0.04426143318414688	-0.00958236400038004	-0.03494640067219734	-0.034135639667510986	0.06540097296237946	-0.021732889115810394	-0.00906345620751381	0.05478377267718315	-0.025076989084482193	-0.009290440008044243	0.04600759595632553	-0.015328655019402504	-0.023579658940434456	0.012891479767858982	-0.022685673087835312	0.01292935200035572	0.046740442514419556	0.06547823548316956	-0.0022091905120760202	-0.04453345388174057	-0.04779345914721489	0.0047546327114105225	-0.025550907477736473	0.021230647340416908	0.04557640105485916	-0.048714976757764816	-0.060206279158592224	-0.053191617131233215	-0.04325822740793228	0.06910949945449829	-0.05530925095081329	0.041179146617650986	-0.0276555884629488	0.026918066665530205	0.023913975805044174	-0.01505716610699892	0.05959707126021385	-0.0035476109478622675	-0.10200891643762589	-0.027161993086338043	0.120704784989357	0.009993134997785091	0.034229326993227005	0.07749281823635101	-0.011690163053572178	0.05370050668716431	0.021413244307041168	0.028547417372465134	-0.004879762884229422	0.0076462142169475555	-0.005951477214694023	0.0594274066388607	-0.043762460350990295	0.07523445039987564	-0.04042207449674606	0.08211412280797958	0.01021824311465025	-0.00859755463898182	0.11491590738296509	0.06170995533466339	0.02689453586935997	-0.0091270562261343	-0.04172678291797638
+0.050080306828022	-0.030132587999105453	-0.024197442457079887	0.04405517876148224	-0.06525301188230515	-0.06868176907300949	0.0021597258746623993	0.03386320918798447	0.006312776822596788	-0.015017510391771793	-0.07974236458539963	-0.02176719345152378	-0.004447357729077339	0.002422318095341325	0.025786329060792923	-0.028310563415288925	-0.058023128658533096	0.03117435611784458	-0.027790071442723274	-0.030465789139270782	0.021997099742293358	-0.013839290477335453	-0.0902172178030014	0.07271896302700043	0.015273544937372208	0.026472797617316246	-0.07770883291959763	0.006020185071974993	-0.007423012051731348	0.027920616790652275	0.04065660759806633	0.005153322126716375	0.036164894700050354	-0.057944878935813904	-0.04037129133939743	0.02116822451353073	-0.04282136633992195	0.08898766338825226	-0.032706957310438156	0.006907043047249317	-0.06217314675450325	0.026874182745814323	-0.07213377207517624	-0.03618113324046135	0.02750415727496147	0.020066965371370316	0.021368136629462242	-0.004956956487149	0.031002352014183998	0.014874087646603584	0.07426720857620239	-0.02356654964387417	-0.006222613155841827	-0.014436440542340279	-0.028316939249634743	0.010682291351258755	0.03510143607854843	-0.038430240005254745	-0.04213261231780052	0.0231024157255888	-0.11407779157161713	-0.005173060577362776	0.004641572013497353	-0.02840355969965458	-0.043665554374456406	-0.01469194795936346	0.0012720078229904175	0.10903815925121307	-0.003461641725152731	-0.03392181545495987	0.03981120511889458	0.0640551745891571	-0.07029115408658981	-0.02746390551328659	0.04654628783464432	-0.06649208813905716	-0.014507866464555264	0.08579793572425842	0.007841167040169239	0.009817141108214855	0.009824590757489204	-0.02420780435204506	0.1350560039281845	-0.005813981406390667	-0.09169232845306396	-0.029132969677448273	-0.004067497327923775	-0.03569580242037773	0.05052381008863449	0.018552372232079506	0.017794804647564888	0.08839606493711472	-0.031023863703012466	0.04182944446802139	0.05163029208779335	0.013291457667946815	0.026330864056944847	-0.11979302763938904	-0.06857723742723465	0.03332051262259483	-0.011153033934533596	-0.0039315237663686275	0.04751597344875336	-0.005787842907011509	0.04549700766801834	0.021916775032877922	-0.03571581467986107	0.0005668101366609335	0.018036190420389175	0.032813359051942825	0.03747778385877609	0.056569840759038925	0.04970404505729675	0.041453808546066284	0.02861655317246914	-0.00653576897457242	0.03796343505382538	-0.03974079340696335	-0.05906665697693825	0.019759733229875565	0.0087739834561944	0.04692038893699646	-0.0038975642528384924	-0.06705670803785324	-0.02288844995200634	0.03518595173954964	-0.10679610073566437	-0.025611817836761475	0.06738735735416412	-0.034650225192308426	0.10413076728582382	0.025037774816155434	-0.005989754572510719	0.025963444262742996	0.07219399511814117	0.02023070491850376	0.019772609695792198	0.01655721478164196	0.007127999793738127	-0.02258923090994358	-0.02388935722410679	0.030399922281503677	-0.019957076758146286	-0.05525560304522514	0.09020699560642242	0.054787810891866684	0.002925289561972022	-0.04169224202632904	-0.026078125461935997	-0.01535737793892622	-0.060455720871686935	-0.07079271972179413	0.009601451456546783	-0.046754054725170135	-0.01851058565080166	0.014424548484385014	0.01938774436712265	0.03161473199725151	-0.03732458874583244	-0.02450101636350155	-0.07716229557991028	-0.02321985922753811	-0.037970539182424545	-0.053714483976364136	-0.03173651918768883	-0.0019819072913378477	-0.01724601909518242	0.03492492809891701	-0.043031271547079086	-0.019328348338603973	-0.003908401355147362	-0.014395876787602901	0.0162171833217144	0.0879494771361351	0.05315983295440674	0.027851255610585213	0.038389358669519424	-0.027203166857361794	-0.000916902965400368	-0.06429511308670044	0.012014572508633137	-0.0069414726458489895	0.041611384600400925	-0.020572755485773087	0.02302776649594307	0.01327272318303585	0.03673100471496582	-0.054290201514959335	0.03659844771027565	-0.009405124932527542	-0.0040725404396653175	-0.05780855566263199	0.0502319261431694	0.09375356137752533	-0.019159415736794472	0.04612976685166359	-0.04080543294548988	0.06358202546834946	-0.02070731297135353	-0.004731860477477312	-0.05122463405132294	0.049071893095970154	0.0030465770978480577	0.07246154546737671	0.030548598617315292	-0.0826408714056015	0.09345788508653641	0.05863938853144646	0.04200156405568123	-0.054270315915346146	0.006396775133907795	0.003706088289618492	-0.01738252118229866	0.0902012512087822	0.08941201865673065	0.011002909392118454	-0.018003279343247414	0.013272703625261784	0.03515790030360222	-0.037283752113580704	0.012041113339364529	0.005839335732161999	-0.10586220771074295	0.015641575679183006	-0.08583512902259827	-0.06922394782304764	-0.024263650178909302	0.047227904200553894	-0.027826998382806778	0.015077803283929825	-0.032768622040748596	0.008533966727554798	-0.013112569227814674	-0.06102849170565605	-0.013016012497246265	0.00024676855537109077	-0.09464722126722336	0.10503674298524857	0.02172146737575531	-0.09499867260456085	-0.058779194951057434	0.01824921928346157	0.03400527685880661	0.0064857047982513905	0.037189289927482605	0.02548249624669552	0.11583739519119263	0.03754280135035515	0.01157771423459053	0.024171998724341393	-0.011885133571922779	-0.029469402506947517	0.00987093336880207	0.016686182469129562	-0.06497468054294586	-0.004516372457146645	0.029477400705218315	-0.04083745926618576	-0.024665359407663345	0.005388680845499039	0.016379809007048607	-0.0004340977466199547	-0.045927148312330246	-0.023442212492227554	0.03270157426595688	-0.05802115797996521	-0.0278665442019701	0.0379333570599556	-0.015263504348695278	0.05500932037830353	-0.06307825446128845	-0.07478466629981995	0.013979270122945309	0.015163627453148365	-0.003892963519319892	-0.07103438675403595	0.0504305362701416	-0.009044899605214596	-0.07150880992412567	0.0008683454361744225	-0.05948338657617569	-0.03154319152235985	-0.033144015818834305	-0.0008930513868108392	-0.02624722570180893	0.0918702483177185	0.005751452408730984	-0.06334761530160904	-0.06327518820762634	-0.02630762755870819	-0.04258137196302414	-0.04157208278775215	0.012036182917654514	-0.007512432057410479	-0.031064923852682114	-0.04733985662460327	0.06346917897462845	-0.036592233926057816	-0.023035792633891106	0.0209734495729208	-0.032905787229537964	-0.09126327931880951	0.05066271871328354	-0.002159136813133955	-0.04531148448586464	0.032769884914159775	-0.059860534965991974	-0.007775501348078251	0.11140525341033936	0.050175536423921585	-0.003695968072861433	0.0642971619963646	0.02384107932448387	0.10061398893594742	-0.02341841720044613	-0.03516538068652153	-0.019745750352740288	-0.030590636655688286	-0.012651395983994007	0.023519184440374374	-0.032931338995695114	-0.0022354426328092813	0.0156908817589283	-0.003369749989360571	0.04004868119955063	-0.014284714125096798	-0.04929286241531372	-0.03148767352104187	-0.013282773084938526	-0.010007351636886597	0.0006399225094355643	0.035381779074668884	0.026061473414301872	-0.03888801857829094	-0.059300631284713745	-0.05799432471394539	-0.03116770274937153	0.035496294498443604	-0.013817748986184597	0.022065289318561554	-0.04760827124118805	-0.01590953953564167	0.008462932892143726	-0.03355697914958	0.08710858225822449	-0.06395642459392548	-0.001843105535954237	0.07318664342164993	0.030851734802126884	-0.06994811445474625	0.023079028353095055	0.06901558488607407	-0.0662253201007843	-0.05387518182396889	-0.010834786109626293	0.005057850852608681	-0.05447002872824669	0.006974386051297188	0.03589426353573799	-0.04721315950155258	-0.01311303861439228	0.03711164370179176	0.035789601504802704	0.03460374101996422	-0.013020601123571396	-0.015628743916749954	0.0017840791260823607	0.01684602163732052	0.04064646363258362	-0.029585134238004684	0.025471597909927368	-0.022024858742952347	0.04454151168465614	-0.05380794033408165	-0.054663680493831635	-0.04598047956824303	0.04236886650323868	0.009657401591539383	-0.004342676140367985	-0.03952760994434357	-0.013280264101922512	-0.037655334919691086	0.012044526636600494	0.04067138954997063	-0.08148123323917389	0.0024965754710137844	-0.06543514877557755	-0.08862454444169998	-0.07926321029663086	-0.04706783965229988	-0.007833419367671013	-0.038283444941043854	0.04065362736582756	-0.033771950751543045	0.004639421124011278	0.009056437760591507	-0.014310026541352272	0.01430666632950306	0.07213994115591049	0.023707721382379532	-0.008678204379975796	0.04129921272397041	-0.033045489341020584	0.06500446051359177	0.03863068297505379	-0.06304139643907547	0.02565247192978859	0.01518947258591652	-0.051461994647979736	0.051928456872701645	-0.03702700510621071	-0.0011444503907114267	-0.00077870610402897	-0.041422419250011444	0.022527243942022324	0.030615583062171936	0.020012713968753815	-0.07712116837501526	0.04006824269890785	-0.04531954601407051	-0.038339320570230484	0.04143742471933365	-0.03161158785223961	-0.053320225328207016	-0.04825820028781891	-0.018843507394194603	-0.004755091387778521	0.027690930292010307	-0.05413820222020149	0.03115876577794552	0.0506134107708931	-0.05749072879552841	-0.0670497789978981	-0.025585012510418892	0.004823256749659777	0.06258273124694824	-0.004741920158267021	0.07490934431552887	0.04940098151564598	-0.00295884907245636	-0.013888176530599594	-0.028727123513817787	0.005044683814048767	-0.05248637497425079	0.019307317212224007	-0.009378181770443916	-0.019721366465091705	-0.0011818466009572148	-0.004284912254661322	-0.010052282363176346	-0.05381248518824577	0.004247086122632027	-0.058739081025123596	0.07260407507419586	0.025399984791874886	0.020784027874469757	0.01130611915141344	-0.030364707112312317	0.019133564084768295	0.000984218786470592	-0.014629155397415161	-0.05615117773413658	-0.01629716157913208	-0.05393015593290329	0.033851370215415955	0.030461644753813744	-0.008919700048863888	-0.02559550292789936	-0.050578340888023376	0.010970036499202251	0.02131420001387596	-0.05255649983882904	-0.015538905747234821	0.034411218017339706	-0.02859526500105858	-0.04339449107646942	-0.007004864513874054	-0.05127504840493202	0.08167724311351776	0.07558773458003998	0.03327559307217598	-0.021336480975151062	-0.012541057541966438	0.001219139201566577	-0.00518953800201416	-0.025823166593909264	-0.038276556879282	-0.07834508270025253	0.01664699614048004	0.07075759768486023	-0.053741805255413055	0.013299260288476944	0.050883933901786804	-0.049778662621974945	0.027573933824896812	0.005010643508285284	-0.03669760003685951	0.07066895812749863	-0.009761498309671879	-0.01564200222492218	0.046963464468717575	-0.09530161321163177	0.10240576416254044	-0.050312791019678116	0.07643347233533859	0.008300487883388996	-0.05240205302834511	0.09560230374336243	0.07783979922533035	-0.010862182825803757	0.004483431112021208	-0.0751403272151947
+0.027244165539741516	-0.02764979749917984	-0.032864153385162354	0.046883512288331985	-0.07728751748800278	-0.05033036321401596	-0.01939493417739868	0.0501365065574646	-0.0006540610920637846	-0.03827977180480957	-0.04518067091703415	-0.0221122857183218	0.002122214762493968	-0.0004012710996903479	0.032541003078222275	-0.06205318123102188	-0.08108019083738327	0.04145100712776184	-0.015040055848658085	-0.02972123585641384	-0.001047237659804523	-0.006396753713488579	-0.07546944171190262	0.05809945985674858	-0.00012062472524121404	0.022131938487291336	-0.04622682183980942	0.022044062614440918	0.004003324080258608	0.04153546318411827	0.07004814594984055	-0.04250068962574005	0.03328942507505417	-0.06872735172510147	-0.05342971906065941	-0.0021342409308999777	-0.036270905286073685	0.08590932935476303	0.01916624791920185	-0.046517349779605865	-0.07081440091133118	0.051810331642627716	-0.03784030303359032	-0.05362701416015625	0.05636210739612579	0.011709564365446568	-0.014882681891322136	-0.006690705195069313	0.020222986117005348	0.0025413227267563343	0.0548362210392952	0.028812110424041748	-0.03545171022415161	-0.03205640986561775	-0.03137366473674774	0.022521592676639557	0.019127970561385155	-0.067075714468956	-0.0596439391374588	0.054251778870821	-0.10117694735527039	0.030569909140467644	0.032486386597156525	0.018448565155267715	-0.018127625808119774	-0.03586963564157486	-0.009647546336054802	0.06885585933923721	0.0035879130009561777	-0.02031877636909485	0.014537557028234005	0.08772656321525574	-0.055128563195466995	-0.03753647580742836	0.046708330512046814	-0.05784901976585388	-0.0027114865370094776	0.012059958651661873	0.005073114763945341	-0.007848040200769901	0.03560209274291992	-0.002089653629809618	0.13923844695091248	0.008889440447092056	-0.04843093454837799	-0.01926070638000965	-0.004007880575954914	-0.04518120363354683	0.06712616235017776	0.010034985840320587	0.027813956141471863	0.05294404923915863	-0.009456056170165539	0.05181952938437462	0.002328205620869994	0.044931843876838684	0.002684891689568758	-0.11342713981866837	-0.01682865619659424	-0.012549088336527348	0.009722394868731499	-0.03211892396211624	0.039326995611190796	0.02466045878827572	0.011855628341436386	0.029679544270038605	-0.07280721515417099	0.010524158366024494	0.04895848408341408	0.02538546361029148	0.052610453218221664	0.1126876175403595	0.03622833639383316	0.0704556256532669	0.07147122919559479	0.029284855350852013	-0.002376225311309099	-0.035816267132759094	-0.024487920105457306	0.04924502223730087	0.005223327782005072	0.014776816591620445	0.03365197777748108	-0.08985389024019241	-0.012931966222822666	0.03663231432437897	-0.11250089108943939	-0.01928616873919964	0.019892781972885132	-0.020579803735017776	0.10508962720632553	0.031155651435256004	-0.011541355401277542	0.03806488960981369	0.04704268276691437	0.029611555859446526	-0.004388485569506884	0.002684706123545766	-0.011370999738574028	-0.03162681683897972	-0.06100481376051903	0.07732078433036804	0.005903090350329876	-0.03461030498147011	0.0849127545952797	0.01023037638515234	0.032128553837537766	0.010410499759018421	-0.06954356282949448	-0.008118914440274239	-0.08795507997274399	-0.057965993881225586	0.05154567211866379	-0.0335116870701313	-0.013385090976953506	0.01519032847136259	0.021904170513153076	-0.008602786809206009	-0.02211163192987442	-0.06997750699520111	-0.01772930473089218	-0.00965715479105711	-0.04053690657019615	-0.09689619392156601	0.013474537990987301	0.0036431869957596064	-0.021793147549033165	0.040872421115636826	-0.07842661440372467	-0.0025269908364862204	0.004830977879464626	0.0017412492306903005	0.033061426132917404	0.07392269372940063	0.028048207983374596	-0.00047740794252604246	0.04023939371109009	-0.033752765506505966	-0.010532197542488575	-0.02430892549455166	0.026901334524154663	-0.03883863240480423	0.06178960204124451	-0.053416669368743896	0.019577395170927048	-0.019103309139609337	0.025424066931009293	-0.06019815057516098	0.037603091448545456	-0.036682382225990295	-0.04274498671293259	-0.05630814656615257	0.06056487187743187	0.0434458926320076	-0.005004891660064459	0.012790593318641186	0.013454628176987171	0.04372744262218475	0.007218511775135994	0.008932462893426418	-0.05026886984705925	0.0331234484910965	0.00039250453119166195	0.029249321669340134	0.010628563351929188	-0.05699613317847252	0.07476222515106201	0.020758751779794693	0.04253997281193733	-0.02722555585205555	0.030886616557836533	0.045459046959877014	-0.017404673621058464	0.03262176364660263	0.08358517289161682	-0.013779763132333755	-0.02567555382847786	0.04531703516840935	0.06215434893965721	-0.07519379258155823	0.010750253684818745	-0.0020797746255993843	-0.08303625136613846	0.013277546502649784	-0.05014559254050255	-0.08876020461320877	-0.005968714132905006	0.02471163123846054	-0.021830473095178604	-0.060872357338666916	0.023535657674074173	0.01840898022055626	0.01236141286790371	-0.016853926703333855	-0.017596900463104248	0.012558041140437126	-0.08376562595367432	0.10473591834306717	-0.019017504528164864	-0.06842034310102463	-0.03964800015091896	-0.002666300628334284	0.01892182230949402	0.01657557114958763	0.00702547887340188	0.060658764094114304	0.08682990819215775	0.0567329078912735	-0.04070303589105606	0.03971700370311737	-0.04097718745470047	-0.0630943551659584	0.03697400540113449	0.08941728621721268	-0.04356870427727699	-0.031094176694750786	-0.004302725661545992	-0.0421283021569252	0.03861309587955475	-0.014575622044503689	0.004135566763579845	-0.04823775216937065	-0.013111003674566746	-0.06064147874712944	0.04081764072179794	-0.0087139792740345	-0.011442821472883224	0.047405123710632324	-0.02306811884045601	0.05022144690155983	-0.011754225008189678	-0.05421120673418045	0.044109467417001724	-0.004388737492263317	0.04617439955472946	-0.04630381986498833	0.06284267455339432	0.012431681156158447	-0.05597207695245743	0.002050344832241535	-0.08131518214941025	-0.08210619539022446	-0.018663862720131874	-0.040373336523771286	-0.033873118460178375	0.09302591532468796	0.040239039808511734	-0.02125721238553524	-0.053713954985141754	-0.04566585645079613	0.005230983719229698	-0.07771121710538864	-0.06444880366325378	0.028562437742948532	-0.07193984091281891	-0.0121916513890028	0.03690702095627785	-0.05331336334347725	-0.023917799815535545	0.029632195830345154	-0.006754045374691486	-0.06643614172935486	0.07429253309965134	-0.021508360281586647	-0.0017396957846358418	0.017153730615973473	-0.06932559609413147	0.0406462587416172	0.066622793674469	0.04087039455771446	0.015130273066461086	0.04289032146334648	0.038766320794820786	0.12645022571086884	0.004407100845128298	-0.036928076297044754	-0.029396923258900642	-0.07589251548051834	-0.05593595653772354	0.06136368587613106	-0.03633105754852295	-0.024627499282360077	0.03160718455910683	0.013649079017341137	0.031928904354572296	-0.03536320850253105	0.019099559634923935	-0.04543326050043106	0.02132323756814003	-0.02956504561007023	0.03199124336242676	-0.02744181826710701	0.00317043112590909	0.008403013460338116	-0.04059159755706787	-0.055484212934970856	-0.028054257854819298	0.06144014745950699	-0.01203550212085247	0.03796026110649109	0.010426902212202549	-0.04257666692137718	0.005557812284678221	-0.0021077687852084637	0.09267988055944443	-0.08550242334604263	0.013969551771879196	0.05416397377848625	0.06543400138616562	-0.029033511877059937	0.02229185216128826	0.005860595963895321	-0.04132262244820595	-0.005644043441861868	-0.01175752468407154	0.04217906296253204	-0.07447180896997452	-0.03794233128428459	0.01675960049033165	-0.027252325788140297	-0.004594451282173395	0.015226652845740318	0.024015506729483604	0.054587796330451965	-0.018021080642938614	-0.0040014139376580715	0.020196858793497086	-0.005051465705037117	0.034024614840745926	0.002186607802286744	0.045650020241737366	-0.016799362376332283	0.05767079070210457	-0.04045511782169342	-0.038923230022192	-0.033479154109954834	0.062314409762620926	-0.02677777409553528	0.03772978484630585	-0.002654064679518342	0.009639079682528973	-0.013211800716817379	0.02562820352613926	0.04148218035697937	-0.06795965880155563	-0.012319300323724747	-0.02465025894343853	-0.07713143527507782	-0.06745453923940659	-0.045362766832113266	-0.010719391517341137	-0.05110015720129013	0.01750904507935047	-0.07212253659963608	0.034580353647470474	0.053047262132167816	-0.009681000374257565	0.029618462547659874	0.054681964218616486	0.008434788323938847	-0.0014725567307323217	0.008596093393862247	-0.007253448478877544	0.07665400207042694	0.020591409876942635	-0.051750000566244125	0.007508218754082918	0.061623506247997284	-0.048392388969659805	0.03206969425082207	-0.014323825016617775	0.030568694695830345	0.028874825686216354	-0.005586146377027035	0.020129695534706116	-0.02520691603422165	0.019751034677028656	-0.0011714942520484328	0.028932349756360054	-0.017021488398313522	-0.05971368029713631	0.03818880021572113	-0.03791351988911629	-0.08789093792438507	-0.025029299780726433	-1.2403201253619045e-05	-0.04036055505275726	0.0535823293030262	-0.03380410373210907	0.016976704820990562	0.060750801116228104	-0.04598832130432129	-0.0462728887796402	0.013066697865724564	-0.06342700868844986	0.05722979083657265	-0.008129412308335304	0.05547817796468735	0.007349265273660421	0.028539374470710754	-0.014867018908262253	-0.026043996214866638	-0.022693568840622902	-0.08043315261602402	0.018766673281788826	-0.004001741297543049	-0.045288123190402985	0.010818548500537872	0.006672750227153301	0.015009155496954918	-0.04639880359172821	0.0007885713130235672	-0.01531864982098341	0.06210514158010483	-0.010635114274919033	0.00240417686291039	0.013529852963984013	-0.04803674295544624	0.0012033847160637379	0.029109925031661987	-0.037211593240499496	-0.08016343414783478	-0.015623766928911209	-0.04399413242936134	0.0538589172065258	0.021071672439575195	0.026317937299609184	-0.026587823405861855	-0.0842280462384224	0.024786265566945076	0.0036016504745930433	-0.03596623241901398	0.0011922386474907398	0.0003749394090846181	-0.044064946472644806	-0.04222399368882179	-0.016015034168958664	-0.08072657138109207	0.08892977982759476	0.04480563849210739	0.044539470225572586	-0.03852842375636101	-0.007687499281018972	0.04215918108820915	-0.03996400162577629	0.02179567888379097	-0.07296505570411682	-0.05644892901182175	0.009200971573591232	0.0940556600689888	-0.059532567858695984	0.024788187816739082	0.053379885852336884	-0.016136882826685905	0.059497762471437454	-0.0064823380671441555	-0.03715762868523598	0.012135069817304611	-0.03086850792169571	-0.0356781929731369	0.0535547211766243	-0.07557348161935806	0.03641393408179283	-0.05867704376578331	0.06972397118806839	0.0011136201210319996	-0.055435001850128174	0.09972192347049713	0.09497017413377762	-0.02197299711406231	0.0508396215736866	-0.09698783606290817
+0.02214418724179268	-0.031373076140880585	-0.02314196340739727	0.015222915448248386	-0.06948439031839371	-0.057329874485731125	0.006793298292905092	0.034261539578437805	-0.010566355660557747	-0.04255795478820801	-0.027799168601632118	0.024491652846336365	0.03040016070008278	0.016898462548851967	0.019957194104790688	-0.05006929114460945	-0.07221236824989319	-0.003778618760406971	-0.016052445396780968	-0.038767315447330475	0.016752954572439194	-0.01801837608218193	-0.09549642354249954	0.050119273364543915	0.023674597963690758	-0.0006833048537373543	-0.04755737632513046	0.006816452834755182	0.018878551200032234	0.025356506928801537	0.08690368384122849	-0.05310352146625519	0.04090103134512901	-0.04311952739953995	-0.07656289637088776	0.03191805258393288	-0.06273377686738968	0.12833338975906372	0.010603880509734154	0.01597278192639351	-0.08259200304746628	0.003267911495640874	-0.008818821050226688	-0.018180659040808678	0.022060589864850044	0.007492639124393463	-0.0024652169086039066	-0.022057602182030678	0.03409656137228012	0.007160877343267202	0.08324176073074341	0.024568898603320122	0.0009372489876113832	-0.02126985602080822	-0.01993408054113388	0.020794836804270744	0.029458019882440567	-0.04969443380832672	-0.10010949522256851	0.02504187449812889	-0.09508997946977615	0.01935768313705921	-3.219326390535571e-05	-0.005143336020410061	-0.009768056683242321	-0.0201270692050457	-0.040086835622787476	0.08036717772483826	-0.02001280151307583	-0.0328894704580307	0.03793102130293846	0.07318904250860214	-0.06906413286924362	-0.06957283616065979	0.029516495764255524	-0.05008609965443611	0.00982681568711996	0.028640178963541985	-0.01891694962978363	0.0018589288229122758	-0.045533113181591034	-0.01571257784962654	0.12488226592540741	-0.02785387821495533	-0.036447953432798386	-0.04899011179804802	-0.02614513598382473	-0.056146200746297836	0.061874859035015106	0.03628353029489517	0.04768684506416321	0.06132170185446739	-0.049313366413116455	0.027156267315149307	0.03275471180677414	-0.011761656031012535	0.023782681673765182	-0.11995338648557663	-0.02556617185473442	0.014610329642891884	-0.006564694456756115	-0.0008149388013407588	0.09294427931308746	-0.007785764988511801	0.04054737091064453	0.04822906479239464	-0.08047515153884888	0.042722851037979126	0.03931093215942383	0.04311539605259895	0.028330422937870026	0.10573969036340714	0.049616046249866486	0.07612939924001694	0.052270714193582535	0.013220404274761677	0.004731729626655579	-0.017460202798247337	-0.03581361100077629	-0.008503371849656105	-0.005619589239358902	0.05765910446643829	0.0014062122208997607	-0.07087038457393646	-0.028459951281547546	0.049419015645980835	-0.12420209497213364	-0.019442394375801086	0.07579659670591354	-0.014432129450142384	0.08158392459154129	0.07952157407999039	0.01812640391290188	0.052874792367219925	0.053231410682201385	0.0012291910825297236	0.004754835739731789	0.017978990450501442	-0.007043173536658287	-0.05136649310588837	-0.03757745027542114	0.032471634447574615	-0.004766685888171196	-0.06308946013450623	0.028176669031381607	0.04678736999630928	0.0210746880620718	-0.02694980800151825	-0.03105621039867401	-0.004196465946733952	-0.05497818440198898	-0.07144047319889069	0.01025409996509552	-0.028376197442412376	0.024821806699037552	0.04529796540737152	0.04738111421465874	0.03557480126619339	0.01834828220307827	0.0017109701875597239	-0.04165714606642723	-0.04202900826931	-0.015018715523183346	-0.07515760511159897	0.007001367397606373	0.0030679134652018547	0.02214105613529682	0.01707747019827366	-0.0554368682205677	-0.010698326863348484	-0.010445227846503258	0.0058779846876859665	0.017686894163489342	0.06151462718844414	0.03770012781023979	-0.006852800492197275	0.04089851304888725	-0.05563756451010704	-0.006465866230428219	-0.04395315423607826	0.023164743557572365	-0.0412849560379982	0.009262453764677048	-0.06240665167570114	-0.019114822149276733	0.008991727605462074	0.02034458890557289	-0.031269606202840805	-0.007642839103937149	-0.01030793972313404	-0.04631740599870682	-0.06539487093687057	0.026932531967759132	0.048094142228364944	-0.05061478912830353	0.046842049807310104	0.012209612876176834	0.06906673312187195	-0.010108509100973606	-0.02376139722764492	-0.05868460237979889	0.015315067023038864	0.003289250424131751	0.017228716984391212	0.021105214953422546	-0.054687995463609695	0.0590202733874321	0.005315569229424	0.03277381509542465	-0.06913971155881882	-0.006351298186928034	0.014014595188200474	0.008406365290284157	0.04835040494799614	0.09203595668077469	-0.05755221098661423	-0.033543579280376434	0.023976193740963936	0.06879720091819763	-0.07663081586360931	-0.009781927801668644	-0.02200537547469139	-0.053659312427043915	0.005758239887654781	-0.04301972687244415	-0.10999130457639694	-0.006976887118071318	0.027788400650024414	-0.027363842353224754	-0.03903144598007202	-0.020686190575361252	0.0450812391936779	0.0003930159437004477	-0.026657605543732643	0.0158479493111372	-0.017933310940861702	-0.07769446820020676	0.08020763844251633	0.009733445942401886	-0.08529659360647202	-0.05783623456954956	-0.012709388509392738	0.0203151386231184	0.0018492810195311904	0.014230757020413876	0.029300356283783913	0.09726344048976898	0.04843616858124733	-0.06226477026939392	0.03374185040593147	-0.03155158832669258	-0.028448373079299927	0.022262509912252426	0.0445903018116951	-0.04375700652599335	-0.017287088558077812	0.036908239126205444	-0.05772116780281067	0.004295013844966888	-0.016890333965420723	0.02118358574807644	-0.018311038613319397	-0.05086912959814072	-0.012099087238311768	0.0454091839492321	-0.02604825235903263	-0.05104914680123329	0.0038509282749146223	-0.030766736716032028	0.08088725805282593	-0.05226897448301315	-0.033548034727573395	0.012645712122321129	0.0057526747696101665	0.014167869463562965	-0.04315744712948799	0.07881899923086166	-0.021622583270072937	-0.08027731627225876	-0.029322486370801926	-0.06636452674865723	-0.059652406722307205	-0.009152562357485294	-0.015829768031835556	-0.03166896849870682	0.07581660896539688	0.0012589464895427227	-0.05456917732954025	-0.03656537085771561	-0.04413682222366333	-0.004298308864235878	-0.04723220318555832	-0.0171585064381361	-0.009987571276724339	-0.020510777831077576	-0.02306055836379528	0.04827097803354263	-0.041989121586084366	0.0057522510178387165	0.05025360733270645	-0.014618446119129658	-0.03737954795360565	0.04968247190117836	-0.07600866258144379	-0.008077038452029228	0.002449647756293416	-0.06920406222343445	0.044340088963508606	0.09177859872579575	0.04818815365433693	0.016993315890431404	0.03937628120183945	0.03445697948336601	0.11072880774736404	0.021411221474409103	-0.0430915430188179	0.007742967922240496	-0.061088960617780685	-0.06681693345308304	0.0594036690890789	-0.02065104804933071	0.001553971553221345	-0.020840497687458992	-0.013311212882399559	0.05019044131040573	0.012522152625024319	-0.026057058945298195	-0.0110867815092206	0.020415978506207466	-0.02226131036877632	0.011417551897466183	0.018778495490550995	-0.003253066446632147	0.016963183879852295	-0.051180485635995865	-0.07300961762666702	-0.06197154149413109	0.04072069004178047	-0.026412691920995712	0.090263731777668	-0.038298558443784714	-0.045955345034599304	0.024418406188488007	0.0016420695465058088	0.10384682565927505	-0.06097344681620598	0.019825486466288567	0.006693655159324408	0.06135764718055725	-0.08722855895757675	-0.014211400412023067	0.038191553205251694	-0.02539796009659767	-0.06609167158603668	0.019441040232777596	0.008980263024568558	-0.047656863927841187	-0.024406731128692627	0.009339983575046062	-0.051049698144197464	0.005767450667917728	-0.002120152348652482	-0.00945359654724598	0.035225480794906616	0.00887098629027605	-0.030187468975782394	0.013557635247707367	0.020746110007166862	0.03781372681260109	-0.04601944610476494	0.03329953923821449	-0.050987642258405685	0.07351849973201752	-0.029541227966547012	-0.03833148628473282	-0.07560983300209045	0.04823935776948929	-0.015709076076745987	-0.015025269240140915	-0.05995089188218117	-0.007897529751062393	-0.03050662949681282	-0.011934352107346058	0.0011110692285001278	-0.11060643196105957	-0.009849637746810913	-0.042656634002923965	-0.06946858018636703	-0.056744832545518875	-0.06979434937238693	0.012621327303349972	-0.03508184105157852	0.035287633538246155	-0.015885338187217712	0.022288067266345024	0.02401152066886425	-0.010756523348391056	0.04402075335383415	0.054859552532434464	0.04204820841550827	0.009587986394762993	0.06993379443883896	0.01964845322072506	0.022873422130942345	-0.004929364658892155	-0.028581807389855385	0.039742156863212585	0.05224289000034332	-0.06427782773971558	0.018749356269836426	-0.04643850028514862	0.05369109660387039	-0.016433248296380043	-0.038065265864133835	0.03275339677929878	-0.0053291176445782185	-0.007303605321794748	-0.035547200590372086	0.0435008741915226	-0.050606559962034225	-0.039327822625637054	0.05741892755031586	-0.07490190118551254	-0.06074558570981026	-0.01989707164466381	-0.023119939491152763	-0.030931569635868073	0.0494350902736187	0.024021651595830917	0.06240834295749664	0.029803236946463585	-0.03171858191490173	-0.05304158478975296	0.014009804464876652	-0.017027679830789566	0.02506827376782894	0.0013006612425670028	0.04552345722913742	0.012409267015755177	0.0045717693865299225	0.046720877289772034	-0.02649778686463833	0.03376215323805809	-0.1004381850361824	-0.003590724663808942	-0.001252621877938509	-0.011218114756047726	-0.008463648147881031	0.009663917124271393	0.029480023309588432	-0.07991144061088562	0.014072188176214695	-0.0522146038711071	0.050430335104465485	-0.000744908582419157	0.035749200731515884	0.014221849851310253	-0.04059106111526489	-0.015960590913891792	-0.019671304151415825	-0.017062082886695862	-0.08414503931999207	0.009297778829932213	-0.04931565001606941	0.03023059479892254	-0.01378429401665926	0.015394605696201324	0.00787173118442297	-0.11424222588539124	0.004140331409871578	0.005861620884388685	-0.038775164633989334	0.010426871478557587	0.013618454337120056	-0.07639827579259872	-0.06551240384578705	-0.001989136915653944	-0.058747999370098114	0.058856572955846786	-0.009344144724309444	0.009350254200398922	-0.04558618739247322	-0.007735621649771929	-0.008477956987917423	-0.029135767370462418	0.03315793350338936	-0.03546822816133499	-0.058662451803684235	-0.0409250445663929	0.08844495564699173	-0.031005827710032463	0.037853408604860306	0.06969500333070755	-0.025800565257668495	0.047912150621414185	0.016578087583184242	-0.051518723368644714	0.020009836181998253	-0.017202256247401237	-0.0026891615707427263	0.03728913143277168	-0.07823608070611954	0.06620533019304276	-0.06884298473596573	0.09338176250457764	0.03938135877251625	-0.002823433605954051	0.09853126853704453	0.06925363093614578	-0.00026152023929171264	0.03331149369478226	-0.06660033762454987
+-0.00907989777624607	-0.07607991248369217	-0.0524737648665905	0.012592985294759274	-0.010020763613283634	-0.04000139981508255	-0.03962958604097366	0.0341937318444252	0.01776423305273056	-0.06511666625738144	-0.054855603724718094	-0.0491541288793087	-0.005713246297091246	-0.04898960888385773	0.058122020214796066	7.8545359428972e-05	-0.05709195137023926	0.02444174326956272	0.01319657638669014	0.011636514216661453	0.004160332027822733	0.01973453164100647	-0.08704885095357895	0.069121353328228	0.0006065308698453009	0.008798823691904545	-0.047068431973457336	0.043608758598566055	-0.013167507015168667	0.10367650538682938	0.019418584182858467	-0.02879735641181469	0.03060046397149563	-0.06278052181005478	-0.00437226053327322	-0.0014186374610289931	-0.02856483682990074	0.08747996389865875	0.048965610563755035	-0.031943660229444504	-0.014903686940670013	-0.0011395688634365797	-0.02222011797130108	-0.10525914281606674	0.025593703612685204	0.0022843428887426853	-0.03579847514629364	-0.04802767559885979	-0.01829061098396778	0.07028074562549591	0.0816720724105835	-0.002599917585030198	-0.0007353519322350621	0.01243688352406025	-0.058377765119075775	-0.007278388366103172	0.02051786705851555	-0.021610818803310394	-0.011958968825638294	0.044429779052734375	-0.06177709624171257	-0.00930349063128233	0.002675085561349988	0.03144308552145958	-0.08444096893072128	-0.011729691177606583	0.012360341846942902	0.04011283814907074	-0.02859770506620407	-0.014241013675928116	-0.03481536731123924	0.013233177363872528	-0.057458776980638504	-0.04922722280025482	-0.007205908186733723	-0.03249160200357437	-0.013821026310324669	-0.0063753011636435986	0.030563320964574814	-0.004516893066465855	0.032451946288347244	0.0040466017089784145	0.11376927047967911	-0.04618429020047188	0.009666703641414642	-0.07115917652845383	-0.015337738208472729	-0.034093227237463	0.017645126208662987	0.0007287742919288576	0.05558225139975548	0.03380563110113144	0.0593925379216671	0.020525068044662476	0.01919744163751602	0.030341651290655136	0.04643918573856354	-0.09299666434526443	-0.012573756277561188	0.0005955442902632058	-0.03931371495127678	0.011829332448542118	0.057362355291843414	0.056423839181661606	0.032634127885103226	-0.016751276329159737	-0.07214215397834778	0.04530841484665871	0.06545316427946091	0.03252562880516052	0.00861535407602787	0.0995878353714943	0.005546048283576965	0.00400934275239706	0.06788379698991776	0.0324128195643425	0.00889586377888918	0.0055628614500164986	-0.006905816495418549	0.028106752783060074	-0.002974890172481537	0.03652837499976158	-0.006802065297961235	-0.07936234772205353	0.09402503073215485	0.09295263886451721	-0.0628095343708992	-0.030703606083989143	-0.05399040877819061	-0.02608705312013626	0.04179692640900612	0.042024366557598114	-0.0007571289897896349	-0.006974526681005955	0.03114769235253334	0.016382237896323204	0.007008429151028395	0.04040471464395523	0.013813587836921215	-0.018517563119530678	-0.04423947632312775	-0.004324531648308039	-0.040186937898397446	0.012425792403519154	0.05776642635464668	0.006102937739342451	-0.01404223870486021	0.00461761886253953	-0.0651206374168396	-0.02000822313129902	-0.06252679228782654	-0.02077518403530121	0.048743877559900284	-0.07260129600763321	0.024209486320614815	-0.006662398111075163	0.027993781492114067	0.050631485879421234	0.0335988886654377	0.03592982888221741	-0.05140404775738716	-0.08616059273481369	0.010531090199947357	-0.06496947258710861	-0.01637452468276024	0.01053532399237156	0.004155322909355164	-0.014890090562403202	-0.017240257933735847	-0.00795040838420391	0.020479397848248482	0.029959842562675476	0.0663665160536766	0.039610207080841064	0.05245008319616318	0.03043181262910366	0.04491959884762764	-0.057348012924194336	-0.035413648933172226	-0.06127597764134407	-0.016688482835888863	-0.008337490260601044	-0.02269979938864708	-0.048700910061597824	-0.017094092443585396	0.02055511623620987	-0.0037103539798408747	0.008421970531344414	0.01054269541054964	-0.017278462648391724	0.007128353230655193	-0.06958895176649094	0.08128974586725235	0.0033493563532829285	0.0072339545004069805	0.013360565528273582	0.045846424996852875	0.08403302729129791	-0.06956701725721359	-0.059967394918203354	-0.03077072650194168	0.01639695093035698	0.054895829409360886	0.002004749607294798	-0.005558764562010765	0.019949402660131454	0.06550560891628265	0.03687799721956253	0.024804895743727684	-0.005953081417828798	0.03616161644458771	0.03629974275827408	-0.02479706145823002	0.020897705107927322	0.10416702926158905	-0.0333734005689621	-0.021920323371887207	0.033064164221286774	0.03948133438825607	-0.09298907965421677	-0.004045397508889437	0.024210629984736443	-0.10962698608636856	-0.007310131099075079	-0.021668458357453346	-0.07876098901033401	0.02089815028011799	0.023742415010929108	-0.0446602888405323	0.01352942455559969	0.021902723237872124	0.012521139346063137	-0.01071157120168209	0.00799604132771492	0.007542040664702654	0.02354506589472294	-0.05576092749834061	0.029241623356938362	-0.061150625348091125	-0.07143326848745346	-0.056621003895998	0.037854548543691635	-0.008282729424536228	0.0056626624427735806	0.02367584966123104	0.04078897088766098	0.08539275079965591	0.05230308696627617	-0.042995672672986984	0.03241570293903351	-0.056534543633461	-0.045222967863082886	-0.0026236444246023893	-0.005294115282595158	0.04435184597969055	-0.04683224484324455	0.0098740104585886	-0.10699079185724258	-0.027931714430451393	-0.002980844583362341	-0.0013070941204205155	-0.056584954261779785	0.017504308372735977	-0.030649341642856598	0.04179850593209267	-0.032853249460458755	-0.03030640259385109	0.054741665720939636	-0.05304386839270592	-0.004276636056602001	-0.08175795525312424	0.012394944205880165	0.031871624290943146	9.357390081277117e-05	-0.008047833107411861	-0.05069201812148094	0.08153819292783737	-0.023405713960528374	-0.03150453418493271	-0.06138106808066368	-0.1151062622666359	-0.002342269755899906	0.05668080598115921	-0.003587137907743454	-0.020333116874098778	0.05227559804916382	0.07473844289779663	-0.02053047902882099	-0.07421670109033585	-0.10575840622186661	-0.041305381804704666	-0.003627452068030834	0.007411740720272064	0.004919916857033968	-0.01753348670899868	-0.0196338202804327	0.08956341445446014	-0.03830298036336899	0.0030927471816539764	0.04723319411277771	0.03211156278848648	-0.053950805217027664	0.038779787719249725	-0.00892682932317257	-0.03555096685886383	0.07863807678222656	-0.06394902616739273	-0.0030892794020473957	0.07487326860427856	0.025746531784534454	0.010913562029600143	0.054945509880781174	0.028709031641483307	0.041156332939863205	-0.03378553315997124	-0.031391702592372894	-0.06767478585243225	-0.02607037127017975	-0.031409841030836105	0.017582371830940247	0.008155249059200287	0.024877449497580528	-0.020685164257884026	-0.022392554208636284	0.01908985525369644	-0.018712390214204788	-0.015011955983936787	-0.05356091260910034	-0.021225839853286743	-0.030467459931969643	0.013759160414338112	-0.026998743414878845	0.04879050329327583	-0.019147755578160286	-0.056224919855594635	-0.04658704251050949	-0.04531434550881386	0.041759803891181946	-0.025170335546135902	0.022382790222764015	0.014378301799297333	-0.04264489561319351	0.013537455350160599	0.004473432432860136	0.08469461649656296	-0.09987697750329971	0.0375785231590271	0.03753078356385231	0.026109199970960617	-0.06177983805537224	-0.03840683400630951	0.00658729812130332	-0.10173120349645615	-0.037501804530620575	-0.020928917452692986	0.02751542627811432	0.00810681376606226	-0.07276304066181183	-0.0198456272482872	-0.03343384340405464	0.04618664085865021	0.06652519851922989	0.0021663003135472536	0.05349968001246452	-0.002582824556156993	-0.05837663635611534	-0.02969779446721077	-0.008468147367238998	0.011569312773644924	-0.0414913035929203	0.048664674162864685	0.016369909048080444	-0.01980108581483364	-0.03585328534245491	-0.033905357122421265	-0.04703159257769585	0.010281219147145748	-0.06950019299983978	0.011238334700465202	-0.02315145544707775	-0.004551443737000227	-0.027420004829764366	0.05542801693081856	0.025972358882427216	-0.02604057639837265	-0.0735037550330162	-0.004767522215843201	-0.04990621283650398	-0.05229954421520233	-0.04148498922586441	0.05573323741555214	0.015410033985972404	0.05330618843436241	-0.0711636170744896	0.02605791762471199	0.007871133275330067	0.02728196047246456	0.041412703692913055	0.0629655048251152	0.027831338346004486	-0.002026359783485532	0.0232241190969944	-0.027770692482590675	0.014775089919567108	-0.0006750496104359627	-0.05800141394138336	0.008957267738878727	0.019966235384345055	-0.04107826203107834	-0.023735230788588524	-0.013717426918447018	0.016592105850577354	-0.0050433906726539135	-0.07912975549697876	-0.002433537971228361	-0.002370027359575033	-0.004548234399408102	0.002494486980140209	0.037596579641103745	-0.03996523469686508	-0.03309733420610428	-0.07396356016397476	-0.030440116301178932	-0.036613572388887405	-0.05510469526052475	-0.030020710080862045	0.00756685808300972	0.019624382257461548	-0.06814800202846527	0.003981567919254303	0.032297708094120026	-0.03328762948513031	0.010688153095543385	0.021576015278697014	0.05878010019659996	-0.00920094270259142	0.06778468936681747	-0.002990999026224017	-0.0297989584505558	-0.022647136822342873	-0.0023862409871071577	-0.0512673556804657	0.019867252558469772	-0.009789426811039448	-0.026312127709388733	0.03952858969569206	0.01839749701321125	0.003983172122389078	0.05631344020366669	0.03217440843582153	-0.09582432359457016	-0.033389486372470856	-0.030186686664819717	0.035374827682971954	0.017449503764510155	0.05582166090607643	0.017383301630616188	-0.10265990346670151	-0.08260390162467957	0.02890441194176674	-0.04259935021400452	-0.08052623271942139	-0.06012515723705292	-0.040219444781541824	0.0736057236790657	0.009705044329166412	0.01733557879924774	-0.05233875662088394	-0.08693791180849075	0.024454565718770027	-0.00595202436670661	-0.04408106207847595	-0.08038748800754547	0.05931537225842476	-0.022593563422560692	0.03880934789776802	-0.009379791095852852	-0.04226319491863251	0.04294130578637123	0.021258581429719925	-0.0032430822029709816	-0.10436778515577316	0.01759708672761917	-0.027737271040678024	-0.0018660728819668293	0.032169610261917114	-0.05528876930475235	-0.08787558227777481	-0.0012484716717153788	0.08568808436393738	-0.026368439197540283	0.06118552014231682	0.06663728505373001	-0.0018337544752284884	0.06511739641427994	0.07032207399606705	0.007265772204846144	-0.06195224076509476	-0.04189734905958176	0.0005490256007760763	0.046342503279447556	-0.0582025982439518	0.03636031225323677	-0.0765838623046875	0.15659348666667938	0.04513875022530556	-0.012926350347697735	0.022933421656489372	0.07528380304574966	0.06993602216243744	0.033388201147317886	-0.09779596328735352
+0.050452154129743576	-0.003939176443964243	0.008260250091552734	0.04519585520029068	-0.08444011956453323	-0.05177067965269089	-0.015446689911186695	0.03201370686292648	0.010990262031555176	-0.03350478410720825	-0.01058354601264	0.01446018647402525	0.0026471298187971115	0.004164605401456356	0.023041458800435066	-0.029556114226579666	-0.05127155780792236	-0.013167442753911018	-0.02695981040596962	-0.02492966689169407	0.03720215708017349	0.04670356214046478	-0.052921369671821594	0.0261748768389225	0.014454545453190804	0.024413196370005608	-0.04090915992856026	-0.01589421182870865	-0.004130539949983358	0.024617506191134453	0.026168562471866608	-0.03960390388965607	0.032691244035959244	-0.05568332225084305	-0.05238019675016403	0.06884028017520905	-0.03051624819636345	0.10238576680421829	0.047941163182258606	-0.04544644057750702	-0.03363161161541939	-0.010480411350727081	-0.027433909475803375	-0.06124440208077431	-0.02951079234480858	0.016974445432424545	-0.007995069026947021	-0.02561584673821926	0.028249680995941162	0.024265799671411514	0.10939984023571014	-0.013174505904316902	-0.03009624034166336	-0.012298396788537502	-0.0032682681921869516	-0.010057292878627777	0.05039439722895622	-0.056725554168224335	-0.08613699674606323	0.03867504373192787	-0.07963532954454422	-0.01116870902478695	-0.0010322877205908298	0.03508751839399338	-0.024952935054898262	-0.03669895604252815	0.00874290894716978	0.07526469975709915	0.05534815788269043	-0.03970332071185112	0.036195047199726105	0.09447426348924637	-0.038833439350128174	-0.05413680523633957	0.030813461169600487	-0.07080547511577606	-0.03673459589481354	0.029416363686323166	0.004620116204023361	0.026519369333982468	0.011028695851564407	-0.07373760640621185	0.1042715460062027	-0.05949785187840462	-0.05219050124287605	-0.010256798006594181	-0.020737331360578537	-0.08363252133131027	0.05554817616939545	0.04311564192175865	0.004269236698746681	0.0817275121808052	-0.08507516235113144	0.05935016646981239	-0.007152083795517683	0.013673955574631691	0.0400225855410099	-0.10091114044189453	-0.064516581594944	-0.01855364441871643	-0.03651026636362076	-0.01143612153828144	0.05739301070570946	0.013458486646413803	0.07561549544334412	0.008885926567018032	-0.02664959244430065	0.0050641633570194244	0.03198852017521858	0.041811466217041016	0.038655247539281845	0.046764373779296875	0.10664218664169312	0.05271584540605545	0.05866311863064766	-0.027938678860664368	0.013424523174762726	-0.048392970114946365	-0.03639828786253929	-0.00952933356165886	0.007760126143693924	0.046237047761678696	-0.029189925640821457	-0.08113545924425125	-0.02561010792851448	0.025109605863690376	-0.092836894094944	-0.02595077082514763	0.047243740409612656	-0.012596395798027515	0.1140853688120842	0.0846170037984848	-0.049433935433626175	0.024474387988448143	0.03150840476155281	0.03333350270986557	-0.023783830925822258	0.052943434566259384	0.009841911494731903	0.001922545488923788	-0.015358890406787395	0.036989666521549225	0.029012011364102364	-0.06537358462810516	0.004927036352455616	0.037594377994537354	0.042122066020965576	-0.034087736159563065	-0.0731779932975769	-0.006766695063561201	-0.050103332847356796	-0.07853589206933975	-0.007934372872114182	-0.07453593611717224	0.015110869891941547	0.05419451370835304	0.04076676815748215	0.037416357547044754	-0.057219620794057846	0.026791563257575035	-0.028669001534581184	-0.04325070232152939	-0.0018633623840287328	-0.10223255306482315	-0.03903310000896454	-0.01827828772366047	0.0012230293359607458	0.00885044690221548	-0.05150556564331055	-0.020201796665787697	-0.030173305422067642	-0.017918450757861137	0.05174923315644264	0.09026239067316055	0.07774371653795242	-0.004479111637920141	0.010097256861627102	-0.06663063913583755	0.03663286194205284	-0.007603519596159458	0.010599449276924133	-0.04447288066148758	0.022803470492362976	-0.06617484986782074	-0.031271643936634064	0.004696170333772898	0.0644889697432518	-0.008955661207437515	0.0045919036492705345	0.017602020874619484	-0.05499587953090668	-0.0466306246817112	0.01781294122338295	0.06934694200754166	-0.01644316501915455	0.042022231966257095	-0.019873928278684616	0.05056462809443474	0.005793592892587185	-0.037723056972026825	-0.058527298271656036	0.023198653012514114	-0.0061969272792339325	0.06141650676727295	0.006707584019750357	-0.05232984572649002	0.0890364795923233	0.03949819132685661	0.01816987618803978	-0.07965380698442459	-0.011735312640666962	0.03631405532360077	0.014602035284042358	0.025607004761695862	0.057839423418045044	-0.051772214472293854	-0.0033336998894810677	0.0004130535526201129	0.04099377617239952	-0.043141920119524	-0.023524004966020584	-0.014036539010703564	-0.04975191503763199	0.01250310055911541	-0.013243470340967178	-0.06086273863911629	-0.05814695358276367	0.025950422510504723	0.03221593797206879	-0.04724320396780968	0.008177614770829678	0.02502562664449215	-0.005021672695875168	-0.013958687894046307	-0.02337975986301899	-0.03770393878221512	-0.10745101422071457	0.07742325961589813	-0.0007896938477642834	-0.04593263939023018	-0.05678780749440193	-0.018567323684692383	0.023950878530740738	0.003146157832816243	0.04300848767161369	0.07453636825084686	0.07257658243179321	0.020990557968616486	0.0032544657588005066	-0.037607792764902115	-0.035959579050540924	-0.04685722291469574	0.009618324227631092	0.025719573721289635	-0.04036124050617218	-0.036002546548843384	0.00769046088680625	-0.06373707950115204	-0.020836595445871353	-0.009036576375365257	0.03907840698957443	-0.01675885170698166	-0.05032647028565407	-0.033475112169981	0.03608199581503868	0.0073175691068172455	-0.026788584887981415	0.026120640337467194	-0.015758177265524864	0.08351434022188187	-0.026398519054055214	-0.022010544314980507	0.05738009139895439	-0.011249029077589512	0.026380762457847595	0.029635244980454445	0.07128269225358963	-0.02827901393175125	-0.06491229683160782	-0.0248107947409153	-0.06928538531064987	-0.02292596735060215	-0.022513004019856453	-0.023963121697306633	-0.06519722938537598	0.08019529283046722	-0.021986575797200203	-0.031871214509010315	-0.027973705902695656	0.03229570761322975	-0.02177836000919342	-0.09939181804656982	-0.014845958910882473	0.005975085310637951	-0.07710837572813034	-0.015654176473617554	0.0016033948631957173	-0.04883107915520668	-0.061280641704797745	-0.0018709903815761209	-0.03896806016564369	-0.06330695748329163	0.03926180675625801	-0.01712196320295334	-0.051679421216249466	-0.042351122945547104	-0.07331889867782593	0.019712451845407486	0.0785372257232666	0.007541762664914131	0.05930463969707489	0.021066315472126007	0.036181699484586716	0.10036028921604156	0.02506040222942829	-0.05076379328966141	-0.009197205305099487	-0.03716963157057762	-0.06661560386419296	0.07177010923624039	-0.034635115414857864	0.018337713554501534	-0.00673655467107892	0.03111586906015873	0.07259556651115417	0.01617666520178318	-0.024249523878097534	-0.00948390830308199	0.035780198872089386	-0.004876128863543272	0.04936280474066734	-0.014615360647439957	-0.009518452920019627	-0.014767938293516636	-0.03131181374192238	-0.060171861201524734	-0.004937350284308195	0.018370719626545906	0.009592466987669468	0.06828796118497849	0.012112585827708244	-0.08387397974729538	-0.009895089082419872	-0.009062686003744602	0.09761306643486023	-0.08331780880689621	0.0040153139270842075	0.005523812957108021	-0.0075714923441410065	-0.1145375519990921	-0.0022644782438874245	0.05139218270778656	-0.03326476365327835	-0.07422436028718948	0.0007317334529943764	0.04291269928216934	-0.07363781332969666	-0.003688669530674815	-0.024910498410463333	-0.050396114587783813	0.03680530935525894	0.0037134212907403708	0.022866925224661827	0.023262619972229004	0.013989254832267761	-0.040428172796964645	-0.002091372152790427	-0.0011914665810763836	0.0016713140066713095	-0.023586537688970566	-0.012659757398068905	-0.06752514094114304	0.06577784568071365	-0.06016532704234123	-0.0074156210757792	-0.046115994453430176	0.04411131143569946	0.011949400417506695	-0.03266472369432449	0.0161927230656147	-0.013338982127606869	0.011087831109762192	0.030587397515773773	0.023063546046614647	-0.027576660737395287	-0.04001374915242195	-0.055969104170799255	-0.005118536297231913	-0.06935874372720718	-0.020527023822069168	0.025295671075582504	-0.08393611758947372	-0.029224444180727005	-0.006077983416616917	-0.030741700902581215	-0.007193136494606733	-0.01969766430556774	0.015613178722560406	0.02343171089887619	0.04634738713502884	-0.061016954481601715	0.03337343782186508	0.007390920072793961	0.04735449701547623	0.018033096566796303	-0.03511253744363785	0.027336878702044487	0.04273650795221329	-0.04431351274251938	0.04215776547789574	-0.035194121301174164	-0.0027245560195297003	0.001771252485923469	0.003443524707108736	-0.001155503443442285	-0.0123305544257164	-0.0024226298555731773	-0.01998485065996647	0.07305360585451126	-0.10533329099416733	-0.04198089614510536	0.03292896971106529	-0.032874103635549545	-0.11105594784021378	-0.033534351736307144	-0.0045922184363007545	-0.05767408013343811	0.01196148432791233	0.023095544427633286	0.017815807834267616	0.05551109462976456	-0.035400547087192535	-0.00875396654009819	0.009020781144499779	-0.027570217847824097	0.021292701363563538	-0.008282908238470554	0.04544448107481003	-0.005774262826889753	0.03584149852395058	0.012104962952435017	0.02444426901638508	0.012836902402341366	-0.08881337940692902	-0.03402983024716377	0.004632672294974327	-0.03814132139086723	0.0029593301005661488	0.01860158145427704	-0.010133357718586922	-0.07433059811592102	-0.04710157960653305	-0.031013362109661102	0.07166004925966263	-0.02846398949623108	-0.01244320347905159	0.06627941131591797	-0.028458114713430405	-0.02047262340784073	0.016896918416023254	-0.028379173949360847	-0.10472627729177475	0.0230341125279665	-0.007605835795402527	0.006712486036121845	0.007940664887428284	0.017009558156132698	-0.032157957553863525	-0.06890781968832016	-0.020509522408246994	0.021007759496569633	-0.0715395137667656	0.038062479346990585	0.015132575295865536	-0.07918979972600937	-0.06978937238454819	0.01572388969361782	-0.04671250656247139	0.007768275681883097	0.02039971575140953	0.03851700946688652	-0.0014964997535571456	0.014003907330334187	-0.02101881057024002	-0.043112341314554214	0.017270836979150772	0.02486596070230007	-0.03305896371603012	-0.044347263872623444	0.07413393259048462	-0.04638143628835678	-0.002821449190378189	0.05542252957820892	-0.03298928216099739	0.02663099579513073	0.058669351041316986	-0.032408736646175385	0.06389930099248886	-0.007200111169368029	-0.03529882803559303	0.09344268590211868	-0.07323450595140457	0.09236345440149307	-0.04597433656454086	0.10990764945745468	-0.0020230808295309544	0.0002190605882788077	0.07508113235235214	0.07645083963871002	-0.011452550068497658	0.021493038162589073	-0.058941058814525604
+0.032220788300037384	-0.0751892551779747	4.0675888158148155e-05	0.057313431054353714	-0.059900201857089996	-0.02181820385158062	-0.023182308301329613	0.03088538721203804	-0.0093834912404418	-0.08964697271585464	-0.0022147223353385925	-0.04859773442149162	-0.042388223111629486	0.016013670712709427	0.06922096759080887	0.026548491790890694	-0.023493394255638123	0.06433896720409393	-0.02112254686653614	-0.038474541157484055	0.00998873170465231	0.0004231512430123985	-0.04851283133029938	0.02700524590909481	-0.033647213131189346	-0.02800462394952774	-0.07308957725763321	0.046590130776166916	0.01286715641617775	0.057717692106962204	0.01986083947122097	-0.024088606238365173	0.03819190710783005	-0.04087512940168381	-0.06521022319793701	0.04610145837068558	-0.03874889388680458	0.1087050512433052	0.021070318296551704	-0.006969427689909935	-0.034444257616996765	0.006639582570642233	-0.02889905869960785	-0.05548141896724701	0.08628023415803909	-0.010956368409097195	0.030847353860735893	-0.011447305791079998	0.01141701452434063	0.08369366079568863	0.08030537515878677	0.034955140203237534	0.03125997260212898	-0.0025837267749011517	-0.006012899801135063	0.008350377902388573	0.057393353432416916	-0.060728807002305984	-0.006592886056751013	0.06797315925359726	-0.09998630732297897	-0.05624350160360336	0.03650287538766861	0.032183267176151276	-0.02832302637398243	-0.0033447998575866222	-0.012725773267447948	0.09689443558454514	-0.030395973473787308	-0.07806093245744705	-0.035322532057762146	0.06579016149044037	-0.022764902561903	-0.08700495958328247	0.030922414734959602	-0.0613953173160553	0.026313213631510735	0.03165986016392708	-0.019271550700068474	-0.017858583480119705	-0.015747109428048134	-0.03765617683529854	0.08736028522253036	-0.007949084043502808	-0.0021885614842176437	-0.03570268303155899	-0.018160631880164146	-0.04695017263293266	0.03802702575922012	0.01342433039098978	0.0346132293343544	0.04611693322658539	-0.032079827040433884	0.00926534179598093	-0.007145661860704422	0.013682504184544086	0.001374166226014495	-0.0837697684764862	-0.012586026452481747	-0.0011217262363061309	0.004108358174562454	-0.004676509648561478	0.030951745808124542	0.02456611767411232	0.06254585087299347	0.010774395428597927	-0.044119104743003845	0.0023991893976926804	0.03160526230931282	0.02502266690135002	0.0002747969701886177	0.10339153558015823	0.06990480422973633	0.026806799694895744	0.04153217375278473	0.025121964514255524	-0.008701551705598831	-0.05905859172344208	-0.019839677959680557	0.002425809158012271	0.003294014371931553	0.03594454750418663	0.016661370173096657	-0.06808406859636307	0.013718374073505402	0.03964430093765259	-0.0790865570306778	-0.03611461818218231	0.07036294043064117	-0.023390164598822594	0.08166315406560898	0.02845381759107113	-0.0217706598341465	0.04180578514933586	0.054698165506124496	0.04539331793785095	-0.016814425587654114	0.0224191565066576	0.0288540031760931	-0.03759489953517914	0.00916746724396944	0.03860202059149742	-0.019620094448328018	-0.06558771431446075	0.05557757988572121	0.04229728877544403	0.027658136561512947	-0.022447310388088226	-0.09245093166828156	0.02956593595445156	-0.03565467521548271	-0.05173765867948532	-0.011723832227289677	-0.11192617565393448	0.0013709928607568145	0.03930332884192467	-0.019801903516054153	0.030214833095669746	0.007021740078926086	-0.021388892084360123	-0.08346228301525116	-0.06871693581342697	-0.011216537095606327	-0.05719703808426857	-0.04432633891701698	-0.015826523303985596	-0.019714614376425743	0.035381853580474854	-0.07026852667331696	-0.04837462678551674	0.005746633280068636	0.002484344644472003	0.07515408843755722	0.05103898420929909	0.09447991847991943	0.03817662596702576	0.01446085050702095	-0.04347775876522064	-0.008777732029557228	-0.035217899829149246	-0.005397344008088112	0.0031131489668041468	0.015218598768115044	-0.08928317576646805	-0.02564927004277706	-0.04086260870099068	0.02652970515191555	-0.01951776258647442	0.007522983010858297	-0.01888592168688774	-0.015966802835464478	-0.045991189777851105	0.07308779656887054	0.08065079152584076	-0.024380052462220192	0.04308886080980301	-0.06418806314468384	0.0822233259677887	-0.08738542348146439	-0.060561470687389374	-0.035887617617845535	0.06159302964806557	0.07321824133396149	0.0042915670201182365	-0.012014313600957394	-0.07518325746059418	0.07431138306856155	0.03652552515268326	0.01758573018014431	-0.03381285071372986	0.02937805838882923	0.03450200706720352	0.012372639030218124	0.04477429389953613	0.042063646018505096	-0.013578200712800026	-0.0014262159820646048	-0.0070356582291424274	0.051796820014715195	-0.051728587597608566	0.027891647070646286	0.004332822747528553	-0.07137758284807205	0.0021852594800293446	-0.04195481538772583	-0.07182061672210693	0.003176246304064989	0.03923945501446724	-0.06048646569252014	0.011998110450804234	0.0016807870706543326	0.0013068101834505796	-0.02047020010650158	-0.0024940280709415674	0.002939312718808651	-0.0431288480758667	-0.04708308354020119	0.07551686465740204	0.015043419785797596	-0.05352586507797241	-0.09550632536411285	-0.02991248294711113	0.04979116469621658	-0.026523737236857414	0.024821912869811058	0.04263269156217575	0.04026702418923378	0.02278514765202999	-0.014473186805844307	0.017560210078954697	-0.035657696425914764	-0.03717513754963875	-0.02952413633465767	0.006083320360630751	-0.042805712670087814	-0.015636973083019257	-0.024864960461854935	-0.07882227003574371	0.0013653809437528253	0.004122147336602211	-0.016803067177534103	-0.06257370859384537	0.014012182131409645	0.009329217486083508	0.01878848671913147	-0.04985276237130165	-0.03468688949942589	-0.01929118111729622	-0.0377507209777832	0.023325439542531967	-0.06807364523410797	0.002689302433282137	0.044607508927583694	-0.010696418583393097	-0.0054921661503612995	-0.07020477205514908	0.02311781607568264	-0.013932274654507637	-0.08675939589738846	-5.699926077795681e-06	-0.10390809178352356	-0.028514588251709938	0.011807051487267017	-0.026514768600463867	-0.02166224829852581	0.10925409942865372	0.04572618380188942	-0.02777918055653572	-0.043720755726099014	-0.06682077795267105	-0.008662157692015171	-0.015898369252681732	0.0043544829823076725	0.005628497805446386	-0.017022782936692238	0.0015406827442348003	0.027346909046173096	-0.05201621353626251	0.0025363320019096136	-0.028230534866452217	-0.018634309992194176	-0.03864128887653351	0.045740608125925064	-0.022089585661888123	-0.0122127216309309	0.05474669858813286	-0.03426085412502289	0.05178959667682648	0.09339527785778046	0.05320858210325241	-0.01452658697962761	0.044190291315317154	0.016187097877264023	0.09040184319019318	0.017369180917739868	-0.002170486841350794	-0.027079250663518906	-0.046648405492305756	-0.011224807240068913	0.037817616015672684	-0.03000747226178646	-0.008852073922753334	0.01344972662627697	-0.020133554935455322	0.09955362975597382	-0.00865195319056511	-0.05472206696867943	0.009028726257383823	0.009588428772985935	0.007379476446658373	-0.001458359882235527	-0.0032716684509068727	0.003410832490772009	-0.03126842901110649	-0.06234891712665558	-0.009990086778998375	-0.0030474024824798107	0.06029851362109184	-0.05843029171228409	0.026091860607266426	-0.05170284956693649	-0.05402472987771034	0.01969623751938343	0.04936766251921654	0.1037382036447525	-0.05741596594452858	0.0304871778935194	0.05594702437520027	0.025614231824874878	-0.07932919263839722	0.022311192005872726	-0.0063829426653683186	-0.05337824299931526	-0.049261920154094696	0.023137694224715233	0.0145296985283494	-0.06668372452259064	-0.033495672047138214	-0.03762279823422432	-0.03941294550895691	-0.008341019973158836	0.007437160238623619	0.005289032123982906	0.04152810201048851	-0.03591299429535866	-0.014303091913461685	-0.04124123230576515	-0.002286625327542424	0.020733783021569252	-0.05793757736682892	0.011359812691807747	0.0036903684958815575	-0.0004788423248101026	-0.05194401368498802	-0.061791084706783295	-0.0025544108357280493	0.046643976122140884	-0.03741760924458504	0.005636902526021004	-0.037961166352033615	-0.06818032264709473	-0.02843335084617138	-0.002573517384007573	0.03025396354496479	-0.04417826980352402	-0.032422300428152084	-0.023456528782844543	-0.04707840457558632	-0.100476935505867	-0.09924250096082687	0.052113715559244156	-0.021188730373978615	0.04299448803067207	-0.051635850220918655	0.001858184696175158	-0.01188543438911438	-0.01351043302565813	0.022022446617484093	0.08089853823184967	0.02428293414413929	0.003777183359488845	0.026095449924468994	-0.023693472146987915	0.013082257471978664	0.03528571128845215	-0.05883830785751343	-0.001174235949292779	-0.02185649424791336	-0.03493082523345947	0.04814495891332626	-0.014984294772148132	0.04693213850259781	-0.025252511724829674	-0.04949081689119339	0.039973482489585876	-0.0313144326210022	-0.01429295539855957	-0.03572765365242958	0.09525823593139648	-0.058013271540403366	-0.024797547608613968	-0.023962359875440598	-0.02773332968354225	-0.10101276636123657	-0.05661986023187637	-0.02142244018614292	0.011851916089653969	0.05997729301452637	-0.04487449303269386	0.02295028790831566	0.011390527710318565	-0.028617097064852715	-0.03480273857712746	-0.05166415125131607	0.007809318136423826	0.01741568185389042	0.049986470490694046	0.0032570259645581245	-0.017636723816394806	-0.01784694381058216	-0.0007573079783469439	-0.01778777875006199	0.004801709670573473	-0.09637017548084259	-0.020392045378684998	-0.014422931708395481	0.009301411919295788	0.014711756259202957	0.0022722703870385885	0.01322744507342577	-0.0647595003247261	-0.023013025522232056	-0.05366430804133415	0.10514847934246063	0.011829274706542492	-0.008221287280321121	0.02905793860554695	-0.0543239563703537	-0.022603396326303482	0.06438912451267242	-0.06948065012693405	-0.07372691482305527	-0.036763567477464676	1.8829814507625997e-05	0.03878414258360863	-0.009163107722997665	0.06373761594295502	-0.05658428743481636	-0.0756182000041008	0.055262427777051926	-0.019244421273469925	-0.08172304183244705	-0.014481555670499802	0.037822507321834564	-0.06810688227415085	-0.0500958114862442	0.0032716512214392424	-0.027501430362462997	0.060050591826438904	0.011331669054925442	0.01600034348666668	-0.06780935823917389	-0.006631616503000259	-0.03532011806964874	-0.0779360830783844	0.056090470403432846	-0.010995976626873016	-0.05718831717967987	-0.024982048198580742	0.08049485087394714	-0.06628301739692688	0.03416968137025833	0.04412202909588814	-0.05166468396782875	0.022833911702036858	0.05194200947880745	-0.007229526527225971	0.04419256001710892	0.0041555906645953655	0.00048414786579087377	0.02613532543182373	-0.0652189701795578	0.056504856795072556	-0.05046974495053291	0.10994371026754379	0.0178537517786026	0.020181184634566307	0.06598154455423355	0.06386742740869522	0.013229304924607277	-0.016531938686966896	-0.10584191977977753
+0.0360809825360775	-0.004813831299543381	0.0009996510343626142	0.031340908259153366	-0.13591594994068146	-0.06291601061820984	-0.02162226475775242	0.0574006587266922	-0.012059228494763374	-0.014679210260510445	-0.018736587837338448	0.004034795332700014	-0.011577282100915909	0.035323068499565125	0.03917735069990158	-0.028458040207624435	-0.05072980374097824	-0.017430618405342102	-0.0693589597940445	-0.033172428607940674	0.044202372431755066	0.036817438900470734	-0.0409516878426075	0.04727273806929588	0.00891746487468481	-0.012017074041068554	-0.05370321869850159	0.022578706964850426	-0.010354090481996536	0.0048764911480247974	0.03052213042974472	-0.014130222611129284	0.03685551881790161	-0.02893349528312683	-0.06555411964654922	0.053003065288066864	-0.05239201709628105	0.09140080958604813	0.04043586179614067	-0.05353027209639549	-0.031189216300845146	0.025403309613466263	-0.01188608631491661	-0.03125402703881264	0.03090401366353035	0.02372973971068859	0.01538732647895813	-0.0358111672103405	0.039385147392749786	0.04268632456660271	0.07516853511333466	0.01984325982630253	0.01242853794246912	-0.03346966207027435	0.011688063852488995	0.009373292326927185	0.031026024371385574	-0.07672177255153656	-0.057223010808229446	0.04050713777542114	-0.09044619649648666	-0.00994161143898964	0.018724024295806885	0.017265338450670242	0.030323242768645287	-0.019834717735648155	0.0011959128314629197	0.092781201004982	0.04948075860738754	-0.025038044899702072	0.027660133317112923	0.09473983198404312	-0.04307849332690239	-0.0308056827634573	0.0513848252594471	-0.03534058481454849	-0.016130374744534492	0.03100135177373886	-0.020532725378870964	0.016772672533988953	0.008224066346883774	-0.05257410928606987	0.0917448177933693	-0.0409969724714756	-0.06197207421064377	-0.02090330794453621	-0.02506260760128498	-0.0852111354470253	0.041967641562223434	0.006809372920542955	0.006805925164371729	0.07879333943128586	-0.07566049695014954	0.020274916663765907	-0.045175351202487946	0.0380672812461853	0.016595643013715744	-0.11797896772623062	-0.006788360886275768	-0.023595189675688744	-0.019274497404694557	-0.005558294244110584	0.04312990978360176	-0.00981475692242384	0.034026384353637695	0.058017291128635406	-0.03639635443687439	-0.007888381369411945	-0.010531710460782051	0.03409098833799362	0.01694806106388569	0.04057366028428078	0.12375717610120773	0.06381909549236298	0.04043744504451752	-0.01535686943680048	-0.023210013285279274	-0.04422162100672722	-0.05053461343050003	-0.02332654409110546	-0.005520868580788374	0.05081994831562042	0.0006601458298973739	-0.06535430997610092	-0.022420065477490425	0.038872793316841125	-0.09418027102947235	-0.011761244386434555	0.07213995605707169	-0.0494922399520874	0.09657896310091019	0.034748055040836334	-0.04663015529513359	0.04402458295226097	0.062168288975954056	0.020999349653720856	-0.012496971525251865	0.026750370860099792	-0.01120215654373169	-0.03787331283092499	-0.030671264976263046	0.05869150906801224	0.04432544484734535	-0.10067310184240341	0.022054621949791908	0.07827364653348923	0.019868478178977966	-0.022158769890666008	-0.0899079367518425	0.017748739570379257	-0.0953894779086113	-0.08523651957511902	-0.03188182786107063	-0.06509146839380264	-0.015404622070491314	0.056928157806396484	0.0031861134339123964	0.0026090515311807394	-0.06303282082080841	-0.030628716573119164	-0.02410179376602173	-0.018848156556487083	0.008371186442673206	-0.08665572851896286	-0.023251989856362343	-0.03180243447422981	-0.008074374869465828	0.019177908077836037	-0.05354875326156616	-0.050523579120635986	-0.032637134194374084	-0.009263228625059128	0.04561467096209526	0.08039297163486481	0.04356485605239868	-0.008001753129065037	0.02866498939692974	-0.02302648313343525	0.002549610799178481	-0.020181361585855484	0.02421664260327816	-0.05387570708990097	0.07099857181310654	-0.07329055666923523	-0.01931767910718918	-0.040260303765535355	0.03517567738890648	-0.04924319311976433	0.011762176640331745	-0.02207370661199093	-0.04153206944465637	-0.049874890595674515	0.03668970242142677	0.10884564369916916	-0.049966391175985336	0.06599998474121094	-0.014349240809679031	0.01926003396511078	-0.028317078948020935	-0.014765110798180103	-0.06642940640449524	0.07695917040109634	0.009264816530048847	0.03675651550292969	0.010293669998645782	-0.09913740307092667	0.07466515898704529	-0.0014307486126199365	-0.001256850897334516	-0.045760657638311386	0.054387547075748444	0.10486842691898346	-0.0010511795990169048	0.040270570665597916	0.0545869916677475	0.005020865239202976	0.01957579329609871	-0.05179212614893913	0.031192203983664513	-0.06911537051200867	0.027240963652729988	0.018991481512784958	-0.05055418610572815	0.03380512073636055	-0.019305504858493805	-0.06925344467163086	-0.0205784160643816	0.023328453302383423	0.027095872908830643	-0.03322761878371239	-0.0023625728208571672	-0.0010724884923547506	0.023034662008285522	-0.017324352636933327	-0.02951810508966446	-0.010118106380105019	-0.0810299813747406	0.09714826941490173	0.013244759291410446	-0.034114908427000046	-0.035106487572193146	-0.01196417585015297	0.010516636073589325	-0.027715343981981277	0.01921256259083748	0.06251349300146103	0.06927596032619476	0.03493824973702431	0.022594677284359932	-0.040007732808589935	-0.05757399648427963	-0.0296272374689579	-0.020562496036291122	0.03845595568418503	-0.029159385710954666	-0.03368407115340233	0.013680445030331612	-0.06817439198493958	-0.030534259974956512	0.0003912572574336082	0.053612641990184784	-0.049617018550634384	-0.050744589418172836	-0.05137082561850548	0.01418375875800848	-0.010943339206278324	-0.0748044028878212	0.015511250123381615	-0.0038676317781209946	0.06793607026338577	-0.0074179950170218945	-0.017487119883298874	0.04655115678906441	0.00800626166164875	0.04370441660284996	-0.0011556849349290133	0.04092807322740555	-0.01078053843230009	-0.06373216211795807	0.0160080436617136	-0.03459978103637695	-0.08895078301429749	-0.025843681767582893	-0.0457448810338974	-0.02247612178325653	0.11504758149385452	0.022438231855630875	-0.02706802450120449	-0.06865081191062927	0.023439208045601845	-0.014376312494277954	-0.03509226813912392	-0.0008333318401128054	0.010560677386820316	-0.0817488357424736	-0.012602732516825199	-0.02228199504315853	-0.049841467291116714	-0.054754775017499924	0.012452686205506325	-0.02944599837064743	-0.06461571156978607	0.046787407249212265	-0.04342280328273773	0.0003652509185485542	-0.018548201769590378	-0.026696398854255676	0.019840558990836143	0.0792713463306427	0.029601244255900383	0.014523155055940151	0.013973531313240528	0.01350186113268137	0.1002211943268776	0.012126881629228592	-0.0282298531383276	-0.0161659624427557	-0.03638935834169388	-0.05295347422361374	0.06829769164323807	-0.06116579473018646	-0.03930972144007683	0.025630662217736244	-0.013148807920515537	0.09584209322929382	-0.01700054109096527	-0.04450737312436104	-0.033668700605630875	0.026289552450180054	0.012343415059149265	0.04630208760499954	-0.001151900738477707	-0.03177831321954727	-0.005650695413351059	-0.038115814328193665	-0.017471224069595337	-0.048660606145858765	0.039595481008291245	0.003424527822062373	0.04554138705134392	-0.015704767778515816	-0.06111103668808937	0.006579491309821606	0.01679009012877941	0.11341892182826996	-0.0715818926692009	0.013826657086610794	0.037677329033613205	0.006406874395906925	-0.051704902201890945	0.01057704072445631	-0.004523542243987322	-0.015355127863585949	-0.0394759438931942	0.02533251792192459	0.04877085983753204	-0.1261165589094162	-0.024838490411639214	-0.017946753650903702	-0.022181786596775055	0.013090070337057114	-0.01972995512187481	0.007667470257729292	0.035641904920339584	-0.008378857746720314	-0.00577111029997468	-0.007020443677902222	0.02628355100750923	0.025592349469661713	-0.031245186924934387	0.03612265735864639	-0.004439618904143572	0.08619876205921173	-0.060789886862039566	-0.04861868545413017	-0.02703508548438549	0.039306625723838806	-0.015367819927632809	-0.010954147204756737	-0.019237274304032326	-0.019384009763598442	0.019560761749744415	0.05079413205385208	-0.02127191238105297	-0.06995374709367752	4.2143907194258645e-05	-0.005276907235383987	-0.0343535840511322	-0.0718374028801918	-0.04306326434016228	0.012573647312819958	-0.08470983803272247	-0.00446490291506052	-0.018977271392941475	-0.009702802635729313	-0.01287362352013588	-0.03666184842586517	0.004501244053244591	0.05025007203221321	0.0425657257437706	-0.007195127662271261	0.04503670334815979	0.018397759646177292	0.03342248871922493	0.0064827753230929375	-0.05573465675115585	0.045136258006095886	0.0396096371114254	-0.06860333681106567	0.05501721426844597	-0.06486256420612335	-0.0006803630967624485	0.006913633085787296	0.02421422488987446	0.019142238423228264	-0.021161949262022972	0.009857074357569218	-0.053475115448236465	0.07446856796741486	-0.07686211913824081	-0.03595385700464249	0.06563929468393326	-0.01311047188937664	-0.10258455574512482	0.0072316634468734264	-0.03894120082259178	-0.02890677936375141	0.039736438542604446	0.01488522905856371	0.0028975578024983406	0.024646196514368057	-0.06370437145233154	-0.0219897348433733	-0.05400674417614937	-0.01672818697988987	0.007595050148665905	0.01435134932398796	0.0558406338095665	0.014068998396396637	0.012161150574684143	-0.011225788854062557	-0.01682678982615471	0.008698112331330776	-0.07994988560676575	-0.006380208767950535	-0.008245911449193954	-0.0106210233643651	0.007972520776093006	0.0019638841040432453	0.012045536190271378	-0.045853447169065475	-0.03266366198658943	-0.02389664202928543	0.08120132982730865	-0.03678837791085243	-0.013664739206433296	0.07106958329677582	-0.024416210129857063	-0.0006307045114226639	0.01260631624609232	-0.046508725732564926	-0.10106334835290909	0.002958911005407572	-0.02400786243379116	0.023085108026862144	0.014673883095383644	0.033562932163476944	-0.031742844730615616	-0.07006507366895676	0.0008050058386288583	0.019755134359002113	-0.021861426532268524	0.04244094714522362	0.02459375187754631	-0.05516315624117851	-0.06762527674436569	-0.02664828673005104	-0.049886900931596756	0.07439564168453217	0.01632755808532238	0.034886837005615234	-0.009460905566811562	-0.017947610467672348	-0.0016801021993160248	-0.06704965233802795	-0.025863641873002052	0.02741444669663906	-0.0460612028837204	-0.02780936472117901	0.08420168608427048	-0.0413828082382679	0.0049667819403111935	0.07855450361967087	-0.05861004441976547	0.0037851505912840366	0.018812410533428192	0.00675726355984807	0.05575713887810707	0.0013697280082851648	-0.03127387538552284	0.06065097823739052	-0.048378836363554	0.06442535668611526	-0.016617346554994583	0.0670645534992218	-0.010946972295641899	-0.0369473434984684	0.08961957693099976	0.07235032320022583	0.005041799042373896	-0.003996227402240038	-0.07812649756669998
+0.042996834963560104	-0.03572828322649002	0.022871656343340874	0.05904754623770714	-0.07181396335363388	-0.023632515221834183	-0.0031852908432483673	0.02561347186565399	-0.010027251206338406	-0.03843237832188606	-0.0006799366092309356	-0.02224445901811123	-0.00623277248814702	0.017335942015051842	0.03347209468483925	-0.03903389722108841	-0.0722174122929573	0.008420216850936413	-0.04307981953024864	-0.026407264173030853	0.04182009398937225	-0.002840882632881403	-0.07938079535961151	0.0544295608997345	-0.02224702574312687	0.018396329134702682	-0.05123146250844002	0.023831285536289215	-0.02714700810611248	0.03490126132965088	0.08492906391620636	-0.011551305651664734	0.04118138924241066	-0.05762399733066559	-0.061700981110334396	0.04347537085413933	-0.032934971153736115	0.1077827736735344	0.026213018223643303	-0.034258902072906494	-0.06259380280971527	0.024032974615693092	-0.005084300879389048	-0.048586655408144	0.05270673334598541	0.005458530969917774	0.020050527527928352	-0.023518621921539307	0.050285983830690384	0.01473773643374443	0.1151098906993866	0.03300299867987633	0.014892644248902798	0.0026790655683726072	-0.011777047999203205	0.0018701665103435516	0.04802246019244194	-0.046869248151779175	-0.07064882665872574	0.05717907473444939	-0.09237170964479446	-0.027896052226424217	0.01212321873754263	-0.01828773133456707	-0.025179890915751457	-0.010573125444352627	-0.026623982936143875	0.08743611723184586	0.030361421406269073	-0.017612537369132042	0.01900615356862545	0.09659101814031601	-0.06096149981021881	-0.07123212516307831	0.04223450645804405	-0.040915098041296005	-0.003065692726522684	0.022545870393514633	-0.0348806157708168	0.02772708795964718	-0.023638760671019554	-0.028027363121509552	0.1277148425579071	-0.028612440451979637	-0.03452068939805031	-0.035218022763729095	0.0031021018512547016	-0.06459242105484009	0.07916991412639618	0.05080338940024376	0.0011866833083331585	0.07463578879833221	-0.048694759607315063	0.057952094823122025	0.022662915289402008	0.013004342094063759	0.026925168931484222	-0.10790007561445236	-0.014119570143520832	0.002497045323252678	0.004279755055904388	-0.0009012401569634676	0.06515225768089294	-0.010523808188736439	0.08410529792308807	0.04383515566587448	-0.06649486720561981	0.007579259108752012	0.03445557877421379	0.03198741376399994	0.0397556908428669	0.11713410168886185	0.0461425743997097	0.027758797630667686	0.04340347647666931	0.014107909053564072	0.009892989881336689	-0.07641538232564926	-0.03226114809513092	0.004023872781544924	-0.009904642589390278	0.05501032620668411	-0.01537943072617054	-0.06583192199468613	-0.0053995754569768906	0.033960867673158646	-0.11833786964416504	-0.023117894306778908	0.09110686928033829	-0.02853994071483612	0.11260946094989777	0.07851108908653259	-0.00029410465504042804	0.053522173315286636	0.06380177289247513	0.03190567344427109	-0.014853832311928272	0.025876067578792572	-0.006197115406394005	-0.03136177733540535	-0.008474950678646564	0.04631851613521576	-0.006838537752628326	-0.0530720092356205	0.03517590090632439	0.058193281292915344	0.03373100981116295	-0.025064976885914803	-0.05281519517302513	0.032978158444166183	-0.06026788055896759	-0.07954701781272888	-0.0011865418637171388	-0.05845395103096962	-0.015759140253067017	0.059771306812763214	0.02141350321471691	0.048444170504808426	-0.003105949144810438	-0.03924949839711189	-0.04443288594484329	-0.06165195629000664	-0.03473309427499771	-0.07176772505044937	-0.011050290428102016	-0.026347612962126732	-0.007129074074327946	0.01460257824510336	-0.03062588907778263	-0.02843651734292507	0.02529599703848362	-0.003343254793435335	0.012278425507247448	0.06579156219959259	0.05674443766474724	0.015442495234310627	0.011822471395134926	-0.05526827275753021	1.0869709512917325e-05	-0.041586972773075104	-0.013635427691042423	-0.013945279642939568	0.02117438055574894	-0.10075483471155167	-0.018089141696691513	-0.003367469646036625	0.04322922229766846	-0.018460219725966454	-0.0029076584614813328	-0.018701650202274323	-0.03064078465104103	-0.05667572468519211	0.039843738079071045	0.05589365214109421	-0.031767334789037704	0.0334143303334713	-0.007068815175443888	0.045756805688142776	-0.02213876321911812	-0.030465979129076004	-0.050186287611722946	0.05757145211100578	0.015002075582742691	0.044188570231199265	0.02413189969956875	-0.04767022281885147	0.06472364813089371	0.010347526520490646	0.004453862085938454	-0.06331225484609604	-0.0013400069437921047	0.011361834593117237	0.03672254830598831	0.03612009435892105	0.07089394330978394	-0.01940685696899891	0.00013759065768681467	0.019167078658938408	0.08542106300592422	-0.06608526408672333	0.009516779333353043	-0.025931593030691147	-0.06170361861586571	0.023053200915455818	-0.032645098865032196	-0.09376534074544907	-0.02629196085035801	0.04285538196563721	-0.006754136644303799	-0.02037874050438404	0.03277083858847618	0.03221193328499794	-0.029112378135323524	0.0012615697924047709	0.028316812589764595	-0.03479188308119774	-0.09782544523477554	0.09618500620126724	-0.0023303793277591467	-0.07406183332204819	-0.05340607091784477	0.004425049293786287	0.053533390164375305	0.013819841668009758	0.050109826028347015	0.030508728697896004	0.08811738342046738	0.03794189170002937	-0.0245317742228508	0.013748551718890667	-0.04134942218661308	-0.06326813250780106	0.011256281286478043	0.025571724399924278	-0.02224115841090679	-0.04133648797869682	-0.00014893557818140835	-0.061211615800857544	0.023481806740164757	-0.023951293900609016	0.028113778680562973	-0.040129221975803375	0.01240657176822424	-0.014383806847035885	0.04386334493756294	-0.04112474247813225	-0.04916583001613617	0.02724829502403736	-0.010459959506988525	0.06504128873348236	-0.038468893617391586	-0.014161481522023678	0.02104121819138527	0.014309486374258995	0.024181673303246498	-0.054078325629234314	0.04981540888547897	-0.007117287255823612	-0.10956145823001862	-0.03553296998143196	-0.09214838594198227	-0.04918224364519119	0.002809305442497134	-0.03204842284321785	-0.061666734516620636	0.07475265860557556	0.016117462888360023	-0.05396535247564316	-0.07217409461736679	-0.04234619066119194	-0.003678411478176713	-0.024907810613512993	-0.030065899714827538	-0.0012606774689629674	-0.04282398894429207	-0.0049135987646877766	0.031746912747621536	-0.06402666866779327	-0.03708770126104355	0.0034619509242475033	-0.004105713218450546	-0.03989599272608757	0.05378406494855881	-0.04427643492817879	0.004938563797622919	0.008933153003454208	-0.08539675921201706	0.06575918942689896	0.09924649447202682	0.052401695400476456	0.04189779981970787	0.025647660717368126	0.017000382766127586	0.10677415132522583	0.02066170983016491	-0.03575243428349495	-0.01068110391497612	-0.04987483099102974	-0.061103902757167816	0.05138583853840828	-0.03534211590886116	0.005236559547483921	-0.00041391729610040784	-0.007224968168884516	0.07091613113880157	0.0036497581750154495	-0.0250713974237442	-0.018061010167002678	0.03466096892952919	-0.016478557139635086	0.02227027341723442	0.008430044166743755	0.0081104077398777	-0.007917201146483421	-0.042235638946294785	-0.049933694303035736	-0.024707218632102013	0.04970049113035202	-0.019572818651795387	0.024721043184399605	-0.01239156536757946	-0.06436529755592346	0.014771989546716213	0.002979963319376111	0.09882283210754395	-0.07283712178468704	0.024873632937669754	0.028424836695194244	0.03607720881700516	-0.11804083734750748	-0.0028520983178168535	0.02349083125591278	-0.03943963348865509	-0.05997326970100403	0.03917737677693367	0.04314670339226723	-0.0626533180475235	-0.010017302818596363	-0.027706122025847435	-0.02621907740831375	0.002255209255963564	0.03250840678811073	0.007158476393669844	0.022721661254763603	-0.012223996222019196	-0.04221475124359131	-0.027070054784417152	0.004689078778028488	0.030215663835406303	-0.02781730704009533	0.03504681587219238	-0.024521904066205025	0.04527853801846504	-0.0415428951382637	-0.05756603553891182	-0.051006440073251724	0.05065135285258293	-0.015086022205650806	0.008188261650502682	-0.01797224022448063	-0.025403251871466637	-0.026811854913830757	-0.004628186114132404	0.005342417862266302	-0.07994815707206726	0.0028964742086827755	-0.023432200774550438	-0.06886924803256989	-0.09533396363258362	-0.04598499834537506	0.014256058260798454	-0.044549379497766495	0.01840384490787983	-0.024946242570877075	0.012275499291718006	0.01568327099084854	-0.025005128234624863	0.03191515430808067	0.06781332939863205	0.028644517064094543	-0.0015119456220418215	0.029937248677015305	0.020743196830153465	0.020440753549337387	0.0020122446585446596	-0.05084220692515373	0.031706321984529495	0.03146885707974434	-0.06203393638134003	0.03832574188709259	-0.0064385621808469296	0.011345223523676395	-0.007249865680932999	-0.021817024797201157	0.0025835055857896805	-0.016664279624819756	-0.040947966277599335	-0.04861829802393913	0.06989654898643494	-0.03980715572834015	-0.018656441941857338	0.009958399459719658	-0.058633673936128616	-0.10212623327970505	-0.025991683825850487	-0.007564309053122997	-0.04134668409824371	0.06315471231937408	0.0077833812683820724	0.023025238886475563	0.03902687504887581	-0.042866308242082596	-0.03305129334330559	-0.010743611492216587	-0.011088229715824127	0.016993865370750427	0.0011342242360115051	0.0735316127538681	0.0029832161962985992	-0.020809214562177658	0.000827518873848021	-0.024871163070201874	0.0231300238519907	-0.0869741290807724	-0.0110976193100214	-0.007111006416380405	-0.022892028093338013	0.014537103474140167	0.023043742403388023	0.0031425112392753363	-0.06662065535783768	-0.01774933747947216	-0.039945654571056366	0.07027033716440201	-0.009702812880277634	0.01634790375828743	0.010848036967217922	-0.03931610658764839	-0.006667010020464659	0.01837959885597229	-0.051951877772808075	-0.08159728348255157	0.012417282909154892	-0.03093665838241577	0.0044453926384449005	-0.01217308733612299	0.02520485781133175	-0.0005772081785835326	-0.10544037073850632	-0.01209697313606739	-0.0003524416533764452	-0.06427785754203796	-0.008440154604613781	0.009494584053754807	-0.07735054939985275	-0.052186209708452225	-0.010685976594686508	-0.06140756234526634	0.05648563429713249	-0.006012667901813984	0.00962079968303442	-0.015231684781610966	0.005035783164203167	-0.033615872263908386	-0.05096907541155815	0.021341195330023766	-0.023499934002757072	-0.04976598918437958	-0.022730456665158272	0.059671711176633835	-0.04839728772640228	0.04541584476828575	0.06780947744846344	-0.00149199937004596	0.04054019972681999	0.05547581985592842	-0.03366786614060402	0.022119158878922462	0.004986674524843693	0.010428257286548615	0.057589758187532425	-0.07576196640729904	0.08235283941030502	-0.0624733567237854	0.10609589517116547	0.006550804246217012	0.008807589299976826	0.07399848103523254	0.09452158212661743	0.02540447935461998	0.014546169899404049	-0.07715226709842682
+0.05754610896110535	-0.03103499300777912	0.018460338935256004	0.06357745826244354	-0.05890551954507828	-0.03574518859386444	-0.00039196255966089666	0.012476436793804169	-0.0005419217050075531	-0.025688601657748222	-0.018153997138142586	-0.01593749411404133	-0.012252867221832275	0.020042484626173973	0.033010758459568024	-0.022585012018680573	-0.06142972409725189	0.017575738951563835	-0.033871885389089584	-0.029501965269446373	0.0356169231235981	0.0054831719025969505	-0.06386303901672363	0.06282290071249008	-0.021128464490175247	0.0284112598747015	-0.061628419905900955	0.028374988585710526	-0.013899687677621841	0.022411014884710312	0.04617920145392418	-0.030998796224594116	0.03246631473302841	-0.08790083229541779	-0.05124920234084129	0.0363168939948082	-0.023980790749192238	0.11153250932693481	0.024142315611243248	-0.03980478644371033	-0.05909053608775139	0.02672351337969303	-0.03760741278529167	-0.048653535544872284	0.055788855999708176	0.01767170988023281	0.021803604438900948	-0.026060227304697037	0.049670133739709854	0.04190036281943321	0.10322935879230499	0.0007629203610122204	-0.026806741952896118	-0.03192725032567978	-0.02661251835525036	0.009001847356557846	0.05311789736151695	-0.07231809198856354	-0.06622155010700226	0.04664071276783943	-0.09657704085111618	-0.029103504493832588	0.006234138272702694	-0.002878872212022543	-0.04171169921755791	-0.029211249202489853	-0.008141789585351944	0.0960305780172348	0.016226254403591156	-0.027793074026703835	0.02718835324048996	0.12360068410634995	-0.06659456342458725	-0.05586763843894005	0.0401591882109642	-0.07867060601711273	-0.008494534529745579	0.04539702832698822	-0.011159374378621578	0.01580721326172352	-0.01263592392206192	-0.039940740913152695	0.1123555600643158	-0.028638135641813278	-0.05656500533223152	-0.02355288527905941	-0.0013499823398888111	-0.06332622468471527	0.053004421293735504	0.039017386734485626	0.008379559963941574	0.060591548681259155	-0.0468619167804718	0.053512681275606155	0.024869153276085854	0.012689786031842232	0.04002672806382179	-0.10491137951612473	-0.025526398792862892	-0.004186397884041071	-0.013202132657170296	-0.0004381611361168325	0.044548746198415756	0.01377972960472107	0.06666412204504013	0.012702764011919498	-0.07624390721321106	0.012200438417494297	0.029434090480208397	0.0439034104347229	0.05025758594274521	0.07884109765291214	0.07634544372558594	0.050003670156002045	0.05804982781410217	-0.024297622963786125	0.0305255725979805	-0.06960336118936539	-0.030425386503338814	0.0017909721937030554	0.02059243433177471	0.06077490374445915	-0.021567443385720253	-0.0845462754368782	-0.004296240396797657	0.03351130709052086	-0.10492530465126038	-0.016456229612231255	0.07370886206626892	-0.02174406126141548	0.11586718261241913	0.056762635707855225	-0.018510181456804276	0.04738109931349754	0.04837888106703758	0.01705952174961567	-0.024638839066028595	0.03963390737771988	0.015566079877316952	0.0034551271237432957	-0.004560236819088459	0.053602807223796844	0.009519505314528942	-0.061787091195583344	0.039900925010442734	0.050235651433467865	0.03673611581325531	-0.03752252832055092	-0.05606779828667641	0.008498829789459705	-0.06875947117805481	-0.06761300563812256	0.008151722140610218	-0.08084747195243835	-0.01128427218645811	0.036807529628276825	0.010465102270245552	0.04312950372695923	-0.018576020374894142	-0.015727514401078224	-0.06639691442251205	-0.06332860887050629	-0.027742652222514153	-0.06685201823711395	-0.048600610345602036	-0.026053037494421005	0.020991042256355286	0.0023753538262099028	-0.03696781024336815	-0.03481745719909668	-0.004432663321495056	0.0013174299383535981	0.0338701494038105	0.07005175948143005	0.08916624635457993	0.034669652581214905	0.01254076138138771	-0.02687094174325466	0.03505011647939682	-0.03030032105743885	0.0005006642313674092	-0.03633739799261093	0.01054171659052372	-0.07140639424324036	-0.012586868368089199	-0.013695020228624344	0.028590001165866852	-0.021177450194954872	0.01709016226232052	0.004181861877441406	-0.03048442304134369	-0.059535667300224304	0.042304471135139465	0.07698963582515717	-0.02231708914041519	0.042819831520318985	-0.03895324468612671	0.05325176566839218	-0.0299563966691494	-0.036978792399168015	-0.052495326846838	0.04773760586977005	-8.981161954579875e-05	0.042358092963695526	0.019020121544599533	-0.04629305377602577	0.07951652258634567	0.032606519758701324	0.02614511549472809	-0.08538636565208435	-0.003783188993111253	0.005410183686763048	0.027612121775746346	0.02874242700636387	0.08400357514619827	-0.017600251361727715	-0.02808014675974846	-0.0002757067559286952	0.04984717816114426	-0.03734814375638962	0.011388237588107586	-0.011393681168556213	-0.08500899374485016	0.030322903767228127	-0.055875495076179504	-0.07426229864358902	-0.02961673028767109	0.04123855382204056	-0.0020874240435659885	-0.01606605015695095	0.004030199721455574	0.01761685498058796	-0.019853156059980392	-0.02930636517703533	-0.0011329427361488342	-0.02467610314488411	-0.09626678377389908	0.10440260916948318	0.02132326364517212	-0.054864250123500824	-0.06144355610013008	0.01053654309362173	0.04577363654971123	-0.0044534034095704556	0.031379591673612595	0.01806841790676117	0.09568308293819427	0.04747001454234123	-0.010382485575973988	0.028176749125123024	-0.04261402785778046	-0.056229542940855026	7.35818175598979e-05	0.02229035086929798	-0.04096353054046631	-0.023532673716545105	-0.011695720255374908	-0.0501265712082386	-0.012221631594002247	-0.011384975165128708	0.016752392053604126	-0.011279348284006119	-0.019468776881694794	-0.008157017640769482	0.05653928592801094	-0.03489598259329796	-0.04812188073992729	0.02260296232998371	0.005047642160207033	0.0544402077794075	-0.03993268311023712	-0.01651797629892826	0.05859823524951935	0.011845381930470467	0.013310967944562435	-0.057858727872371674	0.05646960437297821	-0.00756786810234189	-0.0807664543390274	-0.04013318195939064	-0.08734478056430817	-0.03703350946307182	3.5603386550064897e-06	-0.02896316908299923	-0.031311094760894775	0.08246459066867828	0.012536623515188694	-0.02993134781718254	-0.06208127364516258	-0.029028456658124924	-0.026411738246679306	-0.06136124208569527	-0.021747000515460968	0.014474066905677319	-0.04242097958922386	-0.025414003059267998	0.012816485948860645	-0.04572701454162598	-0.025269394740462303	-0.0392826572060585	-0.027630139142274857	-0.05226302891969681	0.05306106060743332	-0.03347090631723404	-0.023187512531876564	0.020382702350616455	-0.07410578429698944	0.03329550847411156	0.10917014628648758	0.07556772977113724	0.0570281483232975	0.04446108266711235	0.019339533522725105	0.11288610100746155	0.01984502375125885	-0.03859527409076691	-0.017154213041067123	-0.020521890372037888	-0.07364016771316528	0.044648218899965286	-0.025597916916012764	0.003319358918815851	0.003123931121081114	0.001566620427183807	0.07805468887090683	-0.005214942619204521	-0.0508618988096714	-0.02051362209022045	0.026979589834809303	-0.03323705866932869	0.00466473214328289	0.0017377336043864489	0.017011992633342743	-0.011080448515713215	-0.05373363196849823	-0.05488964542746544	-0.02493431232869625	0.04894108697772026	-0.025399161502718925	0.04453567415475845	-0.013898367062211037	-0.048014722764492035	0.014095702208578587	0.02250557579100132	0.09152726829051971	-0.05397138372063637	0.010542494244873524	0.03263722360134125	0.02536749467253685	-0.11036982387304306	0.0052381400018930435	0.028363807126879692	-0.04224742576479912	-0.05614258348941803	0.0123392129316926	0.026027560234069824	-0.05812487378716469	-0.007106729317456484	-0.003072122810408473	-0.059384893625974655	0.0013426271034404635	0.03385232016444206	0.03256869316101074	0.028403080999851227	-0.024868814274668694	-0.04769700765609741	-0.020767059177160263	-0.010197332128882408	0.03315609320998192	-0.037982288748025894	0.013688750565052032	-0.03023032285273075	0.03680967539548874	-0.04873586446046829	-0.03533198684453964	-0.038878876715898514	0.05742963030934334	0.006588266231119633	-0.021697940304875374	-0.013678079470992088	-0.02961752563714981	-0.021282879635691643	0.004087114706635475	0.04388783127069473	-0.05454835295677185	-0.007067624945193529	-0.040147069841623306	-0.0512988455593586	-0.098701111972332	-0.0657339096069336	0.015025953762233257	-0.04966256022453308	0.01940501295030117	-0.017868995666503906	-0.0040423995815217495	0.006332429125905037	-0.0054489499889314175	0.02386915683746338	0.050288110971450806	0.03316984698176384	-0.02331327274441719	0.03055720217525959	-0.01196323148906231	0.052746158093214035	0.031260229647159576	-0.05521165579557419	0.0038889877032488585	0.020189978182315826	-0.05721982568502426	0.04293905943632126	-0.027650045230984688	0.020390266552567482	-0.021458351984620094	-0.008143225684762001	0.05228312313556671	-0.005414089187979698	-0.005877090152353048	-0.029897192493081093	0.06382410228252411	-0.07008478045463562	-0.024149393662810326	0.013837479054927826	-0.04047581925988197	-0.11150937527418137	-0.040988434106111526	0.0009019520948641002	-0.012424016371369362	0.03371712565422058	-0.03353150933980942	0.02890746295452118	0.026767795905470848	-0.032538361847400665	-0.04619855433702469	-0.018369177356362343	-0.016416147351264954	0.0491890050470829	-0.0009103039046749473	0.08185393363237381	0.016423804685473442	0.0013423202326521277	0.004028693772852421	0.004342789761722088	0.007296964526176453	-0.10003379732370377	-0.022826356813311577	-0.027155406773090363	-0.007076850160956383	0.010779505595564842	0.017939288169145584	-0.004337134771049023	-0.0650557279586792	-0.019486170262098312	-0.036348093301057816	0.10097861289978027	-7.332538370974362e-05	0.011875545606017113	0.03770880773663521	-0.03938257694244385	-0.011143028736114502	-0.005708775483071804	-0.041763462126255035	-0.08532622456550598	-0.005951101891696453	-0.010645625181496143	0.014385220594704151	-0.002255692146718502	0.01882336102426052	-0.01315715815871954	-0.0646967887878418	-0.011644565500319004	0.023383142426609993	-0.06903736293315887	0.008530199527740479	0.009154998697340488	-0.07249222695827484	-0.057702645659446716	0.023347796872258186	-0.054066047072410583	0.05606053024530411	0.022180236876010895	0.009311371482908726	0.012254620902240276	-0.0035546168219298124	-0.016042446717619896	-0.0488118901848793	0.024078942835330963	-0.010721713304519653	-0.05489522963762283	-0.008600923232734203	0.05866948515176773	-0.035383958369493484	0.03294476866722107	0.04862996190786362	-0.04859611392021179	0.04819651320576668	0.05085767060518265	-0.040149085223674774	0.035759370774030685	-0.019246449694037437	-0.00939230341464281	0.07880609482526779	-0.07753418385982513	0.09829440712928772	-0.07923705875873566	0.10216757655143738	0.022352835163474083	-0.015334529802203178	0.0884367823600769	0.0880221575498581	-0.0031187226995825768	0.0024714788887649775	-0.07990776002407074
+0.043678898364305496	-0.031157823279500008	0.0184647049754858	0.031639743596315384	-0.08615219593048096	-0.021239614114165306	0.014090340584516525	0.03997774422168732	-0.026160724461078644	-0.0186911653727293	0.0028472733683884144	0.009818123653531075	0.0304573867470026	0.016770513728260994	0.040019094944000244	-0.0517825186252594	-0.058923475444316864	0.004694725386798382	-0.04489158093929291	-0.040487613528966904	0.05437859892845154	-0.010514134541153908	-0.0535748265683651	0.014303481206297874	-0.015546631999313831	0.027112998068332672	-0.05521180108189583	0.024900104850530624	0.005368651822209358	0.008223682641983032	0.08763883262872696	-0.031303513795137405	0.027371501550078392	-0.05258537083864212	-0.05111350491642952	0.03197070583701134	-0.04506731405854225	0.0977877601981163	-0.0009238665807060897	-0.03152908384799957	-0.03179905191063881	-0.00453281169757247	-0.004112033173441887	-0.01505784410983324	0.03455048426985741	0.008924759924411774	0.01627734676003456	-0.024053236469626427	0.033353742212057114	-0.027916179969906807	0.07910268008708954	0.02292056567966938	0.014805218204855919	-0.027048835530877113	-0.006065648049116135	-0.01128179021179676	0.05078659951686859	-0.038994092494249344	-0.09460172057151794	0.07649733126163483	-0.09518207609653473	-0.005184246227145195	-0.016470180824398994	-0.012778179720044136	-0.022253170609474182	-0.03258452191948891	-0.010309345088899136	0.0687234178185463	0.004529346711933613	-0.02977689914405346	0.040833547711372375	0.10266994684934616	-0.057264797389507294	-0.09088476747274399	0.05876370519399643	-0.057968053966760635	0.006072331219911575	0.028361795470118523	-0.0392141118645668	0.01118277758359909	-0.06292058527469635	-0.053680773824453354	0.11693667620420456	-0.039851609617471695	-0.07331645488739014	-0.04628344997763634	-0.011142497882246971	-0.04315781965851784	0.06586980074644089	0.04509791359305382	0.009570027701556683	0.056313615292310715	-0.08881450444459915	0.047427233308553696	0.035041965544223785	0.005300730001181364	0.03654441609978676	-0.10461267083883286	-0.04851439967751503	0.018831389024853706	-0.007943405769765377	-0.022060822695493698	0.08274412900209427	0.022851062938570976	0.04720234125852585	0.041952721774578094	-0.0421634279191494	0.02718786709010601	0.015397471375763416	0.07350143045186996	0.019931668415665627	0.1002613753080368	0.054638467729091644	0.039920054376125336	0.033828746527433395	-0.02561344765126705	-0.015763171017169952	-0.05127697065472603	-0.027827754616737366	0.003662603674456477	0.02623770385980606	0.0535319447517395	-0.029966311529278755	-0.07321502268314362	-0.041351743042469025	0.044379692524671555	-0.12187358736991882	0.0005340123898349702	0.10564363747835159	-0.04428410157561302	0.10711971670389175	0.06950493156909943	0.008616046980023384	0.07864724099636078	0.05803246051073074	0.04459915682673454	0.0035696097183972597	0.03711313009262085	-0.016213955357670784	-0.037990979850292206	-0.02269166335463524	0.019918976351618767	0.01551615260541439	-0.06187968701124191	0.011377973482012749	0.05805780366063118	0.030199719592928886	-0.05505384877324104	-0.028020111843943596	0.022034691646695137	-0.05055006593465805	-0.06591733545064926	-0.012748203240334988	-0.05947031080722809	0.012488218024373055	0.022368766367435455	0.0029439416248351336	0.018019622191786766	-0.01850849948823452	0.0058580245822668076	-0.05144631117582321	-0.06273236125707626	-0.032424792647361755	-0.03933078423142433	-0.008864917792379856	0.003938586916774511	0.018016492947936058	-0.002662567887455225	-0.043699827045202255	-0.017811764031648636	0.021335845813155174	0.010138195008039474	0.013202152214944363	0.05715435743331909	0.03187551349401474	-0.007499325089156628	0.029028326272964478	-0.03940075635910034	0.009880125522613525	-0.040428899228572845	-0.007408343255519867	-0.0014326531672850251	0.03062993474304676	-0.08352763950824738	-0.016196761280298233	-0.012330704368650913	0.042301323264837265	-0.04938314110040665	0.009641817770898342	-0.04071076586842537	-0.022671153768897057	-0.05525944009423256	0.021200943738222122	0.04840948060154915	-0.049864109605550766	0.04419087991118431	-0.017822735011577606	0.03236594796180725	-0.03374013304710388	-0.04369247332215309	-0.03789078816771507	0.04955597221851349	-0.004043018911033869	0.022043267264962196	-0.020290954038500786	-0.05280798673629761	0.048122186213731766	0.00013836933067068458	0.0017604457680135965	-0.08229761570692062	0.001043285708874464	0.019763171672821045	0.023942802101373672	0.047945406287908554	0.07878682017326355	-0.04023076221346855	-0.00248955306597054	0.005367888603359461	0.07537402957677841	-0.03737835958600044	0.016933301463723183	-0.02377752773463726	-0.07484487444162369	0.0005384903051890433	-0.04979703947901726	-0.08115097135305405	-0.01579856313765049	0.020419780164957047	-0.010600031353533268	-0.03222158923745155	-0.014121619053184986	0.04946190118789673	-0.011526254937052727	-0.017133615911006927	0.004147265572100878	-0.009958315640687943	-0.0784095972776413	0.10188743472099304	0.01468875352293253	-0.08049120754003525	-0.0790749192237854	-0.0009985347278416157	0.05541299283504486	-0.0006365422159433365	0.05022239685058594	0.01761772483587265	0.11618143320083618	0.028307344764471054	-0.015628667548298836	0.014357072301208973	-0.02421625703573227	-0.05006425455212593	0.01446590293198824	0.012310298159718513	-0.048056043684482574	-0.00795845314860344	0.03673015534877777	-0.04732430726289749	0.014000405557453632	-0.019633887335658073	0.05075331777334213	-0.01664847694337368	-0.03778231889009476	-0.01019076257944107	0.049809060990810394	-0.04636802524328232	-0.06371647864580154	0.020754728466272354	-0.008858653716742992	0.06472914665937424	-0.011002041399478912	-0.010195422917604446	0.03815687820315361	0.0038839769549667835	0.0312701053917408	-0.06574318557977676	0.04801788181066513	-0.024954622611403465	-0.08499748259782791	-0.015637218952178955	-0.05880142003297806	-0.04787679389119148	-0.01846974715590477	-0.04409906640648842	-0.06452688574790955	0.07631932944059372	-0.007506642024964094	-0.04667079076170921	-0.050095122307538986	-0.024023959413170815	0.022008471190929413	-0.04181024059653282	-0.02423982508480549	0.01623987779021263	-0.040831103920936584	-0.0027567539364099503	0.04630069434642792	-0.06566642224788666	-0.044942114502191544	0.008170252665877342	-0.015495162457227707	-0.06907659024000168	0.058698464184999466	-0.0688280314207077	-0.00020971409685444087	-0.007400555070489645	-0.06176961213350296	0.05280154198408127	0.09031141549348831	0.057092905044555664	0.03701401129364967	0.01933964341878891	0.005562986712902784	0.11513280868530273	0.019190968945622444	-0.022402873262763023	0.010882294736802578	-0.07239285111427307	-0.06043882295489311	0.025789372622966766	-0.035905178636312485	-0.015148463658988476	-0.030183270573616028	-0.008094926364719868	0.0761088877916336	-0.021208126097917557	-0.026626309379935265	0.0013326859334483743	0.033622633665800095	-0.011934613808989525	0.030813688412308693	0.03554047644138336	-0.00270264083519578	-0.0151089271530509	-0.027113640680909157	-0.050501830875873566	-0.020450063049793243	0.056391723453998566	-0.0496562160551548	0.04803934693336487	-0.03592387214303017	-0.07600051164627075	0.02093205600976944	0.03152282163500786	0.08718788623809814	-0.05516522377729416	0.028396960347890854	0.011972562409937382	0.022679543122649193	-0.10018076002597809	0.002525969874113798	0.029514554888010025	-0.008936329744756222	-0.03522256761789322	0.042413778603076935	0.04102695733308792	-0.05288228392601013	0.02844151295721531	0.005038489121943712	-0.033110037446022034	-0.00034250636235810816	0.03365437686443329	-0.01476786844432354	0.00496976962313056	-0.03322404995560646	-0.021646836772561073	-0.03018989786505699	0.02660873532295227	0.03264574706554413	-0.02742944471538067	0.05902373418211937	-0.054821040481328964	0.05988901108503342	-0.03311138600111008	-0.05864231288433075	-0.060320161283016205	0.08591271936893463	0.018282774835824966	-0.006890343967825174	-0.04519945755600929	-0.024368073791265488	-0.007360039744526148	0.009800572879612446	-0.01130098570138216	-0.06835824251174927	-0.0007712039514444768	-0.0320449136197567	-0.09203905612230301	-0.08775655180215836	-0.06479933112859726	0.02611621655523777	-0.04978413134813309	0.0028536266181617975	0.007959477603435516	0.02345455437898636	-0.0019431754481047392	-0.037472374737262726	0.04863768070936203	0.053175367414951324	0.060507528483867645	-0.007780561223626137	0.07317086309194565	0.03935892879962921	0.05541519820690155	-0.022188210859894753	-0.02686009369790554	0.06338359415531158	0.020547322928905487	-0.05909767746925354	0.061323005706071854	-0.019723277539014816	0.037813592702150345	-0.013268468901515007	-0.002296717604622245	0.03515828400850296	-0.024322044104337692	-0.021238187327980995	-0.04739818349480629	0.08357641100883484	-0.07426377385854721	-0.009072341956198215	0.04610225558280945	-0.05063801258802414	-0.09373057633638382	-0.019010262563824654	-0.005736811086535454	-0.03679337352514267	0.03721824288368225	0.015661701560020447	0.022413456812500954	0.02265496924519539	-0.0038550198078155518	-0.021588044241070747	-0.006919221021234989	-0.010720240883529186	0.03873860836029053	-0.005970661994069815	0.0887191966176033	0.02153933420777321	-0.003534466726705432	0.019384579733014107	0.004901252221316099	0.03102850914001465	-0.1050727367401123	-0.0014775777235627174	-0.037333257496356964	-0.008671344257891178	-0.01646992564201355	-0.010702020488679409	0.023215781897306442	-0.07999148219823837	-0.01987571083009243	-0.04737796261906624	0.07583338022232056	-0.002322621177881956	0.01752975583076477	0.019510697573423386	-0.019988324493169785	-0.01757294498383999	-0.021914158016443253	-0.03839520737528801	-0.058908846229314804	0.028309503570199013	-0.034064922481775284	0.024121487513184547	0.00039983962778933346	0.013276427052915096	0.007504922337830067	-0.09179331362247467	-0.015870580449700356	0.03865120932459831	-0.05921141058206558	0.04735716059803963	0.01699971593916416	-0.050996921956539154	-0.04800711199641228	0.005382078234106302	-0.028777988627552986	0.04746457561850548	0.010836041532456875	-0.001957457745447755	-0.022810140624642372	0.010618024505674839	-0.0195932500064373	-0.05261420086026192	0.02255764603614807	-0.020605452358722687	-0.06768833100795746	-0.03967079520225525	0.051777806133031845	-0.07497769594192505	0.04123349487781525	0.03852318972349167	-0.04398117959499359	0.03358713537454605	0.02864711917936802	-0.03825364634394646	0.03156733140349388	-0.009627241641283035	0.024599555879831314	0.0360809825360775	-0.09611200541257858	0.08407037705183029	-0.08180037140846252	0.05744682624936104	0.01661905273795128	-0.0016521674115210772	0.08254265785217285	0.06828730553388596	0.01425009686499834	-0.018323661759495735	-0.07323675602674484
+0.07327040284872055	-0.014397152699530125	-0.025390805676579475	-0.01628677174448967	-0.09289240837097168	-0.04690290614962578	-0.007249726913869381	0.057137228548526764	-0.04991919919848442	-0.022620897740125656	-0.037789907306432724	-0.014250597916543484	0.026755360886454582	-0.0019787081982940435	0.03665667772293091	-0.08412200957536697	-0.0981074795126915	-0.026342760771512985	-0.037218715995550156	0.0014160305727273226	0.048958148807287216	-0.01977337710559368	-0.0901794284582138	0.03874892741441727	0.029188690707087517	0.02984408102929592	-0.016554854810237885	0.012624895200133324	0.010962859727442265	-0.0035110635217279196	0.0864100456237793	-0.025822877883911133	0.0007864452200010419	-0.055563684552907944	-0.06330045312643051	0.045565370470285416	-0.048681214451789856	0.11950387805700302	0.04081738367676735	-0.06665463000535965	-0.08884356170892715	0.04249463975429535	-0.03473382070660591	-0.0559481605887413	0.04968999698758125	0.00031072477577254176	-0.029424408450722694	-0.06315989792346954	0.07063265144824982	-0.017212744802236557	0.06508238613605499	-0.015452324412763119	-0.02036268450319767	-0.005560767371207476	-0.051656827330589294	0.033452216535806656	0.012604652903974056	-0.013675088994204998	-0.07701604813337326	0.016836166381835938	-0.05779888853430748	0.010998853482306004	-0.010061926208436489	0.006563755217939615	0.022113988175988197	-0.04904361814260483	-0.011066579259932041	0.047424107789993286	0.009584563784301281	0.028965316712856293	-0.014278577640652657	0.0876479297876358	-0.06826507300138474	-0.0693272054195404	0.0587683729827404	-0.06502875685691833	-0.032206665724515915	0.005940516944974661	-0.012325606308877468	0.0005107630277052522	-0.021134493872523308	0.005077189765870571	0.14108532667160034	-0.05366752669215202	-0.044547129422426224	-0.052456729114055634	-0.019191686064004898	-0.03707442432641983	0.07257971912622452	0.08663525432348251	0.030539261177182198	0.04741955175995827	-0.043393850326538086	0.03140939027070999	-0.012249697931110859	-0.005988662596791983	0.0625598356127739	-0.10763078182935715	-0.03161275014281273	-0.03529196232557297	0.0034270489122718573	-0.03830106928944588	0.07489536702632904	-0.012886964716017246	0.02337748184800148	0.022450905293226242	-0.06244947388768196	0.03254313766956329	0.023934917524456978	0.05534064769744873	0.016762830317020416	0.09671708941459656	0.07091903686523438	0.05895783752202988	0.06449868530035019	-0.015644164755940437	-0.020196136087179184	-0.056371934711933136	-0.016580499708652496	0.01932491362094879	-0.027171922847628593	0.02199665829539299	-0.020321998745203018	-0.05436491221189499	-0.011713141575455666	0.045316457748413086	-0.09265230596065521	-0.01664160192012787	0.02278570458292961	-0.02569091133773327	0.07784228026866913	0.06451646983623505	-0.021721791476011276	0.05293523520231247	0.07140015810728073	0.015127384103834629	0.06180790811777115	0.07333723455667496	-0.0575338676571846	-0.06331145018339157	-0.032857879996299744	0.03460352122783661	-0.005762650165706873	-0.025462288409471512	0.01967521570622921	0.03832239285111427	0.05089237540960312	-0.02337670512497425	-0.018794812262058258	0.035089630633592606	-0.09888074547052383	-0.050378456711769104	0.01718536950647831	-0.0472097247838974	0.024958427995443344	0.057264361530542374	0.03251562640070915	0.014540721662342548	0.020348547026515007	-0.004064890090376139	-0.024663304910063744	-0.08658631891012192	-0.015667051076889038	-0.09467727690935135	-0.017919903621077538	-0.03365571051836014	0.02608475647866726	-0.015311307273805141	-0.0019462896743789315	-0.01622394286096096	0.030447721481323242	-0.018488261848688126	-0.0017446333076804876	0.05810152739286423	0.017923230305314064	-0.0018125466303899884	0.01177500281482935	-0.03835311159491539	-0.02205497957766056	-0.01751439832150936	0.027131469920277596	-0.054104749113321304	0.003799930214881897	-0.0797114372253418	0.020737817510962486	0.0032003740780055523	0.01990598812699318	-0.03646759316325188	-0.0011786228278651834	-0.026372285559773445	-0.05226128548383713	-0.07661984115839005	0.0009477437706664205	0.02689182758331299	-0.020526953041553497	0.05121849477291107	0.004042900633066893	0.02079126052558422	0.019977696239948273	-0.020695101469755173	-0.06391900777816772	0.04385882243514061	-0.0544172078371048	0.0598873496055603	0.03446890413761139	-0.04460671544075012	0.05053459480404854	-0.02877972088754177	0.03717581555247307	-0.04893874004483223	0.00451193330809474	0.016949394717812538	0.014167177490890026	0.001959604909643531	0.0765218511223793	-0.02285807952284813	-0.019920434802770615	0.04018859192728996	0.10241097211837769	-0.04723775014281273	0.00936468131840229	0.03237159922719002	-0.08932730555534363	0.056217752397060394	-0.0075075700879096985	-0.10043936222791672	-0.045999910682439804	0.033572953194379807	-0.011670063249766827	-0.06006554514169693	0.0006562142516486347	0.028872467577457428	0.017003171145915985	-0.029910249635577202	0.03975493088364601	0.0018028800841420889	-0.07503460347652435	0.07023449242115021	-0.014944339171051979	-0.09257346391677856	-0.032304782420396805	0.012228392995893955	0.0061016869731247425	0.004983719438314438	0.02610064297914505	0.017345262691378593	0.07648332417011261	-0.012970546260476112	-0.023828934878110886	0.04393831640481949	-0.04958290234208107	-0.04818538576364517	-0.011547342874109745	0.03678921237587929	-0.020845230668783188	-0.06725715100765228	0.03076840564608574	-0.019941316917538643	0.022926317527890205	-0.04630590230226517	0.03668196126818657	-0.012516173534095287	-0.04158294200897217	-0.04641161859035492	0.09833451360464096	-0.04053226858377457	-0.04906522110104561	0.027818623930215836	-0.02938651852309704	0.061642128974199295	-0.00037665385752916336	-0.003189706476405263	0.005259744822978973	0.0005274960421957076	0.0247623510658741	-0.03288133814930916	0.06963254511356354	-0.047469913959503174	-0.055511474609375	-0.030549488961696625	-0.06996429711580276	-0.0713200718164444	0.0021237756591290236	-0.029383283108472824	-0.025475362315773964	0.08036260306835175	0.01934746839106083	-0.020687710493803024	-0.04600721225142479	-0.04113632068037987	-0.008725278079509735	-0.03159850090742111	-0.04886946827173233	0.023415591567754745	-0.06464280188083649	-0.04112878814339638	0.07118013501167297	-0.013750818558037281	-0.052194755524396896	0.0355173721909523	0.0018766698194667697	-0.03741368651390076	0.020069545134902	-0.049918241798877716	0.00021809556346852332	-0.0013268544571474195	-0.06630648672580719	0.044286906719207764	0.07976528257131577	0.07624407112598419	0.045370981097221375	0.01046955119818449	-0.010792715474963188	0.10597172379493713	0.007042654324322939	-0.05518898367881775	0.010074539110064507	-0.09727109223604202	-0.11059396713972092	0.038112569600343704	-0.016577057540416718	-0.03664442524313927	-0.03565966710448265	-0.01988999731838703	0.039314109832048416	-0.03303302079439163	-0.001062785042449832	-0.05396853759884834	-0.007270469330251217	-0.0683862492442131	0.037535201758146286	0.00737007986754179	0.03033565916121006	-0.023610973730683327	-0.025295695289969444	-0.053962383419275284	-0.08973225951194763	0.02619931288063526	0.00529246823862195	0.01747865416109562	-0.00228596362285316	-0.003974888473749161	-0.007214776240289211	0.012096087448298931	0.08933805674314499	-0.023714009672403336	0.039972178637981415	-0.003348075784742832	0.05735298991203308	-0.05963106080889702	-0.017309918999671936	0.00449997978284955	-0.017058806493878365	-0.021223248913884163	-0.011989837512373924	0.03920622542500496	-0.056022997945547104	-0.015730837360024452	0.020613688975572586	-0.020107237622141838	0.01346089132130146	-0.009786628186702728	0.007935866713523865	0.05857254937291145	0.005288190674036741	-0.0428888276219368	0.07612942904233932	-0.016740266233682632	0.05889733135700226	-0.014755942858755589	0.03770803287625313	-0.027649156749248505	0.06042731553316116	-0.029030507430434227	-0.05989854782819748	-0.10106983035802841	0.052771296352148056	-0.005823912099003792	-0.025266442447900772	-0.04067260026931763	0.032335370779037476	-0.020233578979969025	0.030076181516051292	0.00035632893559522927	-0.06018020957708359	-0.006371641997247934	-0.03324585780501366	-0.04006561264395714	-0.07014166563749313	-0.003917816560715437	0.0031231404282152653	-0.05836663022637367	0.00564592843875289	-0.03481128066778183	0.007167918607592583	0.058211423456668854	-0.01386196818202734	0.01876724325120449	0.01281770970672369	0.03974075987935066	0.01212974265217781	0.050630223006010056	0.032258518040180206	0.02578117325901985	-0.0062218173407018185	-0.01680625230073929	-0.0007722024456597865	0.06056002154946327	-0.06526964157819748	0.035903990268707275	0.010227090679109097	0.048670053482055664	-0.033390168100595474	0.010617610067129135	0.031652919948101044	-0.04439377039670944	0.008899638429284096	-0.00465345848351717	-0.0020803154911845922	-0.03658555448055267	-0.06633545458316803	0.06397439539432526	-0.04286118224263191	-0.07984188199043274	0.021007025614380836	-0.0038827958051115274	-0.029572274535894394	0.027013786137104034	-0.0006444174214266241	0.023591725155711174	0.043292686343193054	-0.04608612135052681	-0.039239779114723206	-0.027481570839881897	-0.02620256505906582	0.05243292823433876	-0.01582459732890129	0.07092276960611343	0.04173320531845093	-0.010982709936797619	0.06639298796653748	-0.021645423024892807	0.0230727382004261	-0.08110000938177109	-0.01268409751355648	-0.02649928443133831	-0.019274629652500153	0.04866872355341911	0.007575151976197958	0.04366982728242874	5.554353629122488e-05	-0.014935753308236599	-0.04373440518975258	0.06516755372285843	0.016335545107722282	0.03436652943491936	0.028710167855024338	-0.052837029099464417	0.010264229960739613	-0.04128117859363556	-0.035354599356651306	-0.059525489807128906	0.041461341083049774	-0.04967288672924042	0.015491037629544735	0.036040663719177246	0.036648355424404144	0.058163534849882126	-0.09641348570585251	-0.011846396140754223	-0.0009997989982366562	-0.034663643687963486	0.0476151667535305	-0.007799476385116577	-0.014887265861034393	-0.061880212277173996	-0.05210692808032036	-0.07709667831659317	0.05511040240526199	-0.05742223560810089	0.007296487223356962	-0.00770095270127058	0.03392602875828743	-0.0007976299966685474	-0.012297392822802067	-0.012157712131738663	-0.01766985096037388	-0.0568268746137619	-0.0003597498289309442	0.08819431811571121	-0.013596052303910255	0.03279722109436989	0.07607229053974152	0.014458766207098961	0.009615486487746239	0.02237001433968544	-0.04605399817228317	-0.017376992851495743	-0.007609084248542786	-0.010824081487953663	0.06209024414420128	-0.05001366138458252	0.032501041889190674	-0.08649704605340958	0.07807489484548569	-0.001527764368802309	-0.040944840759038925	0.06452185660600662	0.04341893270611763	-0.010965805500745773	0.05204133316874504	-0.027083387598395348
+0.031550757586956024	-0.025874484330415726	0.009637529961764812	0.02022203430533409	-0.1024777963757515	-0.0741385966539383	-0.012279430404305458	0.027345499023795128	0.005234649404883385	-0.02300983853638172	-0.02283012680709362	-0.018597140908241272	0.004574653226882219	0.034467753022909164	0.05942648649215698	-0.03931392356753349	-0.05998154729604721	0.010583330877125263	-0.06036801636219025	0.01869078539311886	0.08962450921535492	0.0048480103723704815	-0.04765050485730171	0.03803553432226181	-0.004619180224835873	0.03375636786222458	-0.052454568445682526	0.02783832512795925	0.00047981124953366816	0.016733624041080475	0.0480833500623703	0.001672432292252779	0.011559983715415001	-0.059384580701589584	-0.03353727236390114	0.03252749517560005	-0.03079291805624962	0.08845251798629761	0.04720107838511467	-0.0345802940428257	-0.05390980839729309	0.002347951754927635	-0.0561036616563797	-0.0381452813744545	0.017294827848672867	-0.00870842207223177	0.028666067868471146	-0.04671946167945862	0.04570886865258217	0.03916195034980774	0.07004687935113907	-0.022382689639925957	-0.022409183904528618	-0.013410123996436596	-0.008543889969587326	-0.014178100973367691	0.03032858483493328	-0.05770431458950043	-0.0645851194858551	0.042304906994104385	-0.09958504885435104	0.0058370428159832954	-0.03658653050661087	0.0077739134430885315	-0.004498549271374941	-0.021902577951550484	0.004955784883350134	0.07783234864473343	0.024835254997015	-0.04695361480116844	0.01865571364760399	0.09425225853919983	-0.061614200472831726	-0.05840405076742172	0.028150590136647224	-0.04233280569314957	0.010527596808969975	0.020346321165561676	-0.03990096598863602	-0.0032152344938367605	-0.01831750199198723	-0.06482904404401779	0.1535959392786026	-0.05393394082784653	-0.062387749552726746	-0.0500032864511013	-0.03040366619825363	-0.05942264944314957	0.03712467849254608	0.049689631909132004	0.024205384775996208	0.07954049110412598	-0.05331498384475708	0.047339361160993576	0.010572207160294056	0.03165382146835327	0.028187865391373634	-0.13325892388820648	-0.04125337302684784	-0.012387391179800034	-0.028530722483992577	-0.0012935055419802666	0.08104326575994492	0.01443429384380579	0.043806061148643494	0.031224025413393974	-0.04706212133169174	0.0037710817996412516	-0.016721123829483986	0.06082339584827423	0.012311381287872791	0.06511641293764114	0.09643255174160004	0.0663386732339859	0.0030995875131338835	-0.04299057275056839	-0.021441835910081863	-0.02732648141682148	-0.03153889998793602	0.006134004332125187	0.03139113262295723	0.045601699501276016	-0.009937421418726444	-0.05421207845211029	-0.017868852242827415	0.04669545218348503	-0.12230876833200455	-0.013072743080556393	0.0529802069067955	-0.07467197626829147	0.11178022623062134	0.07270296663045883	-0.04886642470955849	0.041259948164224625	0.08901365846395493	0.03598036244511604	0.0037560896016657352	0.018997130915522575	-0.031157584860920906	-0.039564941078424454	-0.04174337536096573	0.042529281228780746	-0.01321180909872055	-0.060725122690200806	0.02625000849366188	0.08609135448932648	0.013422759249806404	-0.05360492691397667	-0.04806484282016754	0.017070360481739044	-0.08114943653345108	-0.055399417877197266	0.003413539379835129	-0.06867046654224396	0.0032534312922507524	0.0438784696161747	0.011028029024600983	0.04207007586956024	-0.038561563938856125	-0.0076159462332725525	-0.05288244038820267	-0.06648583710193634	-0.029344050213694572	-0.050861895084381104	-0.012213184498250484	-0.017255457118153572	0.02490314282476902	-0.0285019688308239	-0.0225792545825243	-0.06030930578708649	-0.012357081286609173	0.0025304527953267097	0.01132276188582182	0.06034434214234352	0.034306131303310394	-0.028623702004551888	-0.0024531406816095114	-0.04017013683915138	0.001318473252467811	-0.02797311544418335	0.02792041003704071	-0.021467572078108788	0.022945862263441086	-0.06745190918445587	-0.040944188833236694	-0.012619608081877232	0.016213996335864067	-0.0455828458070755	-0.00527444900944829	-0.03476319462060928	-0.0229975376278162	-0.057565271854400635	0.023030990734696388	0.10421646386384964	-0.05419677123427391	0.06955838203430176	-0.05002754181623459	0.05773824080824852	-0.03812766075134277	-0.012325243093073368	-0.05796832963824272	0.07058746367692947	-0.02238592319190502	0.0655306726694107	0.011240518651902676	-0.05689764767885208	0.06540177017450333	0.002781966235488653	0.006649197544902563	-0.06801244616508484	0.05386258661746979	0.044070493429899216	0.01208774745464325	0.05010669678449631	0.05427169054746628	-0.03795785829424858	-0.003164985915645957	-0.011337594129145145	0.056558363139629364	-0.04530836269259453	0.04086891934275627	0.014354080893099308	-0.06887078285217285	0.012555250898003578	-0.040852513164281845	-0.04610170051455498	-0.020227564498782158	0.02466714009642601	-0.007266503293067217	-0.01209311280399561	-0.009972521103918552	0.013721754774451256	-0.0138970036059618	-0.006589837837964296	0.0072620101273059845	0.011894464492797852	-0.09390139579772949	0.08673743903636932	-0.006004953291267157	-0.05055994912981987	-0.07108887284994125	0.023901667445898056	0.03648022189736366	0.016166647896170616	0.00997499655932188	0.037152498960494995	0.08129365742206573	0.05310330167412758	0.007755933795124292	0.0020436362829059362	-0.06072846055030823	-0.062035173177719116	-0.01739458739757538	-0.00879958551377058	-0.03358881175518036	-0.045360058546066284	0.03722965344786644	-0.06927225738763809	-0.01787261664867401	-0.038544222712516785	0.04914198815822601	-0.028317339718341827	-0.027300475165247917	-0.008885174989700317	0.05549740046262741	-0.02352672629058361	-0.0802096500992775	0.009087854996323586	-0.013759959489107132	0.057899825274944305	-0.02273830771446228	-0.032407745718955994	0.056249238550662994	0.02582143247127533	0.03940277546644211	-0.0191673431545496	0.03912663832306862	-0.02375064603984356	-0.088358573615551	-0.02430790290236473	-0.07527557760477066	-0.06864544749259949	-0.015852494165301323	0.0010389998788014054	-0.03998211771249771	0.07295434921979904	0.020621061325073242	-0.0519234836101532	-0.05418983846902847	-0.002389548346400261	-0.02185344323515892	-0.03634653240442276	-0.018263479694724083	-0.010695917531847954	-0.09169996529817581	-0.035776667296886444	0.0296008437871933	-0.07775361835956573	-0.06734529882669449	0.013887068256735802	-0.05058560147881508	-0.06965744495391846	0.03905900567770004	-0.03451107442378998	-0.009554088115692139	-0.0029384070076048374	-0.06438745558261871	0.05060745030641556	0.08464865386486053	0.035178326070308685	0.035388730466365814	0.040855422616004944	0.004380077589303255	0.06649130582809448	-0.017232170328497887	-0.005167017690837383	-0.020987914875149727	-0.035497717559337616	-0.05170920863747597	0.05307454615831375	-0.05932147800922394	-0.014786774292588234	0.005570457316935062	0.0038595658261328936	0.08346683531999588	-0.023772340267896652	-0.02298903279006481	-0.012460961006581783	0.008143599145114422	-0.010825821198523045	0.022763730958104134	-0.006263932213187218	-0.004711976274847984	-0.021149050444364548	-0.049616437405347824	-0.060297273099422455	-0.044825710356235504	0.027268903329968452	-0.016547132283449173	0.01772536151111126	-0.017293037846684456	-0.049160972237586975	-0.010507792234420776	0.013052389025688171	0.1234046146273613	-0.03088057041168213	0.0354301780462265	0.04353151097893715	-0.007347798440605402	-0.08274810761213303	0.0037069469690322876	0.009376903995871544	-0.012111487798392773	-0.054792460054159164	0.04066024720668793	0.044186633080244064	-0.0846128910779953	-0.009902297519147396	-0.0008754756418056786	-0.018952589482069016	0.019232220947742462	0.03517267853021622	0.0027834256179630756	0.029395803809165955	0.020645836368203163	-0.04887612536549568	-0.005574569571763277	0.004200495779514313	0.022941913455724716	-0.0339697040617466	0.030180085450410843	-0.022522836923599243	0.07954088598489761	-0.041636910289525986	-0.07095091044902802	-0.05666205286979675	0.06073680520057678	0.011523841880261898	-0.03206736221909523	-0.036346685141325	-0.006037609186023474	-0.012843763455748558	0.0698837861418724	-0.007906067185103893	-0.060831934213638306	-0.007442300207912922	-0.0430116280913353	-0.04546431824564934	-0.09957116842269897	-0.032762669026851654	0.006401238031685352	-0.05012940615415573	0.0070890155620872974	-0.019110560417175293	0.02667979709804058	0.00040937517769634724	-0.038523249328136444	0.022953422740101814	0.030465545132756233	0.02655017375946045	-0.006353080738335848	0.039203301072120667	0.008942077867686749	0.04898177832365036	-0.0070886691100895405	-0.04767755791544914	0.039534345269203186	0.02822192572057247	-0.0667487159371376	0.0469503216445446	-0.0671054869890213	-0.008815093897283077	-0.0024131895042955875	0.01256729755550623	0.01947135664522648	0.0023518672678619623	0.018352922052145004	-0.06907059997320175	0.06692701578140259	-0.0965111181139946	-0.024875741451978683	0.04270581528544426	-0.03958781808614731	-0.08927794545888901	-0.004227849654853344	-0.023780692368745804	-0.03274392709136009	0.029921317473053932	-0.016397221013903618	0.024886876344680786	0.03227891027927399	-0.05806615948677063	-0.0076176743023097515	-0.010138577781617641	0.011364218778908253	0.028997741639614105	0.030827026814222336	0.0740414559841156	0.022665413096547127	-0.022186962887644768	0.01953747123479843	-0.008265173062682152	0.014784214086830616	-0.07104663550853729	0.0003506873908918351	0.0019676219671964645	0.024484915658831596	-0.0055320714600384235	0.015098989941179752	0.05993865057826042	-0.05786411836743355	-0.004684676881879568	-0.041428618133068085	0.08782270550727844	0.018460111692547798	0.028098152950406075	0.034462571144104004	-0.03410571441054344	0.004575295373797417	0.00012305603013373911	-0.017843792214989662	-0.08602727204561234	-0.02532372996211052	-0.06468767672777176	0.02836700528860092	0.02306312881410122	-0.0039019451942294836	-0.00488643255084753	-0.04597603157162666	0.004920492880046368	0.02195965126156807	-0.01064587477594614	0.037310171872377396	0.037127431482076645	-0.00041898697963915765	-0.05517633259296417	-0.03536759689450264	-0.05028802528977394	0.03439042344689369	0.014616722241044044	0.02811960130929947	-0.00953886192291975	0.011825475841760635	0.015653757378458977	-0.049918003380298615	0.013739404268562794	0.019519519060850143	-0.06894988566637039	-0.010789654217660427	0.07680867612361908	-0.0327216237783432	0.022074276581406593	0.05893762409687042	-0.02361666038632393	0.033808864653110504	0.03334447368979454	-0.01313833985477686	0.027925610542297363	-0.010303058661520481	-0.0177899319678545	0.052200280129909515	-0.09094881266355515	0.0842449814081192	-0.05987192690372467	0.10700614005327225	0.011520713567733765	-0.03755004331469536	0.09816160053014755	0.059260617941617966	0.04052000492811203	0.017915742471814156	-0.06474729627370834
+0.03635001555085182	-0.00939672440290451	0.04272063821554184	0.032466989010572433	-0.08103030174970627	-0.04653628543019295	-0.020751962438225746	0.01342412643134594	-0.026965904980897903	-0.01230043824762106	-0.016895055770874023	-0.019205857068300247	-0.041181549429893494	0.0026943329721689224	0.057192519307136536	-0.004417733754962683	-0.06442993879318237	-0.008007586933672428	-0.019319850951433182	-0.026609979569911957	0.05342923477292061	0.03533044829964638	-0.049766961485147476	0.022855384275317192	0.01818615198135376	0.014655457809567451	-0.055488720536231995	0.006044985726475716	-0.03203900530934334	0.005120758432894945	-0.029948346316814423	0.01206352561712265	0.057816267013549805	-0.07700823247432709	-0.04488195478916168	0.05017494782805443	-0.027623945847153664	0.07470619678497314	0.057150259613990784	-0.07914615422487259	-0.0516149066388607	0.03643427789211273	-0.03796453773975372	-0.092420294880867	0.04162700101733208	0.025954458862543106	-0.004969611298292875	-0.052969787269830704	0.031324926763772964	0.07968242466449738	0.10988415032625198	-0.057800836861133575	-0.04323494806885719	-0.006217817310243845	-0.005830331705510616	-0.031778883188962936	0.055937573313713074	-0.023338109254837036	-0.08604005724191666	0.04182084649801254	-0.08561310917139053	-0.013902010396122932	0.0020244133193045855	0.03286388888955116	-0.02683892473578453	-0.021050352603197098	0.012382397428154945	0.05119912698864937	0.027632150799036026	-0.025808272883296013	0.0350816510617733	0.1025976613163948	-0.0315217599272728	-0.022928033024072647	0.014979464933276176	-0.016373829916119576	0.019776854664087296	0.07596428692340851	0.006675610318779945	0.035435810685157776	0.04989639297127724	-0.049454446882009506	0.09442048519849777	-0.1054333746433258	0.00670446315780282	-0.022177016362547874	-0.033140603452920914	-0.0553552620112896	0.0362151637673378	0.05940020829439163	0.006415866781026125	0.059674348682165146	-0.04119880497455597	0.020844222977757454	-0.011757924221456051	0.042443644255399704	0.02511746436357498	-0.07335224747657776	-0.03833124414086342	-0.006203817203640938	-0.01977989822626114	-0.033573854714632034	0.025913912802934647	0.020546987652778625	0.06121073663234711	0.0031725941225886345	0.01224056351929903	0.008751384913921356	0.005487211048603058	0.01909445784986019	0.008799921721220016	0.005330461077392101	0.14820285141468048	0.023351427167654037	0.014340929687023163	-0.02030922845005989	-0.007615027483552694	-0.048312656581401825	0.023223968222737312	0.0469694547355175	-0.00845363549888134	0.0008187872590497136	-0.0021068966016173363	-0.0757211223244667	0.023705272004008293	0.019549377262592316	-0.03890432044863701	-0.04638763889670372	0.01680903695523739	-0.0665355995297432	0.12588666379451752	0.02488105185329914	-0.06625356525182724	0.027951853349804878	0.027453836053609848	0.06220126152038574	0.04469294473528862	0.07326440513134003	-0.03300148993730545	-0.0490558035671711	0.006226203870028257	0.041846781969070435	-0.02528287284076214	-0.06318089365959167	-0.008640037849545479	0.044175513088703156	0.034650299698114395	-0.02485557459294796	-0.09962499141693115	0.01992243528366089	-0.09742651879787445	-0.05216140300035477	-0.07117823511362076	-0.09431547671556473	0.023712947964668274	0.08218873292207718	-0.026062816381454468	0.06129700690507889	-0.041675351560115814	0.027629734948277473	-0.04322357848286629	-0.07429344952106476	-0.013486412353813648	-0.07421664893627167	-0.06536363810300827	-0.06362288445234299	0.02027871645987034	0.0033193558920174837	-0.032741729170084	-0.03344718739390373	-0.05901369825005531	-0.036263857036828995	0.031100360676646233	0.024773525074124336	0.07376713305711746	0.008074212819337845	0.018568649888038635	-0.06130906194448471	-0.01162184402346611	-0.02654935047030449	0.019810592755675316	-0.04618946835398674	0.03757869824767113	-0.030179288238286972	-0.03005206771194935	-0.07036182284355164	0.050679177045822144	-0.02432873658835888	0.030149372294545174	0.05791255831718445	-0.047889769077301025	-0.0160814356058836	0.0756315365433693	0.109626404941082	-0.0061511998064816	0.0746399387717247	-0.03545190021395683	0.0377974659204483	-0.047425463795661926	-0.056753601878881454	-0.023382142186164856	0.04605000838637352	0.014521658420562744	0.08537077158689499	-0.006541235372424126	-0.05721278488636017	0.07626432925462723	0.030399521812796593	-0.009114001877605915	-0.050052229315042496	0.04715508595108986	0.032562073320150375	0.02085699513554573	-0.016707615926861763	0.05289483442902565	-0.05269872397184372	0.018225761130452156	-0.02009035088121891	0.04184145852923393	0.008724897168576717	0.018615087494254112	0.05054352059960365	-0.043245311826467514	0.031079651787877083	-0.03543490171432495	-0.06782372295856476	-0.03411773964762688	0.027295691892504692	-0.03245287388563156	-0.003967474680393934	0.03258620947599411	0.03484785929322243	-0.04694207385182381	0.0007133343024179339	-0.017574375495314598	-0.008354113437235355	-0.06150605529546738	0.054624319076538086	-0.02445271983742714	0.000464849203126505	-0.08089292049407959	0.0187789648771286	-0.007983420975506306	0.0397823303937912	0.029376626014709473	0.04067910835146904	0.03482433408498764	-0.01640351675450802	0.04854680225253105	-0.0074893333949148655	-0.059244584292173386	-0.041116803884506226	-0.019213203340768814	-0.0007485580281354487	-0.06852490454912186	-0.024657847359776497	-0.05945068225264549	-0.039543285965919495	-0.03688789904117584	0.01853758655488491	0.009874300099909306	0.005926455371081829	-0.004919089376926422	-0.005501041654497385	0.03245331346988678	0.023160753771662712	-0.06123971939086914	0.023596614599227905	-0.004654364660382271	0.05046207085251808	-0.02700420469045639	-0.005362433847039938	0.0521601140499115	-0.013201371766626835	0.02580440416932106	-0.01911861076951027	0.04340464249253273	-0.02691732347011566	-0.06843332201242447	-0.012320728041231632	-0.08299471437931061	-0.0029452689923346043	-0.030246954411268234	-0.054093800485134125	-0.016908476129174232	0.07848768681287766	0.057334769517183304	-0.023619748651981354	-0.06391152739524841	-0.04021286219358444	-0.029047587886452675	-0.06995712220668793	-0.0096999267116189	0.018175676465034485	-0.07743201404809952	-0.04885929450392723	0.03231019154191017	-0.023036573082208633	-0.07449360936880112	-0.012987182475626469	-0.052813343703746796	-0.06101628765463829	0.022938670590519905	0.009923476725816727	-0.023985330015420914	0.03513938561081886	-0.09290286898612976	0.051725976169109344	0.09072870761156082	0.020507432520389557	0.018515855073928833	0.06822028756141663	0.021062683314085007	0.10386021435260773	0.015231208875775337	-0.015584995038807392	-0.03759007155895233	-0.04209718108177185	-0.09495695680379868	0.044804058969020844	-0.00407625874504447	0.007350824307650328	-0.025075377896428108	-0.017607346177101135	0.09205999970436096	-0.008722672238945961	-0.038289524614810944	-0.01956760324537754	-0.010039069689810276	-0.029668031260371208	0.02452254481613636	0.031207676976919174	0.00537942536175251	-0.009146437048912048	-0.0481283962726593	-0.0016051094280555844	-0.04764467850327492	0.025127116590738297	-0.03109162114560604	0.0069221314042806625	-0.01200094074010849	0.009758428670465946	-0.011470054276287556	0.016436690464615822	0.1049928143620491	-0.03597622737288475	0.0432346872985363	0.03319569304585457	-0.0071233646012842655	-0.10488177090883255	-0.011105158366262913	0.03275091573596001	-0.05445827543735504	-0.020418444648385048	-0.032469313591718674	0.05819573625922203	-0.057232700288295746	-0.026449289172887802	0.011473546735942364	-0.024742107838392258	0.021388979628682137	0.002918628742918372	0.04556860029697418	0.054728176444768906	-0.020433690398931503	-0.025515995919704437	-0.006614301819354296	-0.04885838180780411	0.010473022237420082	0.004154332913458347	-0.017315318807959557	-0.008415844291448593	0.02026393637061119	-0.03172171860933304	-0.026532692834734917	-8.976159733720124e-05	0.05688364803791046	-0.01567942276597023	-0.03475245088338852	0.025740444660186768	0.004401799291372299	-0.013786127790808678	0.05996260046958923	0.022548828274011612	0.0039235670119524	-0.04312676563858986	-0.036872051656246185	-0.004359390586614609	-0.11421670764684677	-0.010470789857208729	0.008913143537938595	-0.08118246495723724	0.029434066265821457	-0.058147262781858444	0.020271195098757744	-0.011342067271471024	-0.021325306966900826	-0.011578680016100407	0.03125416487455368	0.05658775568008423	-0.05845438688993454	-0.011094608344137669	-0.026430178433656693	0.012557114474475384	0.026603568345308304	-0.056463826447725296	0.009963097050786018	0.010839256457984447	-0.0026308405213057995	0.03670864179730415	-0.025327499955892563	-0.0430036298930645	-0.008348872885107994	-0.02119164913892746	0.02343752048909664	-0.003757321508601308	0.01098141074180603	-0.0029443157836794853	0.04813927784562111	-0.07245665043592453	-0.04900134727358818	0.010414298623800278	0.019852321594953537	-0.11148615181446075	-0.010033653117716312	-0.0038032871671020985	-0.02197129838168621	0.02409076876938343	-0.006873616948723793	-6.759577809134498e-05	0.03349872678518295	-0.038737330585718155	-0.016820445656776428	-0.0607328936457634	0.0035901174414902925	0.031218836084008217	0.003663375275209546	0.019158758223056793	-0.010634888894855976	-0.043645914644002914	0.0007862618076615036	-0.022380614653229713	0.016685349866747856	-0.06504784524440765	-0.028836701065301895	-0.04693267494440079	0.016264673322439194	0.08280137181282043	-0.024879982694983482	-0.0024182943161576986	-0.029771946370601654	-0.06683791428804398	-0.031939808279275894	0.043443698436021805	-0.004500873386859894	-0.0064650666899979115	0.10734197497367859	-0.030586522072553635	-0.007950766012072563	0.03146500140428543	-0.06120786443352699	-0.07909535616636276	-0.024062221869826317	-0.017051415517926216	0.0601336732506752	0.04526812210679054	0.01803104393184185	-0.009387078694999218	-0.03138682618737221	-0.019913462921977043	0.0037686787545681	-0.07412748038768768	0.013728220015764236	0.01968863606452942	-0.06687629222869873	-0.08461722731590271	-0.008038671687245369	-0.031488582491874695	0.04595840349793434	0.03774670138955116	0.03676379844546318	-0.00957559421658516	0.021439746022224426	0.01769123785197735	-0.05188558250665665	0.004983153194189072	0.048010021448135376	-0.041567835956811905	-0.0062489998526871204	0.11757928878068924	-0.027601173147559166	0.04780399799346924	0.0670049786567688	-0.07230247557163239	0.02259312942624092	0.029867839068174362	-3.080856549786404e-05	0.025914888828992844	-0.00364285078831017	-0.06639014929533005	0.06367899477481842	-0.0436202809214592	0.07215750217437744	-0.04346670210361481	0.11436096578836441	0.00034486636286601424	-0.050962887704372406	0.06454955041408539	0.04532913491129875	-0.025220980867743492	-0.02490510791540146	-0.07041331380605698
+0.05230317264795303	0.020670751109719276	-0.003996998071670532	0.03511156514286995	-0.10877516120672226	-0.0321548730134964	-0.03943173587322235	0.042865004390478134	0.01051621325314045	-0.03705906495451927	-0.04316583648324013	-0.014168599620461464	-0.0008706833468750119	0.0549439899623394	0.02483411878347397	-0.05642462894320488	-0.07758523523807526	0.006370104383677244	-0.050079695880413055	-0.046749234199523926	0.029607495293021202	-0.007013889495283365	-0.06355520337820053	0.06780289113521576	0.023754741996526718	0.011991636827588081	-0.08821433782577515	0.02929246798157692	0.0011533740907907486	0.03295918554067612	0.05132841318845749	-0.034347642213106155	0.04652651026844978	-0.0583418607711792	-0.02564757689833641	0.07783270627260208	-0.029370687901973724	0.09879017621278763	0.037775810807943344	-0.03428258001804352	-0.05042767524719238	0.02462945692241192	-0.04637327417731285	-0.06966584175825119	0.022223107516765594	0.005090620368719101	-0.05026054009795189	-0.03779534995555878	0.04959141090512276	-0.005256836302578449	0.0500994473695755	-0.03364735469222069	-0.02473071776330471	-0.003390311961993575	-0.02733323909342289	0.007076757028698921	0.032721467316150665	-0.05688796564936638	-0.021210093051195145	0.022247441112995148	-0.06449216604232788	-0.014817061834037304	0.01469514798372984	0.017770424485206604	-0.012920095585286617	-0.021737871691584587	0.039587073028087616	0.07890646159648895	0.039231136441230774	0.016799144446849823	0.002711203880608082	0.0665246918797493	-0.04599718749523163	-0.027219342067837715	0.05947653949260712	-0.09480194002389908	-0.06048053875565529	0.06825564801692963	0.01897377520799637	0.051739443093538284	0.0269311536103487	-0.04566168412566185	0.12229226529598236	0.0004030998097732663	-0.04980722442269325	-0.013503516092896461	-0.007914365269243717	-0.03873898461461067	0.02438403107225895	0.006659610662609339	0.030982356518507004	0.070340096950531	-0.04623435437679291	0.023893363773822784	0.012464608065783978	0.0375845767557621	0.06710594892501831	-0.10978154838085175	-0.007201768457889557	0.013009371235966682	-0.006664622575044632	-0.00427995203062892	0.03626728430390358	0.0023432474117726088	0.042073994874954224	0.02806563302874565	-0.04549919068813324	0.000801545160356909	0.020398885011672974	0.02460355870425701	0.04012561962008476	0.0858546569943428	0.08988216519355774	0.02292162925004959	0.05214777588844299	-0.03150158002972603	0.017571160569787025	-0.07836420834064484	-0.06366771459579468	-0.015147309750318527	-0.026016350835561752	0.0734744444489479	-0.055264346301555634	-0.052433982491493225	-0.010489920154213905	0.018796928226947784	-0.11082328855991364	-0.010935799218714237	0.025504011660814285	-0.0006509188679046929	0.07643776386976242	0.056724995374679565	-0.010793713852763176	0.037806667387485504	0.08183886855840683	0.026818806305527687	0.0400017686188221	0.036316681653261185	-0.0033726789988577366	-0.01202763058245182	-0.022529372945427895	0.03277202323079109	0.03680362179875374	-0.04957376420497894	0.05404185876250267	0.04109181836247444	-0.019951479509472847	-0.0021367804147303104	-0.07773032039403915	-0.02189885452389717	-0.07517969608306885	-0.07546378672122955	0.003686918644234538	-0.05945469066500664	0.001011707354336977	0.03978971764445305	0.016004275530576706	0.01767374575138092	-0.0663471445441246	-0.04677312821149826	-0.04292306303977966	-0.0008979584090411663	0.024935616180300713	-0.07111245393753052	-0.031523942947387695	-0.0035966397263109684	-0.013042833656072617	0.015405774116516113	-0.041596729308366776	0.01327920239418745	-0.014389313757419586	-0.026210062205791473	0.042515866458415985	0.08867541700601578	0.04964086413383484	-0.021952856332063675	0.0399479940533638	-0.046305105090141296	-0.01331996452063322	-0.04865081608295441	0.017293697223067284	-0.0587255135178566	0.028477875515818596	-0.05515424534678459	0.00148190266918391	-0.0008576734689995646	0.03241308778524399	-0.044290099292993546	0.03688782453536987	-0.020807992666959763	-0.05027910694479942	-0.04340750351548195	0.034129440784454346	0.06147254258394241	-0.0441351942718029	0.04438488557934761	-0.016208168119192123	0.03266323730349541	0.018010083585977554	-0.008750014007091522	-0.07549331337213516	0.08340593427419662	0.0014212706591933966	0.06744328886270523	0.042516421526670456	-0.06758861243724823	0.05171641707420349	0.020254746079444885	0.04551666975021362	-0.03478626534342766	-0.002399755408987403	0.03670063614845276	0.0005694455467164516	0.05055977404117584	0.10014262795448303	0.010534066706895828	0.00278307031840086	-0.0036089641507714987	0.03783292695879936	-0.06956931203603745	0.0034910233225673437	0.010371826589107513	-0.0963134914636612	0.019929837435483932	-0.053137119859457016	-0.07637780159711838	-0.01657101698219776	0.021207448095083237	-0.019122332334518433	-0.04949560388922691	0.010626733303070068	0.035435061901807785	-0.015073896385729313	-0.03573025390505791	0.005885683465749025	-0.025484537705779076	-0.06548275053501129	0.10474992543458939	0.001771510229445994	-0.07482223212718964	-0.04052771255373955	0.03460816293954849	0.04274195432662964	0.00201374851167202	0.01271173357963562	0.04287058487534523	0.1280176192522049	0.03476623445749283	-0.02075771987438202	0.0023593269288539886	-0.03039686568081379	-0.06810278445482254	0.0022836560383439064	0.04341839626431465	-0.006652012933045626	-0.03348983824253082	0.015199737623333931	-0.04660487920045853	-0.0026686389464884996	0.017472196370363235	0.04578116163611412	-0.01743379794061184	-0.023488590493798256	-0.08290985226631165	0.046772100031375885	-0.039113305509090424	-0.029124919325113297	0.07623724639415741	-0.03151801973581314	0.033133216202259064	-0.010628682561218739	-0.054342396557331085	0.031186776235699654	0.044507112354040146	0.03336683288216591	-0.038921162486076355	0.08415309339761734	-0.0075475662015378475	-0.03834433853626251	-0.023741932585835457	-0.05410681664943695	-0.04551539570093155	-0.030958788469433784	0.0016383768524974585	-0.028411561623215675	0.07473237812519073	0.015279969200491905	-0.014309871941804886	-0.05317329242825508	0.004999884404242039	-0.018002809956669807	-0.0659688413143158	-0.0037014365661889315	0.012072242796421051	-0.059983029961586	-0.02475014515221119	0.014472217299044132	-0.015013639815151691	-0.05870288237929344	0.01753252185881138	-0.02089567296206951	-0.08468411862850189	0.03672736510634422	-0.01138584315776825	-0.04199414327740669	0.007876788266003132	-0.03842209279537201	-0.012949489057064056	0.12264996767044067	0.03569961339235306	-0.013765959069132805	0.02165057323873043	0.053066644817590714	0.10191002488136292	-0.0031097091268748045	-0.041851624846458435	-0.023196110501885414	-0.05673552304506302	-0.07811031490564346	0.07074242830276489	-0.07223603129386902	-0.01660100370645523	0.003335267072543502	-0.014248747378587723	0.01885760948061943	-0.009349956177175045	-0.060517191886901855	-0.046785883605480194	0.01123220194131136	-0.0052947960793972015	0.06451918184757233	-0.02116715908050537	-0.02793232910335064	-0.04698333889245987	-0.0481429286301136	-0.056564074009656906	-0.059912778437137604	0.05788668617606163	0.008425584062933922	0.01875079981982708	0.00016910825797822326	-0.06847789138555527	0.011366304010152817	-0.024537667632102966	0.08564949780702591	-0.08672575652599335	-0.02103419601917267	0.017902180552482605	0.019969377666711807	-0.0695793479681015	-0.007497183512896299	0.012015093117952347	-0.03426665812730789	-0.026223858818411827	0.00758214620873332	0.017830166965723038	-0.09203160554170609	0.013358676806092262	-0.002935232361778617	-0.037890803068876266	-0.020836643874645233	0.03771451488137245	0.015595882199704647	0.02947639301419258	-0.00824709888547659	-0.03272813931107521	0.017847875133156776	0.04040612652897835	0.007939862087368965	-0.013118603266775608	0.05883469805121422	-0.030267633497714996	0.07101279497146606	-0.028831034898757935	-0.03897133842110634	-0.059309445321559906	0.06342363357543945	0.0016516977921128273	-0.004772360436618328	-0.0004063934029545635	0.014790006913244724	-0.016078989952802658	0.02592954784631729	0.02533150650560856	-0.05086374655365944	-0.03850651532411575	-0.05044308677315712	-0.05448411405086517	-0.06400039792060852	0.020090045407414436	0.02873525768518448	-0.042737118899822235	0.03607005625963211	0.005526326596736908	-0.050185427069664	0.022726155817508698	-0.012199743650853634	0.01324122678488493	0.04223713278770447	0.04038017988204956	-0.012248935177922249	0.037760619074106216	-0.00843136664479971	0.04410688206553459	0.05073664337396622	-0.029897034168243408	0.020616203546524048	0.018387233838438988	-0.06316344439983368	0.06760331243276596	-0.04130919277667999	0.005654689855873585	0.007832611910998821	-0.0005572652444243431	0.021221784874796867	-0.020149625837802887	0.02398824132978916	-0.0627504289150238	0.039186105132102966	-0.0654846802353859	-0.03356611356139183	0.030298303812742233	-0.027845488861203194	-0.11652223020792007	-0.020403392612934113	0.03708469122648239	-0.017168356105685234	0.01049121841788292	-0.023475492373108864	0.016857901588082314	0.0738680362701416	-0.038610056042671204	-0.046141911298036575	-0.030435552820563316	0.01986423321068287	0.0541778989136219	-0.02270655147731304	0.06242244690656662	0.02192489057779312	0.023094113916158676	0.027066867798566818	0.003032335778698325	-0.004775437526404858	-0.035585395991802216	-0.007312439847737551	0.02372903935611248	-0.07556721568107605	0.011190795339643955	0.026844877749681473	-0.002944004023447633	-0.03739086538553238	-0.05486580729484558	-0.04478245973587036	0.07762130349874496	0.0011535106459632516	0.013975527137517929	0.035315945744514465	-0.03271086886525154	0.041123680770397186	-0.009118025191128254	-0.04630227014422417	-0.06656283885240555	0.006162835285067558	-0.04202297702431679	0.018699998036026955	0.008411669172346592	0.0198883805423975	-0.03243980556726456	-0.08923552930355072	0.025310631841421127	-0.009329930879175663	-0.07853943854570389	0.01154919620603323	-0.012564590200781822	0.00198258925229311	-0.04562561586499214	-0.022093433886766434	-0.0993029996752739	0.09131358563899994	0.05958143621683121	0.051390618085861206	0.008593975566327572	-0.027459818869829178	-0.0025598888751119375	-0.050522781908512115	-0.030245671048760414	-0.0012623247457668185	-0.041332557797431946	0.006540311500430107	0.07364010810852051	-0.04936840757727623	0.022347480058670044	0.06071401759982109	-0.012506434693932533	0.02277488447725773	0.04544655606150627	-0.03900613263249397	0.06487838923931122	-0.014880931936204433	-0.03638935089111328	0.07976218312978745	-0.038485657423734665	0.09616915881633759	-0.059863485395908356	0.07322458177804947	-0.01071929931640625	-0.028200920671224594	0.07411061972379684	0.08745861053466797	-0.005605247803032398	0.029704805463552475	-0.039782971143722534
+-0.00021775637287646532	-0.0014470741152763367	-0.04713001847267151	-0.003384567331522703	-0.07401926070451736	-0.052912186831235886	-0.0040604290552437305	0.05066685006022453	0.0059396144933998585	-0.04866020753979683	-0.031183544546365738	-0.0016234347131103277	-0.012269794940948486	-0.016863839700818062	0.021250516176223755	-0.027957554906606674	-0.07930399477481842	-0.015167353674769402	-0.04557278752326965	-0.06736907362937927	0.017712073400616646	0.03065994754433632	-0.07352568954229355	0.05387162044644356	0.02752436138689518	0.02352420799434185	-0.04895039647817612	-0.027722060680389404	-0.012751617468893528	0.033624663949012756	0.029921231791377068	-0.010710574686527252	0.07354693114757538	-0.037292759865522385	-0.05088493973016739	0.08022282272577286	-0.0035362625494599342	0.10483191162347794	0.038635604083538055	-0.03550388664007187	-0.04387179762125015	-0.007055529858916998	-0.04788709059357643	-0.06486557424068451	-0.005212300457060337	-0.032166432589292526	-0.03604601323604584	-0.0188829954713583	0.013325031846761703	0.04067602753639221	0.06172005832195282	-0.009030734188854694	-0.036090187728405	-0.030509203672409058	-0.017939632758498192	0.039881326258182526	0.006893211975693703	-0.062060050666332245	-0.07186705619096756	-0.0013437350280582905	-0.06847618520259857	0.05330094322562218	-0.015516668558120728	0.019786003977060318	-0.0019417204894125462	-0.0471939891576767	-0.003964384086430073	0.09113913029432297	0.028303418308496475	-0.016291338950395584	0.014439982362091541	0.06577558070421219	-0.07173385471105576	-0.038932058960199356	0.053915590047836304	-0.03998119384050369	-0.028603069484233856	0.05072401091456413	-0.006711388472467661	-0.001375666237436235	0.017812084406614304	-0.030650993809103966	0.12158229947090149	-0.053732164204120636	-0.007659949362277985	-0.04157159850001335	-0.028908802196383476	-0.056382521986961365	0.05260339006781578	-0.007392385043203831	0.03458151966333389	0.05744955688714981	-0.042861755937337875	0.042024970054626465	0.014252014458179474	0.026700271293520927	0.025914402678608894	-0.14163251221179962	-0.04604467749595642	-0.002736948663368821	-0.010677952319383621	-0.017492011189460754	0.05346568301320076	0.003454061457887292	0.024970659986138344	0.032050345093011856	-0.03822854906320572	5.6048545957310125e-05	0.04313584044575691	0.006256651598960161	0.04606875404715538	0.0845513567328453	0.095185786485672	0.04453239217400551	0.04771530255675316	0.0033166727516800165	0.007828609086573124	-0.022609751671552658	-0.009477062150835991	0.026951700448989868	-0.01749398373067379	0.007837342098355293	-0.00839676707983017	-0.04880547150969505	-0.04263834282755852	0.0460435152053833	-0.08286202698945999	-0.037205059081315994	0.025551704689860344	-0.045205600559711456	0.10326020419597626	0.05361893028020859	-0.02877143956720829	-0.006158080417662859	0.043868664652109146	0.008747195824980736	0.03649762645363808	0.007993321865797043	-0.022256435826420784	-0.041354551911354065	-0.05677001178264618	0.04858267307281494	-0.022148430347442627	-0.04686562344431877	0.023663461208343506	0.014575636945664883	0.0132299168035388	-0.018419725820422173	-0.08182808756828308	-0.014957807026803493	-0.0969972163438797	-0.09572263062000275	0.0019310161005705595	-0.08970443159341812	0.05122643709182739	0.08100778609514236	0.02975429967045784	0.06204724684357643	0.009726963937282562	-0.0044260527938604355	-0.022444726899266243	-0.032137323170900345	-0.010204083286225796	-0.10588674247264862	-0.019524136558175087	-0.021245762705802917	0.01024316530674696	0.03868462145328522	-0.04924494028091431	-0.011369162239134312	-0.025955790653824806	-0.016823329031467438	0.012439411133527756	0.08266781270503998	0.05587799847126007	0.01901993900537491	0.016287684440612793	-0.05354858189821243	-0.031059877946972847	-0.025996070355176926	0.05254823714494705	-0.07515149563550949	0.037489984184503555	-0.01588159054517746	0.0015633358852937818	-0.01251532044261694	0.02755291946232319	-0.00043900898890569806	0.018056390807032585	0.024316709488630295	-0.059892263263463974	-0.07911910116672516	0.033228274434804916	0.051293645054101944	-0.019320445135235786	0.03208010271191597	0.027191486209630966	0.10243217647075653	-0.015987947583198547	0.003544356906786561	-0.07037746906280518	0.004658637102693319	0.029360098764300346	0.03051670640707016	0.018235137686133385	-0.06285826116800308	0.0846339613199234	0.0009055703994818032	0.03249754756689072	-0.047799017280340195	0.04020630568265915	0.028694594278931618	0.011377481743693352	0.029306935146450996	0.06432470679283142	-0.04195006936788559	-0.020663496106863022	0.001772089395672083	0.04650986194610596	-0.09238851070404053	-0.010872715152800083	0.004297735635191202	-0.06857430189847946	0.03149024769663811	-0.02851308137178421	-0.11204843968153	-0.03746715560555458	-0.0039467285387218	-0.020068537443876266	-0.060331132262945175	-0.022648220881819725	0.04937795549631119	-0.027553288266062737	-0.02937735617160797	0.007573003880679607	-0.002158353105187416	-0.05992111563682556	0.06571731716394424	-0.03171434998512268	-0.06036597862839699	-0.047346677631139755	-0.003151434240862727	-0.010496540926396847	0.0026478308718651533	0.024284623563289642	0.07234157621860504	0.11175541579723358	0.02658179961144924	-0.02811703085899353	-0.011225702241063118	-0.05376725643873215	-0.04552162438631058	0.010462556965649128	0.03926300257444382	-0.04066767543554306	-0.0784616768360138	0.011548684909939766	-0.07149286568164825	-0.0012070140801370144	0.004229466896504164	0.05470402538776398	-0.01701643131673336	-0.03856924548745155	-0.04282689839601517	0.022633317857980728	0.0013220241526141763	-0.03385018929839134	0.029995331540703773	-0.04455210641026497	0.027641858905553818	-0.026112090796232224	-0.02571214735507965	0.009512384422123432	-0.016580691561102867	0.04869512468576431	-0.004638862330466509	0.07460363954305649	-0.03003542870283127	-0.0769423395395279	-0.03462408110499382	-0.05847209692001343	-0.058423276990652084	-0.009341760538518429	-0.022895116358995438	-0.03345436975359917	0.07791159301996231	0.032513972371816635	-0.03578825667500496	-0.027694428339600563	-0.032379478216171265	-0.04685429483652115	-0.09205037355422974	-0.025615543127059937	0.0022799912840127945	-0.08257468789815903	-0.029440734535455704	0.046303749084472656	-0.039978884160518646	-0.01815791241824627	0.06682706624269485	-0.02772272378206253	-0.05541962385177612	0.040424373000860214	-0.0033988002687692642	-0.03837478160858154	-0.005734400823712349	-0.08388756960630417	0.008402583189308643	0.08929205685853958	0.02031419426202774	0.029212433844804764	0.04727572947740555	0.06785298883914948	0.07520930469036102	0.015653042122721672	-0.02394045703113079	-0.005339109338819981	-0.010991639457643032	-0.08225879818201065	0.040744926780462265	-0.021288154646754265	-0.009805943816900253	-0.024661969393491745	-0.001668910845182836	0.042155273258686066	0.005292881280183792	-0.007885674946010113	-0.01796579733490944	0.03576076403260231	-0.014200692996382713	0.05380966141819954	-0.003747513983398676	-0.009514889679849148	0.04131340980529785	-0.07326804101467133	-0.045161060988903046	-0.06828845292329788	0.015595742501318455	0.011959385126829147	0.06009247899055481	0.014530424028635025	-0.05427977815270424	-0.0006276983767747879	-0.017618512734770775	0.11415287107229233	-0.10015300661325455	0.025362584739923477	0.022413568571209908	0.013350986875593662	-0.1101166233420372	-0.03920738399028778	0.03987131267786026	-0.04100780561566353	-0.0583069883286953	0.007590858265757561	0.04923272505402565	-0.05680758133530617	-0.07869472354650497	0.01122433040291071	-0.04225517064332962	-0.015959301963448524	-0.007515043951570988	0.007088674698024988	0.03646264597773552	0.0036252110730856657	0.00554394768550992	0.040215738117694855	0.010284118354320526	0.030976595357060432	-0.025304296985268593	0.02921469137072563	-0.03723914548754692	0.07010077685117722	-0.049562711268663406	-0.046135541051626205	-0.07429274171590805	0.05249570310115814	-0.021622326225042343	-0.021125249564647675	-0.03242810070514679	0.007899153046309948	-0.017570560798048973	0.05564701184630394	0.024433553218841553	-0.037222620099782944	-0.03672458231449127	-0.028033696115016937	-0.02406252548098564	-0.044665612280368805	-0.018287332728505135	-0.006494040600955486	-0.051256902515888214	0.024383796378970146	-0.06101556867361069	-0.028076620772480965	0.011204155161976814	0.0019031008705496788	0.023772994056344032	0.04531748965382576	0.02917891927063465	-0.028485499322414398	0.04049447923898697	-0.006447849795222282	0.03537529706954956	0.016079023480415344	-0.02848452515900135	0.01569138653576374	0.036101534962654114	-0.029143698513507843	0.029498936608433723	-0.030833058059215546	-0.009393511340022087	0.0022260798141360283	-0.03203775733709335	-0.004092802293598652	-0.0011026677675545216	0.03969600424170494	-0.03395645692944527	0.03280263766646385	-0.08107537031173706	-0.04817131906747818	0.03821224346756935	-0.025436673313379288	-0.08663907647132874	-0.022172346711158752	-0.02219640463590622	-0.033952828496694565	0.04859547317028046	-0.0098705580458045	0.03506302088499069	0.04514358192682266	-0.05920346453785896	-0.07154998928308487	-0.012986033223569393	-0.01460037101060152	-0.009631013497710228	0.015949591994285583	0.04420602321624756	-0.012171484529972076	0.009204485453665257	0.008662433363497257	-0.014416595920920372	0.005180977284908295	-0.06994057446718216	-0.006267175544053316	-0.013736892491579056	-0.03824438154697418	0.052668314427137375	0.009254802018404007	0.026344751939177513	-0.042489200830459595	-0.03496289253234863	-0.030543027445673943	0.03214867785573006	-0.030836738646030426	-0.003675646847113967	0.03703974932432175	-0.02053189091384411	-0.0013268364127725363	0.034262750297784805	-0.0015789231983944774	-0.05579565092921257	-0.0013516253093257546	-0.0501624271273613	0.07265745848417282	0.019046040251851082	0.0325312577188015	-0.02927006222307682	-0.079415462911129	-0.030549077317118645	-0.008549923077225685	-0.058909520506858826	-0.012739920988678932	0.02130708284676075	-0.07609028369188309	-0.0770515576004982	-0.018653690814971924	-0.053173430263996124	0.0637081116437912	-0.0126028535887599	0.06690855324268341	-0.040947671979665756	-0.017348255962133408	0.005747171584516764	-0.03459092229604721	0.011829919181764126	0.00808698683977127	-0.07660596817731857	-0.04508022591471672	0.0984029695391655	0.014561504125595093	0.031475745141506195	0.09751612693071365	-0.035135652869939804	0.056815288960933685	0.01888444647192955	-0.04311002790927887	0.02264554612338543	-0.0018727764254435897	-0.06573545932769775	0.03538191318511963	-0.04297535866498947	0.08277875930070877	-0.048300229012966156	0.12271876633167267	-0.0015989611856639385	-0.034989722073078156	0.08933989703655243	0.08675795793533325	-0.016618823632597923	0.041038066148757935	-0.050099246203899384
+-0.0026014933828264475	-0.07435241341590881	-0.04338986054062843	0.02797975018620491	-0.03165707737207413	0.032920800149440765	-0.03230499103665352	0.07145024836063385	0.020660964772105217	-0.019007017835974693	-0.038621291518211365	-0.04878890514373779	-0.009933928959071636	0.015707336366176605	0.03677092120051384	0.0035145252477377653	-0.00417493237182498	-0.006624914705753326	0.06751678884029388	-0.04111307114362717	-0.007646829355508089	0.06335772573947906	0.026773806661367416	0.0632578507065773	0.04260272905230522	-0.0087897265329957	-0.027387157082557678	0.060391657054424286	0.009403449483215809	0.09058547019958496	-0.017993947491049767	-0.03240218013525009	-0.01306798867881298	-0.01059039868414402	0.05048668384552002	0.01891007460653782	-0.020404310896992683	0.08379387855529785	0.04310370236635208	-0.08518992364406586	0.04477235674858093	0.00745429378002882	0.026547139510512352	-0.04084472358226776	0.00869052391499281	-0.033512067049741745	-0.05596306174993515	-0.10654997080564499	-0.009121919050812721	0.06959674507379532	0.06928341835737228	0.003500895807519555	0.03759003058075905	-0.004967079497873783	-0.10283835232257843	-0.0006685021216981113	-0.01412898302078247	-0.011354582384228706	-0.003097724402323365	0.03586592152714729	-0.003764451015740633	-0.04334740340709686	-0.001281831180676818	0.04404563084244728	-0.021732542663812637	-0.0237586572766304	0.03465425595641136	0.03589876368641853	0.03272128850221634	0.023396916687488556	-0.06418997049331665	0.03700268641114235	-0.02735678292810917	-0.047049541026353836	0.025739364326000214	0.004030016250908375	-0.009127354249358177	-0.014132875017821789	0.01717703603208065	0.004103000275790691	0.04790310934185982	-0.040661707520484924	0.02939588949084282	-0.04492328315973282	0.0344301201403141	0.03434847295284271	-0.01929188147187233	-0.040279243141412735	-0.002747997874394059	0.006820473819971085	0.060773711651563644	-0.010671542026102543	0.02652769722044468	0.012696834281086922	-0.0029156282544136047	0.006169759668409824	0.08781888335943222	-0.08289564400911331	-0.00512261176481843	-0.01827823370695114	-0.010274432599544525	0.0008866671123541892	0.06627669930458069	0.05015531927347183	0.005837931763380766	0.008785596117377281	-0.06935873627662659	0.031504396349191666	0.0644800066947937	0.03838416188955307	-0.03141670674085617	0.05245021730661392	0.05023863539099693	-0.0685272365808487	-0.011489130556583405	-0.04950457066297531	-0.05071435868740082	0.029873095452785492	0.005804654210805893	0.05457427725195885	0.020402707159519196	0.0493825264275074	-0.05027244612574577	-0.06578591465950012	0.04677075520157814	0.07787026464939117	-0.008623091503977776	0.06247283145785332	-0.03251432999968529	-0.008028066717088223	0.0020212624222040176	0.023017147555947304	0.006025970913469791	0.033254701644182205	0.03966616839170456	0.029783964157104492	-0.002056610304862261	0.022185519337654114	0.02190304733812809	0.05198110640048981	-0.08007960021495819	0.0343523733317852	0.07581031322479248	0.0043344260193407536	-0.029639463871717453	0.01947980746626854	-0.012599456124007702	0.04332759231328964	-0.028786374256014824	-0.03465823084115982	-0.03553280979394913	-0.01913926564157009	0.06324223428964615	-0.05415702238678932	-0.019039815291762352	0.012590097263455391	-0.028489792719483376	0.07684490829706192	0.06402654200792313	0.004969489760696888	-0.04229132831096649	-0.04727773368358612	0.00907064974308014	-0.010246009565889835	0.031114349141716957	0.003115949220955372	-0.005980105139315128	-0.10841351002454758	0.028565973043441772	0.03396957740187645	0.04247639328241348	0.018913062289357185	0.04757202789187431	0.051020484417676926	0.057056792080402374	0.01733395643532276	0.015413046814501286	0.007742864079773426	-0.05035123974084854	-0.0424216166138649	-0.04709775745868683	0.021593283861875534	-0.035924240946769714	-0.018123406916856766	-0.04948775842785835	-0.026408644393086433	0.05119144171476364	0.01177580002695322	-0.009687754325568676	-0.043629761785268784	0.08119484037160873	-0.04306209459900856	0.03525420278310776	-0.021326903253793716	-0.010092444717884064	0.019798263907432556	-0.0005357779446057975	0.07883838564157486	-0.055518824607133865	0.01579354703426361	0.028878921642899513	0.09247322380542755	0.06255770474672318	-0.02998386323451996	7.631463813595474e-05	0.06783968955278397	0.047418128699064255	-0.025105426087975502	0.02444464899599552	-0.03535088896751404	0.007594768889248371	0.01763705536723137	-0.0013256537495180964	0.012048465199768543	0.07116062194108963	-0.006514161825180054	0.023885410279035568	-0.04185326769948006	0.024288583546876907	-0.018904048949480057	0.04027976095676422	0.0014755657175555825	-0.07783829420804977	0.006720162928104401	-0.017538363113999367	-0.04343823343515396	0.021355142816901207	-0.05053342133760452	0.003988146781921387	-0.02272881753742695	0.06830901652574539	0.032616712152957916	0.010026747360825539	0.03408709540963173	0.006049630232155323	0.0374089851975441	0.040705956518650055	-0.033963948488235474	-0.09690749645233154	0.016353433951735497	-0.07043015956878662	0.01197002176195383	-0.042106565088033676	-0.0031255444046109915	0.008446279913187027	0.06578966230154037	0.05679250508546829	0.07163172215223312	-0.02596389874815941	0.060376979410648346	-0.04972362890839577	-0.042080339044332504	-0.02747761458158493	0.0032149548642337322	0.003042830154299736	-0.05958547443151474	0.004225546959787607	-0.14936736226081848	-0.03327891603112221	-0.04807327687740326	0.009953086264431477	-0.048051416873931885	0.0465538464486599	-0.03340648487210274	0.020342059433460236	-0.009977176785469055	-0.07512127608060837	0.023114195093512535	-0.054252978414297104	-0.013467252254486084	-0.006591171491891146	0.08280102163553238	0.03521289303898811	0.08078667521476746	-0.017316479235887527	-0.052594445645809174	-0.014987298287451267	-0.054865166544914246	-0.054131653159856796	-0.0678659975528717	-0.05289831757545471	-0.0368104949593544	0.06737088412046432	-0.029687628149986267	-0.016751844435930252	-0.019941281527280807	0.03724764660000801	0.03689510002732277	-0.05312643200159073	-0.06345944851636887	-0.012496566399931908	-0.013086711056530476	-0.0017980467528104782	0.013044158928096294	-0.027706975117325783	-0.04959482327103615	-0.004239128436893225	0.014237177558243275	-0.022850576788187027	0.024376176297664642	0.047405585646629333	-0.004716360010206699	0.031354550272226334	0.019479095935821533	0.02101775072515011	0.029526367783546448	-0.04933552071452141	0.04276758432388306	0.0834960862994194	0.044121984392404556	0.0018036452820524573	0.06936503946781158	-0.010741214267909527	-0.0033230821136385202	0.04512054845690727	-0.01975395902991295	0.013682563789188862	0.04958268254995346	-0.011175000108778477	-0.06887493282556534	0.025780189782381058	-0.05057710036635399	0.018356259912252426	-0.08720873296260834	-0.020305700600147247	0.054934341460466385	-0.05111485347151756	-0.08125089854001999	0.04832302778959274	-0.0533771850168705	0.028373634442687035	-0.04207335785031319	-0.0033145491033792496	0.008938850834965706	-0.0013709828490391374	0.05159268155694008	-0.06753767281770706	0.08117004483938217	-0.02912185899913311	-0.022666197270154953	0.07039543241262436	0.015241507440805435	0.013265401124954224	0.01152411662042141	0.031129784882068634	-0.09336454421281815	0.011278614401817322	0.013805197551846504	-0.04144062101840973	-0.054371584206819534	-0.05535805597901344	-0.07710084319114685	-0.06668850034475327	0.010706434957683086	0.00959568191319704	-0.004092995077371597	0.028185490518808365	-0.060253046452999115	-0.0719931423664093	-0.037974532693624496	0.020366525277495384	0.04758685454726219	-0.007803386077284813	0.07412850856781006	-0.014474278315901756	-0.060672223567962646	-0.0010412714909762144	-0.023458493873476982	0.02893860451877117	-0.011461743153631687	0.012119160033762455	0.09969091415405273	-0.028541477397084236	0.010668880306184292	-0.04710366949439049	-0.0683654174208641	-0.0029498019721359015	-0.05050768330693245	0.02858337014913559	-0.013461404480040073	0.02025548368692398	0.048723991960287094	0.07659260928630829	0.012537792325019836	0.04576864093542099	-0.029507234692573547	0.016277538612484932	0.04893139377236366	-0.011978263966739178	-0.06436330825090408	0.06145056337118149	0.03507857769727707	0.05105287954211235	-0.0475328154861927	-0.0016602268442511559	-0.07469567656517029	0.0230021383613348	0.04764213785529137	0.04660232365131378	0.01981520839035511	0.020658647641539574	0.009549944661557674	0.0153147391974926	0.0030974862165749073	0.003809939604252577	-0.07427719235420227	0.03506934270262718	0.013260794803500175	-0.02131183072924614	-0.04799581319093704	0.005003301426768303	0.010035965591669083	-0.04016156122088432	-0.010354651138186455	0.037914078682661057	-0.09332208335399628	-0.014530356042087078	-0.06181193143129349	0.03104270249605179	-0.0460442453622818	0.051129359751939774	-0.04266508296132088	0.052872080355882645	0.00573490047827363	-0.0022048335522413254	-0.025974949821829796	0.019888553768396378	0.02832176722586155	-0.09321281313896179	-0.06898654252290726	0.029172580689191818	-0.03462643921375275	-0.0024454195518046618	0.007269419729709625	0.1023639589548111	-0.026243574917316437	0.12751969695091248	-0.023482954129576683	-0.05739288404583931	-0.07639271020889282	-0.05987859517335892	-0.08508992195129395	0.07211282849311829	0.01464912574738264	-0.012756104581058025	-0.013066797517240047	-0.016679318621754646	0.060026027262210846	0.08905675262212753	0.050487224012613297	-0.03878888487815857	-0.024563411250710487	-0.0038370233960449696	0.030964162200689316	0.030194398015737534	0.03587331622838974	-0.02214978262782097	-0.023422852158546448	-0.05318702757358551	0.01950427144765854	-0.008739049546420574	-0.009533483535051346	-0.05277688428759575	0.02939673699438572	0.04843289405107498	-0.05013906583189964	0.041834212839603424	-0.08818669617176056	-0.044578976929187775	-0.03273054584860802	0.011988355778157711	-0.0711900070309639	-0.047606855630874634	0.024798814207315445	0.0013732040533795953	0.07137276977300644	0.0021606553345918655	-0.03831362724304199	0.04306867718696594	-0.034158769994974136	-0.02074394002556801	-0.023836495354771614	0.014703147113323212	-0.04571743309497833	-0.06947142630815506	0.010514726862311363	-0.04431231692433357	-0.010318531654775143	-0.07961750775575638	0.03405730053782463	0.058212749660015106	0.03290094435214996	0.0632084533572197	-0.015884369611740112	0.07962659001350403	0.10056831687688828	0.029490293934941292	-0.05186213552951813	-0.005214448552578688	0.00941252987831831	0.0798381045460701	0.003906087251380086	0.05841675028204918	-0.028101874515414238	0.13441996276378632	-0.005504127126187086	-0.030853362753987312	-0.011625277809798717	0.03694259747862816	0.08726588636636734	-0.04378440976142883	-0.08474324643611908
+0.04841863363981247	-0.021505117416381836	-0.01569589041173458	0.04805969446897507	-0.08221311867237091	-0.043001938611269	0.004451146349310875	0.018748432397842407	0.004642784595489502	-0.010896760039031506	0.0018337499350309372	-0.010580391623079777	-0.00687806960195303	-0.004349888768047094	0.0333392508327961	-0.036053527146577835	-0.06897242367267609	-0.002842573681846261	-0.031931836158037186	-0.04996085166931152	0.025510862469673157	0.02985091134905815	-0.06985189020633698	0.054475337266922	-0.02190956473350525	0.03086715377867222	-0.06700155138969421	0.002476644469425082	-0.025025656446814537	0.01415992435067892	0.03748688846826553	-0.017673732712864876	0.062123507261276245	-0.05665988102555275	-0.05814172327518463	0.06132100149989128	-0.02072964981198311	0.12723317742347717	0.03567827120423317	-0.053206488490104675	-0.056730251759290695	0.01897892914712429	-0.02851683460175991	-0.05629219114780426	0.025416478514671326	0.012614129111170769	-0.002383715473115444	-0.03094489686191082	0.036660805344581604	0.026101399213075638	0.094876728951931	0.021082008257508278	-0.026378203183412552	-0.025575486943125725	-0.026665743440389633	0.00656806118786335	0.05469062551856041	-0.06283436715602875	-0.08769230544567108	0.04460848122835159	-0.07725061476230621	0.003826314816251397	-0.0006335327634587884	-0.012547865509986877	-0.012311214581131935	-0.046192627400159836	0.007201866712421179	0.08076851069927216	0.024785254150629044	0.006664379965513945	0.033074598759412766	0.11438463628292084	-0.06925103813409805	-0.05375048518180847	0.058194126933813095	-0.0728212371468544	-0.042589060962200165	0.06272539496421814	-0.020184630528092384	0.0262506864964962	-0.01084889192134142	-0.02884179726243019	0.09767074882984161	-0.041716478765010834	-0.049272969365119934	-0.02119985967874527	-0.006332992110401392	-0.05554958060383797	0.03977762535214424	0.024253960698843002	0.007097355090081692	0.05203431472182274	-0.07340968400239944	0.06045445427298546	0.024291036650538445	0.02834261581301689	0.06266021728515625	-0.10574106127023697	-0.04296410083770752	-0.013493767008185387	-0.028124583885073662	-0.027833152562379837	0.03892345726490021	0.0008890628814697266	0.05229168012738228	0.011379346251487732	-0.051820650696754456	0.015008770860731602	0.032159965485334396	0.04799244552850723	0.05136348679661751	0.06325555592775345	0.07955094426870346	0.019793694838881493	0.061915408819913864	-0.018897563219070435	0.03386607766151428	-0.06750979274511337	-0.02530001476407051	0.02537183277308941	-0.015440555289387703	0.03537432476878166	-0.029251020401716232	-0.06421473622322083	-0.04012220352888107	0.033623646944761276	-0.10481639206409454	-0.029033571481704712	0.05609205737709999	-0.031485024839639664	0.1142420619726181	0.05847267434000969	-0.006381274666637182	0.03126184642314911	0.03127901256084442	0.015156558714807034	-0.0022135921753942966	0.0454212911427021	-0.00012625813542399555	-0.00919984932988882	-0.01773393526673317	0.05481331795454025	0.028658626601099968	-0.05183392018079758	0.02108110301196575	0.028749004006385803	0.028495121747255325	-0.029178138822317123	-0.08184501528739929	0.006321799475699663	-0.08035445958375931	-0.08086896687746048	-0.010262143798172474	-0.08042687922716141	0.002667465480044484	0.04953034222126007	0.006146632134914398	0.05478261038661003	-0.005863139871507883	-0.025493551045656204	-0.03556690365076065	-0.037845030426979065	-0.018481291830539703	-0.08118069916963577	-0.04680486395955086	-0.03320435807108879	0.005228916648775339	0.007491393014788628	-0.01759711280465126	-0.004378248006105423	-0.00817891675978899	-0.002864539623260498	0.029842115938663483	0.06472012400627136	0.07061173021793365	0.031682468950748444	0.020577365532517433	-0.033926915377378464	0.030699871480464935	-0.048939298838377	0.0036017335951328278	-0.05857370048761368	0.02796587534248829	-0.04599883407354355	0.000834316189866513	-0.002923608524724841	0.05698099359869957	-0.009181084111332893	0.01660558581352234	0.020251799374818802	-0.04553883895277977	-0.08306884765625	0.037287794053554535	0.0833856537938118	-0.03133146092295647	0.024953998625278473	-0.0061905100010335445	0.05709187313914299	-0.022967331111431122	-0.011119572445750237	-0.040419723838567734	0.047985147684812546	-0.0009997363667935133	0.03767336532473564	0.003202431835234165	-0.049269597977399826	0.08093933016061783	-0.002441402757540345	0.015853827819228172	-0.06613297760486603	0.002699054079130292	0.014778222888708115	0.009530988521873951	0.01331446971744299	0.08145765960216522	-0.02267034351825714	-0.004002307541668415	-0.005626239813864231	0.058301396667957306	-0.04262762516736984	0.008470691740512848	-0.03203734755516052	-0.08833783864974976	0.026323171332478523	-0.044311314821243286	-0.12076401710510254	-0.025104431435465813	0.029192853718996048	-0.0037075504660606384	-0.04131004214286804	0.009257359430193901	0.03314543515443802	-0.005938843358308077	-0.040473416447639465	-0.010819309391081333	-0.01779690384864807	-0.0816461592912674	0.10581757873296738	0.0036932104267179966	-0.06648366898298264	-0.035554856061935425	-0.0018075719708576798	0.024480575695633888	-0.008333748206496239	0.030149079859256744	0.0506841279566288	0.1081865057349205	0.04575705900788307	-0.008724641054868698	0.00956561230123043	-0.04885709285736084	-0.05496562272310257	0.01912717893719673	0.042267657816410065	-0.05222561955451965	-0.02266031689941883	-0.008871443569660187	-0.06474307179450989	0.014877108857035637	-0.0006927490467205644	0.033043209463357925	-0.025795655325055122	-0.04064216837286949	-0.03658635914325714	0.03841942548751831	-0.013298085890710354	-0.04388786107301712	0.048303768038749695	-0.010909954085946083	0.046736378222703934	-0.004564089700579643	-0.01212091650813818	0.031855639070272446	0.01903635822236538	0.046668682247400284	-0.03027280420064926	0.051108211278915405	-0.007830208167433739	-0.09905460476875305	-0.026752859354019165	-0.06981304287910461	-0.04822589084506035	-0.003705714363604784	-0.05277306213974953	-0.044754963368177414	0.080409936606884	0.01291834469884634	-0.02239196188747883	-0.07019343972206116	-0.016923585906624794	-0.013987275771796703	-0.08725184947252274	-0.01946122944355011	0.023667393252253532	-0.07660131901502609	-0.041268009692430496	0.022847559303045273	-0.03124159947037697	-0.022189535200595856	-0.016849113628268242	-0.024565931409597397	-0.06054176762700081	0.04615020379424095	-0.022263232618570328	-0.02742636762559414	-0.008694883435964584	-0.07993204146623611	0.03284875676035881	0.12225203216075897	0.054232850670814514	0.06131887808442116	0.04462651163339615	0.04646254703402519	0.11366430670022964	0.02660054713487625	-0.0396466888487339	-0.0005462800618261099	-0.025410432368516922	-0.07521810382604599	0.03131479769945145	-0.041788775473833084	-0.013581366278231144	-0.003414473496377468	0.004715398419648409	0.05727469548583031	-0.001691124984063208	-0.04850104823708534	-0.02500288188457489	0.05104926601052284	-0.009299123659729958	0.03263779357075691	0.012214262038469315	0.0003319392562843859	0.011257079429924488	-0.05622203275561333	-0.03365807607769966	-0.04841209203004837	0.019108539447188377	-0.0025223675183951855	0.056090932339429855	0.01639283075928688	-0.05974250286817551	-0.0021129727829247713	-0.0028481557965278625	0.10319700092077255	-0.10219582915306091	-0.004254231695085764	0.017115551978349686	0.019258098676800728	-0.10637978464365005	-0.013124077580869198	0.04662315919995308	-0.05062857270240784	-0.03216172382235527	0.01936766318976879	0.044768426567316055	-0.06218363717198372	-0.01564602181315422	0.007489010691642761	-0.04241963103413582	-0.02243869937956333	0.016142288222908974	0.02300526387989521	0.012281638570129871	-0.04636884480714798	-0.014514034613966942	-0.00013824523193761706	0.008859390392899513	0.04073653370141983	-0.04062262549996376	0.015936674550175667	-0.029376676306128502	0.034573961049318314	-0.050054486840963364	-0.024500014260411263	-0.05256430059671402	0.07384075224399567	0.0066169570200145245	-0.026080412790179253	-0.019471608102321625	-0.0010917582549154758	-0.013399973511695862	0.01868610642850399	0.030741140246391296	-0.038856491446495056	-0.01760152541100979	-0.03420070558786392	-0.04367974027991295	-0.07007688283920288	-0.036200542002916336	0.008891105651855469	-0.06566613167524338	0.015962902456521988	-0.025919614359736443	-0.028097491711378098	-0.0025371145457029343	-0.017995629459619522	0.025059835985302925	0.055915702134370804	0.04111115634441376	-0.023223768919706345	0.03223368152976036	0.011260983534157276	0.048298440873622894	0.030158787965774536	-0.04260450229048729	0.020190397277474403	0.026623820886015892	-0.06766568124294281	0.02557079680263996	-0.01076862309128046	0.007159189321100712	-0.0020862130913883448	-0.0027873115614056587	0.024688605219125748	-0.00868905894458294	0.006549420300871134	-0.0306064672768116	0.04312758147716522	-0.07657134532928467	-0.019203318282961845	0.03731304034590721	-0.02326572872698307	-0.09470381587743759	-0.03752467408776283	0.011856691911816597	-0.021329374983906746	0.045446593314409256	-0.022821873426437378	0.026897870004177094	0.03953038901090622	-0.0402192585170269	-0.06688439846038818	-0.03602839261293411	-0.026420049369335175	0.045471809804439545	-0.016906004399061203	0.0735546350479126	0.0017654262483119965	0.013908814638853073	-0.007979897782206535	0.01700940914452076	0.013625748455524445	-0.08822701126337051	-0.013100508600473404	-0.055543918162584305	-0.032724529504776	0.035475004464387894	-0.009605685248970985	0.00042471938650123775	-0.05721992999315262	-0.025728803128004074	-0.03569183126091957	0.08151528984308243	-0.03375864773988724	-0.018822072073817253	0.04945524036884308	-0.006436033174395561	0.00741153908893466	0.010456222109496593	-0.04253492131829262	-0.0739862397313118	0.017596900463104248	-0.00858727190643549	0.030561622232198715	0.013366802595555782	0.02442825399339199	-0.014663894660770893	-0.075607068836689	-0.03295401856303215	0.02943645417690277	-0.09017591178417206	0.011806562542915344	-0.0014782734215259552	-0.0898946225643158	-0.06610570847988129	0.012177685275673866	-0.058430265635252	0.08523634821176529	0.009909417480230331	0.03576928377151489	-0.0002818756620399654	-0.007676724344491959	-0.02313956804573536	-0.04896125942468643	-0.0037244181148707867	-0.000999831478111446	-0.042375966906547546	-0.03783710300922394	0.05593717470765114	-0.014355781488120556	0.033764418214559555	0.07422760874032974	-0.04084029421210289	0.052822425961494446	0.05115588381886482	-0.033986590802669525	0.04525746405124664	-0.006644674111157656	-0.041759833693504333	0.06990211457014084	-0.05073719844222069	0.09833169728517532	-0.04406092315912247	0.0901351347565651	-0.00666406424716115	-0.027316829189658165	0.07128457725048065	0.09851579368114471	-0.01323465071618557	-0.0026456972118467093	-0.06190583482384682
+0.02573505975306034	-0.021929951384663582	0.028332628309726715	0.07915136963129044	-0.07493830472230911	-0.011834298260509968	-0.01479389239102602	0.016074325889348984	0.008747204206883907	-0.04323209077119827	-3.6576209822669625e-05	-0.00697920611128211	-0.006837582681328058	0.026391474530100822	0.045647699385881424	-0.03366364166140556	-0.05967456102371216	0.015243137255311012	-0.021195484325289726	-0.049039989709854126	0.035855330526828766	0.014014119282364845	-0.03868540748953819	0.04905303567647934	-0.011968678794801235	0.010586756281554699	-0.050301868468523026	0.011288435198366642	-0.00965708214789629	0.031002545729279518	0.0772843211889267	0.00011144444579258561	0.03587253764271736	-0.04047387093305588	-0.034966349601745605	0.04836136847734451	-0.014429409988224506	0.08853123337030411	0.013346509076654911	-0.009085671044886112	-0.006773780100047588	-0.018498461693525314	-0.030761096626520157	-0.04563010856509209	0.03703322634100914	-0.00696458900347352	0.01851717382669449	-0.01207294687628746	0.03046586364507675	0.018612859770655632	0.0947161465883255	0.012106005102396011	0.009679163806140423	-0.01626085303723812	-0.0016467783134430647	-0.020726822316646576	0.06264331191778183	-0.06315773725509644	-0.0485687293112278	0.06449007987976074	-0.09493337571620941	-0.030728820711374283	0.027503658086061478	-0.00028993046726100147	-0.014359631575644016	-0.013925529085099697	-0.006708481349050999	0.0876438245177269	0.004579992964863777	-0.05247925594449043	0.04986941069364548	0.10172805935144424	-0.06350753456354141	-0.04828080162405968	0.0636809766292572	-0.05462949350476265	-0.004771197680383921	0.0394396148622036	-0.029309576377272606	0.010387241840362549	0.006482900585979223	-0.052106823772192	0.09581618756055832	-0.03036626987159252	-0.051648303866386414	-0.028135012835264206	0.009933152236044407	-0.059615157544612885	0.07202471047639847	0.015271896496415138	0.025029119104146957	0.08765731006860733	-0.08152074366807938	0.03986907750368118	0.03121021017432213	0.019249942153692245	0.020474841818213463	-0.10100055485963821	-0.056589480489492416	0.025056907907128334	0.014060314744710922	-0.010814551264047623	0.05897951126098633	-0.008438218384981155	0.052804168313741684	0.04664549604058266	-0.0466628260910511	0.0032382674980908632	0.015549569390714169	0.04428590461611748	0.056743793189525604	0.09762918204069138	0.055758215487003326	0.04545576497912407	0.04195452854037285	0.0011453856714069843	-0.009838253259658813	-0.047526679933071136	-0.035009801387786865	0.023873930796980858	0.0056073712185025215	0.07086468487977982	-0.010774804279208183	-0.08070621639490128	-0.03095453418791294	0.0319090336561203	-0.11157172918319702	-0.00900730025023222	0.09523002058267593	-0.04336389899253845	0.12818671762943268	0.03314412385225296	-0.0041699823923408985	0.054993778467178345	0.05647272989153862	0.06278257817029953	-0.03652702644467354	0.020236322656273842	-0.0023385551758110523	-0.016866788268089294	-0.025009308010339737	0.058029282838106155	0.01891970820724964	-0.05997448414564133	0.044515494257211685	0.07040276378393173	0.013132303021848202	0.002881579799577594	-0.03702812269330025	-0.01319415308535099	-0.049318693578243256	-0.03986312076449394	-0.008161772042512894	-0.08990682661533356	-0.005433282814919949	0.05127602443099022	0.0011736428132280707	0.03713433817028999	-0.021694552153348923	-0.025814423337578773	-0.07664677500724792	-0.03164299950003624	-0.01255735568702221	-0.042386494576931	-0.0352913998067379	-0.014579595066606998	-0.022365441545844078	0.02021055668592453	-0.05294160172343254	-0.02424977906048298	-0.0006031065131537616	-0.01686948910355568	0.0511430948972702	0.07845136523246765	0.06435775011777878	0.010213110595941544	0.004563683178275824	-0.06065043807029724	0.0040668160654604435	-0.0388239361345768	-0.014226661995053291	-0.008452263660728931	0.03756105527281761	-0.07388348132371902	-0.04155397415161133	-0.020260417833924294	0.05667538195848465	-0.01578364335000515	0.004304145462810993	-0.032154496759176254	-0.02428014948964119	-0.04716844484210014	0.07118955254554749	0.07840404659509659	-0.04506199061870575	0.01665802113711834	-0.0250694677233696	0.04042710363864899	-0.020736418664455414	-0.029651133343577385	-0.057995591312646866	0.028806699439883232	0.03605027124285698	0.06329693645238876	0.016852525994181633	-0.07163399457931519	0.08574768900871277	0.0186888687312603	0.018366998061537743	-0.07656647264957428	-0.013996022753417492	0.028090564534068108	0.001100613153539598	0.07473458349704742	0.07509197294712067	-0.017270565032958984	0.023923218250274658	-0.012667899020016193	0.07555285841226578	-0.059912294149398804	0.011765570379793644	-0.01622360572218895	-0.0708676427602768	0.0009811443742364645	-0.03128412738442421	-0.0588131882250309	-0.018246743828058243	0.01941457763314247	-0.013036373071372509	-0.004645201843231916	0.01072956994175911	0.05142369121313095	-0.005540214944630861	0.00993598997592926	-0.007645772770047188	-0.015041136182844639	-0.08494182676076889	0.11373317986726761	-0.0020142060238868	-0.06430445611476898	-0.10371220856904984	0.010752303525805473	0.057855814695358276	0.0023679882287979126	0.057627465575933456	0.027708884328603745	0.09782670438289642	0.020805126056075096	0.005516796372830868	-0.0026824811939150095	-0.03060545027256012	-0.0771372988820076	-0.0035720011219382286	0.027283377945423126	-0.04133111238479614	-0.0397583469748497	0.010420788079500198	-0.07786505669355392	0.0007810307433828712	0.0067105526104569435	0.031065981835126877	-0.027138186618685722	-0.00799022801220417	-0.03990854322910309	0.011266260407865047	-0.044598132371902466	-0.03630170226097107	0.01851189322769642	-0.016382642090320587	0.041437119245529175	-0.01751842349767685	-0.00418719369918108	0.034719206392765045	0.01906546950340271	0.008951570838689804	-0.06206202879548073	0.03271010145545006	-0.011299588717520237	-0.07356606423854828	-0.00953342393040657	-0.06398649513721466	-0.03897829353809357	-0.007656073197722435	-0.01974889636039734	-0.0571146197617054	0.08586405217647552	0.009375330060720444	-0.06744559109210968	-0.04945877194404602	-0.018769903108477592	-0.0037734878715127707	-0.03327209874987602	0.00020701684115920216	-0.0006573121063411236	-0.06057853624224663	-0.004617590922862291	0.024721641093492508	-0.05772076174616814	-0.03670201450586319	0.008976740762591362	-0.016838131472468376	-0.060293301939964294	0.03790176287293434	-0.013023209758102894	-0.017439670860767365	-0.011931894347071648	-0.07473331689834595	0.06407608091831207	0.09051208943128586	0.053474169224500656	0.025373810902237892	0.02635767124593258	0.04102090746164322	0.11127173155546188	0.008587511256337166	-0.013195247389376163	0.01951509155333042	-0.05664807930588722	-0.03421344235539436	0.045143187046051025	-0.01864285208284855	-0.006810052786022425	-0.006235799286514521	-0.01051412709057331	0.10520993173122406	-0.00993373990058899	0.0011817084159702063	-0.0006620002095587552	0.05478601157665253	0.020584596320986748	0.03303328529000282	0.006563452538102865	-0.006630220916122198	-0.023265743628144264	-0.06035853177309036	-0.038274090737104416	-0.02728300169110298	0.06940088421106339	-0.015873542055487633	0.03580676019191742	-0.023369302973151207	-0.10886487364768982	0.02216384932398796	0.018652893602848053	0.11221583187580109	-0.0770757719874382	0.004518342670053244	0.03465571999549866	0.00059528584824875	-0.10114709287881851	-0.007395443040877581	0.0034138450864702463	-0.06460670381784439	-0.031834665685892105	0.02980763092637062	0.04579804465174675	-0.052414730191230774	0.0042685866355896	-0.010906980372965336	-0.04023922607302666	0.015684956684708595	0.024915508925914764	-0.004892281722277403	0.023503543809056282	-0.01318554487079382	-0.0315067321062088	-0.05596890300512314	0.03051622584462166	0.003779168939217925	-0.013384624384343624	0.04866102710366249	-0.021624384447932243	0.03238464891910553	-0.05150932818651199	-0.0442892462015152	-0.05071089789271355	0.07213805615901947	-0.016453346237540245	0.01745271310210228	-0.02814687415957451	-0.05145246535539627	-0.00909942016005516	0.021185729652643204	-0.007118441630154848	-0.07567503303289413	-0.01588565856218338	-0.056967154145240784	-0.05993250757455826	-0.08720623701810837	-0.05727533623576164	0.029908867552876472	-0.03919005021452904	0.026564544066786766	-0.0346798300743103	0.012619874440133572	-0.02938944473862648	-0.03718907758593559	0.040939074009656906	0.05441462993621826	0.05160550773143768	-0.01974516361951828	0.04103996977210045	0.010244498029351234	0.055497970432043076	0.01981043443083763	-0.06182892248034477	0.05058479681611061	0.027216067537665367	-0.03822975978255272	0.056962281465530396	-0.027788210660219193	-0.006266563665121794	-0.011546469293534756	-0.011773886159062386	0.021740393713116646	-0.027717623859643936	-0.03749523311853409	-0.065998375415802	0.0766507238149643	-0.07081162929534912	-0.022918807342648506	0.009762855246663094	-0.03128119558095932	-0.0911308228969574	-0.056333836168050766	-0.008609024807810783	-0.022035613656044006	0.049042958766222	-0.016480835154652596	0.004332070238888264	0.04184442013502121	-0.011051633395254612	-0.01646343618631363	-0.008500184863805771	-0.0017904106061905622	0.0034500567708164454	-3.6613462725654244e-05	0.04253685101866722	-0.012586270458996296	-0.008533218875527382	-0.005200524348765612	-0.023551737889647484	0.022447826340794563	-0.07832975685596466	-0.013652341440320015	-0.014613425359129906	-0.033812738955020905	-0.0027559346053749323	0.016525767743587494	0.012256406247615814	-0.07811296731233597	-0.04246259480714798	-0.061676785349845886	0.10148371011018753	-0.010182815603911877	0.04061749204993248	0.0022137982305139303	-0.027772612869739532	-0.006647263187915087	0.0338529497385025	-0.04747645556926727	-0.0803770050406456	-0.01470513641834259	-0.020731745287775993	-0.0030097574926912785	-0.02646486647427082	0.025837162509560585	-0.021544627845287323	-0.077985480427742	0.008300093933939934	0.028217732906341553	-0.0801595076918602	0.015411006286740303	0.02407839708030224	-0.044923100620508194	-0.03337549418210983	-0.002809747587889433	-0.04262280836701393	0.06249672546982765	0.03622007369995117	0.031463511288166046	-0.012333711609244347	-0.004198562819510698	-0.01242684293538332	-0.071950763463974	0.004793104249984026	-0.002029788214713335	-0.05292500928044319	-0.025857796892523766	0.053885702043771744	-0.06660237163305283	0.03589526563882828	0.04625700041651726	-0.057475946843624115	0.034262221306562424	0.05089185759425163	-0.03441263362765312	0.0545804500579834	0.012270127423107624	0.009708750993013382	0.06823237240314484	-0.07803975045681	0.11281609535217285	-0.054228536784648895	0.0870128870010376	-0.004554497543722391	-0.010478546842932701	0.06551337242126465	0.07540921866893768	0.013975746929645538	0.0019599096849560738	-0.06526312232017517
+0.054822567850351334	-0.012840264476835728	-0.010320087894797325	0.015846557915210724	-0.07029163837432861	-0.04273497313261032	-0.02638985775411129	0.06610354781150818	-0.004150159191340208	-0.04715755581855774	-0.05942380055785179	0.008350232616066933	-0.0010795161360874772	0.014416933991014957	0.024788841605186462	-0.0502442866563797	-0.08400139212608337	0.0046555702574551105	-0.041568413376808167	-0.05339067056775093	0.02134154550731182	0.06295011192560196	-0.049361005425453186	0.04377162829041481	0.021463554352521896	0.03303571417927742	-0.03489324450492859	0.004886041861027479	0.003455654252320528	0.00828277412801981	0.08058660477399826	-0.005100498907268047	0.07490678876638412	-0.07121014595031738	-0.02832729183137417	0.06381877511739731	-0.04677397385239601	0.11214181780815125	0.02030901610851288	-0.038644690066576004	-0.04698800668120384	-0.007301263976842165	-0.016837099567055702	-0.0679701641201973	0.03218192234635353	-0.005526604596525431	-0.03922891989350319	-0.0363086499273777	0.005836367607116699	0.01179689820855856	0.08683974295854568	-0.030903201550245285	-0.03527325019240379	-0.04229676350951195	-0.020692231133580208	0.043875910341739655	0.0364566408097744	-0.06822365522384644	-0.037405192852020264	0.015026853419840336	-0.08904536813497543	0.004728358238935471	0.017064493149518967	0.02227606624364853	-0.028233474120497704	-0.030921978875994682	-0.010907403193414211	0.07385823130607605	0.027952641248703003	-0.02022772841155529	0.03632049635052681	0.06046359986066818	-0.04279893636703491	-0.04997855797410011	0.05952135846018791	-0.06432381272315979	-0.004895850550383329	0.04077785834670067	-0.004498910624533892	-0.0033151644747704268	0.03891674429178238	-0.01918106898665428	0.11692693084478378	-0.056930411607027054	-0.044396549463272095	-0.03914954140782356	-0.020329853519797325	-0.06209788843989372	0.05088505521416664	0.00789122935384512	0.0050168223679065704	0.06232593208551407	-0.003708870382979512	0.02291780896484852	-0.009160401299595833	-0.0007387187797576189	-0.009579746052622795	-0.11718041449785233	-0.04721207171678543	-0.009635991416871548	0.020589129999279976	0.017545470967888832	0.07678278535604477	0.015757368877530098	0.05376756936311722	0.03348620608448982	-0.035011060535907745	-0.020470425486564636	0.04071716219186783	0.03033295087516308	0.009454932063817978	0.0846516415476799	0.09000731259584427	0.052065763622522354	0.07687856256961823	-0.024154702201485634	-0.003308137645944953	-0.029476679861545563	-0.02656932920217514	-0.0009397118119522929	-0.017703795805573463	0.026569005101919174	-0.041062772274017334	-0.07766018807888031	-0.05753999203443527	0.02934030443429947	-0.07984542101621628	-0.03539058193564415	0.07179955393075943	-0.014401240274310112	0.12420154362916946	0.03147488459944725	-0.04715501889586449	0.023763036355376244	0.04212029650807381	0.039013806730508804	0.01999565400183201	0.02171347104012966	0.004852761048823595	-0.04524102434515953	-0.0109639223664999	0.055501390248537064	-0.007642337121069431	-0.06859719753265381	0.043010156601667404	0.01985633745789528	0.025972679257392883	-0.03911794722080231	-0.05431963503360748	0.003894770983606577	-0.08311322331428528	-0.075632743537426	0.004384644795209169	-0.061966679990291595	0.02730054222047329	0.03402363881468773	0.001548907719552517	-0.002549394266679883	-0.02950902469456196	-0.033772680908441544	-0.07079752534627914	-0.034084245562553406	-0.006287598982453346	-0.09893738478422165	-0.012465870007872581	-0.0512181781232357	0.008358143270015717	0.0342850498855114	-0.06651554256677628	-0.02933105267584324	-0.038078851997852325	-0.028560996055603027	0.006747035775333643	0.10783300548791885	0.0694161057472229	0.008684154599905014	0.00867895781993866	-0.035928282886743546	-0.01058486383408308	-0.001025002682581544	0.026748310774564743	-0.043814629316329956	0.03010708838701248	-0.0652962177991867	0.02353190816938877	-0.026109728962183	0.03688308224081993	-0.031985267996788025	0.041336789727211	0.022563060745596886	-0.021901456639170647	-0.03434271737933159	0.04269453510642052	0.0723724365234375	0.007102658040821552	0.06188275292515755	-0.009123739786446095	0.06528948247432709	0.02130320854485035	-0.04544765129685402	-0.060602109879255295	0.03739234432578087	0.024601401761174202	0.05291914939880371	0.024141356348991394	-0.07518170773983002	0.10474967956542969	0.031019126996397972	0.04530556872487068	-0.052772849798202515	0.025450123474001884	0.035591088235378265	-0.009443544782698154	0.06737350672483444	0.07507064938545227	-0.024249901995062828	-0.025203010067343712	-0.023714838549494743	0.03153936192393303	-0.06385648995637894	-0.00834852084517479	0.041199617087841034	-0.06525613367557526	0.07683464884757996	-0.04169616103172302	-0.10586713254451752	-0.051949772983789444	0.004892776254564524	0.005522859748452902	-0.03979263827204704	-0.006401659920811653	0.016051756218075752	0.005093154031783342	-0.025434482842683792	-0.02096249721944332	-0.03128582611680031	-0.10085871070623398	0.09171642363071442	0.008508480153977871	-0.07651250809431076	-0.06640902906656265	-0.008980205282568932	0.041115567088127136	-0.022943103685975075	0.016894392669200897	0.05366332083940506	0.08586057275533676	0.003937626723200083	-0.019129693508148193	0.03832218050956726	-0.04374753311276436	-0.04690219834446907	-0.0183566864579916	0.029054438695311546	-0.04699418321251869	-0.06824127584695816	0.013818026520311832	-0.038600653409957886	-0.021136214956641197	-0.014113571494817734	0.03523098677396774	-0.005455587990581989	-0.03627663105726242	-0.046702791005373	0.04977547749876976	-0.04121778532862663	-0.0247162114828825	0.045513592660427094	-0.020064137876033783	0.05088663101196289	-0.009773572906851768	-0.030143223702907562	0.047354478389024734	-0.026818597689270973	0.03621532395482063	-0.020622242242097855	0.09761843830347061	-0.025668270885944366	-0.05394936352968216	0.010396135970950127	-0.03965691477060318	-0.0468822680413723	-0.03687842935323715	-0.06556031107902527	-0.01684568263590336	0.09143362939357758	0.012209998443722725	-0.039462629705667496	-0.02771696075797081	-0.008776122704148293	-0.02769642323255539	-0.0607391856610775	-0.03230758756399155	-0.009773816913366318	-0.055967193096876144	-0.02099248766899109	0.03641324117779732	-0.052079033106565475	-0.02403111569583416	0.027137309312820435	-0.024746306240558624	-0.050239648669958115	0.028582554310560226	-0.04684414714574814	-0.060651637613773346	-0.002776609268039465	-0.06468241661787033	0.01728670485317707	0.07912576198577881	0.05627346411347389	0.008881701156497002	0.03318403661251068	0.027688972651958466	0.11204926669597626	-0.0065453858114778996	-0.011482472531497478	0.0035000299103558064	-0.046695590019226074	-0.04964187368750572	0.03722768276929855	-0.010155930183827877	-0.0553438626229763	-0.004841347690671682	-0.01710827462375164	0.05499593913555145	0.0022782753221690655	-0.03365365415811539	-0.02485828660428524	0.0232835803180933	-0.04459959641098976	0.04411249980330467	0.004876532591879368	0.017329785972833633	-0.016484711319208145	-0.05012695863842964	-0.036406029015779495	-0.055418603122234344	0.03656955808401108	-0.02037135511636734	0.02466229908168316	-0.03255748003721237	-0.04366802051663399	-0.006678655277937651	0.0046846903860569	0.1048923209309578	-0.046010587364435196	0.017134996131062508	0.04074593633413315	0.0221919734030962	-0.07607849687337875	7.093543536029756e-05	0.011323419399559498	-0.04121151193976402	-0.044268541038036346	-0.01643327996134758	0.022864369675517082	-0.0865478664636612	-0.041864536702632904	0.0026923895347863436	-0.045159731060266495	0.004687667358666658	0.0013685404555872083	0.033004991710186005	0.050922609865665436	0.016316398978233337	-0.01476032193750143	0.030763059854507446	0.023218875750899315	0.030831223353743553	-0.02876666560769081	0.009858466684818268	-0.04978438466787338	0.03533969819545746	-0.04636191949248314	-0.051944129168987274	-0.044893234968185425	0.04109322652220726	0.018077295273542404	-0.0044736615382134914	-0.015398459509015083	-0.030505025759339333	-0.0017111391061916947	0.026325969025492668	0.017995931208133698	-0.059568919241428375	-0.03621545061469078	-0.03594086691737175	-0.06022069230675697	-0.08900712430477142	-0.05684901773929596	0.014020310714840889	-0.078401118516922	0.016523465514183044	-0.047665175050497055	0.016594326123595238	0.025217222049832344	0.004825988318771124	0.049997106194496155	0.03517073392868042	0.03700895607471466	-0.02905850112438202	0.024651188403367996	-0.01638856530189514	0.06462661176919937	0.04968656226992607	-0.02403898723423481	-0.02217249386012554	0.017344679683446884	-0.024743476882576942	0.05408367142081261	-0.022806702181696892	0.01730857416987419	0.024008112028241158	-0.02594052441418171	0.03593916818499565	-0.0041258311830461025	0.040623147040605545	-0.0397881343960762	0.026259832084178925	-0.0643574595451355	-0.04671372100710869	0.036519456654787064	-0.025500793009996414	-0.1102704256772995	-0.041369564831256866	-0.02934873104095459	-0.0355718657374382	0.03143428638577461	-0.0076018101535737514	0.019392214715480804	0.007930203340947628	-0.04610113427042961	-0.08207813650369644	-0.03892374411225319	-0.009578926488757133	0.04151145741343498	0.002294989535585046	0.04879946634173393	0.0212249755859375	0.02474171295762062	0.014722451567649841	-0.0262084249407053	-0.007762840948998928	-0.10231736302375793	-0.000445306213805452	-0.03209530562162399	-0.060816098004579544	0.028350112959742546	-0.0009543764754198492	0.017599567770957947	-0.029963761568069458	-0.022926582023501396	-0.05868632346391678	0.08798612654209137	-0.018973350524902344	0.024212008342146873	0.0008749327971599996	-0.05629238113760948	-0.006509534548968077	0.03895098716020584	-0.05108140408992767	-0.074287049472332	0.01655551604926586	-0.018162723630666733	0.03287966921925545	0.008598124608397484	0.018503788858652115	-0.03738394379615784	-0.0808720663189888	0.009679066948592663	-0.003696736879646778	-0.03672252595424652	0.023619603365659714	0.010163752362132072	-0.07548128813505173	-0.06751838326454163	-0.008716659620404243	-0.06757765263319016	0.06689407676458359	0.008181237615644932	0.03887529671192169	-0.03128820285201073	-0.02203844115138054	0.01262329425662756	-0.029949380084872246	-0.01594834215939045	-0.012075014412403107	-0.038997795432806015	-0.034952715039253235	0.07885792851448059	-0.01580536924302578	-0.00901025254279375	0.09847088158130646	-0.06630814075469971	0.016402846202254295	0.014546588063240051	-0.027524536475539207	0.027258461341261864	-0.015825321897864342	-0.05650892108678818	0.0806400403380394	-0.05921977385878563	0.06513962149620056	-0.07637136429548264	0.09087732434272766	0.0062815654091537	-0.02199791744351387	0.07374624907970428	0.05273463949561119	-0.01933220587670803	0.027868956327438354	-0.06677760928869247
+0.06746120750904083	-0.013916239142417908	-0.004686901345849037	0.03697735443711281	-0.06016206741333008	-0.0406002402305603	-0.007685588672757149	0.03349309042096138	-0.01634688302874565	-0.030331457033753395	-0.040551215410232544	0.0005998468841426075	-0.014544740319252014	0.002916918136179447	0.032711148262023926	-0.0497954823076725	-0.08419575542211533	-0.014441553503274918	-0.04141734167933464	-0.04622498154640198	0.03123313933610916	0.02450491301715374	-0.07125196605920792	0.059972021728754044	-0.001655350555665791	0.04325313866138458	-0.034057848155498505	0.004098130855709314	-0.024726826697587967	0.011310647241771221	0.061365336179733276	-0.011747240088880062	0.05825868248939514	-0.07066764682531357	-0.05601748079061508	0.04058937728404999	-0.02953542396426201	0.12157058715820312	0.012450172565877438	-0.06148262321949005	-0.06903667747974396	0.01429620198905468	-0.034703124314546585	-0.06717657297849655	0.03532065451145172	0.0027119803708046675	0.0001355241402052343	-0.026529978960752487	0.028086872771382332	0.01159974280744791	0.09651171416044235	-0.0049698567017912865	-0.0190545953810215	-0.0394657664000988	-0.022124754264950752	0.027235371991991997	0.03921922668814659	-0.042164284735918045	-0.05885213613510132	0.05589564889669418	-0.10079638659954071	-0.023432593792676926	0.01201999094337225	-0.006970481015741825	-0.03308558836579323	-0.029467344284057617	-0.00426888931542635	0.08745524287223816	0.030328888446092606	-0.019466474652290344	0.030420267954468727	0.08054018020629883	-0.04182003438472748	-0.03587234765291214	0.04643885791301727	-0.06877203285694122	-0.0170816108584404	0.034679073840379715	0.006961686071008444	0.03960864245891571	0.003843878861516714	-0.019025640562176704	0.13271908462047577	-0.03580612316727638	-0.06526962667703629	-0.044618476182222366	-0.015594696626067162	-0.06026170030236244	0.058892928063869476	0.021005667746067047	-0.014535918831825256	0.08237306028604507	-0.03280150517821312	0.048035744577646255	0.0047858720645308495	0.0051414091140031815	0.021257268264889717	-0.10097841173410416	-0.033431198447942734	-0.017006196081638336	-0.012795623391866684	0.007445674855262041	0.05323696509003639	0.005736269522458315	0.08229116350412369	0.02424885891377926	-0.05523710697889328	-0.004119004122912884	0.038150910288095474	0.004602482542395592	0.04614860564470291	0.07829741388559341	0.0642818734049797	0.04505261778831482	0.08862229436635971	0.000750584586057812	0.042730968445539474	-0.07139414548873901	-0.02746673673391342	-0.008230132982134819	-0.011558574624359608	0.056651074439287186	-0.019565172493457794	-0.0746869221329689	-0.023043887689709663	0.029422160238027573	-0.09994088113307953	-0.04039188101887703	0.0854005292057991	-0.02063511125743389	0.11311408877372742	0.0716383308172226	-0.007141964975744486	0.02988160401582718	0.02734558656811714	0.009217998012900352	-0.003796614473685622	0.04288721829652786	0.00405182084068656	-0.0286854300647974	-0.0023403603117913008	0.04253339767456055	-0.009999697096645832	-0.06744872033596039	0.03934941068291664	0.033895328640937805	0.010115372948348522	-0.05154314637184143	-0.045175228267908096	0.018906567245721817	-0.08696332573890686	-0.0694313645362854	-0.0024802328553050756	-0.06731989979743958	-0.004014313220977783	0.03827183321118355	0.0233157891780138	0.016289066523313522	-0.0384424589574337	-0.025737855583429337	-0.047136638313531876	-0.046379830688238144	0.002366731408983469	-0.10927669703960419	-0.035108357667922974	-0.03605899587273598	0.0056055705063045025	0.04152349755167961	-0.049067139625549316	-0.008854263462126255	-0.0019782755989581347	-0.015296868048608303	0.012270553037524223	0.07465504109859467	0.05972585082054138	0.014671788550913334	0.015661422163248062	-0.04705895110964775	0.02719879150390625	-0.01592879742383957	0.023251241073012352	-0.03200040012598038	0.028865298256278038	-0.07366126030683517	0.009057500399649143	-0.014623884111642838	0.02427184209227562	-0.017141642048954964	0.031867798417806625	0.0307169146835804	-0.04266662150621414	-0.0609494186937809	0.036583635956048965	0.06875431537628174	-0.0062698000110685825	0.061409927904605865	0.011974270455539227	0.05836820602416992	-0.007346575614064932	-0.039772357791662216	-0.05626576021313667	0.025437399744987488	0.0063365427777171135	0.06510952115058899	0.022024236619472504	-0.05500192567706108	0.09261779487133026	0.031174836680293083	0.01894105039536953	-0.06162891909480095	0.01113024353981018	0.019418859854340553	0.0033485395833849907	0.0533178485929966	0.08858024328947067	-0.009521689265966415	-0.019567403942346573	0.00205448386259377	0.03930621221661568	-0.06316033005714417	-0.03331717848777771	0.008703397586941719	-0.0742291510105133	0.03431081399321556	-0.03672875836491585	-0.08711756020784378	-0.041247688233852386	0.055780161172151566	0.016629669815301895	-0.020169377326965332	0.00046241082600317895	0.0161886103451252	-0.02096201665699482	-0.03352411836385727	-0.008167971856892109	-0.038241442292928696	-0.11745869368314743	0.09499264508485794	0.02277185581624508	-0.10186836123466492	-0.04034489765763283	-0.0009813939686864614	0.04490357264876366	-0.018036719411611557	0.05504397675395012	0.0467601902782917	0.09557786583900452	0.017849357798695564	-0.008818523027002811	0.005970056168735027	-0.03421344980597496	-0.03711104393005371	0.007782366126775742	0.02465345337986946	-0.03376412019133568	-0.055920761078596115	0.0007599800010211766	-0.025591885671019554	-0.011942211538553238	-0.021479355171322823	0.04620971903204918	-0.010228551924228668	-0.0402529239654541	-0.029134809970855713	0.04913722723722458	-0.03664948046207428	-0.018889155238866806	0.04768066853284836	0.008306371048092842	0.06811273843050003	-0.03793254867196083	-0.020596636459231377	0.03841638192534447	-0.008955585770308971	0.01397757325321436	-0.03713905066251755	0.08377989381551743	0.0006409737397916615	-0.06756380945444107	-0.01722823642194271	-0.06763256341218948	-0.022871628403663635	-0.029343053698539734	-0.05321558192372322	-0.038187362253665924	0.0946800634264946	0.01250162161886692	-0.04948022589087486	-0.05441272631287575	-0.020823653787374496	-0.0328807532787323	-0.06333854049444199	-0.013359969481825829	0.005939446855336428	-0.04981490597128868	-0.010690529830753803	0.0334986075758934	-0.04314647242426872	-0.025785356760025024	0.005956599023193121	-0.012630434706807137	-0.0696045383810997	0.039442192763090134	-0.032500434666872025	-0.05496348440647125	0.001184491440653801	-0.08287215977907181	0.008503301069140434	0.09253297001123428	0.059981606900691986	0.03403368219733238	0.03194669261574745	0.0331636406481266	0.1124752014875412	-0.002216010820120573	-0.026013758033514023	-0.016694243997335434	-0.034183550626039505	-0.06314992159605026	0.055032968521118164	-0.017935771495103836	-0.005185242276638746	-0.0171689260751009	-0.00029381297645159066	0.07399042695760727	-0.012896069325506687	-0.04076600819826126	-0.027086127549409866	0.01775677688419819	-0.03084297850728035	0.04600531607866287	0.02261083386838436	0.02209092117846012	-0.010298402979969978	-0.05847180262207985	-0.07497495412826538	-0.043731626123189926	0.029474511742591858	-0.007832313887774944	0.03944355994462967	-0.003131032222881913	-0.053522463887929916	0.00019008632807526737	-0.006694608833640814	0.0906330943107605	-0.07339977473020554	0.0026014784816652536	0.0305052287876606	0.03201628476381302	-0.10677601397037506	-0.005385267082601786	0.03137454763054848	-0.05108754336833954	-0.07126052677631378	-0.005088694393634796	0.049749646335840225	-0.07574950903654099	-0.016404740512371063	-0.009940420277416706	-0.038716286420822144	0.003923478070646524	0.014519824646413326	0.03242990002036095	0.020646903663873672	-0.02143312245607376	-0.04378373548388481	-0.001934582949616015	-0.0071524144150316715	0.039901331067085266	-0.03127476945519447	0.022524667903780937	-0.029784291982650757	0.038960088044404984	-0.061916884034872055	-0.028570538386702538	-0.04141172021627426	0.04753648117184639	0.012048222124576569	-0.009850088506937027	-0.01064298301935196	-0.047403402626514435	-0.022108469158411026	0.007489736191928387	0.019262632355093956	-0.06166079267859459	-0.02623327076435089	-0.024231845512986183	-0.0654945895075798	-0.08063547313213348	-0.04503927379846573	-0.00122078915592283	-0.06288331001996994	0.026499532163143158	-0.030133405700325966	0.008638513274490833	0.03149623051285744	0.0034319537226110697	0.03890921548008919	0.054236672818660736	0.039273105561733246	-0.04096170514822006	0.03804763779044151	-0.00605890853330493	0.0452423095703125	0.02447323128581047	-0.04186025261878967	0.005208211485296488	0.013140581548213959	-0.05640092119574547	0.07098868489265442	-0.009910290129482746	0.009969116188585758	-0.004181478638201952	-0.019052542746067047	0.017696119844913483	0.013193468563258648	0.0019175333436578512	-0.03184517100453377	0.036640316247940063	-0.06384976208209991	-0.03383558988571167	0.02350069396197796	-0.03361073136329651	-0.10491317510604858	-0.04914373159408569	-0.011885189451277256	-0.047800421714782715	0.04135504364967346	0.0024432458449155092	0.01994718238711357	0.030591705814003944	-0.04927270859479904	-0.06117262318730354	-0.027624787762761116	-0.030013442039489746	0.026911497116088867	-0.02069486863911152	0.07837358117103577	0.034767571836709976	0.025473730638623238	0.002405050676316023	-0.0020877530332654715	0.004979305434972048	-0.09512303024530411	-0.011090581305325031	-0.005271327681839466	-0.0477275475859642	0.022266197949647903	0.0168459489941597	-0.01954273320734501	-0.04266386851668358	-0.023345159366726875	-0.044549934566020966	0.07345183938741684	-0.018888041377067566	0.014409171417355537	0.026703083887696266	-0.04307224974036217	0.008896054700016975	0.01127507071942091	-0.03919045999646187	-0.09166700392961502	0.010042446665465832	-0.03089507296681404	0.021051010116934776	-0.010239736177027225	0.006370367482304573	-0.010866501368582249	-0.08174598962068558	-0.010642513632774353	0.0006409426569007337	-0.06069766730070114	0.010365297086536884	0.007382804062217474	-0.0830572247505188	-0.057797666639089584	0.01629718765616417	-0.06600163131952286	0.05510009452700615	0.02086257003247738	0.020456701517105103	0.0007364834891632199	-0.008149297907948494	-0.020659510046243668	-0.020123889669775963	-0.01977395825088024	-0.01944275014102459	-0.05777076631784439	-0.014303961768746376	0.065595842897892	-0.04683159664273262	0.02661418728530407	0.06358052790164948	-0.04359353706240654	0.03392050042748451	0.040712736546993256	-0.03481695428490639	0.04053099825978279	-0.009106160141527653	-0.02804744988679886	0.07832518965005875	-0.05725479871034622	0.07054153829813004	-0.06803876161575317	0.10134834796190262	-0.007061566226184368	-0.01648830808699131	0.07308470457792282	0.07764200121164322	0.009007241576910019	0.0314929373562336	-0.08173573762178421
+0.06519493460655212	-0.016608625650405884	-0.0017214467516168952	0.016063321381807327	-0.10511183738708496	-0.06347797811031342	0.002846043324097991	0.01352105475962162	0.014444679021835327	0.008628848008811474	-0.02770215831696987	-0.00329719390720129	0.007725734729319811	0.012000128626823425	0.016936130821704865	-0.0586087703704834	-0.054651372134685516	0.0005972625804133713	-0.02848838083446026	-0.01663868874311447	0.03496377542614937	0.026206687092781067	-0.05990299582481384	0.028564661741256714	-0.009317115880548954	0.04113532230257988	-0.08122915774583817	0.028794515877962112	-0.007824549451470375	-0.0014837014023214579	0.046538569033145905	-0.013497578911483288	0.04194973409175873	-0.07195311039686203	-0.05580044165253639	0.025170495733618736	-0.05212101712822914	0.10974352806806564	0.026654336601495743	-0.05145866051316261	-0.05782964453101158	0.017922351136803627	-0.02063717693090439	-0.02157379686832428	0.02069529891014099	0.002415471011772752	-0.01954670622944832	-0.03217003867030144	0.00942962896078825	0.004343832843005657	0.09680827707052231	-0.024075990542769432	-0.03803710266947746	-0.026215486228466034	-0.015365618281066418	-0.0027586095966398716	0.018132440745830536	-0.05107637867331505	-0.09466181695461273	0.049623917788267136	-0.07487820833921432	0.025814300402998924	-0.017684154212474823	0.003146799746900797	-0.02518540248274803	-0.046231258660554886	0.02369091287255287	0.08005619794130325	0.00914486963301897	0.007346805185079575	0.05862429738044739	0.09894303232431412	-0.060555703938007355	-0.04584994912147522	0.03446704521775246	-0.08298511058092117	0.0020452584140002728	0.08013895153999329	0.0008454450289718807	0.006723751779645681	-0.008276109583675861	-0.0236858818680048	0.09777233004570007	-0.05361557379364967	-0.06692123413085938	-0.02726556919515133	-0.026661135256290436	-0.07018912583589554	0.022537004202604294	0.033904723823070526	0.02758435346186161	0.038466691970825195	-0.044968049973249435	0.057465534657239914	0.008338234387338161	0.038235459476709366	0.0652553141117096	-0.09896448999643326	-0.03554981201887131	-0.0024138083681464195	-0.032155074179172516	-0.0182174164801836	0.042532484978437424	0.00964769534766674	0.04116608202457428	0.035145774483680725	-0.03469572961330414	0.041351061314344406	0.01523459330201149	0.078924261033535	0.042697589844465256	0.026857096701860428	0.11384184658527374	0.055124424397945404	0.05249989405274391	-0.02334771305322647	0.022782502695918083	-0.048587650060653687	-0.021300286054611206	0.025542328134179115	0.01918202079832554	0.049422070384025574	-0.033004987984895706	-0.06691209971904755	-0.04932786151766777	0.03081098198890686	-0.10048241913318634	-0.019237713888287544	0.04534681513905525	-0.010990244336426258	0.09097999334335327	0.054702337831258774	-0.027501698583364487	0.046795591711997986	0.026074450463056564	0.013340483419597149	0.016137858852744102	0.01620052196085453	0.011552869342267513	-0.028959447517991066	-0.028751607984304428	0.0200366098433733	0.03233550488948822	-0.06103390082716942	-0.005474910605698824	0.025647465139627457	0.041242148727178574	-0.05062041059136391	-0.0871494710445404	-0.014588279649615288	-0.09264472126960754	-0.0724923238158226	-0.01342859398573637	-0.05288936570286751	0.0260451752692461	0.012811310589313507	-0.00817444734275341	0.018278401345014572	-0.035683926194906235	-0.006983010098338127	-0.028200611472129822	-0.030136683955788612	-0.00913114845752716	-0.08581098169088364	-0.004747617989778519	-0.023006213828921318	0.035835545510053635	-0.006231044884771109	-0.03776061162352562	-0.001588165294378996	-0.03831469267606735	0.002055525779724121	0.011635342612862587	0.07204747945070267	0.05653864145278931	0.0028469308745115995	0.03941101208329201	0.009440447203814983	0.028261901810765266	-0.04479337856173515	0.023682285100221634	-0.0339927077293396	0.026457112282514572	-0.056000709533691406	0.004993910901248455	-0.011869738809764385	0.03330598399043083	-0.05509414151310921	0.04776030778884888	0.031411491334438324	-0.024611204862594604	-0.07349953800439835	0.019288619980216026	0.09172925353050232	-0.024086279794573784	0.0689309611916542	-0.022055279463529587	0.06647519022226334	-0.006027766037732363	-0.038361500948667526	-0.01029044296592474	0.0454513356089592	-0.01305756252259016	0.04392189532518387	-0.005482573062181473	-0.07134326547384262	0.08934462070465088	0.019013473764061928	0.0296530369669199	-0.061217062175273895	0.02315557934343815	0.02532053180038929	0.011236079968512058	0.027799123898148537	0.0959286317229271	-0.042748115956783295	-0.002722634468227625	-0.008723200298845768	0.013133579865098	-0.02264297567307949	0.03349322825670242	-0.010346322320401669	-0.060576893389225006	0.03067745827138424	-0.08795325458049774	-0.10709655284881592	-0.007613431196659803	0.025908395648002625	-0.00262125744484365	-0.0632462352514267	0.007749434094876051	0.020678682252764702	-0.004646682646125555	-0.042171090841293335	-0.035461194813251495	0.016186054795980453	-0.11570446938276291	0.08944694697856903	0.009989887475967407	-0.05371060594916344	-0.04281879961490631	0.004305769689381123	0.008764977566897869	0.011910784989595413	0.017285756766796112	0.044315632432699203	0.125372052192688	0.04513140395283699	-0.014577114023268223	0.03331048786640167	-0.060555532574653625	-0.032443817704916	0.03204096108675003	0.014611010439693928	-0.0794064849615097	-0.010245844721794128	-0.0010479000629857183	-0.07138700038194656	-0.02892361581325531	-0.015146967954933643	0.021910326555371284	-0.0006432003574445844	-0.06210834160447121	-0.035307399928569794	0.05674809217453003	0.004104712978005409	-0.06612880527973175	0.04948415234684944	-0.009720862843096256	0.05750405415892601	0.0066617694683372974	-0.029438236728310585	0.05350438877940178	0.016130775213241577	0.049831658601760864	-0.014637064188718796	0.079906165599823	-0.017133420333266258	-0.07230132073163986	-0.022688744589686394	-0.05305219441652298	-0.036968231201171875	-0.026148328557610512	-0.049172524362802505	-0.01069637481123209	0.0688864141702652	0.0042302547954022884	-0.00027915521059185266	-0.06899994611740112	-0.003257298842072487	-0.009283014573156834	-0.07768909633159637	-0.039920270442962646	0.022550659254193306	-0.060262423008680344	-0.034021005034446716	0.02813151106238365	-0.03469691798090935	-0.04321075975894928	-0.014673466794192791	-0.045075803995132446	-0.06363643705844879	0.03118814155459404	-0.04466555640101433	-0.029394136741757393	0.02053476870059967	-0.07911792397499084	0.02709032967686653	0.12344936281442642	0.05974901095032692	0.03646507114171982	0.05802742391824722	0.03714662045240402	0.12360043823719025	0.014622473157942295	-0.05233055725693703	-0.012822834774851799	-0.043349817395210266	-0.06863587349653244	0.032735567539930344	-0.046756450086832047	-0.019907299429178238	0.002970617264509201	0.012592130340635777	0.03709687292575836	-0.016306234523653984	-0.07690446078777313	-0.021540170535445213	0.006506694480776787	-0.042574550956487656	0.014686170034110546	0.03189445659518242	0.00035351671976968646	0.013563284650444984	-0.016745250672101974	-0.047287557274103165	-0.04857714846730232	0.01988636888563633	-0.020682955160737038	0.04870082810521126	-0.00768207898363471	-0.025987738743424416	-0.009681547060608864	-0.015490737743675709	0.10238663852214813	-0.0752691850066185	0.0034626945853233337	0.0033799316734075546	0.024140428751707077	-0.08608991652727127	0.007615790702402592	0.04076780751347542	-0.029947593808174133	-0.018146533519029617	-0.0024807227309793234	0.03271796554327011	-0.06088673695921898	-0.02028018981218338	0.03813643380999565	-0.016807420179247856	-0.006519968621432781	0.02721179649233818	0.025937387719750404	0.02818259783089161	-0.04120028018951416	-0.013868172653019428	0.003866962157189846	-0.002485297154635191	0.05337671563029289	-0.0547599270939827	0.011361297219991684	-0.039581216871738434	0.04479328915476799	-0.006902119144797325	-0.032808151096105576	-0.030803196132183075	0.05813608318567276	0.04798879846930504	-0.009195435792207718	-0.026380296796560287	0.034240562468767166	0.004351326730102301	0.029345626011490822	0.03110453672707081	-0.0461847297847271	-0.019527416676282883	-0.03903677687048912	-0.04032257944345474	-0.08748337626457214	-0.04980216175317764	0.014108468778431416	-0.08366338908672333	0.020000508055090904	0.004173286259174347	-0.01216165628284216	-0.0007605788414366543	-0.03565995767712593	0.03052208572626114	0.06206478923559189	0.026801960542798042	-0.012452361173927784	0.040031082928180695	0.011093348264694214	0.05971867963671684	0.029824810102581978	-0.05534301698207855	0.021098971366882324	0.022252894937992096	-0.0359010174870491	0.025915931910276413	-0.013213548809289932	0.02835572510957718	0.015863873064517975	-0.020543549209833145	0.05399332940578461	0.0005479393294081092	0.028152236714959145	-0.029067669063806534	0.048727232962846756	-0.08748360723257065	-0.016128458082675934	0.062199898064136505	-0.019828785210847855	-0.08904410153627396	-0.007481879089027643	-0.005329723935574293	-0.03603557497262955	0.017237568274140358	-0.029031816869974136	0.03342336416244507	0.02004953846335411	-0.048806894570589066	-0.058941859751939774	-0.04782935231924057	-0.003844490507617593	0.0859294980764389	-0.009221652522683144	0.07160355150699615	0.020546315237879753	0.027803197503089905	0.008779007941484451	0.008066557347774506	0.00828600488603115	-0.08098641782999039	-0.010500775650143623	-0.06595994532108307	-0.02380102127790451	0.023529302328824997	0.002591953380033374	0.017775114625692368	-0.06295143812894821	-0.025204844772815704	-0.07506059855222702	0.0687611997127533	-0.01879188045859337	-0.0008232732652686536	0.02663276344537735	-0.039709605276584625	-0.0015102089382708073	-0.020339887589216232	-0.021253008395433426	-0.08317265659570694	0.011999542824923992	-0.009708523750305176	0.04141511768102646	0.0248896274715662	0.017931237816810608	-0.02012460120022297	-0.07723075151443481	-0.004852535668760538	0.05715576559305191	-0.05580396577715874	0.027291640639305115	0.00583777017891407	-0.07152930647134781	-0.06815820932388306	0.018777616322040558	-0.057674869894981384	0.0777861699461937	0.03197406604886055	0.02290383353829384	-0.0011047053849324584	-0.016587983816862106	0.03457324951887131	-0.06001751124858856	-0.014825806953012943	-0.01192544400691986	-0.023780986666679382	-0.03240251913666725	0.06657445430755615	-0.010395711287856102	0.03606569394469261	0.06604067981243134	-0.05632331222295761	0.033699676394462585	0.029082385823130608	-0.035489026457071304	0.02940775267779827	-0.03441395238041878	-0.053074125200510025	0.06265663355588913	-0.05871487036347389	0.07154158502817154	-0.06291017681360245	0.09602173417806625	0.0008397403289563954	-0.06084011495113373	0.07352881878614426	0.06158650293946266	-0.02045176364481449	-0.0025107813999056816	-0.0657758116722107
+-0.004971206653863192	-0.046302542090415955	0.014356614090502262	0.04375747963786125	-0.0861499086022377	-0.06750430911779404	-0.017524033784866333	0.02734677493572235	0.0013961532386019826	-0.02115658111870289	0.006964932661503553	-0.004948279354721308	-0.03315264359116554	-0.01901278831064701	0.02142767608165741	-0.011330032721161842	-0.08004617691040039	-0.01261127833276987	-0.06284771859645844	-0.018088148906826973	0.041296061128377914	0.031905971467494965	-0.10658739507198334	0.011664499528706074	-0.05042576789855957	0.019756454974412918	-0.07090824842453003	0.027985943481326103	-0.0502161867916584	0.044644664973020554	0.04926937073469162	0.0014119748957455158	0.08230653405189514	-0.06294756382703781	-0.050824616104364395	0.017513757571578026	-0.028721963986754417	0.07861717790365219	0.043753571808338165	-0.028791802003979683	-0.052598368376493454	0.020252650603652	0.000759376329369843	-0.05902807414531708	0.03736399486660957	-0.011428228579461575	0.02008730359375477	-0.04884874075651169	0.05322173237800598	0.0461377389729023	0.09895947575569153	0.03024032525718212	0.005320620257407427	-0.024783248081803322	0.007419959641993046	-0.0016558656934648752	0.015601136721670628	-0.03900131955742836	-0.1022401824593544	0.04460688680410385	-0.08479774743318558	0.0032456261105835438	-0.010501512326300144	0.00356657849624753	-0.033670298755168915	0.012898841872811317	-0.009488300420343876	0.08155209571123123	0.02058350294828415	-0.04848490655422211	0.0317305251955986	0.09629674255847931	-0.06452734768390656	-0.05119374394416809	0.003502774517983198	-0.016025813296437263	0.018067333847284317	0.025363678112626076	-0.01041990052908659	0.018507743254303932	-0.025837456807494164	-0.037201397120952606	0.12425095587968826	-0.06092371046543121	-0.008956893347203732	-0.04950958862900734	-0.02073073200881481	-0.05911020562052727	0.062230635434389114	0.026016350835561752	0.017736205831170082	0.0444222018122673	-0.026506105437874794	0.036234721541404724	-0.006615073885768652	0.0728982612490654	0.05620181933045387	-0.10174615681171417	0.008612618781626225	-0.0028522496577352285	-0.028642848134040833	-0.010281332768499851	0.07753407210111618	0.011822564527392387	0.08066892623901367	0.028198959305882454	-0.03096592240035534	0.0027525390032678843	0.03494754433631897	0.02632935158908367	0.0490378737449646	0.09020525962114334	0.0805983617901802	0.0456952229142189	0.027565885335206985	0.01263124868273735	0.008953013457357883	-0.034504812210798264	0.006972789764404297	0.022965073585510254	-0.019267616793513298	0.014602253213524818	-0.021389346569776535	-0.02859070897102356	0.010374852456152439	0.055515047162771225	-0.09000468254089355	-0.04006853699684143	0.058669593185186386	-0.06363902986049652	0.1112794578075409	0.05592501536011696	0.014810261316597462	0.021712100133299828	0.06042462959885597	0.008246904239058495	-0.013692440465092659	0.020150886848568916	0.011332329362630844	-0.025370635092258453	-0.02102288417518139	0.05161214619874954	-0.03894677385687828	-0.04413967579603195	6.721251702401787e-05	0.04642688110470772	-0.008650119416415691	-0.02525312639772892	-0.09089528769254684	0.06897914409637451	-0.07759013026952744	-0.06729760766029358	-0.019770119339227676	-0.08284129947423935	0.00886332243680954	0.07195877283811569	0.041053637862205505	0.03801390156149864	-0.023334559053182602	0.004788849502801895	-0.023419562727212906	-0.0852862298488617	-0.021950529888272285	-0.08489853143692017	-0.0321490503847599	-0.023047616705298424	0.03246690705418587	0.015020942315459251	-0.003755381563678384	-0.030163617804646492	-0.022275410592556	0.015474547632038593	0.006634220946580172	0.049641698598861694	0.02091103419661522	0.011615768074989319	0.00601411797106266	-0.048590000718832016	0.02107754535973072	-0.06247814744710922	0.006665590684860945	-0.04605288803577423	0.048739656805992126	-0.04101847857236862	-0.03692289814352989	0.0011318096658214927	0.004712055902928114	-0.00860288180410862	0.010851006023585796	0.030549556016921997	-0.03310956060886383	-0.0573999285697937	0.019963178783655167	0.09895313531160355	-0.033796973526477814	0.06705375015735626	0.0363391675055027	0.07364660501480103	-0.04801010340452194	-0.05238593369722366	-0.055762846022844315	0.03224549815058708	0.00475389463827014	0.010019234381616116	-0.007896559312939644	-0.05968844145536423	0.06498858332633972	0.028934398666024208	-0.030391931533813477	-0.06287454068660736	0.038105376064777374	0.03812286630272865	0.07718236744403839	0.012278147041797638	0.06051849573850632	-0.03828894719481468	-0.026070594787597656	0.013018984347581863	0.03726625442504883	-0.06401301175355911	0.01627248525619507	0.005108472891151905	-0.04531561955809593	0.027079619467258453	-0.02481848932802677	-0.09781110286712646	-0.0025485982187092304	-0.006218611262738705	0.010960284620523453	-0.002464523771777749	0.045707277953624725	0.026012346148490906	-0.044863540679216385	-0.009898144751787186	-0.007702308241277933	-0.0017891748575493693	-0.07800008356571198	0.09424808621406555	-0.01815757527947426	-0.04906565696001053	-0.03562936559319496	0.028124254196882248	0.019496282562613487	0.01750655099749565	0.007867093198001385	0.020912256091833115	0.10473588854074478	0.06292607635259628	-0.006899837404489517	-0.041847869753837585	-0.0810282826423645	-0.0526249073445797	0.023222915828227997	0.01259269192814827	-0.02325178124010563	-0.022061947733163834	-0.011489484459161758	-0.0915723592042923	0.012037144042551517	-0.014113591983914375	0.059579525142908096	-0.024009112268686295	-0.003132927231490612	0.013953357003629208	0.05110340937972069	0.02054547518491745	-0.08392596989870071	0.04265191778540611	-0.008517362177371979	0.028828328475356102	-0.05188620835542679	0.0016262829303741455	0.03816314786672592	0.001873929868452251	0.05825500935316086	-0.029552675783634186	0.06479883939027786	-0.020239658653736115	-0.11507242172956467	-0.031224146485328674	-0.08015328645706177	-0.03903369978070259	0.013022704049944878	-0.02647361345589161	-0.042002879083156586	0.0724879577755928	0.036339908838272095	-0.02501991018652916	-0.06367555260658264	-0.017826782539486885	-0.02718690223991871	-0.03464798629283905	-0.015363767743110657	0.007581292185932398	-0.06392233073711395	0.009996339678764343	0.023763170465826988	-0.09239506721496582	-0.01807536371052265	0.040162667632102966	-0.043266765773296356	-0.033251356333494186	0.06873864680528641	-0.03394066169857979	-0.016690295189619064	0.029700705781579018	-0.07080300152301788	0.044595688581466675	0.07957471162080765	0.027956761419773102	0.048354052007198334	0.05975380912423134	0.042176488786935806	0.04938821494579315	0.014601520262658596	-0.025229638442397118	-0.039494588971138	-0.005990886129438877	-0.07160984724760056	0.04584240913391113	-0.041029736399650574	0.015951722860336304	-0.008542519994080067	0.002174000022932887	0.08491889387369156	-0.03426474705338478	-0.02831163816154003	-0.019931841641664505	0.02385075017809868	0.02658201940357685	0.024353597313165665	0.04299521446228027	-0.011009174399077892	0.030467845499515533	-0.05250886082649231	-0.021227434277534485	-0.058109283447265625	0.032398004084825516	-0.02165537327528	0.026932181790471077	0.02010847069323063	-0.05514996498823166	0.018812209367752075	0.004940078593790531	0.07965486496686935	-0.07949184626340866	0.04197681322693825	-0.007188127841800451	0.01840049773454666	-0.12492618709802628	-0.03898051381111145	0.02404111996293068	-0.015326369553804398	-0.06145954132080078	0.05123627558350563	0.03772784769535065	-0.05490962415933609	-0.03967259079217911	-0.003406924195587635	-0.03426064923405647	-0.030703196302056313	0.04520058259367943	0.008547753095626831	0.012267337180674076	0.01210361160337925	-0.04021339863538742	-0.04675940051674843	0.002325979992747307	0.005241017788648605	-0.020750155672430992	0.045815933495759964	-0.017925700172781944	0.03657601401209831	-0.029428724199533463	-0.06865920126438141	-0.04527924209833145	0.0578470453619957	0.002455773064866662	-0.007113603875041008	-0.018221521750092506	-0.029706165194511414	-0.011266190558671951	0.0492413155734539	-0.019613170996308327	-0.048349685966968536	-0.0010032594436779618	0.027447693049907684	-0.0839293822646141	-0.09034605324268341	-0.024599386379122734	0.002316975500434637	-0.05453454703092575	0.025846092030405998	-0.04622006043791771	0.041150376200675964	0.016507375985383987	-0.027903635054826736	0.028827274218201637	0.05516466870903969	0.023909728974103928	0.005349431652575731	-0.0007214283687062562	-0.011286438442766666	0.033695511519908905	-0.01627679541707039	-0.042192600667476654	0.030257970094680786	0.025583524256944656	-0.07132682204246521	-0.0052001080475747585	-0.040311068296432495	-0.033129457384347916	0.013765791431069374	-0.05308277904987335	-0.0031432700343430042	0.047216854989528656	-0.009656442329287529	-0.02759425714612007	0.04953474923968315	-0.04106564819812775	0.007682888302952051	0.028529154136776924	0.009642337448894978	-0.0863649919629097	-0.015694474801421165	-0.035468075424432755	-0.04195808619260788	0.06986250728368759	0.002299996791407466	0.020487602800130844	0.009278022684156895	-0.06298777461051941	-0.010670654475688934	-0.007717425469309092	-0.0013592108152806759	-0.006114852149039507	0.03155027702450752	0.08362866193056107	-0.030608762055635452	0.009398482739925385	-0.006852166261523962	-0.010703862644731998	0.021724507212638855	-0.06906087696552277	-0.000159849165356718	-0.014152598567306995	0.03771529719233513	-0.003241791157051921	0.012076832354068756	0.0024745187256485224	-0.07316044718027115	-0.026740700006484985	0.0016422673361375928	0.06139281392097473	-0.023636097088456154	-0.009166334755718708	0.048180997371673584	-0.06446922570466995	-0.04742524400353432	0.006128888577222824	-0.019226571545004845	-0.09756754338741302	0.0035215409006923437	-0.06058022007346153	0.041454631835222244	0.012303473427891731	0.04251401126384735	-0.026797126978635788	-0.08587450534105301	-0.04511495679616928	-0.0038506430573761463	-0.024616237729787827	-0.02203145995736122	0.034309208393096924	-0.07280930131673813	-0.05138147994875908	-0.04552426189184189	-0.07615045458078384	0.08546187728643417	0.02334669418632984	0.0014311986742541194	-0.02004094421863556	0.000653542170766741	0.011357774958014488	-0.023935791105031967	0.000636812939774245	0.009508270770311356	-0.08388303965330124	-0.0448031947016716	0.06639020889997482	-0.014449345879256725	0.10072901844978333	0.0707152709364891	-0.02604382485151291	0.05860438942909241	0.04920955002307892	0.01942780241370201	0.0006091760587878525	-0.0014981502899900079	-0.0218941830098629	0.027122728526592255	-0.03377852588891983	0.044207863509655	-0.03897041827440262	0.10145090520381927	0.011367601342499256	-0.03196579962968826	0.04595160856842995	0.1010754182934761	0.030201692134141922	-0.00045014385250397027	-0.05568642169237137
+0.057135265320539474	-0.03521261364221573	0.006945373024791479	0.057562150061130524	-0.05839080363512039	-0.039620716124773026	-0.002500971546396613	0.014098241925239563	0.011423136107623577	-0.03461721912026405	-0.07205013930797577	0.0034536453895270824	0.005931701511144638	0.026435453444719315	0.02544519118964672	-0.029253168031573296	-0.04700818657875061	0.042738743126392365	-0.004604189656674862	-0.033873822540044785	0.017320111393928528	0.004243090748786926	-0.050631333142519	0.042489875108003616	0.020395711064338684	0.02405150607228279	-0.05750551447272301	0.013448632322251797	0.014635799452662468	0.021304454654455185	0.07403534650802612	-0.020052509382367134	0.03886477276682854	-0.08613599836826324	-0.04629450663924217	0.032033249735832214	-0.04509246349334717	0.10102197527885437	-0.01613503135740757	0.012872980907559395	-0.05353730171918869	0.004275479819625616	-0.058610789477825165	-0.0322859063744545	0.0500962994992733	0.013380715623497963	0.01026864629238844	-0.009809110313653946	0.029406432062387466	0.012170840054750443	0.10303177684545517	-0.012586766853928566	-0.03451964259147644	-0.034332241863012314	-0.04232778400182724	0.0020551581401377916	0.05983399972319603	-0.05119423195719719	-0.05809283256530762	0.023458972573280334	-0.09829584509134293	-0.028177568688988686	0.01289118081331253	-0.018458852544426918	-0.054587773978710175	-0.010252367705106735	-0.006182398647069931	0.08727661520242691	-0.02242053858935833	-0.04357270523905754	0.05688558891415596	0.09855646640062332	-0.0607980377972126	-0.05797755345702171	0.05130157619714737	-0.08605676889419556	0.016183916479349136	0.07301698625087738	-0.008257323876023293	0.009388688951730728	-0.010279638692736626	-0.048236556351184845	0.11761903017759323	-0.04173901304602623	-0.07407855242490768	-0.025678733363747597	-0.003214881755411625	-0.03258362039923668	0.03376713767647743	0.049913182854652405	0.02073051780462265	0.039397820830345154	-0.029873523861169815	0.03640599176287651	0.03568911552429199	-0.019893266260623932	0.030827056616544724	-0.10067254304885864	-0.06558529287576675	0.0409076064825058	0.011412383988499641	-0.008875491097569466	0.06602466851472855	-0.0017084771534428	0.05982799828052521	0.028989583253860474	-0.0453641414642334	0.019335223361849785	0.03923362120985985	0.06032539904117584	0.038117665797472	0.05100240185856819	0.06561814248561859	0.053601812571287155	0.05825437605381012	-0.025746144354343414	0.01918167620897293	-0.05859373137354851	-0.030795782804489136	0.01726054586470127	0.03219269588589668	0.05313178524374962	-0.032475344836711884	-0.06242036819458008	-0.037861235439777374	0.005803379695862532	-0.10631837695837021	-0.00995318591594696	0.07693738490343094	-0.013502613641321659	0.13443827629089355	0.020537953823804855	-0.02299416996538639	0.04603424668312073	0.04343749210238457	0.038735631853342056	0.008641844615340233	0.022934960201382637	0.02536052279174328	-0.03030225820839405	-0.002553024096414447	0.04237695410847664	-0.003566480241715908	-0.05287684500217438	0.08294001966714859	0.04159553349018097	0.05113798752427101	-0.05512985587120056	-0.03410105034708977	-0.02652454376220703	-0.07116550952196121	-0.04987097531557083	0.0185262318700552	-0.05861835554242134	0.005467787850648165	-0.003683878341689706	0.030170520767569542	0.02199472486972809	-0.01815655827522278	-0.0009539249585941434	-0.08911733329296112	-0.03323650732636452	-0.03158821538090706	-0.053021349012851715	-0.021938247606158257	-0.004493068438023329	0.0083905765786767	0.011026337742805481	-0.05898240581154823	-0.039658498018980026	-0.024348745122551918	-0.011825604364275932	0.041857536882162094	0.08600426465272903	0.07757207751274109	0.00136988062877208	0.017041543498635292	-0.05020555481314659	0.010593242943286896	-0.01981501653790474	0.01171152014285326	-0.011951475404202938	0.0075682830065488815	-0.04095204174518585	0.02328624576330185	-0.003774438053369522	0.0337773896753788	-0.054489169269800186	0.028500260785222054	0.005944517906755209	-0.035433195531368256	-0.04974941164255142	0.038688041269779205	0.0960511714220047	-0.01615300215780735	0.0451316311955452	-0.05079037696123123	0.052307575941085815	0.008627154864370823	-0.03057674691081047	-0.0438975915312767	0.024488314986228943	-0.015599231235682964	0.06392014771699905	0.011429565027356148	-0.09669890254735947	0.09482746571302414	0.050993479788303375	0.04315691068768501	-0.0705750435590744	-0.026226161047816277	-0.005696124862879515	-6.11685318290256e-05	0.06561245769262314	0.08968788385391235	-0.004018165171146393	-0.024200698360800743	-0.002490944927558303	0.05876540392637253	-0.02361648716032505	0.01337294839322567	-0.005214848555624485	-0.0873471274971962	0.019052624702453613	-0.09299222379922867	-0.0731581524014473	-0.038027532398700714	0.014785999432206154	-0.009386320598423481	-0.01131532620638609	-0.02383582293987274	0.013716117478907108	0.015249286778271198	-0.028831282630562782	-0.020085658878087997	-0.006557471584528685	-0.09290987998247147	0.0935736745595932	0.028991587460041046	-0.07613392919301987	-0.08137490600347519	-0.010499417781829834	0.057570166885852814	-0.012152404524385929	0.03789805248379707	0.02053127996623516	0.09124193340539932	0.024454372003674507	-0.009556546807289124	0.035086046904325485	-0.0007566068670712411	-0.04187057539820671	0.00028306766762398183	0.009679260663688183	-0.06683064252138138	-0.0024212468415498734	0.029604412615299225	-0.051567647606134415	-0.017192231491208076	0.005475339945405722	-0.002983938902616501	0.02382609061896801	-0.058833763003349304	-0.0031268373131752014	0.0585988350212574	-0.03654824197292328	-0.0310051366686821	0.010733462870121002	-0.01914145052433014	0.0752279981970787	-0.050005584955215454	-0.03513370826840401	0.028829898685216904	-0.016171032562851906	0.0022576141636818647	-0.06494537740945816	0.06936471909284592	-0.016934223473072052	-0.0600404366850853	-0.0067695691250264645	-0.03832758218050003	-0.04673325642943382	-0.021586209535598755	-0.006500087212771177	-0.009067323058843613	0.06596755981445312	-0.014405345544219017	-0.04672643914818764	-0.044745225459337234	-0.023136986419558525	-0.020063307136297226	-0.06542487442493439	-0.009983201511204243	-0.024290749803185463	-0.04091948643326759	-0.0189158096909523	0.02294539473950863	-0.019876889884471893	-0.009005332365632057	0.00043141457717865705	-0.032909926027059555	-0.06806424260139465	0.03837652876973152	-0.029414331540465355	-0.055056024342775345	0.0053061204962432384	-0.05674102529883385	0.03015049546957016	0.09168074280023575	0.06234224513173103	0.014824575744569302	0.05039803683757782	0.012718532234430313	0.10992925614118576	-0.021792007610201836	-0.0262470506131649	-0.0010135392658412457	-0.059740711003541946	-0.05074554309248924	0.03409452363848686	-0.004535480868071318	-0.021041618660092354	0.0035541842225939035	0.003203734289854765	0.044642530381679535	-0.003757144557312131	-0.05296974256634712	-0.026481829583644867	0.018279079347848892	-0.026296082884073257	-0.009797055274248123	0.023767709732055664	0.041683755815029144	-0.037142544984817505	-0.05435096099972725	-0.04425329715013504	-0.021544324234128	0.05210913345217705	-0.018471326678991318	0.029607631266117096	-0.0710957944393158	-0.03391004353761673	-0.001788015360943973	0.026615861803293228	0.08943483978509903	-0.025800863280892372	0.003865736536681652	0.03900039941072464	0.028624366968870163	-0.08811990171670914	0.027510765939950943	0.0440790131688118	-0.04390803724527359	-0.04689325392246246	0.00039470044430345297	0.0002754177257884294	-0.06037050113081932	0.01887102983891964	0.016252197325229645	-0.04771430790424347	0.003271703841164708	0.026650691404938698	0.030712876468896866	0.03753561154007912	-0.00878177210688591	-0.031107168644666672	0.0007678574766032398	0.0058907028287649155	0.03300042822957039	-0.031083570793271065	0.001234962372109294	-0.05135786533355713	0.029918625950813293	-0.03324709087610245	-0.027391523122787476	-0.06077112630009651	0.03894598409533501	0.03445173799991608	-0.015480694361031055	-0.028740976005792618	-0.031008003279566765	-0.009727677330374718	-0.015010355971753597	0.04146100580692291	-0.08668215572834015	-0.008303776383399963	-0.08680993318557739	-0.07681330293416977	-0.09731433540582657	-0.061856720596551895	0.010912938974797726	-0.06347109377384186	0.02052786573767662	-0.015966879203915596	0.0015373908681795	-0.0030838840175420046	-0.0029400165658444166	0.02730230987071991	0.050311628729104996	0.029736250638961792	-0.013001617975533009	0.041234090924263	-0.036180563271045685	0.06540173292160034	0.03929394856095314	-0.0569608211517334	-0.002444198355078697	0.005334509536623955	-0.05363532900810242	0.04230576381087303	-0.03938983008265495	0.008770843967795372	-0.0073264772072434425	-0.020422130823135376	0.06612017750740051	0.006730426102876663	0.013180692680180073	-0.04359588772058487	0.055683642625808716	-0.06962642073631287	-0.034454457461833954	0.044339340180158615	-0.026996605098247528	-0.07893601059913635	-0.052944935858249664	-0.01003551296889782	-0.023365164175629616	0.022919464856386185	-0.04692157357931137	0.03363597393035889	0.014713788405060768	-0.031133383512496948	-0.06890377402305603	-0.023109007626771927	-0.030157875269651413	0.06935479491949081	-0.004040519706904888	0.07470623403787613	0.025900021195411682	0.01073104701936245	0.026656823232769966	-0.00544850854203105	0.001485117245465517	-0.08980005234479904	-0.010089744813740253	-0.05207201465964317	-0.025945331901311874	-0.030144700780510902	0.0004781397292390466	0.009886598214507103	-0.04885674640536308	0.005077033769339323	-0.09042157232761383	0.11133603751659393	0.029134435579180717	0.015791036188602448	0.01251682173460722	-0.0348077192902565	0.006978255696594715	0.016767047345638275	-0.04500063881278038	-0.08000481873750687	-0.007596578914672136	-0.012873013503849506	0.027559304609894753	0.028625261038541794	-0.009623490273952484	-0.011515728197991848	-0.028888730332255363	0.021662069484591484	0.032457347959280014	-0.05254293978214264	0.017741790041327477	0.018733428791165352	-0.0525987334549427	-0.07341095805168152	0.015254481695592403	-0.07174228876829147	0.0761067271232605	0.0710141584277153	0.01244475319981575	-0.008815659210085869	-0.0056058005429804325	0.007326064631342888	-0.012416080571711063	0.021273238584399223	-0.007173657417297363	-0.04778498038649559	0.011517094448208809	0.06232579052448273	-0.038056641817092896	0.023550454527139664	0.03821025788784027	-0.06840100139379501	0.02551564946770668	0.03674514964222908	-0.05412692949175835	0.06149401515722275	-0.012721304781734943	-0.0069350008852779865	0.07234105467796326	-0.09529539942741394	0.09987980872392654	-0.06596093624830246	0.07638531923294067	0.03395413979887962	-0.008212694898247719	0.07733798027038574	0.04641026258468628	-0.029459666460752487	0.0045898957177996635	-0.07768695801496506
+0.039517343044281006	0.017842117697000504	0.007185718510299921	0.0452791228890419	-0.10742444545030594	-0.040177781134843826	0.00825684703886509	0.021687181666493416	0.03341181203722954	0.03162136301398277	-0.021529298275709152	0.019583258777856827	-0.01105027087032795	0.025508301332592964	-0.0026041087694466114	-0.04464863985776901	-0.06553799659013748	-0.01659237965941429	-0.03649287298321724	-0.03300187364220619	0.0378146693110466	0.03114263527095318	-0.05435359105467796	0.04478427767753601	-0.004490541759878397	0.05466408282518387	-0.05011427029967308	-0.004950827453285456	-0.028964262455701828	-0.025118427351117134	0.028282852843403816	0.0059232451021671295	0.06876424700021744	-0.060705848038196564	-0.055253613740205765	0.04144186154007912	-0.017456352710723877	0.0924801155924797	0.05058390274643898	-0.0645560771226883	-0.044979892671108246	0.009011300280690193	-0.05276552215218544	-0.03654612600803375	-0.007371063344180584	0.0033471982460469007	-0.022503428161144257	-0.006445964798331261	0.03360014408826828	0.02438754215836525	0.09005642682313919	0.007564169354736805	-0.0518857017159462	-0.03751738741993904	-0.025196930393576622	0.005494758952409029	0.026009738445281982	-0.056803345680236816	-0.1047528013586998	0.022586459293961525	-0.06930533796548843	0.03400935232639313	-0.01846900023519993	-0.006442769896239042	-0.022753320634365082	-0.047345858067274094	0.011889979243278503	0.07586393505334854	0.061448242515325546	0.001693949569016695	0.06449376791715622	0.10915690660476685	-0.07907280325889587	-0.0003765837464015931	0.05155372619628906	-0.03383518382906914	-0.03402538597583771	0.06445489823818207	-0.033429354429244995	0.017468303442001343	0.00708434684202075	-0.0460156574845314	0.09228197485208511	-0.07060615718364716	-0.0614132359623909	-0.007899189367890358	0.020989032462239265	-0.07976119220256805	0.015073271468281746	0.02355288341641426	-0.021162167191505432	0.036403730511665344	-0.06759468466043472	0.07822556048631668	0.01511099748313427	0.06018320843577385	0.04836055636405945	-0.1337522715330124	-0.061658214777708054	0.008139265701174736	-0.011858304962515831	-0.023049497976899147	0.03547775000333786	-0.026834191754460335	0.041776277124881744	0.028668424114584923	-0.04259500652551651	0.01698349043726921	0.011030466295778751	0.018206851556897163	0.08484846353530884	0.010510937310755253	0.0798768401145935	0.03905686363577843	0.05220465362071991	-0.015311069786548615	0.040049124509096146	-0.05223795399069786	-0.018483923748135567	0.02585717849433422	0.01248858217149973	0.04571399465203285	-0.025407535955309868	-0.05440317839384079	-0.06607993692159653	-0.011279743164777756	-0.09361834824085236	-0.014497239142656326	0.023827407509088516	-0.04210459813475609	0.10040614008903503	0.04612419754266739	-0.03668318688869476	-0.000368658424122259	0.011363161727786064	0.008358744904398918	0.008183526806533337	0.0117446044459939	-0.000974583497736603	-0.002823198912665248	-0.015600042417645454	0.060757897794246674	0.04337023198604584	-0.05244576930999756	0.015076729469001293	0.048032473772764206	0.04459627717733383	-0.011933987960219383	-0.09772685915231705	-0.02922734245657921	-0.0972466692328453	-0.07404719293117523	-0.01866290532052517	-0.07622089236974716	0.021412039175629616	0.06248403713107109	0.012445369735360146	0.08195134997367859	-0.030097011476755142	-0.02144077979028225	-0.003388823475688696	0.01204659417271614	-0.01983763463795185	-0.07110428065061569	-0.00357985170558095	-0.021245624870061874	0.017818935215473175	0.0022280572447925806	-0.03314982354640961	0.024491271004080772	-0.024022933095693588	0.0013011845294386148	0.01543895248323679	0.050394244492053986	0.06678509712219238	-0.0170657429844141	-0.006467422936111689	-0.037643078714609146	0.005948430392891169	-0.008129402995109558	0.02417381852865219	-0.05777667835354805	0.03765375167131424	-0.03653814643621445	0.007651231717318296	-0.030639078468084335	0.03873122110962868	-0.03860732540488243	0.019972138106822968	0.014230492524802685	-0.046865858137607574	-0.045414913445711136	0.03155488893389702	0.06626123189926147	-0.02112440951168537	0.02449950762093067	-0.0010422258637845516	0.03169829025864601	0.014384271577000618	0.004090941045433283	-0.023793373256921768	0.04039975628256798	-0.019886307418346405	0.05809476971626282	0.016262879595160484	-0.06892121583223343	0.06729301065206528	-0.01301516778767109	-0.0008483902201987803	-0.05986957624554634	0.022463247179985046	0.00999873224645853	0.015034175477921963	0.005790193565189838	0.08767518401145935	-0.030322030186653137	0.011849240399897099	-0.01268547773361206	0.04172592982649803	-0.03923797607421875	0.03210534155368805	-0.014772976748645306	-0.04400527849793434	0.015109575353562832	-0.05684398114681244	-0.08955347537994385	-0.026639964431524277	0.009563297033309937	0.019402800127863884	-0.056942619383335114	0.014426382258534431	0.032583869993686676	-0.014731531962752342	-0.04367103800177574	-0.03634728863835335	0.04519687592983246	-0.0782642811536789	0.07708506286144257	-0.010599070228636265	-0.025668369606137276	-0.03141998499631882	0.012660437263548374	0.009259064681828022	0.025665177032351494	0.038401417434215546	0.06680282950401306	0.12021639943122864	0.02722877450287342	0.004181237891316414	-0.01764308102428913	-0.07044302672147751	-0.056947011500597	0.05436013638973236	0.07307889312505722	-0.051255904138088226	-0.03245291858911514	-0.01493821945041418	-0.05763251334428787	-0.02036442793905735	0.03441883251070976	0.056736934930086136	0.016078894957900047	-0.045374516397714615	-0.05301474779844284	0.0013703765580430627	0.010071874596178532	-0.03716706112027168	0.019918112084269524	-0.007394089363515377	0.06407049298286438	0.01416473276913166	-0.015256219543516636	0.02175375632941723	0.02115986868739128	0.0816384106874466	0.006208376493304968	0.030580880120396614	-0.025351423770189285	-0.07658890634775162	-0.028291314840316772	-0.05542868748307228	-0.061916783452034	-0.04517224058508873	-0.04550167918205261	-0.05240103602409363	0.0656154677271843	0.0176068302243948	-0.009445816278457642	-0.08653846383094788	0.002185953315347433	-0.022930411621928215	-0.09181814640760422	-0.018551025539636612	0.013064105994999409	-0.10646462440490723	-0.04718545451760292	-0.013375201262533665	-0.030092338100075722	-0.04316555708646774	0.0105068888515234	-0.04252735152840614	-0.08160951733589172	0.04296127334237099	0.0049391984939575195	-0.030566515401005745	-0.04506542161107063	-0.10183294862508774	0.03185313567519188	0.09721750020980835	0.012463569641113281	0.06042921170592308	0.027792152017354965	0.0350513756275177	0.09514540433883667	0.013318577781319618	-0.041063494980335236	0.016273504123091698	-0.02787114679813385	-0.08372890949249268	0.030653566122055054	-0.03387376293540001	0.004773066844791174	-0.021394474431872368	0.010616070590913296	0.034831900149583817	0.009963404387235641	-0.03320775553584099	-0.03854773938655853	0.037655510008335114	-0.018602808937430382	0.03468838706612587	0.004331969190388918	-0.01891709491610527	0.045485757291316986	-0.03243396058678627	-0.017049433663487434	-0.031037891283631325	0.005362287163734436	0.015059034340083599	0.032481543719768524	0.0248852726072073	-0.05701594054698944	0.0203305184841156	-0.027797475457191467	0.10003794729709625	-0.11131652444601059	-0.0024518133141100407	0.026673033833503723	-0.0076098134741187096	-0.09066405892372131	-0.032974664121866226	0.05270588397979736	-0.04670841991901398	-0.00849558599293232	0.009006191976368427	0.0626695528626442	-0.03958672657608986	-0.019133081659674644	0.0023411260917782784	-0.00018638456822372973	-0.00287174666300416	0.031875330954790115	0.0415106937289238	0.023174097761511803	-0.045919641852378845	0.005140488035976887	0.01015452854335308	0.028761960566043854	0.050587695091962814	-0.0214238278567791	0.026378922164440155	-0.009911776520311832	0.0823366716504097	-0.023522797971963882	-0.027021434158086777	-0.057403814047575	0.03670458123087883	0.028202448040246964	-0.021443963050842285	-0.01592637225985527	0.01187350694090128	0.03666793927550316	0.04824431985616684	0.03311067819595337	-0.01167347189038992	-0.031720541417598724	-0.039410300552845	-0.02448124997317791	-0.0930476039648056	-0.014442257583141327	0.0027350052259862423	-0.08222397416830063	-0.007530366536229849	-0.03057284653186798	-0.057555075734853745	-0.02185191586613655	-0.028108537197113037	0.0014194033574312925	0.04581237956881523	0.03187776729464531	-0.026011846959590912	0.019472109153866768	0.001073481049388647	0.06245657801628113	-0.008154540322721004	-0.05613157898187637	0.041636642068624496	0.042119115591049194	-0.03639151528477669	0.027444662526249886	-0.03746916726231575	-0.03831523656845093	0.014615233056247234	0.01620473898947239	-0.002824115566909313	-0.00034660790697671473	0.006877040024846792	-0.04191068559885025	0.044663384556770325	-0.0970228910446167	0.0035069319419562817	0.06917872279882431	-0.008386234752833843	-0.09710949659347534	-0.00637872563675046	-0.009045328944921494	-0.04600612074136734	0.0220786165446043	-0.003238607430830598	-0.002367437584325671	0.051373787224292755	-0.06553635746240616	-0.052217960357666016	-0.040138278156518936	-0.04178284853696823	0.03106309473514557	-0.03153214603662491	0.07055684924125671	0.01819709688425064	0.0028916618321090937	-0.020166683942079544	0.03300455957651138	-0.003703249851241708	-0.06020288169384003	-0.034523654729127884	-0.0556870736181736	-0.03636862710118294	0.030821342021226883	-0.011053592897951603	-0.002287140116095543	-0.04499376192688942	-0.02683681808412075	-0.03464588150382042	0.05767084285616875	-0.0423613004386425	-0.0360076017677784	0.057677507400512695	0.01007215864956379	0.029196960851550102	0.02448841743171215	-0.010336169973015785	-0.054426584392786026	0.032145339995622635	-0.02096087858080864	0.03204605355858803	0.04860549047589302	0.0036253684666007757	0.0014345500385388732	-0.03598303347826004	-0.06804586201906204	0.04547661170363426	-0.07507960498332977	0.03741142898797989	-0.00884505920112133	-0.073542520403862	-0.06079302728176117	0.011165689677000046	-0.048859670758247375	0.07770437002182007	0.015258424915373325	0.0634663850069046	0.033583518117666245	-0.01197305228561163	0.020899789407849312	-0.03476744890213013	-0.008447753265500069	0.014020390808582306	-0.037500664591789246	-0.022235840559005737	0.05833493918180466	-0.0008387641864828765	0.035919152200222015	0.07908385246992111	-0.05204514041543007	0.066714346408844	0.026430804282426834	-0.050858158618211746	0.017864102497696877	-0.022505272179841995	-0.07882393151521683	0.07718244194984436	-0.028641007840633392	0.0994739830493927	-0.03310262784361839	0.10181696712970734	-0.02487914264202118	-0.057367954403162	0.08668865263462067	0.07997994124889374	-0.028351614251732826	-0.009853466413915157	-0.04428738355636597
+0.040476251393556595	-0.011886943131685257	-0.014640248380601406	0.03181144595146179	-0.10715261101722717	-0.04935647174715996	-0.04038766026496887	0.0342983677983284	-0.00355303636752069	-0.046294599771499634	-0.06227535381913185	-0.03983897343277931	-0.0005873869522474706	-0.03270766884088516	0.018420595675706863	-0.03514193370938301	-0.05903629586100578	0.031080495566129684	-0.031050587072968483	-0.0035958937369287014	-0.005068142898380756	0.040406644344329834	-0.0711955726146698	0.03032493218779564	0.024895079433918	0.017026567831635475	-0.03201119601726532	0.0055500841699540615	-0.0268811397254467	0.02863694541156292	0.059729546308517456	0.02375420555472374	-0.005096933338791132	-0.013418182730674744	-0.07388201355934143	0.05431748554110527	-0.03223227337002754	0.08017270267009735	-0.004354453645646572	-0.029833653941750526	-0.014214633964002132	0.013596358709037304	-0.0045827049762010574	-0.038898270577192307	0.0322481244802475	0.003095763036981225	-0.015663722530007362	-0.02008470892906189	0.00926345493644476	0.008644369430840015	0.1290120631456375	0.009502632543444633	-0.01673823595046997	-0.018867095932364464	-0.031709980219602585	0.020101431757211685	0.003116897540166974	-0.05607868358492851	-0.033873844891786575	0.027870262041687965	-0.07004573941230774	0.021876877173781395	0.03335881605744362	0.002975048962980509	-0.012762983329594135	-0.008996056392788887	-0.02859126776456833	0.1138114333152771	0.001070477650500834	-0.002630883129313588	0.03689256310462952	0.07711215317249298	-0.050846051424741745	-0.05372815951704979	0.07353011518716812	-0.08153561502695084	-0.04573206603527069	0.04888063296675682	-0.01054642628878355	-0.05979862064123154	0.04043271020054817	-0.022653762251138687	0.10523118078708649	-0.05962839722633362	-0.06954391300678253	-0.02832649089396	-0.01116805151104927	-0.07954155653715134	0.07176705449819565	0.06942865252494812	0.035102978348731995	0.08655530214309692	-0.02735532633960247	0.039680879563093185	-0.022869832813739777	-0.0025364237371832132	0.039452873170375824	-0.11668156832456589	-0.06787195801734924	-0.009397574700415134	0.021105652675032616	-0.013555584475398064	0.03541675582528114	-0.014339588582515717	0.006133323535323143	0.04534080997109413	-0.006174575071781874	-0.0008319857297465205	0.005061164498329163	0.08247026801109314	0.02214128151535988	0.0388970673084259	0.0967884436249733	0.1113472580909729	0.05199730023741722	0.012140265665948391	-0.031082555651664734	-0.02309701405465603	-0.04175758361816406	0.036090996116399765	-0.0016435834113508463	0.024264005944132805	-0.002084884326905012	-0.06660474836826324	-0.024917887523770332	0.025350244715809822	-0.07562711089849472	0.024802478030323982	0.037411388009786606	-0.020204834640026093	0.09717092663049698	-0.009110230021178722	-0.059843216091394424	0.02418164350092411	0.05162603780627251	0.052710406482219696	0.0057716527953743935	-0.008043036796152592	-0.038028571754693985	-0.016848336905241013	-0.02547437697649002	0.02388748899102211	-0.0030586211942136288	-0.044265128672122955	0.05395927652716637	0.006642326712608337	0.07864955812692642	-0.019785471260547638	-0.035365838557481766	-0.0009651476866565645	-0.08286586403846741	-0.07078138738870621	0.01663832925260067	-0.05778026208281517	0.03327976167201996	0.028885846957564354	0.041242945939302444	-0.008179528638720512	-0.010919983498752117	-0.002306331880390644	-0.02808590978384018	-0.03241487219929695	-0.04132840037345886	-0.09282047301530838	-0.0031564158853143454	-0.05215238034725189	-0.028575902804732323	0.005696659907698631	-0.052945252507925034	-0.05913881957530975	-0.013974146917462349	0.02275990881025791	0.02441716380417347	0.13987450301647186	0.061423469334840775	0.020131276920437813	0.05313247814774513	-0.008277767337858677	-0.04502492770552635	-0.013780168257653713	0.0028160461224615574	0.005811005365103483	0.0416082888841629	-0.06184876337647438	0.01096345391124487	0.018306784331798553	0.05781838670372963	-0.019979851320385933	0.04228070750832558	0.007473750971257687	0.0009419916896149516	-0.06745056062936783	0.049695055931806564	0.07525347918272018	-0.00040686564170755446	0.028978219255805016	-0.025590302422642708	0.06217823177576065	0.015997635200619698	-0.02634490095078945	-0.05083717033267021	0.01266173180192709	-0.011812875978648663	0.08602967858314514	0.03779415041208267	-0.10911133140325546	0.11282909661531448	0.04229900613427162	0.031742822378873825	-0.031229155138134956	0.023579424247145653	0.04583490267395973	0.015737712383270264	0.07177366316318512	0.06989191472530365	-0.008401995524764061	-0.003135895822197199	0.013665096834301949	0.0606747530400753	-0.049900319427251816	0.04324505478143692	0.008880558423697948	-0.08391033858060837	0.046918269246816635	-0.021008813753724098	-0.06502871215343475	-0.04646875336766243	0.020308781415224075	0.0026599345728754997	-0.007132504601031542	0.021909689530730247	0.006598649080842733	0.02762501873075962	-0.015225469134747982	-0.012075497768819332	0.01142866164445877	-0.13411717116832733	0.07329599559307098	-0.012779355980455875	-0.06501882523298264	-0.0636349767446518	-0.009128892794251442	-0.006115943193435669	0.00029068783624097705	0.04120931029319763	0.07222519814968109	0.09046249091625214	0.04252252355217934	0.03536631539463997	0.024395890533924103	-0.0767502710223198	-0.051165007054805756	-0.011418935842812061	0.0012600444024428725	-0.04422751069068909	-0.04620274156332016	0.03591049462556839	-0.09304036945104599	-0.023546678945422173	0.002005576388910413	-0.005271402653306723	-0.02457340434193611	-0.0595853328704834	-0.05541863664984703	0.05050653591752052	-0.03967596963047981	-0.06543517112731934	0.0030143405310809612	-0.032819729298353195	0.06787823885679245	-0.020008595660328865	-0.03751514106988907	0.02860809862613678	-0.027312029153108597	0.04111607000231743	0.0018874842207878828	0.06058478727936745	-0.051196713000535965	-0.051558468490839005	0.018813489004969597	-0.05590309202671051	-0.07324591279029846	-0.0013712855288758874	-0.040331728756427765	-0.04274478182196617	0.08517132699489594	-0.02147301658987999	-0.03774869069457054	-0.050213128328323364	-0.0005500683910213411	-0.031512144953012466	-0.01991196908056736	-0.0470951683819294	-0.015926096588373184	-0.05144072324037552	-0.043887682259082794	0.054932281374931335	-0.025803234428167343	-0.04428331181406975	0.03576991334557533	-0.025282662361860275	-0.05006903037428856	0.02957076020538807	-0.02636074274778366	0.002992520108819008	0.016518179327249527	-0.04889627918601036	0.02922319620847702	0.08340784907341003	0.04158756136894226	0.039324842393398285	0.0492907278239727	-0.01013156957924366	0.09132541716098785	0.026854947209358215	-0.08491251617670059	-0.011055889539420605	-0.07654976099729538	-0.022250615060329437	0.042290639132261276	0.01730637066066265	-0.06170198321342468	0.012401135638356209	0.02391137182712555	0.05880902707576752	-0.011081216856837273	0.0037350228521972895	-0.033007752150297165	0.014914003200829029	-0.025787344202399254	-0.0003520427271723747	-0.025211099535226822	0.03407838195562363	-0.023333927616477013	-0.013393968343734741	0.0003294571361038834	-0.022635433822870255	0.016934989020228386	0.02635958045721054	0.014988603070378304	-0.06443063914775848	-0.02682878077030182	0.006199185270816088	-0.02320103533565998	0.1095704510807991	-0.044897232204675674	0.03969397395849228	0.05879639834165573	0.03285478055477142	-0.03179514408111572	0.0015235845930874348	0.03105808049440384	-0.0552535243332386	-0.012987788766622543	-0.025933297351002693	0.011463451199233532	-0.07412370294332504	-0.043955691158771515	0.015812255442142487	-0.025877567008137703	0.052600037306547165	-0.02518576942384243	0.003258878132328391	0.028356317430734634	0.0020260061137378216	-0.021429017186164856	0.02252587489783764	-0.004670994821935892	0.016738515347242355	-0.05415504053235054	0.005527023691684008	-0.03559667617082596	0.031954068690538406	-0.03172336891293526	-0.05464013293385506	-0.05020682513713837	0.02375727705657482	-0.00025159568758681417	-0.004293177276849747	-0.013540544547140598	0.008394397795200348	0.009055272676050663	0.04132367670536041	0.004754367750138044	-0.0832875519990921	-0.01633213646709919	-0.07014741003513336	-0.03089992143213749	-0.051655419170856476	-0.051837895065546036	-0.0022112783044576645	-0.07322748005390167	-0.006919908802956343	-0.05957166478037834	-0.01599929668009281	-0.004041688982397318	-0.02906143292784691	0.025953048840165138	0.028568947687745094	0.025466835126280785	0.0098697729408741	0.024871651083230972	-0.017574656754732132	0.045903369784355164	0.027144094929099083	-0.09300816059112549	0.014244441874325275	0.06816757470369339	-0.012614506296813488	0.045157961547374725	-0.013310860842466354	0.025856295600533485	0.047773294150829315	0.018285954371094704	0.02613261342048645	-0.04303007200360298	-0.013039171695709229	-0.02219022996723652	0.03166833519935608	-0.05147367715835571	-0.08114609867334366	0.04911009967327118	-0.04457146301865578	-0.07871507108211517	0.00041035879985429347	-0.05142455920577049	-0.02313382737338543	0.011580049060285091	-0.030466746538877487	0.0167400985956192	0.0156572088599205	-0.09186317026615143	-0.041408516466617584	-0.06754600256681442	-0.007309327833354473	0.06412913650274277	0.029310574755072594	0.03690697252750397	0.0006943149492144585	-0.016351990401744843	-0.0079854940995574	-0.015555504709482193	0.011403405107557774	-0.07143792510032654	-0.020334778353571892	-0.03998417407274246	-0.03453266620635986	0.019477570429444313	0.031991146504879	0.015592776238918304	-0.05054909363389015	-0.017827516421675682	-0.06248093023896217	0.07091997563838959	-0.030879218131303787	0.02363544888794422	0.006418025586754084	-0.047188736498355865	-0.008833114989101887	0.035658352077007294	-0.030404329299926758	-0.08186417818069458	0.01775217428803444	-0.02689884789288044	0.03100547008216381	0.059873972088098526	0.036855705082416534	-0.04679715260863304	-0.06083502247929573	0.020489320158958435	0.04988306388258934	-0.02914176695048809	-0.007501486223191023	0.029414283111691475	-0.0709444135427475	-0.06913841515779495	-0.01439789216965437	-0.04262084141373634	0.0663393959403038	0.006411758717149496	0.03188798576593399	-0.05152202025055885	0.01561095379292965	0.034644074738025665	-0.03349726274609566	-0.007014406379312277	-0.023146595805883408	-0.050938256084918976	-0.026233473792672157	0.08442668616771698	-0.03229641914367676	0.0013555752811953425	0.07665905356407166	-0.0349903330206871	0.001441893633455038	0.010214654728770256	-0.04731160029768944	0.035753779113292694	0.025763636454939842	-0.013188029639422894	0.047016043215990067	-0.08558560162782669	0.055483609437942505	-0.03459964320063591	0.09279511868953705	-0.0037161242216825485	-0.044264644384384155	0.044899120926856995	0.06019104644656181	-0.02590063028037548	0.04953188821673393	-0.055650047957897186
+0.00498773530125618	-0.004231366794556379	-0.0021464736200869083	0.038694702088832855	-0.07310724258422852	-0.07991703599691391	-0.010802948847413063	0.027638955041766167	0.021822229027748108	-0.02450587786734104	-0.006548024248331785	-0.009299957193434238	-0.006802389398217201	0.02628563903272152	0.0318506620824337	-0.030672507360577583	-0.06940703839063644	-0.008576865307986736	-0.005988192744553089	-0.03739970922470093	0.01935632713139057	-0.015340607613325119	-0.10933707654476166	0.013056247495114803	-0.008107771165668964	-0.015901828184723854	-0.059074725955724716	0.008759263902902603	0.016316711902618408	0.03564987704157829	0.06120672449469566	-0.03223210945725441	0.10846004635095596	-0.06090269610285759	-0.0817478597164154	-0.007852017879486084	-0.06141729652881622	0.1207313984632492	0.053367745131254196	-0.022064780816435814	-0.07519492506980896	-0.022952746599912643	-0.034602582454681396	-0.021185295656323433	-0.01042611338198185	-0.010133382864296436	-0.017738236114382744	-0.00704169413074851	0.031719569116830826	0.03562389686703682	0.06835199892520905	0.013713163323700428	-0.006915236823260784	-0.04208831861615181	-0.00803625863045454	-0.008091172203421593	0.053056877106428146	-0.0659043937921524	-0.10423359274864197	0.014500831253826618	-0.1243574321269989	0.012005684897303581	-0.008961000479757786	0.027173686772584915	-0.029268046841025352	-0.04543248191475868	-0.017933044582605362	0.04911984130740166	-0.025654636323451996	-0.056921057403087616	0.04033500701189041	0.07605165243148804	-0.07500952482223511	-0.06157849729061127	-0.011807273142039776	-0.025050213560461998	0.02052394486963749	0.009313536807894707	-0.0022882448974996805	0.04318061098456383	-0.016865458339452744	-0.04490127041935921	0.10454286634922028	-0.011922432109713554	0.018785377964377403	-0.04558298736810684	-0.013975470326840878	-0.050664059817790985	0.03070250153541565	0.0024339586962014437	0.01421669963747263	0.024493034929037094	-0.05066216364502907	0.034574974328279495	0.037830766290426254	0.04273048788309097	0.01582752726972103	-0.10212438553571701	-0.0045787072740495205	0.028217332437634468	-0.005782514810562134	0.010892651975154877	0.06646478921175003	0.02207977883517742	0.0542759969830513	0.03657940402626991	-0.06367610394954681	0.05147372558712959	0.07674787193536758	0.029475023970007896	0.05195099860429764	0.07755627483129501	0.044706422835588455	0.023681359365582466	-0.0004425728984642774	0.0511188767850399	0.026903290301561356	-0.01670331321656704	0.013499204069375992	0.0309350173920393	0.013002939522266388	0.019166378304362297	-0.022441040724515915	-0.043121375143527985	-0.023414256051182747	0.04310495778918266	-0.0472666397690773	-0.06651198118925095	0.028250345960259438	-0.020716071128845215	0.09055142849683762	0.08583596348762512	0.015355793759226799	0.04288950935006142	0.05945545434951782	0.024106644093990326	0.005549320951104164	0.016766836866736412	0.006556718144565821	-0.05106288939714432	-0.04912218451499939	0.053434036672115326	-0.027944272384047508	-0.0826343297958374	0.052586834877729416	0.08273549377918243	0.01940949819982052	-0.021013552322983742	-0.10406478494405746	-0.021093159914016724	-0.04916990175843239	-0.027022507041692734	0.015923066064715385	-0.08696658909320831	0.023410430178046227	0.08063439279794693	0.022184524685144424	0.04891909658908844	0.0064366720616817474	0.006988239008933306	-0.03181765228509903	-0.04382643848657608	0.034265730530023575	-0.06884032487869263	-0.05137249827384949	-0.013733744621276855	0.026968324556946754	0.06119159981608391	-0.08581622689962387	0.0002054283831967041	0.011140433140099049	0.0002449967432767153	0.06083909049630165	0.037986211478710175	0.014462065882980824	-0.001174759934656322	0.004140376579016447	-0.08743339031934738	0.01613982953131199	-0.04722853749990463	0.06679487973451614	-0.058994732797145844	0.013018731027841568	-0.0389886349439621	0.005721222143620253	0.0022899117320775986	0.018430255353450775	0.007639105431735516	-0.006808736827224493	0.00018785271095111966	-0.07950309664011002	-0.040307801216840744	0.021366028115153313	0.04339148849248886	-0.0053336056880652905	0.060048457235097885	-0.0044047636911273	0.050978485494852066	-0.030826816335320473	-0.028336623683571815	-0.048340778797864914	0.013011576607823372	0.03775554522871971	0.0005222341860644519	-0.02023167908191681	-0.04863962531089783	0.06817019730806351	0.025312207639217377	0.02044333890080452	-0.050595592707395554	0.008292349986732006	0.038977038115262985	0.01038362830877304	0.023780224844813347	0.08197669684886932	-0.05423649027943611	0.00727787846699357	0.014315121807157993	0.07986506819725037	-0.07412845641374588	0.022000271826982498	0.022873863577842712	-0.046706490218639374	0.020286232233047485	-0.05729147046804428	-0.11374595016241074	-0.0013683970319107175	-0.0063406964763998985	-0.049791429191827774	-0.04173438996076584	-0.029919907450675964	0.010600380599498749	-0.016042612493038177	-0.008184045553207397	-0.05841931700706482	-0.016703598201274872	-0.03131328150629997	0.09594555199146271	0.00028033246053382754	-0.06565146893262863	-0.04056008532643318	-0.017389893531799316	-0.01639954373240471	-0.02478841133415699	-0.001951458165422082	0.050481539219617844	0.07969187945127487	0.02747606858611107	-0.03753483667969704	-0.005409801844507456	-0.055797748267650604	-0.07583063840866089	0.045824870467185974	0.05686574429273605	-0.05843374505639076	0.028272373601794243	0.01871456764638424	-0.06167206913232803	0.02281871996819973	-0.010862999595701694	0.010593647137284279	0.00320737948641181	-0.0011205334449186921	-0.006849698722362518	0.06323906779289246	0.0016841928008943796	-0.03140951693058014	0.006651163566857576	-0.0039908187463879585	0.0524514839053154	-0.031573086977005005	-0.012314852327108383	0.025867031887173653	-0.0060470225289464	0.0008869062294252217	0.005894200410693884	0.0707167536020279	-0.010255269706249237	-0.06350886076688766	-0.0169503316283226	-0.07825438678264618	-0.037426065653562546	0.004570263437926769	0.005175102036446333	-0.016348225995898247	0.09428923577070236	0.032679617404937744	-0.03270747512578964	-0.021579938009381294	-0.054005492478609085	-0.007169659715145826	-0.08313267678022385	0.004195850342512131	0.01132181566208601	-0.07646700739860535	-0.03268708661198616	0.025815479457378387	-0.07563049346208572	0.033095479011535645	0.048919662833213806	-0.06946780532598495	-0.06679888814687729	0.031989455223083496	-0.06848617643117905	-0.034682780504226685	0.016492607071995735	-0.0563543438911438	0.04668783023953438	0.05549640208482742	0.00043571757851168513	-0.011401536874473095	0.049517735838890076	0.04055614769458771	0.07346340268850327	-0.02277340553700924	-0.03823157027363777	-0.009255974553525448	-0.06148006021976471	-0.06421734392642975	0.06407754868268967	-0.04831119254231453	0.04688625410199165	-0.015954624861478806	-0.02687019668519497	0.06864294409751892	-0.020542601123452187	-0.03234658017754555	0.0027588848024606705	0.03460310027003288	-0.025074228644371033	0.038807813078165054	0.043354108929634094	-0.025902455672621727	0.017112210392951965	-0.08721315115690231	-0.034919995814561844	-0.06020466238260269	0.006599050015211105	-0.055609330534935	0.04800510033965111	-0.01791195198893547	-0.04857935756444931	0.012720247730612755	0.018196726217865944	0.09433265775442123	-0.09585470706224442	-0.0016716653481125832	-0.006094740703701973	0.03373051434755325	-0.09242893755435944	-0.035441819578409195	0.031364165246486664	-0.05351346358656883	-0.053070876747369766	0.041561249643564224	-0.00022075588640291244	-0.07557893544435501	0.0019450358813628554	0.0015711582964286208	-0.03797776624560356	-0.015374751761555672	0.02554209902882576	0.002555318409577012	0.035454608500003815	-0.002737807808443904	-0.03333953395485878	-0.037040211260318756	0.009615853428840637	0.02539205364882946	-0.021805554628372192	0.06070627272129059	-0.061654336750507355	0.04869581013917923	-0.029525279998779297	-0.02229875512421131	-0.0979422926902771	0.01709134690463543	0.0027717771008610725	-0.025552555918693542	-0.03435629978775978	-0.011658946052193642	-0.0335037037730217	-0.007854524068534374	0.028181958943605423	-0.08609376102685928	-0.05307784676551819	-0.016599280759692192	-0.02035919390618801	-0.060408033430576324	-0.04334573820233345	0.028774866834282875	-0.08102469891309738	0.03411082923412323	-0.03073924221098423	0.02047269232571125	-0.015014718286693096	-0.04356760531663895	0.015082419849932194	0.07596204429864883	-0.0056070731952786446	0.0029795619193464518	0.049136679619550705	-0.003467253176495433	0.01703144796192646	0.018973426893353462	-0.014086157083511353	0.02396838553249836	0.03419574350118637	-0.07955759018659592	-0.005033719819039106	-0.06852727383375168	0.017975328490138054	-0.01837078295648098	-0.051470234990119934	0.03806073218584061	0.025064971297979355	0.013441115617752075	0.013751385733485222	0.04091167449951172	-0.04000716283917427	-0.022428561002016068	0.004080156330019236	-0.020534398034214973	-0.06500986218452454	-0.03381853923201561	-0.013726930133998394	-0.06718948483467102	0.059704434126615524	0.02860962226986885	0.048901092261075974	0.02922271192073822	-0.06819666922092438	-0.03318610042333603	0.021790100261569023	-0.009716379456222057	-0.005469764117151499	-0.03709575533866882	-0.002726413309574127	-0.021226957440376282	0.002510484540835023	0.05545993521809578	-0.01429894007742405	0.01757594756782055	-0.07941415905952454	-0.036648329347372055	0.017171869054436684	0.018033163622021675	-0.012542495504021645	-0.03215313330292702	0.02830265462398529	-0.09493552893400192	0.006462042219936848	-0.03599806874990463	0.05824810266494751	-0.004946890287101269	-0.018673619255423546	0.039467886090278625	-0.05611103028059006	-0.018661178648471832	0.03391372039914131	-0.00114969233982265	-0.06357827037572861	-0.0071483636274933815	-0.04962025582790375	0.06057288497686386	0.019883353263139725	0.03406452015042305	-0.008848157711327076	-0.08256524801254272	-0.006683070212602615	0.004898224491626024	-0.08852633088827133	0.005556551273912191	0.009272057563066483	-0.05879170447587967	-0.06691942363977432	-0.004393837880343199	-0.05173688381910324	0.07573064416646957	0.04714400693774223	0.01315703708678484	-0.06233731284737587	-0.02845281921327114	-0.02015477977693081	-0.04793577641248703	0.03113633021712303	9.42712722462602e-05	-0.045471858233213425	-0.02640279196202755	0.09625982493162155	-0.025616100057959557	0.05290655419230461	0.06428315490484238	-0.042120058089494705	0.018390370532870293	0.01666051149368286	-0.019029445946216583	-0.011365730315446854	-0.03869135305285454	-0.06139145419001579	0.04224628582596779	-0.057275399565696716	0.0668618306517601	-0.03658045455813408	0.10028687119483948	0.039974067360162735	-0.024305686354637146	0.07114526629447937	0.07156762480735779	-0.008956512436270714	0.016596009954810143	-0.06506290286779404
+0.06012289226055145	-0.016965243965387344	-0.012460312806069851	0.06160622835159302	-0.07734066247940063	-0.0396253764629364	-0.008036181330680847	0.05442778021097183	0.00432105315849185	-0.04379531368613243	0.010631955228745937	0.0008345089736394584	0.0002839972439687699	0.02018403448164463	0.012309478595852852	-0.03439106419682503	-0.050909798592329025	-0.004258034750819206	-0.04066137969493866	-0.04090459272265434	0.029942985624074936	0.024380434304475784	-0.05824192240834236	0.05418854206800461	-0.0031321262940764427	0.008624144829809666	-0.025580205023288727	0.017009491100907326	-0.015069845132529736	0.04917945712804794	0.07116379588842392	-0.03716614097356796	0.03953941538929939	-0.047336701303720474	-0.0581418015062809	0.07043492794036865	-0.05403375253081322	0.1387200504541397	0.02662636525928974	-0.017753807827830315	-0.060751594603061676	-0.0004998067743144929	-0.003235389944165945	-0.029861876741051674	0.03330227732658386	-0.003664435585960746	-0.012573405168950558	-0.023404767736792564	0.030144115909934044	0.03273165971040726	0.1148836761713028	0.008301419205963612	0.010327765718102455	0.002320304047316313	-0.027186574414372444	0.027613716199994087	0.029823264107108116	-0.055940087884664536	-0.0697374939918518	0.026811683550477028	-0.0498456135392189	-0.026148490607738495	0.016176920384168625	-0.006866659969091415	0.001339983893558383	-0.012719296850264072	-0.001860413234680891	0.0762903243303299	0.0570610947906971	-0.006894735619425774	0.028531253337860107	0.09586653858423233	-0.07380352169275284	-0.07604499161243439	0.039325229823589325	-0.05208604037761688	-0.020088041201233864	0.021730422973632812	-0.007324084173887968	0.04221215099096298	-0.01311760488897562	-0.01564343273639679	0.1097198948264122	-0.04469515010714531	-0.03585971146821976	-0.013453050516545773	4.9110924010165036e-05	-0.0713246688246727	0.0754600390791893	0.02348260022699833	-0.004155196249485016	0.05575485900044441	-0.06858718395233154	0.03463970497250557	0.009297430515289307	-0.0025450121611356735	0.04081018269062042	-0.11663680523633957	-0.030690547078847885	-0.004178457893431187	-0.019316038116812706	-0.005319131538271904	0.06253615766763687	-0.003609405132010579	0.0750761553645134	0.0739469900727272	-0.057747237384319305	0.015074759721755981	0.04115225374698639	0.03491907939314842	0.028196655213832855	0.08271586149930954	0.08008819818496704	0.018720325082540512	0.06862714141607285	-0.021497124806046486	0.016092544421553612	-0.050809383392333984	-0.05766171216964722	-0.019720960408449173	-0.021685771644115448	0.07303859293460846	-0.04725644737482071	-0.06187812238931656	-0.04875950515270233	0.017566848546266556	-0.13127993047237396	-0.022672254592180252	0.08083190023899078	-0.0005250121466815472	0.09104210883378983	0.06377101689577103	0.00045144432806409895	0.02806403674185276	0.051545411348342896	0.006852343212813139	-0.01872961036860943	0.0250754002481699	0.025077665224671364	-0.0006183445802889764	0.011805611662566662	0.056314677000045776	0.020070426166057587	-0.06148405000567436	0.0007111697923392057	0.06956379860639572	0.04096081852912903	-0.019054751843214035	-0.07834732532501221	0.019605709239840508	-0.072027787566185	-0.09687874466180801	-0.019509604200720787	-0.04959771782159805	-0.007232235744595528	0.038607995957136154	0.023595672100782394	0.04237499088048935	-0.009728079661726952	-0.024401681497693062	-0.03136889263987541	-0.03375093638896942	0.00102048902772367	-0.07334236055612564	-0.011945176869630814	-0.02930796705186367	-0.003917788155376911	0.0015671857399865985	-0.024301258847117424	-0.02078995108604431	-0.008792136795818806	-0.004260399844497442	0.02087859995663166	0.09695743024349213	0.07837662845849991	0.03668128326535225	0.008182956837117672	-0.044787727296352386	0.008503529243171215	-0.03321896865963936	-0.004621479660272598	-0.04784014821052551	0.007479620166122913	-0.08027785271406174	-0.003612007712945342	0.0016663686838001013	0.04600687697529793	-0.0009445312316529453	0.0017800491768866777	0.007246098946779966	-0.04023788869380951	-0.06753094494342804	0.03236621990799904	0.06041634827852249	-0.02841884084045887	0.050251707434654236	-0.0002692250127438456	0.05605383962392807	-0.006893743760883808	-0.026197703555226326	-0.039115212857723236	0.047177452594041824	0.02371608093380928	0.03316853567957878	0.046789348125457764	-0.04720648005604744	0.0851580798625946	0.0053190249018371105	0.009791653603315353	-0.07394536584615707	-0.008091877214610577	0.014917445369064808	0.021536163985729218	0.034922461956739426	0.057010069489479065	-0.018953803926706314	-0.015129948034882545	0.0011378256604075432	0.04519219696521759	-0.05702327564358711	-0.011731348931789398	-0.037203673273324966	-0.05819946527481079	0.05285888537764549	-0.02700556255877018	-0.0978979766368866	-0.029405158013105392	0.02687428705394268	-0.0037492357660084963	-0.060910921543836594	0.010498200543224812	0.03103572502732277	0.003943224903196096	-0.046673547476530075	0.001545773004181683	-0.019214028492569923	-0.10170818120241165	0.09737173467874527	-0.004826766438782215	-0.06126806139945984	-0.04216865450143814	-0.008608967065811157	0.03770890831947327	-0.019027890637516975	0.056182749569416046	0.06842512637376785	0.08320627361536026	0.03830302134156227	-0.01968110166490078	0.007244448643177748	-0.06289513409137726	-0.018772808834910393	-0.00259673991240561	0.03370673954486847	-0.03153071179986	-0.0513954684138298	0.01325319241732359	-0.06669009476900101	-0.016263989731669426	-0.014964899979531765	0.05909929424524307	-0.046010833233594894	-0.023570334538817406	-0.03861800953745842	0.02393978089094162	-0.022617164999246597	-0.04813116788864136	0.029929732903838158	-0.03233260661363602	0.05506300553679466	-0.0329258069396019	-0.0069819181226193905	0.030586063861846924	0.015795856714248657	0.02454857900738716	-0.005594160873442888	0.07317672669887543	-0.04125393182039261	-0.1006477028131485	-0.04870205372571945	-0.057342689484357834	-0.05763091519474983	-0.013351778499782085	-0.042375899851322174	-0.03727627173066139	0.07310104370117188	0.010362643748521805	-0.013699257746338844	-0.07782469689846039	-0.010453876107931137	-0.020503470674157143	-0.0699370801448822	-0.009470724500715733	-0.00734338816255331	-0.04829563573002815	-0.019730515778064728	-0.008540251292288303	-0.028100963681936264	-0.002372700022533536	-0.006385187618434429	0.004058363847434521	-0.032636046409606934	0.03853731229901314	-0.06778156757354736	-0.024421630427241325	-0.010416544042527676	-0.06317877024412155	0.04358208552002907	0.09586526453495026	0.08066637068986893	0.050166092813014984	0.02784314565360546	0.05406429246068001	0.10234944522380829	0.036483705043792725	-0.023730609565973282	0.0017728094244375825	-0.012063208967447281	-0.06481721252202988	0.038476210087537766	-0.03314760699868202	-0.024536248296499252	0.007403504103422165	-0.0071356832049787045	0.05987975001335144	0.028790656477212906	-0.05468751862645149	-0.006260258611291647	0.06802947074174881	-0.004055491182953119	0.06056126952171326	0.015554881654679775	-0.01904269866645336	0.02335108071565628	-0.021146822720766068	-0.027936581522226334	-0.05679258704185486	0.03783485293388367	0.00023219214926939458	0.05270572379231453	0.010366742499172688	-0.061569131910800934	0.004770999308675528	0.006548390258103609	0.09380478411912918	-0.08286892622709274	-0.011701909825205803	0.027662672102451324	0.013713037595152855	-0.13123759627342224	-0.018100209534168243	0.020784582942724228	-0.025241656228899956	-0.08494855463504791	0.01761854998767376	0.04668973758816719	-0.057682812213897705	-0.02663799561560154	-0.02729126438498497	-0.04391441494226456	-0.013183606788516045	0.029281025752425194	0.0017115178052335978	0.02568468451499939	-0.008837379515171051	-0.0405210442841053	0.0060652499087154865	0.01948912814259529	0.04551992192864418	-0.05407169461250305	0.00030727870762348175	-0.028041407465934753	0.06373655796051025	-0.04002247378230095	-0.046538691967725754	-0.047291770577430725	0.041110560297966	0.00842775497585535	-0.02967742830514908	-0.052473291754722595	-0.00668457243591547	0.00047149157035164535	-0.007425760384649038	-0.004651063121855259	-0.06463168561458588	-0.016065316274762154	-0.01782463677227497	-0.03967341408133507	-0.08401865512132645	-0.05622159317135811	0.028063936159014702	-0.06490965187549591	-0.017218327149748802	-0.021616583690047264	-0.03780817985534668	-0.012043110094964504	-0.010465702973306179	0.04418380931019783	0.0649874284863472	0.03309103474020958	-0.011969504877924919	0.064643494784832	0.03003847971558571	0.024118486791849136	0.023581570014357567	-0.02873327024281025	0.015459312126040459	-0.001998869702219963	-0.05568566173315048	0.025109561160206795	-0.0216362401843071	0.019258012995123863	-0.015363665297627449	-0.025384018197655678	0.025416143238544464	-0.01607346348464489	-0.00712156668305397	-0.044578369706869125	0.0598161481320858	-0.06689624488353729	0.017524510622024536	0.03879789263010025	-0.03552970290184021	-0.08207736909389496	-0.02943737804889679	-0.014604671858251095	-0.036377839744091034	0.05121229961514473	0.018829647451639175	0.017668887972831726	0.008732864633202553	-0.059882376343011856	-0.06533841788768768	-0.04332788288593292	-0.017960859462618828	0.013201138004660606	-0.006815695203840733	0.053445395082235336	0.00441991351544857	0.02627573162317276	-0.013660303317010403	-0.020514801144599915	0.01109160017222166	-0.05857997387647629	-0.02828091010451317	-0.008520890958607197	-0.042083367705345154	0.016736293211579323	0.020730920135974884	0.007526482455432415	-0.04797046259045601	-0.024229614064097404	-0.05910651013255119	0.07558473199605942	-0.027194393798708916	0.00488600879907608	0.01035546325147152	-0.023547440767288208	-0.012944874353706837	0.013774602673947811	-0.03464148938655853	-0.08259642869234085	0.029156822711229324	0.01677369326353073	0.00012279448856133968	-0.019665271043777466	0.038758452981710434	-0.024120330810546875	-0.09903885424137115	-0.030700549483299255	0.013907532207667828	-0.06222954019904137	0.023483887314796448	-0.0071096173487603664	-0.11706112325191498	-0.052649591118097305	-0.0013448981335386634	-0.0568460077047348	0.05998128280043602	-0.04172457754611969	0.015966640785336494	-0.003026840277016163	-0.015688955783843994	-0.035716328769922256	-0.06658779829740524	0.019003096967935562	-0.012780530378222466	-0.013397639617323875	-0.05757574737071991	0.049730923026800156	0.0030706264078617096	0.02819475345313549	0.0727633386850357	-0.03099106252193451	0.03688720241189003	0.05932643637061119	-0.016284802928566933	0.03165493905544281	0.0012205791426822543	-0.025815611705183983	0.08598586171865463	-0.0461466908454895	0.08502406626939774	-0.06521226465702057	0.1122126504778862	0.014735912904143333	0.01796138286590576	0.07710425555706024	0.06923144310712814	0.016934014856815338	0.02239915169775486	-0.06906925141811371
+0.03689461946487427	-0.009789944626390934	-0.03305753320455551	0.06579888612031937	-0.07353381812572479	-0.04408648982644081	-0.014685351401567459	0.027426576241850853	0.018404515460133553	-0.04694460332393646	-0.02007359080016613	0.009704327210783958	-0.0044874451123178005	0.0026380859781056643	0.04213469848036766	-0.018236620351672173	-0.06997733563184738	0.0031644694972783327	-0.009308799169957638	-0.013172999955713749	0.03181106597185135	-0.033823903650045395	-0.09498424828052521	0.06780664622783661	0.018231544643640518	0.03150762617588043	-0.048620350658893585	0.02224653959274292	-0.015451310202479362	0.047003354877233505	0.029050147160887718	-0.02532823756337166	0.04327679052948952	-0.08349897712469101	-0.07399433851242065	0.05457938089966774	-0.03451378643512726	0.11613423377275467	0.057150185108184814	-0.0015834657242521644	-0.08699926733970642	0.0303665641695261	-0.09443604946136475	-0.09159897267818451	0.026980463415384293	0.02521544322371483	0.012629833072423935	-0.011270565912127495	0.08012401312589645	0.030680416151881218	0.04947188124060631	-0.02009950764477253	-0.0419335700571537	-0.01139825489372015	-0.04023445397615433	0.028951233252882957	0.06825566291809082	-0.04018421471118927	-0.06036468595266342	0.015205160714685917	-0.11265596002340317	0.0027541914023458958	-0.01056298054754734	0.008020947687327862	-0.019291475415229797	0.0006575347506441176	-0.00011483782873256132	0.0341142863035202	-0.008001484908163548	-0.054296281188726425	0.016335491091012955	0.08822676539421082	-0.06311594694852829	-0.03155258670449257	0.008166502229869366	-0.05754145234823227	-0.04542386531829834	0.04138173162937164	-0.01177210547029972	0.034473419189453125	-0.003908831626176834	-0.04208728298544884	0.13670353591442108	-0.01832706481218338	-0.00957051757723093	-0.047856174409389496	-0.017584718763828278	-0.032187625765800476	0.02735656127333641	0.03387648984789848	0.008988872170448303	0.07589312642812729	-0.04800441861152649	0.021161630749702454	0.060913704335689545	0.02684061974287033	0.03052755631506443	-0.11240577697753906	-0.04075126349925995	0.024017632007598877	-0.03735281899571419	-0.01630871742963791	0.04114630073308945	0.008027677424252033	0.07712023705244064	-0.008633307181298733	-0.0618821382522583	0.032025162130594254	0.04490674287080765	0.02002992108464241	0.056964483112096786	0.06366022676229477	0.05006464943289757	0.033705610781908035	0.04313097521662712	-0.013294948264956474	0.030891725793480873	-0.045953419059515	-0.03547688573598862	0.003397677792236209	-0.03255352005362511	0.023666808381676674	0.020005611702799797	-0.07008952647447586	0.004120996687561274	-0.003306059865280986	-0.09889254719018936	-0.05520980805158615	-0.007934455759823322	-0.0665305033326149	0.09225636720657349	0.06121382117271423	-0.010468276217579842	0.003559721168130636	0.04025471955537796	0.04647327959537506	0.028933562338352203	0.07370209693908691	0.001956026302650571	-0.015212786383926868	0.011500337161123753	0.029336364939808846	-0.03509146347641945	-0.04100446775555611	0.08142552524805069	0.03706492856144905	-0.010151148773729801	-0.004689617548137903	-0.06638942658901215	-0.0018905154429376125	-0.052433133125305176	-0.02965734340250492	0.018842492252588272	-0.0598936527967453	0.00630490155890584	0.08519145101308823	0.06993956118822098	0.06580298393964767	-0.022402489557862282	-0.0015434235101565719	-0.037664368748664856	-0.016883453354239464	-0.008762981742620468	-0.07403209060430527	-0.06383811682462692	-0.03003934770822525	0.019470270723104477	0.015037310309708118	-0.03304002806544304	1.6700592823326588e-05	-0.029889855533838272	0.0045580375008285046	0.054043978452682495	0.02410445176064968	0.08586783707141876	0.007655306253582239	0.006926093716174364	-0.1145046204328537	0.019219914451241493	-0.039104267954826355	0.008918107487261295	-0.05627641826868057	-0.006488162558525801	-0.016226135194301605	-0.025977373123168945	0.001563330995850265	0.029056748375296593	0.007642883341759443	-0.0030900726560503244	0.005250905640423298	-0.07853198796510696	-0.06344623863697052	0.05562928691506386	0.06107471138238907	-0.021263903006911278	0.0396491177380085	-0.008489521220326424	0.04416268691420555	-0.011510741896927357	-0.009143310599029064	-0.07018633931875229	0.04443911463022232	-0.016803685575723648	0.07323221862316132	0.056884750723838806	-0.035171497613191605	0.045257531106472015	0.0028112661093473434	0.02154245413839817	-0.05007021874189377	-0.0014901241520419717	0.002134366426616907	-0.007449678611010313	-0.003802388906478882	0.07874546945095062	-0.024309704080224037	-0.039792731404304504	0.030772825703024864	0.08213109523057938	-0.07505181431770325	-0.009217049926519394	-0.006364777684211731	-0.0857287272810936	-0.007427911274135113	-0.039119213819503784	-0.07556789368391037	-0.015475384891033173	0.06257791072130203	-0.04998532310128212	0.0048299068585038185	-0.013527781702578068	0.017737174406647682	-0.024804838001728058	-0.04836028441786766	0.0034808318596333265	-0.016508495435118675	-0.058168429881334305	0.09801874309778214	-0.019695410504937172	-0.07611263543367386	-0.05540009215474129	0.03409169986844063	0.05386319011449814	-0.009329498745501041	0.028980916365981102	0.04415089264512062	0.04007997736334801	0.02586844377219677	-0.016310472041368484	0.011009669862687588	-0.0408155657351017	-0.04510832205414772	0.01667073182761669	0.07298580557107925	-0.01204090379178524	0.00852564163506031	-0.001235012081451714	-0.023678958415985107	0.028911443427205086	0.03261469304561615	0.007956395857036114	-0.010729272849857807	-0.018483497202396393	-0.025728996843099594	0.04105587303638458	-0.029241185635328293	-0.01527368649840355	0.03341655805706978	-0.03259485587477684	0.052642643451690674	-0.08733954280614853	-0.055786214768886566	0.014170806854963303	0.028011448681354523	0.015457631088793278	-0.028750717639923096	0.03887840732932091	-0.016152357682585716	-0.091190405189991	-0.03327420353889465	-0.09700195491313934	-0.012800654396414757	-0.022160153836011887	0.009894735179841518	-0.0498897023499012	0.08503250777721405	0.03791636973619461	-0.03343912214040756	-0.06804854422807693	-0.028957678005099297	-0.013420509174466133	-0.08984864503145218	0.0026046980638056993	0.007485860958695412	-0.079757921397686	-0.06766798347234726	0.054859403520822525	-0.02304091490805149	-0.0206370297819376	0.0017819629283621907	-0.05251944437623024	-0.08060352504253387	0.025148028507828712	-0.02048504166305065	-0.06766798347234726	0.017090916633605957	-0.08702477067708969	0.03380541503429413	0.10298529267311096	0.009486538358032703	0.023117896169424057	0.014769715256989002	0.0321543850004673	0.07964245229959488	-0.040121279656887054	-0.004050503019243479	-0.01696883700788021	-0.04753992706537247	-0.06791375577449799	0.0620293952524662	-0.044333573430776596	0.022227417677640915	-0.02584630623459816	0.005983810871839523	0.06868954747915268	0.008577701635658741	-0.01147457491606474	-0.020377377048134804	0.008128785528242588	0.003478436265140772	0.04133250564336777	0.0017679883167147636	0.015364361926913261	-0.022964999079704285	-0.06320392340421677	-0.05907978117465973	-0.04132021963596344	0.02581779658794403	0.010705425404012203	0.03932267799973488	0.00684514781460166	-0.05507679283618927	0.01564999856054783	-0.0010265731252729893	0.09002736955881119	-0.07902351021766663	0.0354594960808754	0.048124171793460846	0.009474343620240688	-0.07761861383914948	-0.011148315854370594	0.0839875191450119	-0.06624250113964081	-0.07060661166906357	0.001881227595731616	0.015464513562619686	-0.0450017973780632	-0.0016525546088814735	0.020055726170539856	-0.05418568104505539	-0.00436517596244812	0.027620546519756317	0.04199755936861038	0.0038026340771466494	-0.0018742138054221869	-0.019276631996035576	0.014684473164379597	-0.020703688263893127	0.015246146358549595	-0.011369489133358002	-0.0009459634311497211	-0.03942827880382538	0.06000302731990814	-0.06346666067838669	0.004484433680772781	-0.060068000108003616	0.0749303326010704	0.001300001866184175	-0.03775457292795181	-0.026919860392808914	-0.0352700799703598	-0.05851268768310547	0.003701062873005867	0.044547684490680695	-0.057781871408224106	-0.05400937795639038	-0.05173422396183014	-0.05217153578996658	-0.08215320110321045	-0.005439813248813152	0.021661346778273582	-0.04365695267915726	0.02336316928267479	-0.06802382320165634	-0.0005025292630307376	0.03918399289250374	0.0016740723513066769	-0.02227034419775009	0.03300582244992256	0.042955026030540466	-0.02034050039947033	0.019161337986588478	-0.04118391498923302	0.03112855739891529	0.02525535598397255	-0.008046284317970276	0.0001011350832413882	0.029423726722598076	-0.06484737992286682	0.02388639561831951	-0.042360659688711166	-0.017946189269423485	-0.0018048291094601154	-0.04055493697524071	0.007796057034283876	0.021889328956604004	0.0335175022482872	-0.019194884225726128	0.016088129952549934	-0.057072367519140244	-0.016876498237252235	0.024220915511250496	-0.04196606203913689	-0.07698533684015274	-0.0491504929959774	0.005281645338982344	-0.0156889408826828	0.030994560569524765	0.004270449746400118	0.03954486921429634	0.0717114508152008	-0.04281499609351158	-0.035669632256031036	-0.0015212246216833591	-0.03577854856848717	0.034002870321273804	-0.03719340264797211	0.041429001837968826	0.01484858337789774	-0.018489448353648186	0.028781214728951454	0.031861647963523865	-0.0027181371115148067	-0.050288986414670944	-0.0201080571860075	0.015784921124577522	-0.014825724996626377	0.011393208056688309	-0.028779199346899986	0.011746125295758247	-0.03138304501771927	-0.02933058701455593	-0.022586120292544365	0.08718990534543991	0.023507848381996155	-0.008190651424229145	0.09293781220912933	-0.02424636483192444	0.045266181230545044	0.006144452374428511	-0.04785935953259468	-0.07422346621751785	-0.0408039316534996	-0.06520860642194748	0.032564982771873474	0.04151550680398941	-0.01742377132177353	0.008771384134888649	-0.035920001566410065	0.0004234600346535444	-0.02775215171277523	-0.08254066854715347	0.02034359984099865	-0.0065168170258402824	-0.02667796052992344	-0.06851892173290253	-0.025880012661218643	-0.05468770116567612	0.07021711021661758	0.03583681955933571	0.0584743395447731	-0.008664336986839771	-6.125695654191077e-05	-0.015928372740745544	-0.017549637705087662	0.021842528134584427	-0.0018269382417201996	-0.04715150222182274	0.009563104249536991	0.09065120667219162	-0.029518762603402138	0.04374527186155319	0.03267757594585419	-0.00787531677633524	0.04251473397016525	0.04813515022397041	-0.05569550395011902	0.021414879709482193	-0.01040409505367279	-0.0577956922352314	0.06854864209890366	-0.05449645221233368	0.08439623564481735	-0.047655947506427765	0.09181243181228638	-0.010334741324186325	-0.011201180517673492	0.09871142357587814	0.08985117077827454	-0.0023923255503177643	0.029994679614901543	-0.04097658395767212
+0.0037766676396131516	-0.03528663516044617	-0.03083331137895584	0.03573618456721306	-0.08114873617887497	-0.04415154829621315	0.01626487821340561	0.03244591876864433	-0.00459632184356451	-0.03548472002148628	-0.0677676573395729	0.03938979282975197	0.008476904593408108	0.03981216624379158	0.031027082353830338	-0.048045385628938675	-0.06374285370111465	-0.0022792399395257235	-0.016990352421998978	-0.0357758067548275	0.0193954948335886	-0.01875467598438263	-0.06704656034708023	0.07077495753765106	0.03260710835456848	0.00839230790734291	-0.04144778475165367	0.003640925046056509	0.025224069133400917	0.011437800712883472	0.07063370198011398	-0.042418140918016434	0.006461961194872856	-0.06552361696958542	-0.08142969012260437	0.051081933081150055	-0.027617597952485085	0.12779530882835388	0.00721342908218503	0.011957066133618355	-0.04381735622882843	0.00914456695318222	-0.057260990142822266	-0.04153618961572647	0.02173852175474167	-0.02745753712952137	0.009651822969317436	-0.016771607100963593	0.024193942546844482	0.00705826748162508	0.06653077155351639	0.018165959045290947	-0.018562357872724533	-0.03810744360089302	-0.05359407886862755	0.017701560631394386	-0.0015294270124286413	-0.03215302899479866	-0.047150008380413055	0.016677245497703552	-0.09320899844169617	0.002167815575376153	0.009423423558473587	0.002111377427354455	-0.0155847929418087	0.0049186632968485355	-0.012820343486964703	0.08317999541759491	-0.017790144309401512	-0.03717753663659096	0.022799722850322723	0.054821521043777466	-0.03696000203490257	-0.03495265915989876	0.03583541512489319	-0.04870069399476051	0.008383159525692463	0.0291399285197258	-0.005647457670420408	-0.007070257794111967	-0.004099621437489986	-0.015276315622031689	0.12442555278539658	-0.012365717440843582	-0.04644342139363289	-0.034120649099349976	-0.008101865649223328	-0.04211272671818733	0.034630727022886276	0.015575550496578217	0.04152493551373482	0.0829986184835434	-0.053028278052806854	0.04664112627506256	0.01672167330980301	-0.02341047115623951	0.01681869849562645	-0.15175743401050568	-0.0393436998128891	0.016634613275527954	0.012573241256177425	-0.01876993291079998	0.07386244833469391	-0.047726113349199295	0.045987680554389954	0.049109768122434616	-0.08594311028718948	0.013188179582357407	0.028321020305156708	0.004545231349766254	0.049500398337841034	0.10641832649707794	0.03525964915752411	0.09816458821296692	0.07990972697734833	0.002249916084110737	0.0059064822271466255	-0.04378931224346161	-0.05800633504986763	-0.014425233006477356	0.0009970724349841475	0.06517887115478516	0.012049769051373005	-0.06946724653244019	-0.027945412322878838	0.004414617083966732	-0.12947268784046173	0.021729568019509315	0.0652690976858139	-0.007871889509260654	0.0854463130235672	0.04475947469472885	0.016356613487005234	0.03600511699914932	0.04717959463596344	0.010151505470275879	0.011825173161923885	0.014509934931993484	0.010006982833147049	-0.045031093060970306	-0.043742407113313675	0.05482589453458786	0.04240591451525688	-0.010869220830500126	0.03897971287369728	0.05259627848863602	0.002926236018538475	-0.006142914295196533	-0.011134201660752296	-0.017160335555672646	-0.08465860038995743	-0.05942448973655701	0.02968706004321575	-0.06873201578855515	0.04310208559036255	0.03737189620733261	0.07278940826654434	0.038411736488342285	-0.017274770885705948	-0.0016183708794414997	-0.03356432914733887	-0.022003445774316788	0.010851913131773472	-0.0724576860666275	0.018674837425351143	0.027547650039196014	-0.01914292760193348	0.0015155774308368564	-0.04267187789082527	-0.01801641285419464	-0.01596950553357601	-0.034922949969768524	0.02959900163114071	0.07573401182889938	0.01787605695426464	-0.02721545472741127	0.035551320761442184	-0.06798114627599716	-0.005300261080265045	-0.009716430678963661	0.01741734892129898	-0.05795792490243912	0.03182923048734665	-0.06913243979215622	-0.04778791591525078	-0.013603241182863712	-0.0018906022887676954	-0.04735349491238594	-0.009054101072251797	-0.04112385958433151	-0.05349794775247574	-0.05745001509785652	0.030564531683921814	0.06357232481241226	-0.021881788969039917	0.05711865425109863	0.018954811617732048	0.06558937579393387	0.017393626272678375	0.00401132320985198	-0.08724141865968704	0.0021496948320418596	-0.008736995048820972	0.031054317951202393	0.029452862218022346	-0.07201194018125534	0.08213566988706589	0.027223140001296997	0.058684341609478	-0.08681036531925201	-0.01194101944565773	0.03703383356332779	-0.01091776043176651	0.07338862121105194	0.08600103110074997	-0.04162681847810745	-0.032104816287755966	0.02629394456744194	0.034945711493492126	-0.07352295517921448	-0.0028608867432922125	0.010035128332674503	-0.0482371561229229	-0.017291491851210594	-0.03143179789185524	-0.08093041926622391	-0.03284015506505966	0.01831991970539093	-0.01278790831565857	-0.03778602555394173	-0.04066629707813263	0.05591527000069618	-0.002938556019216776	-0.028377514332532883	-0.010712401010096073	0.013478259555995464	-0.049752116203308105	0.06486544758081436	-0.022159548476338387	-0.10061783343553543	-0.06363977491855621	-0.014344324357807636	0.022852616384625435	0.03933937847614288	0.021362194791436195	0.0332736037671566	0.09843231737613678	0.029214899986982346	-0.06668738275766373	0.007700562477111816	-0.016596674919128418	-0.022966137155890465	0.0056474413722753525	0.057380467653274536	-0.04061054065823555	-0.058140870183706284	0.01289015356451273	-0.07292458415031433	-0.01944315992295742	-0.011834083124995232	0.04845018312335014	-0.0014015575870871544	-0.07235456258058548	-0.01775754801928997	0.02523176558315754	-0.0014361325884237885	-0.01851409673690796	-0.03166680783033371	-0.034947242587804794	0.08418764173984528	-0.06317237764596939	-0.05098075047135353	-0.011270705610513687	0.005342189688235521	-0.002797051565721631	-0.06064912676811218	0.04142328351736069	-0.03606775403022766	-0.04068330302834511	-0.0146676329895854	-0.0491422563791275	-0.06494029611349106	-0.029888799414038658	0.002753081265836954	-0.02277964912354946	0.10173582285642624	0.0013030500849708915	-0.049590595066547394	-0.00397598696872592	-0.04336349666118622	-0.004770907107740641	-0.03657836839556694	0.012779703363776207	-0.011294588446617126	-0.05159097537398338	-0.0023162115830928087	0.04170557111501694	-0.023145467042922974	-0.011709986254572868	0.07535645365715027	9.719527588458732e-05	-0.03611128404736519	0.05077815428376198	-0.032739024609327316	-0.030696632340550423	-0.02504858560860157	-0.08651009202003479	0.01878279633820057	0.08614612370729446	0.02222011424601078	0.03887493535876274	0.029960697516798973	0.027956634759902954	0.08172955363988876	-0.005118721164762974	-0.038143258541822433	0.036244675517082214	-0.05823684111237526	-0.07945875823497772	0.06346169859170914	-0.01641097106039524	0.017056291922926903	-0.020849423483014107	0.007138127461075783	0.05657048150897026	-0.0016595785273239017	-0.005204035900533199	-0.02981697767972946	0.014279207214713097	-0.004259643144905567	0.009843643754720688	-0.003930542152374983	-0.0058617671020329	-0.005690522957593203	-0.061730340123176575	-0.1029522716999054	-0.049469321966171265	0.0506022609770298	0.011679728515446186	0.06813821941614151	-0.02409982495009899	-0.07047845423221588	0.018543485552072525	-0.0005199009319767356	0.1004096046090126	-0.0751325935125351	0.0050680553540587425	0.0079458337277174	0.045770421624183655	-0.0734398365020752	0.005613062996417284	0.01977504789829254	-0.04719587415456772	-0.07174373418092728	-0.00336312223225832	0.04413497820496559	-0.058397326618433	-0.018163051456212997	-0.0005216897116042674	-0.02358906902372837	0.0342971533536911	0.027136001735925674	-0.00925587397068739	0.06450708210468292	0.03143232315778732	-0.018085118383169174	0.04733025282621384	0.03548547998070717	0.03185954689979553	-0.04601339250802994	0.04437440261244774	-0.02938561700284481	0.11896965652704239	-0.002621722873300314	-0.028439458459615707	-0.09153547883033752	0.019075067713856697	-0.019855212420225143	0.0005206488422118127	-0.07414206117391586	-0.015151442028582096	0.0062600430101156235	0.016046306118369102	0.020688500255346298	-0.084596186876297	-0.006246240809559822	-0.05483311042189598	-0.06428005546331406	-0.06279823184013367	-0.055915914475917816	0.03225955367088318	-0.04339636117219925	0.0360872782766819	-0.04805067181587219	-0.013488145545125008	0.02734241634607315	-0.023962486535310745	0.008372521959245205	0.022320501506328583	0.04412367567420006	-0.02128678746521473	0.06338781863451004	-0.009049644693732262	0.05236516520380974	-0.017372390255331993	-0.037276335060596466	0.06203703209757805	0.060728076845407486	-0.03348029404878616	0.04284784942865372	-0.04889887943863869	0.011053805239498615	-0.015121332369744778	-0.022960085421800613	0.009830212220549583	0.02842782996594906	-0.029345225542783737	-0.06377172470092773	0.023137956857681274	-0.07119414955377579	-0.049893490970134735	0.045895062386989594	-0.05306868255138397	-0.07695100456476212	-0.04387296363711357	-0.03893909230828285	-0.02777125872671604	0.03741980344057083	0.01207214780151844	0.04986058175563812	0.04002120718359947	-0.031927209347486496	-0.005186344031244516	0.021477699279785156	-0.04594572260975838	0.0009585974621586502	-0.012466835789382458	0.06152626872062683	0.0286308191716671	0.0174171831458807	0.044237878173589706	-0.014327085576951504	0.02728063240647316	-0.06983362138271332	-0.004265477880835533	0.009327428415417671	-0.02916034869849682	-0.0031551753636449575	0.03632713109254837	0.031317856162786484	-0.06691189110279083	-0.007962021976709366	-0.06713271141052246	0.05057181045413017	0.018469925969839096	0.05539952963590622	0.019292693585157394	-0.04019835591316223	0.0033318737987428904	-0.012324529699981213	0.009640373289585114	-0.0824342742562294	0.022007431834936142	-0.06480805575847626	0.00836145505309105	-0.018134858459234238	0.006664304528385401	0.033505622297525406	-0.10604789108037949	0.014012421481311321	-0.011572184972465038	-0.03412175923585892	0.021912498399615288	0.019383126869797707	-0.022797459736466408	-0.05157775804400444	-0.005553259048610926	-0.05813750624656677	0.052675262093544006	0.016408409923315048	0.030710458755493164	-0.03780650347471237	-0.020810933783650398	0.006826036609709263	0.014345834031701088	0.03350261598825455	-0.03193214535713196	-0.08784497529268265	0.006678740028291941	0.10343201458454132	-0.023816822096705437	0.017690634354948997	0.052145954221487045	-0.018134603276848793	0.0339001789689064	0.0021052390802651644	-0.03682170808315277	0.02834627777338028	-0.014706706628203392	0.001329049002379179	0.04757988825440407	-0.04969208687543869	0.06635654717683792	-0.040282074362039566	0.08038730174303055	0.008424127474427223	-0.00262811197899282	0.10399208962917328	0.06198018789291382	0.013637261465191841	0.042220789939165115	-0.03858012333512306
+0.025477338582277298	-0.039370302110910416	0.02089177444577217	-0.015834517776966095	-0.07135546952486038	-0.042540840804576874	0.04473195597529411	0.006422932259738445	0.007199607323855162	-0.021329812705516815	-0.03495839238166809	-0.001494969823397696	0.015982089564204216	0.01710551045835018	0.04005417972803116	-0.04466450214385986	-0.040693722665309906	0.019915426149964333	-0.04814482107758522	-0.019040951505303383	0.045250169932842255	-0.0014506917214021087	-0.043574489653110504	0.03023471310734749	-0.019855549558997154	-0.006547365803271532	-0.06148844212293625	0.010758754797279835	-0.006519792601466179	0.0011318374890834093	0.07630430907011032	-0.01325696799904108	0.04003902152180672	-0.05980173498392105	-0.03243749216198921	0.02107928693294525	-0.04293267801403999	0.09447038173675537	0.008833540603518486	-0.028609350323677063	-0.030331850051879883	0.009701071307063103	-0.046025440096855164	-0.014427275396883488	0.04326445609331131	0.009576407261192799	0.024773286655545235	-0.01778292842209339	0.025801794603466988	0.00828295387327671	0.07533975690603256	0.03968008980154991	-0.03328187018632889	-0.03173850104212761	-0.009384524077177048	-0.0026429174467921257	0.050958842039108276	-0.06501975655555725	-0.0806959792971611	0.06328736245632172	-0.09475293010473251	0.014611349441111088	0.013512550853192806	-0.019893024116754532	-0.009736238978803158	-0.048021070659160614	-0.022293120622634888	0.08075954765081406	-0.006944002117961645	-0.020842043682932854	0.028997894376516342	0.10627976804971695	-0.0725422203540802	-0.08808838576078415	0.07553550601005554	-0.041452351957559586	0.026427512988448143	0.04279386252164841	-0.04717385396361351	-0.012341598980128765	-0.06518132984638214	-0.05458242446184158	0.10421115905046463	-0.05863034725189209	-0.0622573122382164	-0.07124128937721252	-0.0006620963686145842	-0.052067939192056656	0.0473371259868145	0.046932727098464966	0.01741645112633705	0.02583232894539833	-0.04562436044216156	0.051154088228940964	0.007070700637996197	-0.023347312584519386	-0.0014737910823896527	-0.10467642545700073	-0.045415040105581284	-0.007227292750030756	0.034927867352962494	-0.019286472350358963	0.055007342249155045	0.007611632347106934	0.03833513334393501	0.05865898355841637	-0.049546148627996445	0.04104900360107422	0.017878349870443344	0.07701773196458817	0.003864484606310725	0.08134371042251587	0.05630894750356674	0.05172407999634743	0.035991840064525604	-0.01150534488260746	-0.016370059922337532	-0.048427339643239975	0.0058630467392504215	0.011528009548783302	0.030872253701090813	0.019375337287783623	-0.005614998750388622	-0.0635400265455246	-0.03447785973548889	0.06398548185825348	-0.10819436609745026	-0.019964976236224174	0.06487998366355896	-0.037656020373106	0.10724764317274094	0.04317593201994896	-0.038040343672037125	0.03842306137084961	0.057172127068042755	0.017781812697649002	-0.003162299282848835	0.018723513931035995	-0.02868187613785267	-0.08118237555027008	-0.02993905544281006	0.055773552507162094	0.004903675056993961	-0.042735371738672256	0.0351739227771759	0.04236852750182152	0.06247866526246071	-0.057424504309892654	-0.06441441178321838	0.013771343044936657	-0.09094952791929245	-0.09284725785255432	0.0042388527654111385	-0.0644650012254715	0.004680676851421595	0.015594725497066975	-0.006513991393148899	0.03277895972132683	0.009003503248095512	-0.012127591297030449	-0.054694708436727524	-0.0577830970287323	-0.07313679158687592	-0.05191750079393387	0.0031277902889996767	-0.0019314937526360154	0.04126753658056259	0.0103029515594244	-0.0393407829105854	-0.06030290946364403	0.01345754973590374	0.0005917911184951663	0.017111357301473618	0.03934914246201515	0.03303252160549164	-0.0015810331096872687	0.016779020428657532	-0.03579774126410484	-0.0017601882573217154	-0.03797226771712303	0.02065381035208702	-0.01664324663579464	0.04068055748939514	-0.0708184614777565	0.017666080966591835	-0.017428843304514885	0.02574867755174637	-0.07051067799329758	-0.017653705552220345	-0.04929925501346588	-0.029746195301413536	-0.07855448126792908	0.0267194714397192	0.07526732981204987	-0.031149808317422867	-0.0030899641569703817	-0.02013000287115574	0.037078093737363815	-0.023177897557616234	-0.026654701679944992	-0.04450313374400139	0.04273396357893944	-0.005139884073287249	0.010368738323450089	-0.029218070209026337	-0.08722137659788132	0.05107233673334122	-0.01888812519609928	0.03160768002271652	-0.05091007426381111	0.02446695603430271	0.02353280410170555	-0.013108080253005028	0.0072809141129255295	0.06343600898981094	-0.04220367968082428	0.004251721315085888	-0.007078062742948532	0.09332401305437088	-0.019871456548571587	0.04406328499317169	0.00246185390278697	-0.08210841566324234	0.008233784697949886	-0.05540591478347778	-0.0998670905828476	-0.01692487858235836	0.011748719960451126	-0.025112207978963852	-0.028503606095910072	-0.028757369145751	0.010313606821000576	0.0028329864144325256	-0.00750725669786334	0.02652391791343689	0.018858255818486214	-0.07286936789751053	0.07550505548715591	-0.0006042924942448735	-0.0490758903324604	-0.034036777913570404	-0.03221919760107994	0.045031700283288956	-0.001779181300662458	0.028264785185456276	0.007417794317007065	0.09608612954616547	0.022318782284855843	-0.023849304765462875	-0.0015887734480202198	0.001409337273798883	-0.04645316302776337	-0.001419705688022077	0.02263396605849266	-0.06474193930625916	-0.03023575060069561	0.026920732110738754	-0.04980731010437012	-0.0014111086493358016	0.00042498059337958694	0.02551928348839283	-0.013510869815945625	-0.03595270588994026	0.004997629206627607	0.04543645307421684	-0.013166243210434914	-0.056904468685388565	0.013465428724884987	-0.006300073582679033	0.0648512914776802	0.006925210822373629	-0.022305551916360855	0.017549123615026474	-0.022522414103150368	0.03914300724864006	-0.06880148500204086	0.03371012583374977	-0.018920186907052994	-0.09503057599067688	-0.038931433111429214	-0.04544192552566528	-0.07972422987222672	0.009406371042132378	-0.03458646312355995	-0.039830565452575684	0.06476344913244247	0.007688834797590971	-0.05856676399707794	-0.0634334608912468	-0.056436795741319656	-0.016913339495658875	-0.03191174194216728	-0.042076531797647476	-0.0316033810377121	-0.04618425667285919	-0.0031140956562012434	0.048707593232393265	-0.06297680735588074	-0.029835248365998268	-0.0005888406885787845	0.00591088505461812	-0.052069395780563354	0.04714798927307129	-0.04890169948339462	-0.0034415749832987785	-0.007536239456385374	-0.05350203439593315	0.03995385766029358	0.08896857500076294	0.0597228929400444	0.050224192440509796	0.009701820090413094	-0.003197501180693507	0.10906796157360077	0.0013529073912650347	-0.05555029213428497	-0.014745175838470459	-0.051136601716279984	-0.08377667516469955	0.0009952967520803213	-0.04558531939983368	-0.028945857658982277	0.000664390972815454	-0.0023857231717556715	0.05754565820097923	-0.031803641468286514	-0.011311547830700874	-0.021729687228798866	0.03769805282354355	-0.028340844437479973	-0.02188703790307045	0.009807415306568146	0.02879713475704193	-0.0031136195175349712	-0.05403503403067589	-0.04412390664219856	-0.007530387956649065	0.0323503240942955	-0.02837018482387066	0.06222805008292198	-0.06218435987830162	-0.057408154010772705	-0.01474571693688631	0.037807341665029526	0.10867280513048172	-0.028236614540219307	0.032404765486717224	0.032865747809410095	0.015927676111459732	-0.07920634746551514	0.03597037121653557	0.02586122788488865	-0.005444462411105633	-0.01843130774796009	0.016619941219687462	0.05202986299991608	-0.05502935126423836	-0.0035437874030321836	0.009366762824356556	-0.010533593595027924	-0.01403333991765976	0.022552713751792908	-0.009683437645435333	0.04366869851946831	-0.03888869658112526	0.019814101979136467	-0.0023032589815557003	0.028279347345232964	0.05018434301018715	-0.012265590950846672	0.016102472320199013	-0.029438965022563934	0.057106420397758484	-0.01776600070297718	-0.04803017899394035	-0.07080523669719696	0.0660143494606018	-0.025039933621883392	-0.0152296032756567	-0.02199799381196499	0.0027157652657479048	-0.002604434732347727	-0.0008154841489158571	-2.5561068468960002e-05	-0.07636116445064545	0.048110924661159515	-0.024197760969400406	-0.06206541508436203	-0.08958107233047485	-0.058620233088731766	-0.00385095551609993	-0.05026210844516754	-0.025671623647212982	-0.0061386749148368835	0.024876846000552177	-0.0005184956826269627	-0.023743899539113045	0.005777797661721706	0.06006108224391937	0.033776186406612396	0.005693863146007061	0.055399827659130096	0.03143120929598808	0.049047667533159256	-0.018709596246480942	-0.06271478533744812	0.03181973844766617	0.021178703755140305	-0.0601467601954937	0.030780069530010223	-0.04119620472192764	0.03245449811220169	-0.015771297737956047	0.024850280955433846	0.01391240768134594	-0.035575397312641144	-0.016443924978375435	-0.03852308541536331	0.0866134762763977	-0.07245754450559616	-0.05498390272259712	0.05707301199436188	-0.0577240027487278	-0.0765007734298706	0.01829158142209053	-0.029153624549508095	-0.038640573620796204	0.03577012941241264	-0.051183536648750305	0.0411546528339386	0.0004644088912755251	-0.03357592597603798	-0.040052060037851334	-0.017635317519307137	-0.049580298364162445	0.03183791786432266	0.015609031543135643	0.11093689501285553	0.024497373029589653	-0.01669522374868393	0.0051744673401117325	-0.03025946021080017	0.04363342747092247	-0.13668473064899445	0.01639103703200817	-0.07942033559083939	0.021731335669755936	-0.020022446289658546	-0.03538540005683899	0.0583631694316864	-0.0638037696480751	-0.0026583357248455286	-0.044063083827495575	0.08033966273069382	-0.0332157239317894	-0.007891693152487278	0.020243613049387932	-0.006878538988530636	-0.02599656768143177	0.017166849225759506	-0.052623070776462555	-0.07002866268157959	0.015023562125861645	-0.044833600521087646	0.04198068380355835	0.043325114995241165	0.004050794988870621	0.010747474618256092	-0.0575866624712944	-0.02960793673992157	0.03593515604734421	-0.03896321728825569	0.02954634092748165	0.036635685712099075	-0.061660829931497574	-0.061029378324747086	0.008903422392904758	-0.03192131966352463	0.05997229367494583	-0.008071305230259895	0.038760874420404434	-0.025409908965229988	0.001881696400232613	-0.006416020914912224	-0.013730989769101143	0.05520026385784149	-0.0023498605005443096	-0.10279015451669693	0.00041578919626772404	0.07391613721847534	-0.05834948271512985	0.020510151982307434	0.06503444910049438	-0.04626207426190376	0.0341653935611248	0.026614580303430557	-0.05558840185403824	0.01721913553774357	-0.02714480645954609	0.015253799967467785	0.012805934064090252	-0.10798240453004837	0.09206321835517883	-0.055068790912628174	0.07734650373458862	0.02181006222963333	-0.007681171875447035	0.10013345628976822	0.07018665969371796	-0.012074708007276058	-0.03365832567214966	-0.09036075323820114
+0.04974745959043503	-0.012028916738927364	0.001579237636178732	0.042157165706157684	-0.06911596655845642	-0.05529918149113655	-0.03842950612306595	-0.008058994077146053	0.023892289027571678	-0.02761896140873432	-0.03131391853094101	-0.03000405803322792	-0.016371218487620354	0.01500878855586052	0.05694463104009628	-0.024680597707629204	-0.07502221316099167	0.026043152436614037	-0.04372924566268921	0.01882767677307129	0.061849046498537064	-0.011212963610887527	-0.0793847143650055	0.044934339821338654	-0.02666858583688736	0.048175934702157974	-0.05027827247977257	0.018289128318428993	-0.015029914677143097	-0.006068319547921419	0.04266536608338356	-0.001678332919254899	0.024936288595199585	-0.08087190985679626	-0.04257550090551376	0.044227007776498795	-0.006283941678702831	0.10072973370552063	0.04108654707670212	-0.03825054317712784	-0.07475167512893677	0.010792586952447891	-0.09166333824396133	-0.0902126282453537	0.01795578934252262	-0.020262422040104866	0.024257991462945938	-0.020246844738721848	0.03736087679862976	0.047129180282354355	0.07710570096969604	-0.015879442915320396	-0.0495029017329216	-0.019644442945718765	-0.027828490361571312	0.004261726513504982	0.05672827363014221	-0.0777943879365921	-0.04342751204967499	0.043009914457798004	-0.08480983227491379	0.0018184840446338058	-0.008755248039960861	0.029320169240236282	-0.037921883165836334	-0.03290485218167305	0.023042405024170876	0.07266382873058319	0.010325589217245579	-0.03959719091653824	0.005349467042833567	0.07633379101753235	-0.06291856616735458	-0.03985942527651787	0.018502309918403625	-0.080299511551857	-0.04269622266292572	0.0371205173432827	-0.0019760297145694494	-0.002896880265325308	0.012886208482086658	-0.04522355645895004	0.13196100294589996	-0.017643973231315613	-0.05541079863905907	-0.05218637362122536	-0.008410539478063583	-0.035309337079524994	0.01669389009475708	0.05286845564842224	0.010521607473492622	0.0867539495229721	-0.039323244243860245	0.056422315537929535	0.02263239398598671	0.052431318908929825	0.03201071545481682	-0.12227655202150345	-0.03969084843993187	-0.02972775511443615	-0.009488849900662899	-0.010327656753361225	0.029483167454600334	0.007713806815445423	0.05531725287437439	-0.005007381085306406	-0.06990979611873627	-0.011787659488618374	0.003587619634345174	0.040933649986982346	0.05219632014632225	0.06333227455615997	0.06840824335813522	0.08190786838531494	0.049523696303367615	-0.008563938550651073	0.03236335143446922	-0.06122448295354843	-0.026866722851991653	0.022445393726229668	0.006699270568788052	0.01440069917589426	-0.008581027388572693	-0.05533084645867348	-0.0034747424069792032	0.02121463045477867	-0.10500681400299072	-0.04353317990899086	-0.0011909001041203737	-0.03799765184521675	0.10296440124511719	0.07752038538455963	-0.039904315024614334	-0.02350561134517193	0.0617036297917366	0.03652756288647652	0.0017120020929723978	0.03185693547129631	-0.015269177965819836	-0.01830974780023098	-0.009824848733842373	0.033682044595479965	-0.017464064061641693	-0.04930737987160683	0.0578855536878109	0.033115558326244354	0.017792973667383194	-0.026006236672401428	-0.07148587703704834	0.0019590153824537992	-0.08576125651597977	-0.026756247505545616	0.022477462887763977	-0.10193245112895966	0.01993606984615326	0.05417095497250557	0.03844725340604782	0.05837201699614525	-0.027420252561569214	-0.02417517453432083	-0.04319207742810249	-0.054951947182416916	0.00011973157234024256	-0.08443926274776459	-0.060110703110694885	-0.043101850897073746	0.009065925143659115	0.0020756933372467756	-0.01889035664498806	-0.038698192685842514	-0.013480393216013908	0.0005587379564531147	0.04526420310139656	0.048214804381132126	0.06309577077627182	-0.014169483445584774	-0.014639859087765217	-0.05245882645249367	0.011639785021543503	-0.026976199820637703	0.004633202683180571	-0.03219812735915184	-0.011849019676446915	-0.07240413129329681	-0.021420247852802277	0.013325558044016361	0.0010124279651790857	-0.0008820206858217716	-0.0013450321275740862	-0.0006517188739962876	-0.07121746242046356	-0.05109846219420433	0.05565238371491432	0.09409105777740479	-0.02913518436253071	0.0467662438750267	-0.03810161352157593	0.05485016107559204	-0.009552904404699802	-0.032363537698984146	-0.0712895467877388	0.03299841284751892	-0.03153176233172417	0.0793677419424057	0.008495990186929703	-0.06460333615541458	0.07070667296648026	0.034337498247623444	0.022617105394601822	-0.0470133014023304	0.001632970874197781	0.02507035806775093	-0.0032060618977993727	0.027376027777791023	0.07605689764022827	-0.030985182151198387	-0.027015039697289467	0.041907377541065216	0.05640079081058502	-0.04550706967711449	0.025577958673238754	0.025317545980215073	-0.0802532434463501	0.03001932054758072	-0.02283843420445919	-0.06413444876670837	-0.04203523322939873	0.023884713649749756	-0.03389820083975792	-0.0025817370042204857	0.011078188195824623	0.0018412554636597633	-0.007143063936382532	-0.04022054001688957	0.005952159408479929	-0.013897831551730633	-0.0971297174692154	0.09144308418035507	0.012998241931200027	-0.07870881259441376	-0.057654883712530136	0.02425469271838665	0.035992302000522614	0.0019309952622279525	-0.004900300409644842	0.040056079626083374	0.08765870332717896	0.03728032857179642	0.005128536373376846	0.015792280435562134	-0.07734263688325882	-0.09943588078022003	0.016278613358736038	0.02886146679520607	-0.02599438838660717	-0.03533109650015831	-0.006037768442183733	-0.03233485296368599	-0.0009859991259872913	0.0056639015674591064	0.02699529565870762	-0.014503797516226768	-0.017643915489315987	-0.029090749099850655	0.07336779683828354	-0.03754792734980583	-0.029387837275862694	0.022261422127485275	-0.0007167780422605574	0.05855504795908928	-0.02845459245145321	-0.049000080674886703	0.05143477022647858	-0.006625973153859377	0.0305135790258646	-0.004694618284702301	0.05205918848514557	-0.012795777060091496	-0.07607214897871017	-0.020847199484705925	-0.11486058682203293	-0.037057116627693176	-0.005764137487858534	0.02023637667298317	-0.050539951771497726	0.10316243022680283	0.02293858490884304	-0.04252801835536957	-0.04293854907155037	-0.02016240544617176	-0.020447323098778725	-0.05856681987643242	-0.009336492046713829	0.003509227652102709	-0.09985601902008057	-0.03881765156984329	0.056961964815855026	-0.04929886385798454	-0.031944431364536285	-0.01880050078034401	-0.06027248129248619	-0.07050193101167679	0.024184107780456543	-0.012637929059565067	-0.042871180921792984	0.0022549109999090433	-0.07337801903486252	0.028970105573534966	0.08717729151248932	0.01786940172314644	0.038715001195669174	0.0246264711022377	0.0062356023117899895	0.07826594263315201	-0.019989263266324997	-0.029036710038781166	-0.035229817032814026	-0.05413122475147247	-0.06669045239686966	0.07303695380687714	-0.04536303132772446	0.006258917506784201	-0.012102539651095867	0.027007605880498886	0.07557108998298645	-0.025752339512109756	-0.01512423437088728	-0.003040989162400365	0.004262960981577635	0.006361483596265316	0.018637455999851227	-0.01639642007648945	-0.0012046026531606913	-0.051158469170331955	-0.07888741046190262	-0.06818556785583496	-0.01864766888320446	0.00378281413577497	-0.007363464683294296	0.03664456680417061	-0.015801886096596718	-0.08113127946853638	0.0026325711514800787	0.001953710801899433	0.11931245774030685	-0.05643293261528015	0.01523328572511673	0.04919116571545601	0.013199010863900185	-0.06576022505760193	-0.01340547576546669	0.04027123004198074	-0.05762583389878273	-0.0506155751645565	0.002587602473795414	0.021123571321368217	-0.06646531820297241	-0.0105034951120615	0.00750745041295886	-0.03370046988129616	0.031475819647312164	0.03778994083404541	0.012406541034579277	-0.00024817537632770836	-0.011674659326672554	-0.03778433799743652	0.006792580708861351	0.005897230934351683	0.025684339925646782	-0.047838836908340454	0.011265740729868412	-0.024079740047454834	0.053762711584568024	-0.026777971535921097	-0.02816741354763508	-0.05842595547437668	0.05227968841791153	0.01119470875710249	-0.04052945598959923	-0.00881644431501627	-0.020044155418872833	-0.02480805478990078	0.04797298461198807	0.031503308564424515	-0.04791795834898949	-0.06368259340524673	-0.0627845823764801	-0.03536779806017876	-0.0997878909111023	-0.017695797607302666	0.00784618966281414	-0.05430058389902115	0.009679488837718964	-0.03112582303583622	-0.017686523497104645	0.02411147579550743	-0.04630450904369354	0.014146392233669758	0.029821285977959633	0.016266144812107086	-0.04315157234668732	0.009574636816978455	-0.03560252860188484	0.04717537760734558	0.03014262393116951	-0.03203234076499939	0.007999291643500328	0.045783236622810364	-0.047587085515260696	0.04082857072353363	-0.03949836269021034	0.01118371170014143	0.01440872810781002	0.008683626540005207	0.004642813000828028	0.017896661534905434	0.010377583093941212	-0.03149750828742981	0.028114384040236473	-0.08961223065853119	-0.046419039368629456	0.012204292230308056	-0.053495679050683975	-0.09720553457736969	-0.029124150052666664	0.019728876650333405	-0.022862084209918976	0.008770477958023548	-0.02444915845990181	0.04055692255496979	0.034187570214271545	-0.07184344530105591	-0.002826107433065772	-0.028836634010076523	-0.01886814460158348	0.05005979910492897	-0.016231875866651535	0.058105915784835815	0.001458796439692378	-0.00034105675877071917	0.038138248026371	0.03339851647615433	-0.017165465280413628	-0.07971692085266113	-0.035058680921792984	0.01211338210850954	-0.00591960409656167	0.014570346102118492	0.0028606103733181953	0.01840689405798912	-0.0559106282889843	-0.012547077611088753	-0.025182081386446953	0.10176803171634674	-0.0002799487265292555	0.004888100083917379	0.05103868618607521	-0.030390284955501556	0.026223277673125267	0.016776764765381813	-0.0310114286839962	-0.08120639622211456	-0.014861691743135452	-0.037765394896268845	0.027251070365309715	0.03739988058805466	0.004260491579771042	-0.0053195045329630375	-0.05519583821296692	0.018127890303730965	0.0040055206045508385	-0.06795569509267807	0.006469212472438812	0.03164737671613693	-0.0214510727673769	-0.056621212512254715	0.0003473820397630334	-0.07177840173244476	0.04798388481140137	0.025167416781187057	0.05742912366986275	-0.013493388891220093	0.005900762975215912	0.0029215621761977673	-0.0254734605550766	0.026031499728560448	0.015469283796846867	-0.07563167065382004	0.025224480777978897	0.06974093616008759	-0.0486251637339592	0.017498835921287537	0.05898132175207138	0.0017943363636732101	0.034775033593177795	0.03313339129090309	-0.038590990006923676	0.011243317276239395	-0.03728611394762993	-0.04878351464867592	0.06878156960010529	-0.07440679520368576	0.07553880661725998	-0.06127941235899925	0.10959075391292572	-0.006033794023096561	-0.032078295946121216	0.079345703125	0.07912394404411316	-0.0064621251076459885	0.046237606555223465	-0.028727902099490166
+0.017038829624652863	-0.004805784206837416	0.026816673576831818	0.03655153140425682	-0.09031940996646881	-0.04596344754099846	-0.0103262634947896	0.05917137488722801	-0.005464916117489338	-0.018360817804932594	-0.010479504242539406	-0.008204459212720394	0.010833028703927994	0.020771503448486328	0.0254660714417696	-0.06003422662615776	-0.0779682993888855	-0.005542764440178871	-0.05338359996676445	-0.043873000890016556	0.04428340867161751	0.02009439654648304	-0.08304707705974579	0.019879860803484917	-0.005921436008065939	-0.0017492510378360748	-0.06727488338947296	0.02623818628489971	0.002903128508478403	0.016075341030955315	0.09619253128767014	-0.003576918039470911	0.04627010226249695	-0.05748571828007698	-0.037255510687828064	0.04612216725945473	-0.05354719236493111	0.09167240560054779	0.0066908602602779865	-0.02643267624080181	-0.02611648105084896	0.0018966844072565436	0.030617859214544296	-0.012222860008478165	0.019776491448283195	-0.0093822181224823	-0.009893352165818214	-0.042326223105192184	0.027245718985795975	-0.018971998244524002	0.11053778976202011	-0.00034411641536280513	0.027157116681337357	-0.01757504604756832	-0.004582368303090334	0.011545256711542606	0.020917948335409164	-0.04414268210530281	-0.07888022810220718	0.024551978334784508	-0.08551033586263657	0.016504498198628426	-0.011065312661230564	-0.004056828562170267	-0.027750596404075623	-0.00913461297750473	-0.03692617639899254	0.09195201098918915	0.022295933216810226	-0.009873571805655956	0.04174085706472397	0.09143298864364624	-0.07382041960954666	-0.09231851994991302	0.06217578426003456	-0.047814518213272095	-0.010334965772926807	0.03563618287444115	-0.03132277727127075	0.010387358255684376	-0.0034608482383191586	-0.03807036578655243	0.09569413959980011	-0.03682392090559006	-0.0333264023065567	-0.034908492118120193	-0.015340885147452354	-0.05417947098612785	0.0850737988948822	0.059032127261161804	0.03073495626449585	0.08291836827993393	-0.042253900319337845	0.03687926009297371	0.02332749031484127	0.014524642378091812	0.027664680033922195	-0.11203360557556152	-0.031664781272411346	0.011164628900587559	0.02859128825366497	0.004503834061324596	0.0940052717924118	0.0005266568041406572	0.0528777614235878	0.07299797236919403	-0.03588753193616867	-0.0008450561435893178	0.020615916699171066	0.08431174606084824	0.016389383003115654	0.12428166717290878	0.0688306987285614	0.04850750416517258	-0.0020505248103290796	-0.01747415028512478	-0.020370006561279297	-0.0221380777657032	-0.04938056692481041	0.01602969504892826	-0.013806815259158611	0.0404234416782856	-0.05216380953788757	-0.05880322679877281	-0.03837226703763008	0.05456950142979622	-0.07148973643779755	-0.009211058728396893	0.09597001224756241	-0.01752343215048313	0.09840279817581177	0.06464540958404541	0.02454303205013275	0.054089467972517014	0.10010907799005508	0.053766679018735886	0.017385385930538177	0.011441781185567379	-0.02108217217028141	-0.025098424404859543	-0.04835709556937218	0.034392256289720535	0.00783372949808836	-0.05300033837556839	0.002846610965207219	0.06620721518993378	0.021406710147857666	-0.022477371618151665	-0.03036615438759327	0.02045910432934761	-0.041283879429101944	-0.09157418459653854	-0.01691831648349762	-0.06474164128303528	0.030249113216996193	0.058108601719141006	-0.0011635235277935863	0.03026219829916954	-0.011415960267186165	-0.006338781211525202	-0.025537176057696342	-0.07889427244663239	-0.02323942258954048	-0.04394252598285675	-0.007826593704521656	-0.035199154168367386	0.0033553768880665302	-0.003929188009351492	-0.029253166168928146	-0.026085278019309044	-0.0022153928875923157	-0.01178035233169794	-0.009528424590826035	0.0934242308139801	0.008173109032213688	0.010995937511324883	0.04270050674676895	-0.021098541095852852	-0.024046186357736588	-0.06914715468883514	-0.009920813143253326	-0.030012037605047226	0.02664356864988804	-0.08384263515472412	-0.021131830289959908	0.016305964440107346	0.056559186428785324	-0.02317091077566147	0.006708938162773848	-0.012325634248554707	0.0023989614564925432	-0.03424090892076492	0.0307709202170372	0.05293392017483711	-0.02051471918821335	0.05757232755422592	-0.01666492223739624	0.03847508132457733	-0.008432591333985329	-0.04869156703352928	-0.05461398512125015	0.053571246564388275	0.026694582775235176	0.007782625500112772	0.006490727420896292	-0.05582144483923912	0.07252408564090729	0.023267053067684174	0.030191069468855858	-0.06844986230134964	-0.007089906372129917	0.0369473397731781	0.04336165636777878	0.05639338493347168	0.06285922229290009	-0.0468270368874073	-0.012287415564060211	0.007855862379074097	0.04769686609506607	-0.04473969712853432	0.03311964496970177	0.0166678037494421	-0.060309793800115585	0.06446211785078049	-0.034839458763599396	-0.10428279638290405	-0.030919071286916733	-0.012880595400929451	-0.019726719707250595	-0.02528783679008484	0.004330012481659651	0.03253241255879402	-0.026139436289668083	-0.019666096195578575	-0.005618364550173283	-0.008966144174337387	-0.07817602157592773	0.11106441915035248	-0.016250675544142723	-0.05633348226547241	-0.07730422168970108	0.010594098828732967	0.0167042538523674	0.023505639284849167	0.012073519639670849	0.023670727387070656	0.1228245422244072	0.04079800471663475	-0.016322975978255272	0.02818923257291317	-0.05800250172615051	-0.06499606370925903	-0.002869012765586376	0.011497185565531254	-0.051426101475954056	-0.01881306804716587	0.02765699103474617	-0.06024086847901344	0.019799474626779556	-0.030626026913523674	0.0474231094121933	-0.013838476501405239	0.002027283888310194	-0.013887595385313034	0.08111359924077988	-0.03948531299829483	-0.09192893654108047	0.038577232509851456	-0.014495809562504292	0.05798523128032684	-0.013767201453447342	-0.04188728705048561	0.0295382272452116	0.019212450832128525	0.03734184801578522	-0.04211345687508583	0.05980939790606499	-0.04509741812944412	-0.08972494304180145	-0.014530331827700138	-0.06299836188554764	-0.044699475169181824	-0.006840245798230171	-0.05293025076389313	-0.05391507223248482	0.08289185166358948	-0.01705755479633808	-0.03830977901816368	-0.028474953025579453	-0.027192311361432076	0.00802508182823658	-0.01815401017665863	-0.03121861442923546	0.00149970140773803	-0.0508657805621624	-0.018208200111985207	0.06811374425888062	-0.09267687052488327	-0.042114634066820145	0.043607182800769806	-0.035102035850286484	-0.014342560432851315	0.058873388916254044	-0.08575287461280823	0.0270695798099041	0.006472887005656958	-0.04603531211614609	0.04471427947282791	0.0927722305059433	0.03456849977374077	0.031320057809352875	0.05510106682777405	0.013035074807703495	0.10661260038614273	0.054631344974040985	-0.06742129474878311	0.010736956261098385	-0.05305016040802002	-0.06626810878515244	0.023032093420624733	-0.03618612512946129	-0.02998233027756214	-0.012493104673922062	-0.005953144747763872	0.07337567210197449	-0.014310156926512718	-0.012347178533673286	0.0016102511435747147	0.03715784102678299	-0.014026745222508907	0.015708092600107193	0.03468118607997894	-0.031136415898799896	-0.0061118751764297485	-0.04273708537220955	-0.02908233180642128	-0.05395437404513359	0.03944064676761627	-0.03508022427558899	0.023181432858109474	-0.02837703749537468	-0.05151838809251785	0.02095077745616436	0.005682977847754955	0.09202596545219421	-0.06383158266544342	0.03213777765631676	0.015065165236592293	0.023953836411237717	-0.08851280808448792	-0.009113158099353313	0.024937843903899193	-0.022386545315384865	-0.039754800498485565	0.02895302139222622	0.020843790844082832	-0.05950583517551422	0.009440670721232891	0.01615745946764946	-0.0553421713411808	-0.002163201803341508	0.0385434627532959	-0.03095651976764202	0.03225567936897278	0.019749222323298454	-0.02037295512855053	-0.015359224751591682	0.035935673862695694	0.013621320016682148	-0.03315843641757965	0.04849988594651222	-0.08210135996341705	0.06742507219314575	0.002012003445997834	-0.0719081237912178	-0.06785646826028824	0.04812171310186386	0.0004132860922254622	-0.014240855351090431	-0.0302136167883873	0.012228230945765972	-0.008403494954109192	0.02615860290825367	-0.0031279195100069046	-0.08673587441444397	0.006363397464156151	-0.02194494754076004	-0.06111829727888107	-0.0765477642416954	-0.06601536273956299	0.029884107410907745	-0.07536027580499649	0.005444708280265331	-0.01646319590508938	0.03142042085528374	-0.010771943256258965	-0.04823355749249458	0.05016425624489784	0.04150207340717316	0.042588625103235245	-0.005698889959603548	0.04975636675953865	0.0355670265853405	0.03387238830327988	-0.006864418741315603	-0.023958152160048485	0.05573110654950142	0.04905169829726219	-0.019544262439012527	0.0280794408172369	-0.02370058186352253	0.040932852774858475	0.0009299669764004648	-0.025951648131012917	0.021939704194664955	-0.005999419838190079	-0.03317910432815552	-0.04501381888985634	0.04756367951631546	-0.04773624986410141	-0.026494814082980156	0.03931863233447075	-0.04922327771782875	-0.08695054054260254	0.0009860086720436811	-0.029374362900853157	-0.04159022867679596	0.036683183163404465	0.025506289675831795	0.023141421377658844	-0.0016750351060181856	-0.04268844798207283	-0.012623326852917671	-0.01562550850212574	0.01780896820127964	0.025427594780921936	0.006095452234148979	0.06507308781147003	-0.009478847496211529	-0.030700117349624634	0.02923545055091381	-0.03975299373269081	0.04314475134015083	-0.10001400858163834	-0.003381642745807767	-0.012778394855558872	-0.009334908798336983	0.014705442823469639	-0.0022491367999464273	0.026379022747278214	-0.08440528810024261	-0.02331451326608658	-0.036030709743499756	0.050388120114803314	-0.028742745518684387	0.029985014349222183	0.0002549003984313458	-0.033894702792167664	-0.02245827578008175	-0.015963900834321976	-0.017095312476158142	-0.04925911873579025	0.044064100831747055	-0.04750090837478638	0.018866581842303276	0.004060877487063408	0.025336384773254395	-0.02019546739757061	-0.12502382695674896	-0.02605527453124523	0.01521212887018919	-0.04910546541213989	0.01314798928797245	-0.003823267761617899	-0.059734974056482315	-0.05702674388885498	-0.02226184494793415	-0.034179702401161194	0.07008423656225204	-0.0012108496157452464	0.002117430791258812	-0.04117759317159653	-0.014469131827354431	0.003243820508942008	-0.04602137953042984	-0.0032156645320355892	-0.02855069190263748	-0.06443795561790466	-0.04788188263773918	0.05572837218642235	-0.03418651595711708	0.011553154326975346	0.07996657490730286	-0.0331091545522213	0.0151930321007967	0.006404595449566841	-0.012813313864171505	0.000298659666441381	-0.017582230269908905	0.0034014780540019274	0.022161956876516342	-0.08393187075853348	0.08345773816108704	-0.06260590255260468	0.05991513282060623	0.027490487322211266	-0.009511933661997318	0.06603141129016876	0.09101361781358719	-0.001533963019028306	-0.003803481115028262	-0.0525931790471077
+0.011035604402422905	-0.003019123338162899	0.02539399452507496	0.01697932928800583	-0.07928023487329483	-0.07949209958314896	0.001346887438558042	0.019326331093907356	-0.012633186765015125	-0.07259121537208557	-0.061930544674396515	-0.046662587672472	-0.012131065130233765	-0.02345386892557144	0.007222308311611414	-0.008019552566111088	-0.048150353133678436	0.025550885125994682	-0.0002419260999886319	-0.036188554018735886	0.021201305091381073	0.01233469508588314	-0.0789995789527893	0.022353239357471466	0.03973492607474327	-0.05160611867904663	-0.04945402219891548	0.007496196310967207	-0.0011522653512656689	0.06485936790704727	0.027867237105965614	-0.017406508326530457	0.06182139739394188	-0.06981044262647629	-0.01550284679979086	0.03042992576956749	-0.015216529369354248	0.06032729521393776	0.022850340232253075	-0.031541988253593445	-0.021560270339250565	0.04404279589653015	0.001405562972649932	-0.07754435390233994	0.015476047061383724	0.017383666709065437	-0.05779072269797325	-0.013173871673643589	0.01484087947756052	0.04779663309454918	0.09103535860776901	-0.02295702137053013	-0.05213586613535881	-0.026570608839392662	0.014416918158531189	-0.049150899052619934	0.04926254600286484	-0.021465647965669632	-0.03556329384446144	0.04434022679924965	-0.0820639356970787	0.0002030265750363469	0.06666930764913559	0.004775941837579012	-0.026496877893805504	-0.016220159828662872	0.024401215836405754	0.07972066104412079	-0.004143280442804098	-0.03777141869068146	0.0333532951772213	0.06376665830612183	-0.03547628968954086	-0.03785388544201851	0.058789629489183426	-0.05634365230798721	0.05705443397164345	0.0898536741733551	0.023637942969799042	0.022059567272663116	0.013632837682962418	-0.05545588582754135	0.1146671250462532	-0.06782908737659454	-0.005154472775757313	-0.0507286973297596	0.007100770715624094	-0.04755832254886627	0.0534578412771225	0.01851324364542961	0.028376884758472443	0.01978566125035286	-0.030160758644342422	0.010985573753714561	-0.04526780918240547	-0.010828819125890732	0.03570358455181122	-0.09550482034683228	-0.03567193076014519	0.06330364942550659	-0.001591130392625928	-0.04312301054596901	0.008200647309422493	0.015536002814769745	0.0061821406707167625	0.036644190549850464	0.03491785004734993	0.04476584866642952	0.08577372878789902	0.05844488739967346	0.022427059710025787	0.04760599508881569	0.1184866726398468	0.03995787352323532	0.02801559492945671	0.017086714506149292	-0.018413960933685303	-0.04219355061650276	0.03280492126941681	0.05307129770517349	0.004485962446779013	0.02027924731373787	-0.015580710023641586	-0.056434888392686844	0.01589583419263363	0.05831508710980415	-0.050687987357378006	-0.018639886751770973	0.01670614629983902	0.023336950689554214	0.11438920348882675	-0.027421150356531143	-0.042749278247356415	0.056850966066122055	0.053507376462221146	0.07596676051616669	0.034154802560806274	0.046243663877248764	-0.005143499933183193	-0.09443270415067673	-0.008427541702985764	0.022891448810696602	-0.013054252602159977	-0.03596120700240135	0.0435258224606514	0.041721150279045105	0.03956170752644539	-0.025215893983840942	-0.06525585055351257	-0.012714719399809837	-0.08001084625720978	-0.07650743424892426	-0.01028754748404026	-0.07763722538948059	0.011490574106574059	0.012073041871190071	0.006472339853644371	0.00015059327415656298	-0.05801571533083916	0.047598764300346375	-0.0671113058924675	-0.05787055194377899	-0.02107149548828602	-0.06541736423969269	-0.0253219585865736	0.015492076054215431	0.004378545563668013	0.0516587495803833	-0.09642461687326431	-0.061295319348573685	-0.0110250785946846	-0.04015803337097168	0.06061505153775215	0.07434805482625961	0.04276968911290169	-0.00796603411436081	0.07744908332824707	-0.0688629224896431	-0.02657822147011757	-0.04530409350991249	0.042258795350790024	-0.03109322115778923	0.09079518169164658	-0.04423733800649643	-0.002641953993588686	-0.011621796526014805	0.019718948751688004	-0.07694990187883377	0.028975244611501694	0.016834134235978127	-0.06165793165564537	-0.02506847493350506	0.03932426497340202	0.07531425356864929	0.016544846817851067	0.04590224474668503	-0.017814267426729202	0.07176689058542252	-0.01197874266654253	-0.0409138984978199	-0.08273725211620331	0.014403458684682846	0.026287319138646126	0.05396043881773949	0.007378149311989546	-0.11432769894599915	0.061585672199726105	0.06299790740013123	0.004415769129991531	-0.03201833739876747	0.051719825714826584	0.04124036058783531	0.021202392876148224	0.03583918884396553	0.05110837519168854	0.004399123135954142	0.03604748472571373	-0.02363293431699276	0.060626354068517685	-0.020089808851480484	0.04480947181582451	0.028461068868637085	-0.08358906954526901	0.010794157162308693	-0.08182878792285919	-0.0628436878323555	-0.025957660749554634	-0.013401338830590248	-0.03190486878156662	-0.03649703785777092	0.012751189060509205	0.04410611838102341	-0.01956823840737343	0.010754706338047981	-0.014659170992672443	0.010965103283524513	-0.06296399235725403	0.0735025554895401	-0.03156835213303566	-0.05138310790061951	-0.043109580874443054	-0.02150600217282772	0.0015967877116054296	0.013938253745436668	0.05111519619822502	0.03110404498875141	0.07138431072235107	0.0005049171741120517	0.012512062676250935	-0.05331943556666374	-0.021101925522089005	-0.05227518081665039	-0.03218057006597519	-0.002689989050850272	-0.07135850191116333	-0.03913084790110588	-0.008858464658260345	-0.09475596994161606	-0.016273247078061104	0.007858077995479107	-0.026688838377594948	0.010720997117459774	-0.030821409076452255	-0.03339491784572601	0.03425358608365059	0.010067674331367016	-0.06341054290533066	0.04272795468568802	-0.022565225139260292	0.03319668397307396	-0.00996578298509121	-0.0018483622698113322	0.017584985122084618	-0.04746664687991142	-0.008474518544971943	-0.0515323206782341	0.0796704888343811	-0.024242330342531204	-0.02500084601342678	0.00010175845090998337	-0.027991391718387604	-0.04484366998076439	0.001058523659594357	-0.016687897965312004	0.012551816180348396	0.03551282361149788	0.04024820402264595	-0.04644421115517616	-0.04131842404603958	-0.03758416324853897	-0.0569925457239151	-0.06982097774744034	-0.05439985543489456	-0.011692679487168789	-0.016855239868164062	0.015795446932315826	0.03020179644227028	-0.00940080638974905	-0.041984912008047104	0.04583483561873436	-0.003308422164991498	-0.07207668572664261	0.038209181278944016	0.0018127674702554941	-0.05117117241024971	0.063956119120121	-0.031395073980093	0.014530200511217117	0.07982409000396729	0.027350282296538353	0.001334466738626361	0.05999700352549553	0.027572069317102432	0.0790715143084526	-0.020451528951525688	-0.04456328973174095	-0.023348545655608177	-0.09801071882247925	-0.0790705680847168	0.019924655556678772	-0.00010097122139995918	-0.007818260230123997	0.016748912632465363	-0.008006368763744831	0.04827127978205681	-0.028918316587805748	-0.026512136682868004	-0.0462338924407959	0.001758130849339068	-0.050847578793764114	0.004899473395198584	0.016318075358867645	0.04065734148025513	-0.023102307692170143	-0.04257367551326752	-0.01204188447445631	-0.04348921403288841	0.049464866518974304	-0.03088393621146679	0.02636747993528843	-0.05122224986553192	-0.0006838087574578822	-0.05332963913679123	0.012727171182632446	0.08676628023386002	-0.03546976298093796	0.010881072841584682	0.01869342289865017	0.040530670434236526	-0.09554656594991684	0.011870874091982841	0.011582854203879833	-0.014691400341689587	0.019741646945476532	-0.05590425059199333	0.015028766356408596	-0.07782074064016342	0.014759590849280357	0.019099729135632515	0.0005753933219239116	0.009054507128894329	0.035221878439188004	-0.000698684249073267	0.06319254636764526	-0.010004555806517601	0.006995949428528547	-0.027596740052103996	-0.034831978380680084	-0.02468208782374859	0.03294648602604866	0.023337945342063904	-0.02811666950583458	-0.013503178022801876	-0.011578723788261414	-0.052046019583940506	-0.056062571704387665	0.03375855088233948	-0.048944421112537384	0.009192212484776974	0.028177535161376	0.02570413425564766	0.02118653431534767	0.015213321894407272	0.01860923320055008	-0.04224695637822151	0.018477503210306168	-0.0401579812169075	-0.04356757923960686	-0.06956138461828232	-0.0004765494086313993	0.01075997669249773	-0.05431551858782768	0.01005680300295353	-0.0522197000682354	0.026827415451407433	-0.0060387952253222466	0.008763096295297146	-0.0034097684547305107	0.06554652005434036	0.02306043915450573	0.02074986882507801	0.01850777678191662	-0.04605140537023544	0.04184863343834877	0.044196147471666336	-0.09154714643955231	-0.01146052498370409	0.001696965773589909	-0.011869988404214382	0.04672298580408096	-0.04186644032597542	-0.027563096955418587	-0.01104089617729187	-0.01679840125143528	0.021373143419623375	-0.04189641773700714	0.0012758842203766108	0.01794464699923992	0.057048991322517395	0.007232521194964647	-0.09073865413665771	0.013794813305139542	0.006770285777747631	-0.10491462051868439	-0.0006542779156006873	-0.02127332054078579	-0.028261147439479828	0.04164007678627968	-0.05766332894563675	0.06440208107233047	0.017884407192468643	-0.03890969976782799	-0.04311913996934891	-0.021335294470191002	-0.016228660941123962	0.034192994236946106	0.005373071413487196	0.039085663855075836	0.004819553345441818	-0.00026726629585027695	0.02709336020052433	-0.06580428779125214	0.038752321153879166	-0.06628736853599548	0.02394324168562889	-0.050928302109241486	0.0001387492084177211	0.031710442155599594	-0.009204530157148838	0.01797986403107643	-0.034034378826618195	-0.08004920184612274	-0.07653737813234329	0.040734369307756424	-4.503955278778449e-05	0.004277677275240421	0.041947975754737854	-0.09278001636266708	-0.045401427894830704	0.06424060463905334	-0.03827989101409912	-0.07452888786792755	-0.019400568678975105	-0.044727303087711334	0.055279441177845	0.06027369946241379	0.07465539872646332	-0.04029637202620506	-0.06551316380500793	0.015564382076263428	0.03535018488764763	-0.03645063191652298	-0.004755151923745871	0.028041861951351166	-0.037740208208560944	-0.05701940879225731	-0.030264977365732193	-0.04720187559723854	0.061749450862407684	0.07753214985132217	0.010494561865925789	-0.05506044253706932	0.028549781069159508	0.03735635429620743	-0.040309205651283264	0.027234258130192757	0.01723836548626423	-0.05234844982624054	-0.012137382291257381	0.15018926560878754	-0.027316998690366745	0.061834078282117844	0.04615817964076996	-0.06236520782113075	0.02036363072693348	0.04170014336705208	-0.01420142874121666	0.06842275708913803	-0.010372745804488659	-0.029234008863568306	0.03755956143140793	-0.06834939867258072	0.07779911160469055	-0.03267311677336693	0.09548220783472061	0.0121758496388793	-0.034849684685468674	0.058824118226766586	0.03749392554163933	-0.009705545380711555	0.002141804900020361	-0.08854222297668457
+0.024708978831768036	-0.0820334181189537	-0.013907495886087418	0.05390428006649017	-0.0698109120130539	-0.04771481826901436	-0.013635753653943539	0.035926464945077896	-0.02120964229106903	-0.07284638285636902	-0.012822955846786499	-0.027021700516343117	-0.01081413310021162	0.026467936113476753	0.05392146483063698	-0.023494645953178406	-0.04772511124610901	0.05003061145544052	-0.042169537395238876	-0.030213605612516403	0.025460615754127502	0.021856190636754036	-0.04622882604598999	0.054925642907619476	-0.008161994628608227	0.013936004601418972	-0.05158950388431549	0.023509986698627472	0.007238956168293953	0.04518847540020943	0.035813555121421814	-0.02742227166891098	0.03159954398870468	-0.04979380592703819	-0.06525876373052597	0.0606655552983284	-0.0630711019039154	0.13845223188400269	0.005399886518716812	-0.016239328309893608	-0.05621422827243805	-0.0011417377972975373	-0.022741030901670456	-0.07185271382331848	0.03906606510281563	-0.01727086305618286	0.015956567600369453	-0.025568237528204918	0.006297013722360134	0.06065965071320534	0.10433384776115417	0.03628821671009064	0.019430214539170265	0.0038763766642659903	-0.02576717920601368	0.004086052533239126	0.017226915806531906	-0.03526325151324272	-0.03694432973861694	0.04493391141295433	-0.08628590404987335	-0.04455601051449776	0.017236661165952682	0.015763191506266594	-0.00963777769356966	-0.011333509348332882	-0.016905879601836205	0.08023083955049515	0.004572663456201553	-0.027841711416840553	-0.018289443105459213	0.08200910687446594	-0.03389958664774895	-0.08909621089696884	0.03310265764594078	-0.052324287593364716	0.021758917719125748	0.011547798290848732	-0.005148948170244694	0.007271636743098497	-0.008259251713752747	-0.01935502141714096	0.13970701396465302	-0.031977392733097076	-0.012541692703962326	-0.012842155061662197	0.001152080949395895	-0.053440239280462265	0.043643396347761154	0.019093697890639305	0.02229732647538185	0.05699000880122185	-0.03310377523303032	0.04815800115466118	0.002156222937628627	-0.004060719162225723	0.032598864287137985	-0.12619662284851074	-0.00677256565541029	-0.0062981718219816685	-0.01563260331749916	-0.012323549948632717	0.07275941222906113	0.01788182742893696	0.07178611308336258	0.02937791496515274	-0.05711911991238594	0.010938726365566254	0.038010064512491226	0.01524757407605648	0.0012835855595767498	0.111658476293087	0.06781893223524094	0.02493865415453911	0.04142788052558899	-0.005826594773679972	-0.004064368549734354	-0.05550538748502731	-0.06026630103588104	-0.0031725475564599037	0.007219442632049322	0.04818521812558174	-0.03918532282114029	-0.053732726722955704	-0.005758672021329403	0.03334754332900047	-0.11569718271493912	-0.007373907137662172	0.07986776530742645	-0.02619524486362934	0.10785917937755585	0.05170006677508354	-0.00852876715362072	0.05203906446695328	0.05472402647137642	0.020791249349713326	-0.002108643064275384	0.022377479821443558	0.011166004464030266	-0.027374478057026863	-0.005274367518723011	0.06542845815420151	0.010765092447400093	-0.04029009863734245	0.024789152666926384	0.05733467638492584	0.016354940831661224	-0.03797252103686333	-0.08011427521705627	0.03176986798644066	-0.07454463839530945	-0.08088361471891403	-0.00589391216635704	-0.0700564756989479	0.008539199829101562	0.010513011366128922	0.0272989459335804	0.03961682692170143	-0.012811911292374134	-0.01841423101723194	-0.06559612601995468	-0.06030290573835373	-0.0034094916190952063	-0.06733386218547821	-0.008707752451300621	-0.0031031023245304823	-0.02678230032324791	-0.004500456620007753	-0.057715415954589844	-0.05297595262527466	0.003981234505772591	-0.012904197908937931	0.03483618050813675	0.06867692619562149	0.050312455743551254	0.023198451846837997	0.008682268671691418	-0.0434894934296608	-0.027460265904664993	-0.006065274588763714	0.015295792371034622	-0.030786486342549324	0.02397928014397621	-0.06773003190755844	-0.01077386923134327	-0.010812785476446152	0.019408974796533585	-0.031250905245542526	-0.0014836250338703394	-0.01257970742881298	-0.027781028300523758	-0.052585601806640625	0.0362711139023304	0.06504055112600327	-0.03238338977098465	0.07843706756830215	-0.035161156207323074	0.08629026263952255	-0.05449512600898743	-0.03554239124059677	-0.03365210071206093	0.06217433139681816	0.017414219677448273	0.015175234526395798	-0.0035060292575508356	-0.06885580718517303	0.07326023280620575	0.03557921200990677	0.01916678249835968	-0.06751113384962082	0.03723067790269852	0.02485893853008747	0.018129074946045876	0.06177907437086105	0.042813077569007874	-0.02156767249107361	-0.009616853669285774	0.01981584168970585	0.03301070258021355	-0.05951441451907158	0.009667226113379002	-0.005150508135557175	-0.07107794284820557	0.00880566518753767	-0.03381112590432167	-0.08207257091999054	-0.03490332141518593	0.041876330971717834	-0.016633689403533936	-0.044758088886737823	0.0003635767207015306	0.03686822950839996	-0.024917470291256905	-0.02197818085551262	0.0031815015245229006	-0.02884744666516781	-0.08205562084913254	0.07128655910491943	0.008662015199661255	-0.08088476210832596	-0.07696793228387833	-0.035181351006031036	0.009984225034713745	0.008692587725818157	0.03672987222671509	0.0702437087893486	0.07289683073759079	0.05201386287808418	-0.026532938703894615	0.01619303598999977	-0.0388239324092865	-0.023953929543495178	-0.013383403420448303	0.0020322338677942753	-0.01801462657749653	-0.037658337503671646	-0.005831445101648569	-0.09903708845376968	-0.026339713484048843	-0.025690414011478424	0.03520369529724121	-0.05938144773244858	-0.013886751607060432	0.009631454013288021	0.021584605798125267	-0.038492731750011444	-0.03884842246770859	-0.003705994924530387	-0.026006300002336502	0.06192683428525925	-0.061801452189683914	-0.003891533240675926	0.035596270114183426	-0.011892864480614662	0.025975534692406654	-0.0430402047932148	0.054882872849702835	-0.037133391946554184	-0.08938940614461899	-0.006444470025599003	-0.07617735862731934	-0.07582749426364899	-0.027693331241607666	-0.033476270735263824	-0.022379055619239807	0.10031772404909134	0.023256510496139526	-0.0319732241332531	-0.051373664289712906	-0.06075349077582359	-0.0056101116351783276	-0.04650804027915001	0.03124121017754078	-0.019645821303129196	-0.030770793557167053	-0.006434581708163023	0.010421659797430038	-0.04940437898039818	-0.0009508185321465135	0.014175410382449627	0.01331760361790657	-0.039333853870630264	0.07977011799812317	-0.03470060974359512	-0.005923720542341471	0.004892438184469938	-0.06504157930612564	0.04019364342093468	0.08992526680231094	0.044568903744220734	0.011013460345566273	0.06558658182621002	0.03717987239360809	0.07927501946687698	0.029475629329681396	0.003982660360634327	-0.01964675262570381	-0.02417300082743168	-0.047862522304058075	0.0609411858022213	-0.028881564736366272	0.003761834232136607	0.01814914122223854	-0.01597677730023861	0.06111091747879982	-0.0049595097079873085	-0.07740119099617004	-0.011703566648066044	0.038007378578186035	-0.008406108245253563	0.01465913187712431	0.004041633568704128	0.018713954836130142	-0.0022470077965408564	-0.03754802048206329	-0.04506199061870575	-0.0329241119325161	0.05600981041789055	-0.056564249098300934	0.03785434365272522	-0.023206263780593872	-0.04226134344935417	-0.005892002955079079	0.007040750235319138	0.12497704476118088	-0.05753067880868912	0.00863735657185316	0.05837835744023323	0.05162317305803299	-0.12057028710842133	0.011127732694149017	-0.01661684550344944	-0.040783386677503586	-0.08414687216281891	0.026272308081388474	0.047596365213394165	-0.08973512053489685	-0.058339789509773254	-0.040921952575445175	-0.008845623582601547	0.006289991550147533	0.03440435603260994	0.002954928670078516	0.049772217869758606	-0.005292462185025215	-0.059824034571647644	0.008742352947592735	0.006368069909512997	0.029583832249045372	-0.08975045382976532	0.024926360696554184	-0.007363150827586651	0.05590330436825752	-0.029612086713314056	-0.06314632296562195	-0.03731238469481468	0.01687685213983059	-0.005289230961352587	-0.028712108731269836	-0.04744598641991615	-0.034206826239824295	-0.002185856457799673	0.005922545213252306	0.023211749270558357	-0.04234381392598152	-0.04080142825841904	-0.029983211308717728	-0.06052069365978241	-0.07313291728496552	-0.058022934943437576	0.018837857991456985	-0.03575124964118004	0.03886345028877258	-0.04037601500749588	-0.020015614107251167	-0.002587111433967948	-0.014415695331990719	0.04124007374048233	0.06419423967599869	0.01932680420577526	-0.02448912151157856	0.05698158219456673	-0.005305658094584942	0.01682732254266739	0.01657562330365181	-0.04194711148738861	0.0214226171374321	0.0013747462071478367	-0.06558635830879211	0.036600224673748016	-0.03473934158682823	0.022791080176830292	-0.001987054944038391	-0.03586762771010399	0.020556770265102386	-0.0023017036728560925	-0.007303705904632807	-0.0439099557697773	0.05568403750658035	-0.07568097114562988	-0.006069813389331102	-0.0135769322514534	-0.04857271909713745	-0.08440417051315308	-0.050301212817430496	-0.039642948657274246	-0.012246808037161827	0.041342902928590775	-0.0031380660366266966	0.013516735285520554	0.014864564873278141	-0.051996029913425446	-0.02960813418030739	-0.02421603724360466	-0.005512634292244911	0.009822358377277851	0.028987085446715355	0.03229944035410881	-0.0028831332456320524	0.004509792663156986	0.004091168288141489	-0.010097969323396683	0.01415292639285326	-0.06367094069719315	-0.006179553922265768	-0.010326940566301346	-0.010300843045115471	0.02328462153673172	0.028810398653149605	0.028837228193879128	-0.07475647330284119	-0.0043107084929943085	-0.03199099376797676	0.06338158249855042	0.027191346511244774	0.021661939099431038	0.018339861184358597	-0.05184716731309891	-0.016442786902189255	0.03282048925757408	-0.04140182584524155	-0.07203008234500885	0.007893357425928116	-0.014798332005739212	0.06014294549822807	-0.014076360501348972	0.04409303516149521	-0.03889406472444534	-0.09426113218069077	0.0214201919734478	-0.02247188426554203	-0.05768235772848129	-0.022578155621886253	0.04080537334084511	-0.08651604503393173	-0.05090600624680519	0.00913534127175808	-0.061295609921216965	0.04888450726866722	-0.003430956741794944	0.014747265726327896	-0.057762302458286285	-0.012166711501777172	-0.0379444845020771	-0.039144158363342285	0.03706693649291992	-0.0020233693066984415	-0.05026393011212349	-0.03451888635754585	0.08108200877904892	-0.029393568634986877	0.036602020263671875	0.08308444172143936	-0.011381524614989758	0.044830698519945145	0.038045432418584824	-0.00282489275559783	0.04981602728366852	0.0075789764523506165	-0.011675882153213024	0.047156792134046555	-0.0656830370426178	0.06286147236824036	-0.06974521279335022	0.12942488491535187	0.026161503046751022	0.018067657947540283	0.0773741602897644	0.07107646018266678	0.03654779493808746	0.025642164051532745	-0.08964800834655762
+-0.0437428243458271	-0.09392964094877243	-0.07120588421821594	0.014872635714709759	0.009852949529886246	0.007233824115246534	0.01955236867070198	0.04855118319392204	0.03539668023586273	-0.04943825677037239	-0.022061053663492203	0.014999640174210072	0.04003400355577469	-0.02274390123784542	0.03975825756788254	0.024833930656313896	0.011434288695454597	-0.007224378176033497	0.04035082831978798	-0.005987413693219423	-0.022067364305257797	0.07788075506687164	0.0005328040570020676	0.06578410416841507	0.05781399458646774	-0.0202247966080904	-0.009768609888851643	0.0720292255282402	0.013426332734525204	0.10522468388080597	0.0373486690223217	-0.01886424608528614	0.021932873874902725	-0.0295536071062088	0.05878184735774994	0.00727404560893774	-0.04674484208226204	0.08658818155527115	0.02096208930015564	0.01784510724246502	0.0640229880809784	-0.04675620049238205	0.03118930011987686	-0.047740668058395386	0.004576993640512228	-0.018840471282601357	-0.004151506349444389	-0.08306749910116196	-0.0023718662559986115	0.06312879920005798	0.07642657309770584	-0.014015862718224525	-0.0020427368581295013	-0.03425131365656853	-0.04684680327773094	0.026416366919875145	-0.022749602794647217	-0.02068028226494789	-0.008911249227821827	0.02165043354034424	-0.006186977494508028	0.004126854706555605	-0.00903063453733921	-0.009925506077706814	-0.025381222367286682	0.028706779703497887	0.028491085395216942	-0.016779273748397827	-0.01497417502105236	-0.0245527233928442	0.01316548977047205	0.03450194001197815	-0.07487171143293381	-0.057758625596761703	-0.0008895635255612433	0.033835481852293015	0.06876478344202042	-0.007474525365978479	0.009101259522140026	-0.004139534663408995	-0.0009877667762339115	-0.009622441604733467	0.03227600082755089	-0.07724794745445251	0.06396223604679108	-0.04618343338370323	-0.011892559006810188	-0.02745840512216091	0.00016600779781583697	-0.02287069708108902	0.046952761709690094	-0.03391193598508835	0.05905507877469063	-0.008733365684747696	0.032612960785627365	-0.053267691284418106	0.024746309965848923	-0.08973117917776108	-0.011365188285708427	0.036117471754550934	-0.04462429881095886	0.02430236153304577	0.12005427479743958	0.049491725862026215	0.01566334441304207	0.0559253916144371	-0.05511922016739845	0.06333006918430328	0.08364762365818024	0.09638293087482452	-0.07032895088195801	0.02801474928855896	0.029481662437319756	-0.03630060330033302	-0.002567947143688798	-0.058410510420799255	-0.03655783087015152	0.08420158177614212	-0.005514624062925577	0.019473852589726448	0.011610427871346474	0.03531545773148537	-0.03446159511804581	-0.04918031767010689	0.001955458428710699	0.08501147478818893	-0.034869853407144547	-0.004033288452774286	-0.00971041526645422	-0.027023935690522194	0.0554225817322731	-0.014503004029393196	0.009553560987114906	0.0016026126686483622	-0.003927652258425951	-0.0007665042066946626	-0.002183085773140192	0.01518412958830595	0.06532283127307892	0.001497462042607367	-0.013258880004286766	0.0011843646643683314	0.026631422340869904	0.003315760986879468	-0.016758348792791367	0.017874309793114662	-0.049446914345026016	-0.011907360516488552	-0.017758402973413467	-0.05073245242238045	-0.033666398376226425	-0.03727664798498154	0.058515653014183044	0.007600793614983559	-0.010694547556340694	-0.07253360003232956	0.03398318588733673	0.03555070981383324	0.04860448092222214	0.06530255824327469	-0.07705465704202652	-0.03513358160853386	-0.0008526326855644584	-0.010820794850587845	0.04832138121128082	-0.009294352494180202	0.05305076390504837	-0.10044905543327332	0.035052862018346786	-0.028871610760688782	-0.048932794481515884	0.07469019293785095	0.024732626974582672	0.027124794200062752	0.060124799609184265	0.03460276871919632	0.0434272326529026	-0.013115542940795422	-0.016748279333114624	-0.04316764697432518	-0.040276508778333664	0.012229850515723228	-0.03720200061798096	0.0380062460899353	-0.04009997472167015	0.0023800653871148825	0.02611183375120163	0.004190796986222267	-0.03582174703478813	0.009350134059786797	0.04971728101372719	-0.06213325634598732	0.04024527966976166	0.023061204701662064	-0.015439477749168873	0.04373420774936676	0.051602255553007126	0.0967341810464859	-0.048174623399972916	-0.021765878424048424	0.015991991385817528	0.028109688311815262	0.07971443980932236	-0.0614326111972332	0.0031424376647919416	0.01958044245839119	0.02749439887702465	0.00025728929904289544	0.0007946608238853514	-0.02896752767264843	0.008978077210485935	-0.011298676952719688	-0.04592868313193321	0.008954167366027832	0.03999209403991699	-0.012055217288434505	-0.048198211938142776	-0.054871879518032074	-0.022931749001145363	-0.04421041160821915	0.020733870565891266	-0.010805425234138966	-0.07395239174365997	0.02116008847951889	-0.061863429844379425	-0.060093414038419724	0.05392478406429291	-0.04459800943732262	-0.004029469098895788	0.004146582446992397	-0.009252442978322506	-0.008699512109160423	0.026189977303147316	-0.012153522111475468	-0.014070521108806133	0.0679057389497757	-0.01856018789112568	0.0036322614178061485	-0.08554813265800476	0.0045990291982889175	-0.059629470109939575	0.013704122975468636	-0.0017726871883496642	-0.05420508235692978	0.025981100276112556	0.027675576508045197	0.01975259743630886	0.08109475672245026	-0.05174042657017708	0.05268179625272751	-0.028943495824933052	0.047927893698215485	-0.057550325989723206	-0.020811621099710464	0.004454419948160648	-0.01600581221282482	0.05211711674928665	-0.1411607563495636	-0.04705028980970383	-0.018811620771884918	0.007179108913987875	-0.04184494540095329	0.0009223305387422442	-0.005953305866569281	0.02170049212872982	-0.0008654732955619693	-0.09020788222551346	0.0733252614736557	-0.03629568964242935	-0.02395470440387726	-0.06745833903551102	0.03799283877015114	0.011629913002252579	0.03770497441291809	-0.004706409759819508	-0.04827912524342537	0.009508111514151096	-0.07687269896268845	-0.09286975860595703	-0.04919781535863876	0.0032807060051709414	-0.028889603912830353	0.029117252677679062	-0.023626647889614105	0.038734350353479385	-0.053051892668008804	0.015087790787220001	0.019379016011953354	-0.07744704186916351	-0.06495094299316406	-0.022365782409906387	-0.0156547874212265	0.006029040552675724	-0.059184469282627106	0.008567376993596554	-0.003397636581212282	0.0202354546636343	-0.006190613843500614	0.04575009271502495	0.02426476776599884	0.05946769565343857	-5.856527423020452e-05	0.0403088703751564	-0.0745486319065094	-0.03573213517665863	0.030301488935947418	-0.029905840754508972	0.038633864372968674	0.04626556858420372	0.04696979373693466	0.02541658841073513	0.06259835511445999	0.03311028331518173	-0.014671807177364826	-0.024146970361471176	0.03710586205124855	-0.011882688850164413	0.08495280891656876	-0.016111960634589195	-0.08286362141370773	0.031577009707689285	-0.08282456547021866	0.02183462865650654	-0.053065910935401917	0.011281249113380909	0.04130150005221367	-0.0627274438738823	-0.03541064262390137	0.06917781382799149	-0.015238559804856777	-0.0036966134794056416	0.009055690839886665	0.048910073935985565	0.033468566834926605	0.0027103242464363575	0.027513261884450912	-0.06529112160205841	0.05518826097249985	-0.039600830525159836	0.07380792498588562	0.032437119632959366	-0.006310788448899984	-0.0390414297580719	0.03256755322217941	0.06442492455244064	-0.04757072404026985	0.03129158169031143	0.038918256759643555	-0.051409073173999786	-0.06248017027974129	-0.006267423275858164	-0.024796513840556145	-0.03591042384505272	-0.030405301600694656	0.019514499232172966	0.0020680269226431847	0.03950100392103195	-0.0885993167757988	-0.007935465313494205	-0.0403408445417881	-0.01552200224250555	0.06007307395339012	-0.023614132776856422	0.016277959570288658	-0.00468801474198699	-0.013713615946471691	-0.027532363310456276	-0.009616550989449024	0.0005035243812017143	-0.02916775271296501	-0.023571982979774475	0.014709305949509144	-0.03894207999110222	-0.001706320676021278	-0.008457478135824203	-0.057001225650310516	0.008586019277572632	-0.015444973483681679	-0.0007407700759358704	-0.06544242054224014	-0.02076033316552639	0.03427574038505554	0.0139907356351614	-0.029716061428189278	-0.023386966437101364	-0.01981055550277233	0.025790361687541008	-0.017071204259991646	-0.009032396599650383	-0.096912682056427	0.0323534794151783	0.01788187213242054	-0.000594937358982861	-0.07229690253734589	0.05939004570245743	-0.05912353843450546	0.055429402738809586	0.04943501949310303	0.059329044073820114	0.04897361993789673	0.011536600068211555	0.06562766432762146	0.009078585542738438	0.0027061649598181248	-0.02394409105181694	-0.03007235936820507	0.027837861329317093	-0.04633787274360657	-0.018127236515283585	-0.1013747900724411	-0.04228014498949051	-0.025425182655453682	0.02266881614923477	-0.08726785331964493	0.04376645013689995	-0.010589688085019588	0.029645057395100594	-0.029899394139647484	0.024967674165964127	-0.051450371742248535	0.08742621541023254	-0.018211353570222855	-0.0004105523112230003	0.08145595341920853	-0.012626037001609802	-0.09049174934625626	0.008585535921156406	0.04158376529812813	-0.07466427981853485	-0.013742553070187569	-0.047307662665843964	-0.022342516109347343	-0.04156966507434845	0.01249927468597889	0.055942993611097336	-0.02501317672431469	0.1174626499414444	0.0042075421661138535	-0.06074198707938194	-0.04132886230945587	-0.06039877608418465	-0.0941796824336052	0.05656808242201805	-0.006867398973554373	0.01994577795267105	-0.048627033829689026	0.056267961859703064	-0.012699075043201447	0.028859330341219902	0.11034666746854782	-0.027917390689253807	-0.029863251373171806	-0.016958756372332573	0.03927365690469742	0.027415147051215172	0.039506617933511734	-0.010470809414982796	-0.04460105299949646	-0.10970713198184967	0.014781850390136242	-0.04684869572520256	-0.04740038514137268	-0.07286394387483597	0.023800838738679886	0.06853815913200378	-0.03706611692905426	-0.006963727064430714	-0.1053333505988121	-0.020875941962003708	-0.04674115777015686	0.013767671771347523	0.014797566458582878	-0.0526435486972332	0.0579504668712616	-0.055295735597610474	0.04074764996767044	0.0029805528465658426	-0.007189467083662748	0.05590125545859337	-0.020718226209282875	-0.014015207067131996	-0.056440748274326324	-0.037991996854543686	-0.027645885944366455	-0.01633547991514206	0.025682590901851654	-0.025253325700759888	-0.02908036857843399	-0.09706678241491318	0.048121377825737	0.07433107495307922	0.051204387098550797	0.055585239082574844	-0.04297013580799103	0.061730679124593735	0.06396742910146713	0.041764017194509506	-0.07046094536781311	-0.04079852253198624	0.005813035648316145	0.057228926569223404	-0.04024868085980415	0.049859676510095596	-0.058350104838609695	0.09397821128368378	0.01883549988269806	0.019006697461009026	0.02402031607925892	0.046647809445858	0.11826911568641663	-0.03437943756580353	-0.07568065077066422
+-0.015188600867986679	-0.10361302644014359	-0.08623034507036209	0.017489902675151825	-0.010063081048429012	-0.03758369758725166	-0.01773030310869217	0.04906617850065231	-0.010156631469726562	-0.06483469903469086	-0.05490504950284958	-0.023500457406044006	-0.028280118480324745	-0.061639633029699326	0.05851306393742561	0.03230588138103485	-0.05653005093336105	0.014130780473351479	-0.008330360986292362	-0.002281659282743931	0.013093356974422932	0.05196264013648033	-0.040263328701257706	0.05817599222064018	0.02042408287525177	-0.024251049384474754	-0.0005663749761879444	0.012305772863328457	-0.014955754391849041	0.09305804967880249	0.05708847939968109	-0.024501973763108253	0.014790918678045273	-0.025718828663229942	-0.006837776862084866	0.05616945028305054	-0.014903596602380276	0.08796201646327972	0.034206487238407135	-0.020215751603245735	0.03190060704946518	-0.058358997106552124	-0.016270073130726814	-0.09863050282001495	0.05345398187637329	0.0012896371772512794	-0.04568575695157051	-0.09423799067735672	-0.0079794405028224	0.09552010893821716	0.11087729781866074	-0.03848110884428024	0.010374648496508598	0.028935102745890617	-0.08502606302499771	-0.018792908638715744	0.030243657529354095	-0.04403559863567352	0.01320154033601284	0.024618927389383316	-0.03847327455878258	-0.027299467474222183	0.024438444525003433	0.06114055961370468	-0.04411745443940163	0.007272439543157816	0.03016442246735096	0.01333628874272108	-0.009945554658770561	-0.018472716212272644	-0.04929228127002716	-0.008201268501579762	-0.0147897033020854	-0.05413780361413956	-0.0070085711777210236	0.0035242168232798576	-0.014283777214586735	-0.02769550122320652	0.02458546683192253	0.012297684326767921	0.06300386786460876	0.004158141557127237	0.09777078032493591	-0.06879639625549316	0.030970033258199692	-0.04443398118019104	-0.03413449600338936	-0.02616793103516102	0.04227275401353836	-0.0044153714552521706	0.04150991886854172	0.05592263489961624	0.022699479013681412	0.0010590765159577131	-0.006527952384203672	-0.006243713665753603	0.015270019881427288	-0.08634431660175323	-0.04592882841825485	-0.004249333404004574	-0.01515688095241785	0.004271346144378185	0.08245101571083069	0.05286295711994171	0.019465772435069084	0.004267226438969374	-0.04695866256952286	-0.008853285573422909	0.04422299191355705	0.03493887558579445	-0.04167142137885094	0.0854094997048378	0.04497101530432701	-0.011273815296590328	0.07878760993480682	-0.03394342213869095	-0.06637980043888092	-0.00433906726539135	-0.011248314753174782	0.03463716804981232	-0.006970411166548729	0.005070795305073261	-0.03699963912367821	-0.0644456222653389	0.03805161267518997	0.05824197083711624	-0.06947452574968338	-0.022527942433953285	-0.012481879442930222	-0.040814537554979324	0.0792565569281578	-0.006082421168684959	-0.04531415179371834	0.0016867649974301457	0.03681809827685356	0.03586059808731079	0.013696065172553062	0.035152535885572433	-0.01129298284649849	-0.012480637989938259	-0.0240190327167511	0.03940199315547943	-0.03114885464310646	0.03160007670521736	0.03200525417923927	0.008026918396353722	-0.0031074448488652706	0.011913180351257324	-0.04342733696103096	0.0007887103129178286	-0.10051476210355759	0.0027632066048681736	0.030126797035336494	-0.09134377539157867	0.026920732110738754	-0.014391648583114147	-0.002410072134807706	0.03783506155014038	0.016632502898573875	0.04678477719426155	-0.05776716768741608	-0.0880754217505455	0.0359872505068779	-0.06275950372219086	-0.040945060551166534	-0.020288486033678055	-0.040856968611478806	-0.03090576082468033	0.03157218545675278	-0.07314343005418777	-0.027337465435266495	-0.006496104411780834	0.0626484602689743	0.10121244937181473	0.060839857906103134	0.06817423552274704	0.02145821787416935	-0.0615500807762146	-0.037464894354343414	-0.05828798562288284	-0.03985118493437767	-0.013312592171132565	-0.015013894066214561	-0.045707155019044876	-0.03112228401005268	-0.01813911274075508	0.038093339651823044	0.01199441496282816	-0.0002602159511297941	-0.01525925938040018	-0.008993132039904594	-0.0726136639714241	0.08730638027191162	0.044352591037750244	0.008389039896428585	0.01514582708477974	0.043010953813791275	0.09225757420063019	-0.04340146854519844	-0.04854597523808479	-0.054268911480903625	0.01695891283452511	0.05976983904838562	0.014072966761887074	0.01694384217262268	0.0011352604487910867	0.134500652551651	0.048433881253004074	0.04010263830423355	-0.013321968726813793	0.021769538521766663	0.08701038360595703	-0.053009916096925735	0.04263443499803543	0.04995286464691162	-0.023106545209884644	-0.04747780039906502	0.006744604557752609	0.038060445338487625	-0.053138576447963715	-0.009891369380056858	0.05138580873608589	-0.08108615130186081	0.05522740259766579	0.01718061976134777	-0.09480045735836029	-0.0121212312951684	-0.029363388195633888	-0.04584385082125664	0.01660607010126114	0.0012120368191972375	0.009526358917355537	0.03765115514397621	0.020107995718717575	-0.01951429806649685	-0.009479684755206108	-0.041195452213287354	0.04740443080663681	-0.10171469300985336	-0.06280984729528427	-0.05813151225447655	0.02029675804078579	-0.009616076946258545	-0.012090954929590225	0.019489578902721405	0.04744189605116844	0.045630618929862976	0.03312121704220772	-0.014941233210265636	0.023670481517910957	-0.06299939006567001	-0.026761287823319435	-0.05020972341299057	-0.029463110491633415	0.018259039148688316	-0.0978863388299942	0.017932428047060966	-0.108394555747509	-0.035724516957998276	0.006410623900592327	0.05135192722082138	-0.046509549021720886	-0.03584139049053192	-0.035230811685323715	0.052239663898944855	-0.015057907439768314	-0.04034131020307541	0.03439372032880783	-0.06472928076982498	-0.03851265087723732	-0.065999835729599	-0.0017441778909415007	0.02173667773604393	-0.0294415894895792	0.004747127182781696	-0.00690747145563364	0.08092855662107468	-0.05375251919031143	-0.031074969097971916	-0.016130339354276657	-0.07750935852527618	-0.019725963473320007	0.044439125806093216	-0.015560775995254517	-0.008790750056505203	0.048718590289354324	0.04497969150543213	-0.00417855940759182	-0.02302219346165657	-0.05514939874410629	-0.04513199254870415	0.0010868271347135305	0.01986416056752205	-0.02973019890487194	-0.04246923327445984	-0.004984298720955849	0.07474959641695023	-0.009740456938743591	-0.0016208996530622244	0.05160518363118172	0.04115234315395355	-0.01896892674267292	0.02041148580610752	-0.02912697196006775	-0.043597809970378876	0.044535160064697266	-0.04116830602288246	0.002785530174151063	0.05640764907002449	0.024202682077884674	0.03459418565034866	0.04903453215956688	0.026664702221751213	0.017976609990000725	-0.04303910583257675	0.005597483366727829	-0.04545965790748596	-0.002329331124201417	-0.0023197003174573183	0.004297524690628052	0.03432163596153259	-0.0624748058617115	0.014959699474275112	-0.030382752418518066	0.07351327687501907	-0.0215897336602211	0.003842718666419387	-0.05760594829916954	0.006091642659157515	-0.009349995292723179	0.05409026890993118	-0.023176953196525574	0.02757526934146881	-0.02923339605331421	-0.05387502536177635	0.005140072666108608	-0.053653329610824585	0.035966094583272934	-0.008044738322496414	0.008123517036437988	-0.00010827305959537625	-0.06478521227836609	0.0065138996578752995	0.024306556209921837	0.08966885507106781	-0.07708317786455154	0.024454372003674507	0.04238806292414665	-0.01611645705997944	-0.04266432300209999	-0.02499447576701641	-0.018009819090366364	-0.08481002599000931	-0.06128174066543579	-0.014238685369491577	0.052337806671857834	-0.03643989562988281	-0.060879506170749664	-0.03363342210650444	-0.059944868087768555	0.04709396883845329	0.022290576249361038	0.010462531819939613	0.062375329434871674	0.06137120723724365	-0.06282874941825867	0.0023200931027531624	0.027031362056732178	0.0005731605342589319	-0.0635121762752533	0.03675249591469765	-0.004861125722527504	-0.02197381854057312	-0.0303396824747324	-0.05557598918676376	-0.05476566404104233	0.014051014557480812	-0.04739822447299957	-0.02433791756629944	-0.034722767770290375	-0.009813153184950352	0.024109255522489548	0.06854753941297531	-0.019921312108635902	-0.056351691484451294	-0.06804963946342468	-0.012670462019741535	-0.019923847168684006	-0.06692028790712357	-0.04023327678442001	0.06623737514019012	-0.03727637231349945	0.0021653620060533285	-0.08818239718675613	0.02820674516260624	-0.02913726679980755	0.004266895353794098	0.05232895165681839	0.035666707903146744	0.022900760173797607	-0.014836214482784271	0.015032068826258183	-0.02345927618443966	0.011903861537575722	0.020809154957532883	-0.01930629462003708	0.014646608382463455	-0.01513574831187725	-0.03880062326788902	-0.02409512549638748	-0.023145025596022606	-0.010834848508238792	0.0023951430339366198	-0.06287646293640137	0.021220076829195023	-0.018097935244441032	0.016120579093694687	-0.020879125222563744	-0.00046995613956823945	-0.058612160384655	-0.041119400411844254	-0.03853333741426468	0.006510550621896982	-0.0284382626414299	-0.06810757517814636	-0.014972743578255177	0.01610572077333927	0.04765590652823448	-0.031010271981358528	-0.030124805867671967	-0.026258589699864388	-0.02429683692753315	0.014371666125953197	-0.028348367661237717	0.04901552572846413	-0.04306905344128609	0.0737987607717514	0.00135968963149935	-0.0523601695895195	-0.017002258449792862	-0.049493491649627686	-0.0804879441857338	-0.0031459135934710503	0.007687245029956102	-0.056918930262327194	0.027683835476636887	0.025787049904465675	0.008670609444379807	0.04384523257613182	0.04389268159866333	-0.0438043586909771	-0.0522078238427639	-0.05547051876783371	0.07549244165420532	-0.022143352776765823	0.07846721261739731	0.006932266987860203	-0.07369300723075867	-0.08735460788011551	0.06266948580741882	-0.0582786463201046	-0.07687564939260483	-0.028890371322631836	0.013631915673613548	0.041227541863918304	0.010961106978356838	0.019199689850211143	-0.07446443289518356	-0.061865974217653275	0.012743847444653511	0.01619626022875309	-0.01229103934019804	-0.0503200963139534	0.07127652317285538	-0.043814413249492645	0.030749941244721413	-0.022019067779183388	-0.02145824022591114	0.060775596648454666	-0.022345099598169327	0.008812347427010536	-0.13243468105793	0.0010130454320460558	-0.0311270784586668	-0.005006885156035423	0.020117169246077538	-0.033396121114492416	-0.07775843143463135	-0.020745448768138885	0.06124371290206909	-0.0018889742204919457	0.005538254510611296	0.09285556524991989	-0.03542803227901459	0.03282136842608452	0.06327877938747406	0.03262845426797867	-0.047733668237924576	0.0038510034792125225	0.008656036108732224	0.06996340304613113	-0.061594702303409576	0.029312461614608765	-0.04351755604147911	0.12397340685129166	0.06333189457654953	0.025384198874235153	0.02433878555893898	0.033401742577552795	0.04171205684542656	0.04494180157780647	-0.08218509703874588
+0.06678157299757004	0.0054410407319664955	0.039384759962558746	0.0566108413040638	-0.08966681361198425	-0.036974452435970306	-0.019752608612179756	0.0264019463211298	-0.00310320220887661	-0.04537505656480789	-0.025560975074768066	-0.017911965027451515	-0.013041839934885502	0.02003730647265911	0.02953660674393177	-0.025779958814382553	-0.05694376304745674	0.011854348704218864	-0.0382121242582798	-0.0466165617108345	0.015967292711138725	0.023284249007701874	-0.07604912668466568	0.055935971438884735	-0.001927934936247766	0.024310190230607986	-0.06863107532262802	0.01692843623459339	-0.020009247586131096	0.01961546577513218	0.03999757021665573	-0.010548673570156097	0.061603743582963943	-0.04817835986614227	-0.04433836415410042	0.037509676069021225	-0.03491060063242912	0.10316966474056244	0.025327159091830254	-0.0459812767803669	-0.035059865564107895	0.005707006435841322	-0.01631997525691986	-0.055754683911800385	0.014779390767216682	0.01224935706704855	0.0020751727279275656	-0.017517264932394028	0.05102477967739105	0.028880346566438675	0.09127670526504517	0.022803209722042084	-0.01360038761049509	-0.03979675844311714	0.006030757911503315	0.007061753422021866	0.05019408464431763	-0.06928759813308716	-0.057947590947151184	0.044039249420166016	-0.11274432390928268	-0.017637118697166443	0.025590570643544197	0.009763970039784908	-0.026897119358181953	-0.03571050986647606	-0.019075291231274605	0.10945352911949158	0.0333859883248806	-0.034999068826436996	0.03931884095072746	0.11804667115211487	-0.07082857191562653	-0.040345095098018646	0.05003456771373749	-0.08191604167222977	-0.03334895521402359	0.04196027293801308	-0.0072620767168700695	0.0019237854285165668	-0.00020392716396600008	-0.06122785061597824	0.0895456075668335	-0.03126592934131622	-0.06043729558587074	-0.020673684775829315	0.004129065666347742	-0.0850905179977417	0.05796601250767708	0.02236361801624298	0.006263073533773422	0.07150005549192429	-0.05795915424823761	0.03737092763185501	0.019438359886407852	0.02068009227514267	0.033691778779029846	-0.10565119236707687	-0.03362622484564781	-0.010330218821763992	-0.009924191050231457	0.015854066237807274	0.03184598684310913	0.01441943645477295	0.06526581943035126	0.010454987175762653	-0.02986406907439232	0.012750170193612576	0.03057895042002201	0.04259439557790756	0.04456876218318939	0.058563653379678726	0.08403822779655457	0.04982462525367737	0.030582493171095848	-0.012840918265283108	0.028182070702314377	-0.05660165846347809	-0.04440971836447716	-0.009780156426131725	-0.005335740279406309	0.07375819236040115	-0.02514680288732052	-0.08430320769548416	-0.02305617183446884	0.054240066558122635	-0.06673816591501236	-0.02683621272444725	0.07566887885332108	-0.021736664697527885	0.10702207684516907	0.05588085949420929	-0.018801217898726463	0.04125906154513359	0.05137866362929344	0.02000216580927372	-0.03142628073692322	0.03281952440738678	0.010530602186918259	0.00024677295004948974	0.0051760319620370865	0.04016818478703499	0.013662213459610939	-0.09363550692796707	0.03675849363207817	0.04388095438480377	0.02654540352523327	-0.03886774927377701	-0.05921368673443794	0.005771361291408539	-0.030536994338035583	-0.08176626265048981	0.0008329807897098362	-0.07793566584587097	-0.015737663954496384	0.0657665953040123	0.010698902420699596	0.018231678754091263	-0.02955436147749424	-0.00810332503169775	-0.07425105571746826	-0.03719931095838547	-0.013356249779462814	-0.06291565299034119	-0.0651554986834526	-0.028727181255817413	0.0036573184188455343	0.041476424783468246	-0.06999298930168152	-0.018430491909384727	-0.0015840681735426188	0.012955225072801113	0.03189245983958244	0.0816011130809784	0.0834592804312706	0.025413306429982185	0.00575125589966774	-0.029232127591967583	0.033110860735177994	-0.017829466611146927	0.0015745148994028568	-0.0307048000395298	0.04211672395467758	-0.05545692518353462	-0.002898075617849827	-0.011048056185245514	0.04868382215499878	-0.0019153071334585547	0.034481845796108246	0.01796816661953926	-0.004584536422044039	-0.04073416441679001	0.024172231554985046	0.06624362617731094	-0.032975271344184875	0.04194092005491257	-0.042249493300914764	0.036485325545072556	-0.0264876838773489	-0.04469679296016693	-0.04459996521472931	0.05231645330786705	0.016427073627710342	0.06464117020368576	0.03059753030538559	-0.07197399437427521	0.05875382572412491	0.04309447854757309	-0.0031331174541264772	-0.07487665116786957	0.002952503738924861	0.009218798018991947	0.04073967784643173	0.052066605538129807	0.08084529638290405	-0.018624888733029366	0.015969546511769295	-0.02032565325498581	0.050075337290763855	-0.04683181643486023	-0.0018529505468904972	-0.008356895297765732	-0.09828720986843109	0.028993120416998863	-0.04528553783893585	-0.05717280134558678	-0.015820499509572983	0.04635176435112953	0.004336244892328978	-0.01584860309958458	0.020666973665356636	0.01099404040724039	-0.018785813823342323	-0.03228095546364784	-0.010392576456069946	-0.04217148944735527	-0.09380872547626495	0.12022033333778381	0.046600159257650375	-0.06577068567276001	-0.06424519419670105	-0.003323744051158428	0.0414087250828743	-0.01834336295723915	0.041098613291978836	0.035368189215660095	0.12222184985876083	0.03652478754520416	0.004267457872629166	-0.0013399046147242188	-0.048263631761074066	-0.07489611953496933	0.010569868609309196	0.040183331817388535	-0.044405415654182434	-0.005665370263159275	0.009270143695175648	-0.054483622312545776	-0.021615905687212944	0.006999239791184664	0.020311936736106873	-0.010720305144786835	-0.0060727959498763084	-0.03872239217162132	0.03915708512067795	-0.0501079224050045	-0.05187135189771652	0.03980015218257904	0.01580984704196453	0.06412741541862488	-0.02181958593428135	-0.012046228162944317	0.06879545748233795	0.014997914433479309	0.01841653883457184	-0.040733542293310165	0.04614138603210449	-0.018290767446160316	-0.06350207328796387	-0.014292033389210701	-0.08693866431713104	-0.045978572219610214	-0.013360313139855862	-0.0369332879781723	-0.05204825475811958	0.08483431488275528	-0.007725184317678213	-0.04539532959461212	-0.05399180203676224	0.00673617422580719	-0.03923245146870613	-0.05745368450880051	-0.0021736768539994955	0.006960784085094929	-0.025841979309916496	-0.041220393031835556	-0.002121932106092572	-0.0674520954489708	-0.030749160796403885	-0.023263055831193924	-0.043565671890974045	-0.09484390914440155	0.056085702031850815	-0.018659722059965134	-0.017707519233226776	0.014695328660309315	-0.06705862283706665	0.019161337986588478	0.11319788545370102	0.05984823405742645	0.031821753829717636	0.0475802980363369	0.02307865582406521	0.12082234770059586	0.02491099201142788	-0.0502079576253891	-0.016840264201164246	-0.02448437362909317	-0.05848116800189018	0.04435201734304428	-0.025605127215385437	-0.00024811940966174006	-0.008566385135054588	-0.0007057567709125578	0.07156727463006973	0.00753466971218586	-0.05856454372406006	-0.016331462189555168	0.024494385346770287	-0.021097853779792786	0.010558919981122017	0.0026204027235507965	0.003763970686122775	-0.02289215661585331	-0.04743028059601784	-0.03064688853919506	-0.01689833402633667	0.03969138488173485	-0.02267572656273842	0.04217271879315376	-0.0345226414501667	-0.05978097766637802	0.016221173107624054	-0.01321784034371376	0.08289793878793716	-0.06439381837844849	0.026967354118824005	0.03486182913184166	0.016898222267627716	-0.10182832181453705	-0.019421342760324478	0.04010191187262535	-0.036275509744882584	-0.04264727979898453	0.01836646907031536	0.015302084386348724	-0.0662447065114975	-0.00438537634909153	0.0021665228996425867	-0.055727891623973846	-0.011717965826392174	0.012340703047811985	0.04420195147395134	0.009790914133191109	-0.0326174795627594	-0.032412752509117126	-0.049101412296295166	-0.001816804171539843	0.02153490111231804	-0.012470104731619358	0.0228557251393795	-0.03316374868154526	0.03192111477255821	-0.07159506529569626	-0.029048386961221695	-0.028807947412133217	0.05657313019037247	0.01181997824460268	-0.0066159917041659355	0.01845879852771759	-0.04750484973192215	-0.015693848952651024	0.006584726274013519	0.03355984389781952	-0.029205208644270897	-0.015476929023861885	-0.02824494056403637	-0.03646725416183472	-0.06357184797525406	-0.0511339046061039	-0.005252189468592405	-0.042899876832962036	0.027910824865102768	0.0003803193976636976	-0.017155373468995094	-0.007240852806717157	-0.020211689174175262	0.031092386692762375	0.03460925444960594	0.0444367378950119	-0.0069822086952626705	0.017547355964779854	-0.011006468906998634	0.048287227749824524	0.02558700367808342	-0.07424420863389969	0.011686090379953384	0.028431382030248642	-0.06742499023675919	0.057561688125133514	-0.039146631956100464	0.012207938358187675	-0.00819668173789978	0.002900203922763467	0.03619743511080742	-0.021610381081700325	-0.009108438156545162	-0.03817751258611679	0.07588155567646027	-0.07144838571548462	-0.03122682496905327	0.01777404174208641	-0.03829973563551903	-0.10391487926244736	-0.029322374612092972	-0.028140295296907425	-0.018524810671806335	0.03756219521164894	-0.02010735496878624	0.0177521463483572	0.04564261808991432	-0.048548322170972824	-0.058106280863285065	-0.029261423274874687	0.012470402754843235	0.05704313889145851	-0.010617841966450214	0.052176397293806076	0.022555042058229446	-0.004936078563332558	0.004665113054215908	0.011214200407266617	0.02935735508799553	-0.10533116012811661	-0.012013547122478485	-0.02041669189929962	-0.0117460573092103	0.013317457400262356	0.009966667741537094	-0.004236581735312939	-0.08499594032764435	-0.031856462359428406	-0.01560758426785469	0.08345307409763336	-0.014818443916738033	-0.006144121289253235	0.0412033274769783	-0.039761438965797424	-0.011942756362259388	0.009152498096227646	-0.04913559556007385	-0.07562647014856339	0.001747627044096589	-0.022659782320261	0.01305561326444149	0.0032424486707895994	0.03742768242955208	-0.04111147299408913	-0.049665436148643494	-0.0018193420255556703	0.02760445885360241	-0.08112791925668716	0.00496806250885129	0.01062807161360979	-0.07351861149072647	-0.06578885763883591	0.007860194891691208	-0.04179227352142334	0.04855482652783394	0.03608524799346924	0.01559448055922985	0.00622906070202589	0.00885697454214096	-0.015462188981473446	-0.05734774470329285	-0.010192894376814365	0.012017694301903248	-0.046948306262493134	-0.03020966798067093	0.06491455435752869	-0.061643086373806	0.031137222424149513	0.05115179345011711	-0.06292169541120529	0.030464904382824898	0.038960784673690796	-0.04357961192727089	0.06795404106378555	-0.0006375201046466827	-0.01612071320414543	0.07915809005498886	-0.07348739355802536	0.09117283672094345	-0.05338557809591293	0.08527866005897522	-0.00574207678437233	-0.032823022454977036	0.07211283594369888	0.09338842332363129	0.0017282682238146663	-0.006016084458678961	-0.07788202166557312
+0.004461822099983692	-0.047570306807756424	-0.006641245912760496	0.014780589379370213	-0.08407412469387054	-0.05390362814068794	0.006287906784564257	0.03812777251005173	-0.0006857072585262358	-0.03530755266547203	-0.03675166144967079	-0.008247201330959797	-0.0012126967776566744	0.022164946421980858	0.026970529928803444	-0.05064808204770088	-0.06818844377994537	0.004591029603034258	-0.06336195021867752	-0.02815503627061844	0.026963209733366966	0.04194588214159012	-0.05218794196844101	0.03626667708158493	-0.018954114988446236	0.0018204894149675965	-0.059136614203453064	0.015047351829707623	0.011746765114367008	0.023864300921559334	0.08221952617168427	0.0009429413476027548	0.045463692396879196	-0.06262373924255371	-0.03640852868556976	0.046045996248722076	-0.04001401364803314	0.11048883199691772	0.018973786383867264	-0.024937551468610764	-0.029582476243376732	-0.006804840639233589	-0.009718143381178379	-0.04033175855875015	0.03128747269511223	-0.03961671143770218	0.011547993868589401	-0.02899855002760887	0.004130995366722345	0.029231995344161987	0.08382521569728851	0.01233537308871746	0.014927016571164131	-0.021236784756183624	-0.013938508927822113	0.020847758278250694	0.0068696029484272	-0.052377838641405106	-0.04902610555291176	0.041557349264621735	-0.07621195912361145	-0.0007778832805342972	0.013752228580415249	0.013632669113576412	-0.01971866935491562	-0.02001900225877762	-0.00972208846360445	0.10892923921346664	0.007267470471560955	-0.026050832122564316	0.016908377408981323	0.07647804915904999	-0.05278674140572548	-0.06631676107645035	0.03176983818411827	-0.05350042134523392	0.011083101853728294	0.020064344629645348	-0.016676660627126694	-0.020311851054430008	-0.0032434614840894938	-0.023378051817417145	0.11540193855762482	-0.030326491221785545	-0.04127714782953262	-0.05230613052845001	-0.02080366015434265	-0.0779009759426117	0.05776229873299599	0.00894138216972351	0.05327802151441574	0.06836488842964172	-0.02530522830784321	0.03297495096921921	0.0029535209760069847	0.01331400964409113	0.012723107822239399	-0.13155749440193176	-0.027972042560577393	-0.017892440780997276	0.023052772507071495	0.005132115446031094	0.08490534871816635	0.00036624440690502524	0.04806779325008392	0.06504391878843307	-0.06440792232751846	-0.0011312866117805243	0.015383041463792324	0.04068947583436966	0.02384170889854431	0.13117294013500214	0.07759267091751099	0.05966371297836304	0.045418575406074524	-0.004484493751078844	-0.014061235822737217	-0.023972276598215103	-0.024877863004803658	-0.005294006317853928	0.0035334951244294643	0.03738723322749138	-0.016170930117368698	-0.06653697043657303	-0.039307285100221634	0.06824532896280289	-0.10290507227182388	-0.012933127582073212	0.0791720598936081	-0.02310710772871971	0.10395676642656326	0.05603746324777603	-0.02066257782280445	0.03348403424024582	0.06701314449310303	0.005437872372567654	-0.021721981465816498	0.00242499727755785	0.008370431140065193	-0.033995311707258224	-0.06667087227106094	0.05777455121278763	0.011316088028252125	-0.05074228346347809	0.0033889252226799726	0.06433321535587311	0.015783216804265976	-0.037721723318099976	-0.06291337311267853	0.01716902293264866	-0.0682181715965271	-0.08076228946447372	-0.0021915347315371037	-0.08674927800893784	0.025711657479405403	0.046468667685985565	0.0027002720162272453	0.02358882501721382	-0.012544776313006878	-0.02122279442846775	-0.04696812480688095	-0.06347029656171799	-0.00223580002784729	-0.0938350111246109	-0.002441396936774254	-0.019709240645170212	0.008214212022721767	-0.0005513968062587082	-0.036758992820978165	-0.05002354085445404	-0.021506184712052345	-0.022037651389837265	0.017334934324026108	0.0843699499964714	0.03026718646287918	0.012081308290362358	0.017264513298869133	-0.02122802473604679	0.0029377341270446777	-0.0515444278717041	0.007877068594098091	-0.03146515041589737	0.03359039127826691	-0.09001631289720535	-0.02378019504249096	0.002961948048323393	0.022617213428020477	-0.03632260859012604	-0.00568868825212121	-0.027121201157569885	-0.008114369586110115	-0.07166575640439987	0.02721039019525051	0.08530937880277634	-0.021986182779073715	0.05291076749563217	-0.012531881220638752	0.0796133354306221	-0.02520746923983097	-0.03379035368561745	-0.07907060533761978	0.041904013603925705	0.016248468309640884	0.002164514036849141	-0.0042632026597857475	-0.07663656026124954	0.09364752471446991	0.017275433987379074	0.039872609078884125	-0.07700857520103455	0.023206735029816628	0.05673999339342117	0.005850596819072962	0.06855180114507675	0.06272929161787033	-0.030343810096383095	-0.013990577310323715	0.0017545248847454786	0.028910303488373756	-0.06463352590799332	0.01973898522555828	0.021659262478351593	-0.06690815836191177	0.03424673154950142	-0.0326683335006237	-0.09597209841012955	-0.020236656069755554	0.0032988341990858316	-0.003166035283356905	-0.030465206131339073	4.4126049033366144e-05	0.026312388479709625	-0.008746437728404999	-0.007580752484500408	-0.01530580036342144	-0.008662903681397438	-0.08861060440540314	0.07716760039329529	-0.009136408567428589	-0.07588693499565125	-0.06041574478149414	-0.017702320590615273	0.007939974777400494	-0.00021602398192044348	0.01264411024749279	0.016242630779743195	0.10605453699827194	0.04140554368495941	-0.042577456682920456	0.0019841499160975218	-0.045571859925985336	-0.0542333647608757	-0.023781778290867805	0.018987519666552544	-0.04092487320303917	-0.06551730632781982	0.0223728958517313	-0.09440488368272781	-0.01576959900557995	-0.021345920860767365	0.06136338412761688	-0.04027603566646576	-0.01989119127392769	-0.007753141690045595	0.047146741300821304	-0.020905978977680206	-0.059235796332359314	0.02244088239967823	-0.0043767280876636505	0.04909985512495041	-0.01614544540643692	-0.023388104513287544	0.039546865969896317	0.005267821252346039	0.026969414204359055	-0.037818435579538345	0.0742674320936203	-0.013528969138860703	-0.08156634122133255	-0.027872303500771523	-0.05185603350400925	-0.057431090623140335	0.005067650228738785	-0.031320489943027496	-0.03398291394114494	0.08809653669595718	0.015986286103725433	-0.04493536427617073	-0.034432679414749146	-0.044878192245960236	-0.024985037744045258	-0.01961737684905529	-0.025946730747818947	-0.017032815143465996	-0.06124219298362732	0.009660592302680016	0.040921248495578766	-0.07934301346540451	-0.029836293309926987	0.042386285960674286	-0.003221943974494934	-0.021771831437945366	0.06083589419722557	-0.05737310275435448	-0.004105080850422382	-0.0024959028232842684	-0.06252780556678772	0.023917587473988533	0.08039618283510208	0.05955037474632263	0.0390683077275753	0.03557904437184334	0.023061029613018036	0.09039543569087982	0.012348202057182789	-0.03900592029094696	-0.021673783659934998	-0.020294059067964554	-0.06399957090616226	0.04675193130970001	-0.04066569730639458	-0.019383346661925316	0.012856065295636654	0.007542379200458527	0.08463425934314728	-0.024392595514655113	-0.027417950332164764	-0.019437339156866074	0.034576985985040665	-0.015417080372571945	0.016246022656559944	-0.0009056053240783513	-0.003570572705939412	-0.0017101367702707648	-0.061481062322854996	-0.0697430968284607	-0.040261466056108475	0.04287209361791611	-0.018361477181315422	0.0513647124171257	-0.01620892994105816	-0.07529664784669876	0.008104946464300156	-0.0013530735159292817	0.1193506270647049	-0.06870859861373901	0.010687186382710934	0.012923257425427437	0.03573676571249962	-0.08777010440826416	0.007992718368768692	-0.009097223170101643	-0.029961105436086655	-0.07001306116580963	0.018382778391242027	0.036254215985536575	-0.07242365181446075	-0.04114053025841713	-0.005270324647426605	-0.03037336654961109	0.004394873511046171	0.018921146169304848	-0.022352058440446854	0.05389988794922829	0.0264848992228508	-0.02461152896285057	0.0029974610079079866	0.035768311470746994	0.022124307230114937	-0.055559150874614716	0.038382165133953094	-0.028779128566384315	0.06036299094557762	-0.013605091720819473	-0.05957305431365967	-0.057966046035289764	0.03247452899813652	-0.01709650829434395	0.0036318451166152954	-0.045471079647541046	-0.013758142478764057	-0.0001099380970117636	0.0367201529443264	-0.01656447723507881	-0.08295925706624985	0.002622063970193267	-0.010820084251463413	-0.05553779751062393	-0.08374765515327454	-0.06856569647789001	0.032647501677274704	-0.0514395572245121	0.008674824610352516	-0.033755380660295486	0.026021428406238556	0.003874537767842412	-0.027808312326669693	0.04550154134631157	0.06125328317284584	0.028140278533101082	-0.02337692305445671	0.05305872857570648	0.009466439485549927	0.055293768644332886	0.001528931432403624	-0.04781879857182503	0.030076993629336357	0.03627864643931389	-0.04048547521233559	0.022930681705474854	-0.04227841645479202	0.029018741101026535	-0.009188308380544186	-0.02599306032061577	0.01317709218710661	0.001051583094522357	-0.01666264794766903	-0.05117302015423775	0.05738833546638489	-0.08251450210809708	-0.03908253088593483	0.030912110581994057	-0.038899920880794525	-0.08109762519598007	-0.01831982284784317	-0.04829994589090347	-0.020461834967136383	0.049957334995269775	-0.024193741381168365	0.039325036108493805	-0.0013462781207635999	-0.049143239855766296	-0.017241355031728745	-0.006609362084418535	-0.007085418328642845	-0.004076511599123478	0.03307195380330086	0.0712842121720314	0.000480505550513044	0.006291747558861971	0.004713346716016531	-0.05005231127142906	0.024308044463396072	-0.10757777094841003	-0.0026475535705685616	-0.0015994928544387221	-0.008269097656011581	0.011694120243191719	0.029585782438516617	0.030798636376857758	-0.07014816254377365	-0.016090435907244682	-0.0416376069188118	0.06335408985614777	-0.014357637614011765	0.040991105139255524	-0.005888674408197403	-0.05402552708983421	-0.03585183620452881	0.009858191013336182	-0.023789411410689354	-0.08755236864089966	0.0068205855786800385	-0.03127796947956085	0.03146990388631821	-0.00887047778815031	0.042722512036561966	-0.03195495158433914	-0.1176026463508606	-0.0021943249739706516	0.004683093633502722	-0.029531849548220634	0.0007078283815644681	0.04732561111450195	-0.05933665484189987	-0.039309658110141754	-0.0018491094233468175	-0.05546421557664871	0.0564243458211422	-0.002110147150233388	0.03958137705922127	-0.043286096304655075	-0.024713879451155663	0.0018831779016181827	-0.03733297809958458	0.014791407622396946	-0.01907206140458584	-0.08157823979854584	-0.029302578419446945	0.07474150508642197	-0.030128544196486473	0.021342389285564423	0.08018821477890015	-0.05044962838292122	0.03554237633943558	0.01962900720536709	-0.01188613474369049	0.014157922007143497	-0.017736593261361122	-0.006967614870518446	0.02670910581946373	-0.0827312245965004	0.07838655263185501	-0.04933379590511322	0.10561440140008926	0.02122110314667225	-0.011958491057157516	0.07944397628307343	0.08277233690023422	0.021933704614639282	0.016272231936454773	-0.06753835082054138
+0.03848421946167946	-0.05751781538128853	0.009001349098980427	0.036171022802591324	-0.09211258590221405	-0.0672956258058548	-0.008262515999376774	0.03779260814189911	-0.012574461288750172	-0.06295930594205856	0.007977383211255074	-0.01706046611070633	-0.011358357034623623	0.004478147719055414	0.00910783652216196	0.005510841030627489	-0.07008122652769089	0.030464351177215576	-0.09013497829437256	-0.02838604338467121	0.015045195817947388	0.025346551090478897	-0.07013891637325287	0.01249882485717535	-0.0430745929479599	-0.003413354977965355	-0.0679081603884697	0.0021129371598362923	-0.03265608102083206	0.030518976971507072	0.04637191817164421	-0.002099658129736781	0.07258624583482742	-0.035722989588975906	-0.06393195688724518	0.07524513453245163	-0.031868673861026764	0.10964196920394897	0.009522972628474236	-0.030298449099063873	-0.06011808291077614	-0.0030545589979737997	0.0036587496288120747	-0.06936130672693253	0.05072172358632088	0.012280355207622051	0.008554483763873577	-0.0217204038053751	0.042051903903484344	0.05228250101208687	0.10104106366634369	0.03151027113199234	0.007220532279461622	0.004643857944756746	-0.0028741550631821156	0.00683827418833971	0.028677141293883324	-0.038924042135477066	-0.05357503145933151	0.04788889363408089	-0.08773627132177353	-0.03735607862472534	0.026232749223709106	0.02284221723675728	-0.021772727370262146	-0.007941004820168018	-0.0187558401376009	0.10399995744228363	0.04198288172483444	-0.029887327924370766	-0.004894958343356848	0.07799611985683441	-0.020158767700195312	-0.06915311515331268	0.03066279925405979	-0.07393564283847809	-0.013253330253064632	0.028510190546512604	-0.022987203672528267	0.0031949877738952637	-0.02809646725654602	-0.027834320440888405	0.13661977648735046	-0.054622404277324677	-0.03770972788333893	-0.02219998463988304	0.00538683170452714	-0.0722411572933197	0.06336784362792969	0.0253896564245224	-0.0007416913285851479	0.06297816336154938	-0.044531676918268204	0.022639986127614975	-0.024646222591400146	0.027436010539531708	0.04365592822432518	-0.10019479691982269	-0.02088378369808197	-0.0164803396910429	-0.01593116484582424	0.0029978197999298573	0.05294986441731453	0.022119561210274696	0.06807146221399307	0.006623762659728527	-0.006853390485048294	-0.016947221010923386	0.020765189081430435	0.015284114517271519	0.008735744282603264	0.0975903868675232	0.09099637717008591	0.0273926742374897	0.05369356647133827	-0.009029787965118885	0.012775221839547157	-0.06925088167190552	-0.02785368077456951	-0.020776204764842987	-0.017387812957167625	0.03207893297076225	-0.0428880974650383	-0.049667973071336746	-0.021726617589592934	0.035241540521383286	-0.10869298875331879	-0.027379320934414864	0.08259350806474686	-0.04118458554148674	0.11116684228181839	0.040408581495285034	-0.03664873540401459	0.04529852792620659	0.06150466203689575	0.006618898827582598	-0.02651098184287548	0.026309974491596222	0.008118371479213238	-0.019567379727959633	0.02753094956278801	0.06157146021723747	-0.025767002254724503	-0.05333547294139862	0.021810920909047127	0.04758591577410698	0.025632360950112343	-0.04049703851342201	-0.08603101223707199	0.0808064192533493	-0.046900324523448944	-0.09784094989299774	-0.0355285182595253	-0.07308092713356018	-0.007497282698750496	0.04782130941748619	-0.0011420088121667504	0.01596921868622303	-0.03915778920054436	-0.019105130806565285	-0.06022820249199867	-0.059768155217170715	-0.01815764605998993	-0.08591268211603165	-0.044743601232767105	-0.03573782369494438	-0.014162442646920681	0.027864346280694008	-0.03784501180052757	-0.053999654948711395	-0.008653898723423481	-0.001371506368741393	0.013795719482004642	0.08646805584430695	0.07226680964231491	0.032027725130319595	0.0014279107563197613	-0.03787214681506157	0.00770705146715045	-0.0317843072116375	0.0026665034238249063	-0.022282520309090614	0.04539426043629646	-0.0726337656378746	0.007539559621363878	-0.002675470197573304	0.037077028304338455	-0.024645213037729263	0.016999565064907074	0.01115355547517538	-0.017245782539248466	-0.0469144731760025	0.01156522799283266	0.08593285083770752	-0.02971441112458706	0.05604463070631027	-0.02680695615708828	0.07496144622564316	-0.04259638860821724	-0.056101758033037186	-0.05179908499121666	0.0662846714258194	0.005970608908683062	0.038062840700149536	0.02115820161998272	-0.07993801683187485	0.07335187494754791	0.04064885526895523	-0.010467815212905407	-0.061000365763902664	0.03831251338124275	0.029779359698295593	0.05781736224889755	0.04611801356077194	0.0449906587600708	-0.008497883565723896	-0.010243762284517288	0.005791965872049332	0.05362788960337639	-0.037669092416763306	0.0016989752184599638	-0.0007812241674400866	-0.07407554984092712	0.020340094342827797	-0.02231626957654953	-0.07804150134325027	-0.02952127531170845	0.046822790056467056	-0.008976774290204048	-0.010865073651075363	0.027862602844834328	0.014947391115128994	-0.02808830514550209	-0.02275385707616806	1.79279795702314e-05	-0.04566483199596405	-0.09543537348508835	0.09384644776582718	0.029431628063321114	-0.07615970820188522	-0.03281201049685478	-0.004191589541733265	0.048570334911346436	-0.015077408403158188	0.02948817051947117	0.03694567829370499	0.0849849060177803	0.0449192076921463	0.0016459899488836527	-0.02264741249382496	-0.06171652674674988	-0.05185306444764137	-0.009941253811120987	0.010989835485816002	-0.027304962277412415	-0.03487756848335266	-0.00433902395889163	-0.08421709388494492	-0.010834872722625732	0.013829167000949383	0.04941532388329506	-0.04734663665294647	-0.024828223511576653	-0.00478014349937439	0.025573084130883217	-0.04475032165646553	-0.05287126824259758	0.024859340861439705	-0.0035716048441827297	0.03229501098394394	-0.03899349644780159	-0.0042032175697386265	0.05275675281882286	-0.007791911717504263	0.054362062364816666	-0.03280007839202881	0.07768598198890686	-0.0231217872351408	-0.08813463151454926	-0.000533511396497488	-0.08919849991798401	-0.052047599107027054	-0.014820237644016743	-0.05138274282217026	-0.04409101605415344	0.07606782019138336	0.019876474514603615	-0.023196980357170105	-0.06588288396596909	-0.017065610736608505	-0.03860299661755562	-0.02774452604353428	-0.0028068057727068663	-0.009283799678087234	-0.019746903330087662	-0.0050771161913871765	0.0020266841165721416	-0.058501534163951874	-0.016494741663336754	0.0009578356985002756	-0.014418763108551502	-0.046774860471487045	0.07188352197408676	-0.0252639539539814	-0.012327891774475574	0.019164016470313072	-0.05167543888092041	0.00887143611907959	0.10016568005084991	0.06068289652466774	0.03597337380051613	0.05244527757167816	0.02580147422850132	0.07035938650369644	0.030818283557891846	-0.01604594476521015	-0.03566053882241249	-0.013621002435684204	-0.03903457894921303	0.04731905087828636	-0.03999835252761841	-0.022659655660390854	0.014023423194885254	0.005186147056519985	0.07652080804109573	-0.006074734963476658	-0.07567247003316879	-0.01286320947110653	0.029333649203181267	-0.006895591039210558	0.036528751254081726	0.00800552498549223	0.016179144382476807	-0.017080338671803474	-0.034886475652456284	-0.005125483963638544	-0.010901888832449913	0.03396489471197128	-0.028706887736916542	0.01812773570418358	-0.024170342832803726	-0.0567617304623127	0.004244781564921141	-0.018901402130723	0.10435301065444946	-0.054687585681676865	0.00843153428286314	0.0422840416431427	0.04445343092083931	-0.11325539648532867	0.006188164930790663	0.006966099608689547	-0.0032670346554368734	-0.061023496091365814	0.03259425237774849	0.028496647253632545	-0.09311888366937637	-0.02103467285633087	-0.03159124031662941	-0.022547587752342224	-0.032355617731809616	0.008679194375872612	0.02940344624221325	0.01482557412236929	0.00023473016335628927	-0.05577357858419418	-0.02042974717915058	0.020430071279406548	0.013670165091753006	-0.061209145933389664	0.02596101723611355	-0.01995309814810753	0.014992867596447468	-0.052670668810606	-0.0783446878194809	-0.012327826581895351	0.057899393141269684	0.007331481669098139	-0.03521638363599777	-0.0017312084091827273	-0.0545574426651001	0.00261778780259192	0.015839917585253716	-0.005550302565097809	-0.03560507670044899	-0.020626701414585114	-0.01738877221941948	-0.06878479570150375	-0.099882572889328	-0.03873942047357559	0.012615201063454151	-0.04960250109434128	0.00943157821893692	-0.0246630497276783	-0.009803690947592258	0.003221154911443591	0.005524446722120047	0.0360943041741848	0.05813430994749069	0.022308064624667168	0.007928906939923763	0.01403613481670618	-0.010732931084930897	0.03298744559288025	0.024165501818060875	-0.05276571214199066	-0.0001022660726448521	-0.013192699290812016	-0.08776114881038666	0.0518026240170002	-0.03346008434891701	-0.0008317476022057235	0.0028457948938012123	-0.01676054857671261	0.008226285688579082	0.0006258525536395609	0.0007289338391274214	-0.027545155957341194	0.054786570370197296	-0.05657476931810379	-0.012189055792987347	0.027658190578222275	-0.025204980745911598	-0.10956579446792603	-0.03856101259589195	-0.0112101249396801	-0.009801791980862617	0.05741669237613678	-0.010372020304203033	0.013451792299747467	0.007615027483552694	-0.05202894285321236	-0.05714133381843567	-0.05267162248492241	-0.00023690768284723163	0.0376962274312973	0.01507189217954874	0.06578787416219711	0.0038912626914680004	0.011846005916595459	-0.01863935962319374	0.0014414330944418907	0.0211266428232193	-0.07446786761283875	-0.011695189401507378	-0.019651489332318306	0.002578719286248088	0.0026802343782037497	0.014154736883938313	-0.0012967531802132726	-0.057003818452358246	-0.02360229752957821	-0.015326322987675667	0.08635009825229645	-0.01853119768202305	-0.003616139991208911	0.03046604059636593	-0.058236341923475266	-0.023067861795425415	0.03264668211340904	-0.058728545904159546	-0.08683817088603973	0.0368584506213665	-0.026853561401367188	0.019514232873916626	0.018684515729546547	0.06494493037462234	-0.04603623226284981	-0.0798313319683075	-0.0007670836639590561	0.006059073377400637	-0.046861086040735245	-0.0065400912426412106	0.03126921132206917	-0.09866286814212799	-0.05318143963813782	-0.01977580599486828	-0.054152488708496094	0.07407523691654205	-0.0016697306418791413	0.017852669581770897	-0.03374934569001198	-0.0015833238139748573	-0.02921341173350811	-0.0466526560485363	0.007015712093561888	0.015674715861678123	-0.05300316959619522	-0.04028219357132912	0.055017273873090744	-0.05520627647638321	0.05050680786371231	0.07248660922050476	-0.04406788572669029	0.0547119565308094	0.04505172744393349	-0.01974397897720337	0.07873282581567764	0.03226421773433685	-0.025758612900972366	0.03614988923072815	-0.06598948687314987	0.0497501976788044	-0.054153136909008026	0.10638750344514847	0.023818176239728928	-0.0006637903279624879	0.05425948649644852	0.08863126486539841	0.011390918865799904	0.010984152555465698	-0.06828108429908752
+0.0590759739279747	0.046740975230932236	0.010259551927447319	0.022932378575205803	-0.06786609441041946	-0.06938403099775314	-0.020711392164230347	0.003025898477062583	-0.007769490592181683	-0.018055755645036697	-0.03459911048412323	-0.04283124953508377	-0.05462392792105675	-0.007152678444981575	0.042369112372398376	-0.048466842621564865	-0.09095963835716248	-0.008427342399954796	-0.010896602645516396	-0.020387861877679825	0.06084796413779259	-0.005383624229580164	-0.09379130601882935	0.09155606478452682	0.03149573504924774	0.0015343907289206982	-0.03901078179478645	0.013949843123555183	-0.012036945670843124	0.025130052119493484	0.05364248529076576	-0.01896907016634941	0.054906949400901794	-0.04161743074655533	-0.007735908962786198	0.04092051088809967	-0.00898699089884758	0.1085958182811737	0.06650258600711823	-0.03506935015320778	-0.0770765095949173	0.01815393753349781	-0.045393653213977814	-0.054781489074230194	0.020013868808746338	0.012403790839016438	-0.03791229799389839	-0.047169171273708344	0.07707986980676651	0.07066188752651215	0.10041587054729462	-0.03951364383101463	-0.051757827401161194	-0.017694346606731415	0.015789847820997238	0.015957631170749664	0.04685356840491295	-0.04547961801290512	-0.050676971673965454	0.012113586068153381	-0.08794248104095459	0.0057997494004666805	0.011641242541372776	-0.003306934842839837	0.006994251627475023	-0.061087317764759064	-0.01212542038410902	0.08914843946695328	0.01263861358165741	-0.00855480320751667	0.010303087532520294	0.11910520493984222	-0.11810054630041122	-0.025603411719202995	0.06552774459123611	-0.07703088968992233	-0.049174122512340546	0.058414384722709656	0.017254682257771492	0.0384981743991375	0.01770763471722603	-0.03972436860203743	0.10319749265909195	-0.04888514056801796	-0.0400385819375515	-0.023900866508483887	-0.02766992151737213	-0.04644948989152908	0.06386414170265198	0.0576883926987648	0.024406787008047104	0.054954446852207184	-0.04438694566488266	0.016643324866890907	0.002477702684700489	0.011236222460865974	0.05855490267276764	-0.06565166264772415	-0.009413711726665497	-0.01478248368948698	-0.0026479391381144524	0.002177360001951456	0.025954481214284897	-0.012011329643428326	0.052014872431755066	0.009055263362824917	-0.06143086403608322	0.014776644296944141	0.039679449051618576	0.04380608722567558	0.05539094656705856	0.05997520685195923	0.11323846876621246	0.03183886781334877	0.0528743714094162	0.0051376670598983765	0.04264966771006584	-0.06343887746334076	-0.03881027549505234	0.02563517913222313	-0.034419137984514236	0.07418812066316605	-0.012002270668745041	-0.0399371013045311	0.0067409249022603035	0.061497416347265244	-0.05469207838177681	-0.07437168806791306	0.0223984494805336	-0.0021418691612780094	0.09329333156347275	0.07447180151939392	-0.00020188474445603788	0.004914327058941126	0.08474033325910568	-0.0047651007771492004	0.014540459029376507	0.06202610954642296	-0.032269734889268875	-0.024437014013528824	-0.003918970003724098	0.03274311497807503	-0.009996733628213406	-0.06051698327064514	0.03208457678556442	0.05079479515552521	0.02262139692902565	0.010574307292699814	-0.04992996156215668	-0.019047580659389496	-0.07725278288125992	-0.04472273215651512	0.009324107319116592	-0.08603861927986145	-0.029108306393027306	0.06874453276395798	-0.011116649024188519	0.05819308012723923	-0.008197891525924206	-0.017891107127070427	-0.048686712980270386	-0.06214413419365883	-0.006638019345700741	-0.06135769560933113	-0.08495371788740158	-0.029039962217211723	0.011871750466525555	0.01834198459982872	-0.034332167357206345	-0.011551993899047375	-0.017849387601017952	-0.03663096949458122	0.03589947521686554	0.06265732645988464	0.06385640054941177	0.027561604976654053	0.002076153876259923	-0.03894169628620148	0.002690146444365382	-0.06476136296987534	0.0338069349527359	-0.0816122442483902	0.03638231009244919	-0.02739180251955986	0.003408547258004546	-0.005514319986104965	0.05096963047981262	0.009080829098820686	0.018035195767879486	0.05875876545906067	-0.03930441662669182	-0.032524045556783676	0.05745444819331169	0.07068509608507156	-0.039244893938302994	0.023677390068769455	-0.02658791095018387	0.0407995842397213	-0.0023020822554826736	-0.039359454065561295	-0.07617966085672379	0.02534295618534088	0.025196099653840065	0.070070281624794	0.08119229972362518	-0.05810421332716942	0.07283169776201248	0.029997168108820915	0.0220614243298769	-0.04631449282169342	-0.01670125685632229	0.0036603042390197515	0.018491776660084724	-0.003793334821239114	0.06772005558013916	-0.022063983604311943	0.012387176044285297	-0.016337493434548378	0.05789870396256447	-0.0299964789301157	-0.020312698557972908	0.019508883357048035	-0.08931954205036163	0.0643865093588829	-0.044526517391204834	-0.07913997024297714	-0.012720034457743168	0.041001852601766586	-0.02993912063539028	-0.01908866874873638	0.012435774318873882	0.01271339226514101	-0.029120974242687225	-0.022952625527977943	0.0361957810819149	-0.02836190164089203	-0.08352550119161606	0.12669365108013153	-0.003823330393061042	-0.056036267429590225	-0.06367090344429016	0.03670286759734154	0.015934975817799568	0.014119300991296768	0.01760142296552658	0.02854299172759056	0.0921708345413208	0.02370949275791645	0.003291365224868059	0.013193107210099697	-0.054680295288562775	-0.06409787386655807	0.008808452636003494	0.028553975746035576	-0.05403110012412071	-0.05399249121546745	-0.014721173793077469	-0.00891050137579441	0.01855379343032837	-6.606603619729867e-07	0.020986372604966164	-0.013116602785885334	-0.002976687392219901	-0.029898492619395256	0.0725054144859314	-0.04350453242659569	-0.050717029720544815	0.0859840139746666	-0.002755257301032543	0.029531512409448624	-0.0036724198143929243	-0.01262915413826704	0.014087128452956676	0.03960270434617996	-0.0023074778728187084	-0.03229180723428726	0.06282108277082443	0.0007477716426365077	-0.053907059133052826	-0.03922726586461067	-0.1121753603219986	-0.049654290080070496	0.00743114622309804	-0.01824812963604927	0.004830567631870508	0.054808277636766434	0.03512744605541229	-0.04662598296999931	-0.05876310542225838	-0.015071162953972816	-0.04923464357852936	-0.05495404452085495	-0.02740715816617012	0.01579413004219532	-0.06354334205389023	-0.04744838923215866	0.02794092707335949	-0.02968551777303219	-0.01817253604531288	-0.01623186096549034	-0.04317127540707588	-0.04952240362763405	-0.002455979585647583	0.0005406607524491847	-0.0008930914918892086	0.01798616163432598	-0.040215760469436646	0.041270557790994644	0.12068846076726913	0.06557218730449677	0.0343812070786953	0.05559806153178215	0.04587079584598541	0.11998863518238068	0.026410505175590515	-0.056934185326099396	-0.02759196236729622	-0.05475090444087982	-0.09645207971334457	0.03474141284823418	-0.027213837951421738	-0.01729085110127926	-0.02226872928440571	0.0088404705747962	0.045636121183633804	-0.002468890743330121	-0.025367723777890205	-0.04864542558789253	-0.0031800782307982445	-0.020182345062494278	-0.005149966571480036	-1.3801515706290957e-05	-0.018310468643903732	-0.017233941704034805	-0.08327788859605789	-0.03915181756019592	-0.08842248469591141	0.03417091444134712	-0.007759284693747759	0.045171622186899185	-0.0036505144089460373	-0.02886907383799553	-0.020178750157356262	0.012178516946732998	0.08996644616127014	-0.04457893967628479	0.016004590317606926	0.03588378056883812	0.03731611743569374	-0.07393364608287811	-0.029766671359539032	0.0386321134865284	-0.04516008123755455	-0.02137649431824684	-0.014808314852416515	0.03167565166950226	-0.05137546360492706	-0.00023619552666787058	0.011878516525030136	-0.08248714357614517	-0.015469585545361042	-0.004652345087379217	0.027958521619439125	0.01669219508767128	-0.0037190995644778013	-0.038948506116867065	-0.011843674816191196	-0.021853435784578323	0.017755789682269096	0.006347005721181631	-0.0008825944387353957	-0.03605111315846443	0.006220301613211632	-0.056044671684503555	-0.019892917945981026	-0.06140681728720665	0.0588192455470562	-0.03618793934583664	-0.03176956996321678	0.030085867270827293	0.02082393877208233	-0.03061695769429207	0.023391056805849075	0.048428162932395935	-0.03543199226260185	-0.030119983479380608	-0.025837143883109093	-0.0032984011340886354	-0.08138284087181091	0.007900366559624672	-0.03145841509103775	-0.03267462179064751	0.04161224886775017	-0.010070592164993286	-3.2357507734559476e-05	-0.01211242750287056	-0.014125263318419456	0.03586968779563904	0.03472253680229187	0.0267885010689497	0.014064152725040913	-0.0034208488650619984	0.022455619648098946	0.012824532575905323	0.039624132215976715	-0.055350739508867264	-0.01832861453294754	0.030022071674466133	-0.04647647589445114	0.041958875954151154	-0.03761054947972298	0.007311858236789703	-0.06461618840694427	0.007253557909280062	0.018133534118533134	-0.03511391952633858	0.01984471082687378	-0.014120432548224926	0.012002553790807724	-0.057309843599796295	-0.04971988499164581	0.03527839481830597	-0.027831465005874634	-0.07508423924446106	-0.014997174963355064	0.013785235583782196	0.004252529703080654	0.03165014460682869	-0.037693824619054794	0.030130885541439056	0.0399736650288105	-0.043756574392318726	-0.05182991921901703	-0.05208795145153999	0.007585136219859123	0.06241869181394577	-0.028936032205820084	0.03920722007751465	-0.0037524409126490355	-0.014116199687123299	0.02644442208111286	-0.02083664759993553	0.02238057181239128	-0.0895867571234703	-0.0077970074489712715	-0.007791350595653057	0.006184482481330633	0.059595827013254166	0.0021591235417872667	0.0038268582429736853	-0.0065717571415007114	-0.0326511524617672	-0.028822654858231544	0.08850039541721344	-0.026633232831954956	0.020081650465726852	0.046803586184978485	-0.028970668092370033	0.03700583428144455	-0.0094095254316926	-0.03877435252070427	-0.05339854583144188	-0.01135228294879198	-0.020013058558106422	0.00418673362582922	0.015237294137477875	0.01978345960378647	-0.006760598160326481	-0.044193413108587265	0.0042636943981051445	0.015775581821799278	-0.06495092064142227	0.0012654417660087347	-0.02279827743768692	-0.047972872853279114	-0.06097998470067978	-0.0117882639169693	-0.0580153726041317	0.07003428041934967	0.01088494062423706	0.03106609359383583	0.015740353614091873	0.008996398188173771	0.030225979164242744	-0.024102551862597466	-0.02553197182714939	0.020617833361029625	-0.028858857229351997	0.016100628301501274	0.0596969835460186	-0.016037587076425552	0.021256063133478165	0.06672809273004532	-0.016413407400250435	0.035405512899160385	0.07635583728551865	-0.03526163101196289	0.03559735044836998	-0.019239049404859543	-0.04588734731078148	0.08256275951862335	-0.056928545236587524	0.1108688935637474	-0.05570562556385994	0.08597613126039505	-0.0008739158511161804	-0.060819756239652634	0.08972293138504028	0.07980386167764664	-0.024975795298814774	0.0466291718184948	-0.06238403916358948
+0.029765676707029343	-0.042044345289468765	-0.02017994411289692	0.008260580711066723	-0.059389181435108185	-0.0665791854262352	-0.010821499861776829	0.03255336731672287	-0.012576879933476448	-0.07370785623788834	-0.046011898666620255	-0.02303045243024826	-0.018011752516031265	-0.0029114470817148685	0.04767473042011261	-0.052516572177410126	-0.06012015789747238	0.032613374292850494	-0.04625170677900314	-0.020665699616074562	0.029071325436234474	0.03275236859917641	-0.03354647383093834	0.046066902577877045	0.0020028501749038696	0.004905312322080135	-0.02584468387067318	0.013136443682014942	0.02075904607772827	0.05431470647454262	0.0653780996799469	-0.028863878920674324	0.044129181653261185	-0.05224251747131348	-0.02881160005927086	0.061681635677814484	-0.03063160367310047	0.11200553923845291	0.03416234999895096	-0.0037733418866991997	-0.05040673539042473	-0.004399490542709827	-0.010143526829779148	-0.08024680614471436	0.040670622140169144	0.0009018100099638104	-0.018285183236002922	-0.02676815167069435	-0.002726529026404023	0.046612128615379333	0.09456519782543182	0.0009943563491106033	-0.02647065371274948	-0.005416490603238344	-0.009846379980444908	-0.003826743457466364	0.034112442284822464	-0.04005107656121254	-0.02195671759545803	0.03999777510762215	-0.10376782715320587	-0.017201734706759453	0.014902870170772076	0.007750696036964655	-0.012085271067917347	-0.035504285246133804	-0.00719645107164979	0.08143902570009232	-0.006326816510409117	-0.035417575389146805	0.00566897913813591	0.06961759179830551	-0.04583047702908516	-0.07840348780155182	0.04706348106265068	-0.08334863930940628	0.023182464763522148	0.016183657571673393	-0.02210407890379429	0.0022239263635128736	-0.004625349771231413	-0.02129644900560379	0.16168716549873352	-0.06254705786705017	-0.0378008671104908	-0.03823421895503998	-0.03691031411290169	-0.06984203308820724	0.04558485746383667	0.01676749624311924	0.04176083579659462	0.04205525293946266	-0.011550882831215858	-0.0018008206970989704	-0.019654124975204468	-0.0071405391208827496	0.01911189965903759	-0.08755313605070114	-0.018897417932748795	-0.010899411514401436	-0.008834908716380596	0.005360405892133713	0.06714410334825516	0.02883952483534813	0.04295012727379799	0.027080239728093147	-0.0391126424074173	0.009561476297676563	0.044027235358953476	0.028819594532251358	0.012701332569122314	0.10835850983858109	0.09203352779150009	0.0354062058031559	0.08625133335590363	0.0028932837303727865	-0.008065000176429749	-0.05609880015254021	-0.01273687370121479	-0.004060552455484867	0.0007598349475301802	0.059524811804294586	-0.01767730340361595	-0.07374904304742813	-0.005377717316150665	0.03850173577666283	-0.11525406688451767	-0.030542826279997826	0.04907206445932388	-0.02292305789887905	0.1126682385802269	0.05371789634227753	-0.04539939761161804	0.05108584836125374	0.04474002495408058	0.018007606267929077	-0.013462075032293797	0.01844174973666668	0.00335697247646749	-0.058719515800476074	-0.019249020144343376	0.05024055391550064	-0.020118478685617447	-0.024396341294050217	0.043398141860961914	0.041377414017915726	0.027532411739230156	-0.04237581789493561	-0.06472204625606537	0.01300223357975483	-0.0771363154053688	-0.05866158753633499	0.006204820238053799	-0.05251484736800194	0.010284420102834702	0.009852513670921326	0.0020375673193484545	0.0024006529711186886	-0.01885480247437954	-0.025539293885231018	-0.05833086371421814	-0.05227118730545044	-0.017730362713336945	-0.08688373863697052	-0.0036182499025017023	0.01731269434094429	-0.00162318570073694	0.003675974439829588	-0.08552782982587814	-0.059372156858444214	-0.026464849710464478	-0.031161896884441376	0.036100201308727264	0.08088359981775284	0.06836944073438644	0.007429931778460741	0.019743267446756363	-0.07006502151489258	-0.014897959306836128	-0.03119133785367012	0.03827493265271187	-0.04269286245107651	0.03525601327419281	-0.0735425353050232	-0.001862517325207591	-0.009285865351557732	0.014745797030627728	-0.04609860107302666	0.032935433089733124	0.011488054879009724	-0.0289508868008852	-0.049680691212415695	0.03479396924376488	0.06979572772979736	-0.01768394559621811	0.03921836242079735	-0.014970839954912663	0.09046073257923126	-0.02913644351065159	-0.04927099496126175	-0.08997107297182083	0.021226724609732628	0.023923957720398903	0.03421008214354515	0.027380159124732018	-0.06674490869045258	0.0965481847524643	0.024679042398929596	0.03238997980952263	-0.04126490652561188	0.047368329018354416	0.04771818965673447	-0.003855524817481637	0.0441700778901577	0.05811526998877525	-0.02928849682211876	-0.005905604921281338	-0.01600663922727108	0.04199735447764397	-0.06094665825366974	-0.010040265507996082	-0.008864271454513073	-0.0818387120962143	0.0045723081566393375	-0.05518757551908493	-0.08037608861923218	-0.02343638986349106	0.03708109259605408	-0.00822014082223177	-0.041727129369974136	0.0005580749711953104	0.039483506232500076	-0.008899894542992115	0.019232824444770813	0.0147599708288908	-0.04512573406100273	-0.09639304131269455	0.07312840968370438	-0.009000635705888271	-0.08684064447879791	-0.05968862771987915	-0.023225922137498856	0.03629901260137558	-0.006441698409616947	0.027876827865839005	0.04538824409246445	0.06298863887786865	0.029995517805218697	-0.04827660694718361	0.007696017622947693	-0.01715429499745369	-0.03178463876247406	-0.019164806231856346	-0.015001283027231693	-0.02508855052292347	-0.0830264762043953	0.020279420539736748	-0.08797330409288406	-0.007308025844395161	-0.02406412363052368	0.029734358191490173	-0.04665464535355568	-0.028880475088953972	-0.022504134103655815	0.03287555277347565	-0.029722394421696663	-0.03500175103545189	0.04184682294726372	-0.03698103874921799	0.024542706087231636	-0.024384967982769012	0.013286272063851357	0.03978748619556427	-0.027124706655740738	0.016151389107108116	-0.04736843332648277	0.09787139296531677	0.01289601530879736	-0.0404626689851284	-0.008625823073089123	-0.06421559303998947	-0.06075160577893257	0.003459550440311432	-0.03264647349715233	-0.014471291564404964	0.05075020343065262	0.05654826760292053	-0.039305124431848526	-0.04419698938727379	-0.047161541879177094	-0.03391411527991295	-0.04829057306051254	-0.03664205223321915	-0.02086688205599785	-0.0419916994869709	0.004671374335885048	0.023090871050953865	-0.03651433438062668	-0.018924543634057045	0.0204376969486475	0.012236150912940502	-0.05430091917514801	0.01667489856481552	-0.03766223043203354	-0.02575082890689373	0.0004027810937259346	-0.04359439015388489	0.03239533677697182	0.09022440761327744	0.07653097063302994	0.018544308841228485	0.035978738218545914	0.02396157756447792	0.08889313042163849	-0.010031060315668583	0.0045256223529577255	-0.03009512834250927	-0.05746808648109436	-0.04650954157114029	0.061739884316921234	-0.011319085024297237	-0.018428446725010872	0.02419799566268921	0.010776415467262268	0.0467374250292778	-0.022183114662766457	-0.041806310415267944	-0.033377908170223236	0.017428604885935783	-0.03397052735090256	0.017803898081183434	-0.020478058606386185	0.0468888022005558	-0.014466872438788414	-0.061987362802028656	-0.06437742710113525	-0.05310309678316116	0.060866110026836395	-0.020471232011914253	0.042175132781267166	-0.03560579568147659	-0.058589283376932144	-0.04520396515727043	0.025235973298549652	0.13205869495868683	-0.03688863292336464	0.012944434769451618	0.03611099347472191	0.06997771561145782	-0.09964504092931747	0.01897904835641384	-0.01561931986361742	-0.02483556419610977	-0.06025674194097519	0.0015791625482961535	0.03792308270931244	-0.09885473549365997	-0.03267687186598778	-0.03498711809515953	-0.011204468086361885	0.021191520616412163	0.0019738192204385996	-0.005253619980067015	0.052794214338064194	0.02880559116601944	-0.04816114529967308	0.009814346209168434	0.0027311518788337708	0.022073546424508095	-0.04301358014345169	0.016115637496113777	-0.040157608687877655	0.012782329693436623	-0.05168985202908516	-0.05846007913351059	-0.03232496231794357	0.045867856591939926	-0.025361280888319016	-0.005055674817413092	-0.017844779416918755	-0.026958800852298737	-0.019683534279465675	0.007632306776940823	0.004040453117340803	-0.06359541416168213	-0.019784841686487198	-0.035281527787446976	-0.05905522406101227	-0.08294844627380371	-0.03846123069524765	0.009186165407299995	-0.010547995567321777	0.028203461319208145	-0.02147388458251953	0.02355147711932659	0.01417036633938551	0.020327838137745857	0.05874031409621239	0.06802377104759216	0.021732481196522713	0.003388600191101432	0.03607594966888428	-0.0014524252619594336	0.0527363196015358	0.037734534591436386	-0.05516660585999489	-0.028378929942846298	-0.00477454112842679	-0.06042502075433731	0.05100931599736214	-0.03995324298739433	0.004055737983435392	-0.01517920009791851	-0.013328075408935547	0.01684674620628357	-0.025842983275651932	0.029567649587988853	-0.025318244472146034	0.04928234592080116	-0.07369427382946014	-0.04958019033074379	0.020560242235660553	-0.03488921746611595	-0.10123370587825775	-0.06000319495797157	-0.021931735798716545	-0.017023799940943718	0.04419795051217079	-0.03646602854132652	0.04027475416660309	0.009293705224990845	-0.014332077465951443	-0.043965648859739304	-0.01202221680432558	-0.009910517372190952	0.017641326412558556	0.01680043712258339	0.043468691408634186	0.00725078210234642	0.0257873497903347	0.011873702518641949	-0.031792052090168	0.015162245370447636	-0.07970190793275833	0.0071890405379235744	-0.004548981785774231	-0.015769818797707558	0.0043710749596357346	0.029818210750818253	0.028177410364151	-0.05100081115961075	-0.016628053039312363	-0.06744679808616638	0.07429789751768112	0.009694305248558521	0.06520510464906693	0.007952307350933552	-0.0749792754650116	-0.01305738277733326	0.03504294157028198	-0.04918168857693672	-0.0902593657374382	-0.016293883323669434	-0.03367789089679718	0.03578489273786545	-0.008341934531927109	0.036392707377672195	-0.025651708245277405	-0.08254151046276093	0.060272328555583954	0.008588952012360096	-0.015362435020506382	0.005394038278609514	0.046089377254247665	-0.04768813028931618	-0.031574901193380356	0.00988968275487423	-0.07189327478408813	0.04917377978563309	0.02098667621612549	0.03766365349292755	-0.04729780554771423	-0.0025299310218542814	-0.00044897248153574765	-0.029865393415093422	0.03270047903060913	-0.009448870085179806	-0.030177636072039604	-0.011964138597249985	0.094611257314682	-0.028130529448390007	0.020760372281074524	0.07570861279964447	-0.03401275351643562	0.04802250862121582	0.06452469527721405	-0.021618040278553963	0.048920754343271255	0.009463027119636536	-0.012578791007399559	0.0501231923699379	-0.09003681689500809	0.0738983303308487	-0.06968828290700912	0.12397162616252899	0.04294953495264053	-0.005344643257558346	0.08423355221748352	0.0557464174926281	0.01797940768301487	0.05667022988200188	-0.09127475321292877
+0.02699943631887436	0.008596190251410007	-0.0012504929909482598	0.04097555950284004	-0.09276903420686722	-0.06813247501850128	-0.002109889406710863	0.028929395601153374	-0.010920917615294456	-0.03241172432899475	-0.010008685290813446	-0.036903202533721924	-0.035566460341215134	-0.019616004079580307	0.025804467499256134	-0.030004126951098442	-0.08941188454627991	0.0148231852799654	-0.061968978494405746	-0.013967934995889664	0.05690714716911316	0.012801028788089752	-0.07707129418849945	0.0557003989815712	-0.004290327895432711	0.008463392034173012	-0.03252352029085159	0.01846005767583847	-0.03709867596626282	0.040302909910678864	0.08426361531019211	0.006496127229183912	0.0447152778506279	-0.04894101247191429	-0.030369775369763374	0.06118669733405113	-0.015252680517733097	0.11002402752637863	0.0448867492377758	-0.01631052792072296	-0.055584702640771866	0.017563432455062866	-0.020693369209766388	-0.03676688298583031	0.05701485276222229	-0.012688224203884602	-0.025871748104691505	-0.03714093565940857	0.05352114140987396	0.054791685193777084	0.09951487928628922	-0.006920658051967621	-0.017530620098114014	-0.004359954036772251	-0.008928279392421246	0.024316884577274323	0.036372821778059006	-0.056199587881565094	-0.06617006659507751	0.025271257385611534	-0.05378574877977371	-0.002920767292380333	0.013258037157356739	-0.022762252017855644	0.00414315378293395	-0.016208088025450706	-0.014140504412353039	0.08280418068170547	0.021572014316916466	-0.015183763578534126	0.018898088485002518	0.10577836632728577	-0.09863021969795227	-0.06604785472154617	0.058335937559604645	-0.05452572926878929	-0.027913950383663177	0.05262238532304764	-0.009162903763353825	0.016864225268363953	-0.018474960699677467	-0.0308298971503973	0.11428987979888916	-0.07203715294599533	-0.041577283293008804	-0.033631112426519394	-0.02172618731856346	-0.04240218177437782	0.0680779442191124	0.042846761643886566	0.0047873202711343765	0.04231198504567146	-0.05739142745733261	0.014312030747532845	0.0010019660694524646	0.006637537851929665	0.050301387906074524	-0.11932346969842911	-0.021497737616300583	0.011332608759403229	-0.016226258128881454	-0.012343306094408035	0.045884087681770325	-0.003854174166917801	0.0582076832652092	0.055749110877513885	-0.050750888884067535	0.006543694995343685	0.020582139492034912	0.04961450770497322	0.03720274195075035	0.0829678550362587	0.11263246089220047	0.03840533271431923	0.059935346245765686	-0.012825275771319866	-0.008068650029599667	-0.048046328127384186	-0.05422932282090187	-0.0017971692141145468	-0.02750842273235321	0.0447622686624527	-0.02235131897032261	-0.006516458932310343	-0.02385207638144493	0.03426464647054672	-0.11716371774673462	-0.038625460118055344	0.058743271976709366	-0.029293345287442207	0.11019254475831985	0.028748299926519394	0.0031134099699556828	0.0032137581147253513	0.09805290400981903	0.014948639087378979	0.022817181423306465	0.021585971117019653	-0.029494255781173706	-0.025578144937753677	0.007582367863506079	0.03575200214982033	-0.02796073630452156	-0.04391292855143547	0.001762260915711522	0.06738930195569992	0.033792056143283844	-0.012493574991822243	-0.07564153522253036	0.02982190065085888	-0.11228221654891968	-0.0715213194489479	-0.022117938846349716	-0.09916001558303833	0.0026066855061799288	0.05371086299419403	0.01272626779973507	0.05527516081929207	-0.016680965200066566	-0.005940444767475128	-0.035943467170000076	-0.05161634832620621	-0.01735702157020569	-0.06728143244981766	-0.05253289267420769	-0.035352326929569244	-0.0013220852706581354	0.0015968297375366092	0.002143630525097251	-0.05694128945469856	-0.03574339672923088	-0.011081933043897152	0.01833394356071949	0.0872480720281601	0.051730673760175705	0.028286796063184738	0.006983434781432152	-0.04455381631851196	-0.01375820953398943	-0.06207270920276642	0.008406288921833038	-0.04944507032632828	0.05668777599930763	-0.05060313642024994	0.003933603409677744	-0.006514804903417826	0.03684595972299576	-0.01602838560938835	0.014125055633485317	0.0249288659542799	-0.05090031772851944	-0.0607466958463192	0.05180111899971962	0.0997554287314415	-0.04497656226158142	0.04162856936454773	-0.01207822561264038	0.047532107681035995	-0.004266698844730854	-0.04035377502441406	-0.07105765491724014	0.036052122712135315	0.0051473346538841724	0.042279746383428574	0.05380730703473091	-0.08077075332403183	0.07250829041004181	0.01309886109083891	-0.009757963009178638	-0.058334991335868835	0.017856650054454803	0.02711939997971058	0.03955041989684105	0.012196301482617855	0.046723466366529465	-0.0031072392594069242	-0.006551104597747326	-0.011723041534423828	0.056083135306835175	-0.05106585845351219	0.0197162963449955	0.005534593015909195	-0.07365166395902634	0.07477825880050659	-0.03389616310596466	-0.09719634801149368	-0.002911935094743967	0.01564965397119522	-0.02559347078204155	-0.020435458049178123	-0.00037338086985982955	0.033730193972587585	-0.014522007666528225	-0.03168767690658569	0.016797324642539024	0.010041601955890656	-0.10205641388893127	0.12406081706285477	-0.013257190585136414	-0.06006072834134102	-0.06559149920940399	0.02211989276111126	0.018482176586985588	0.0027001560665667057	0.032925575971603394	0.029398083686828613	0.09613896161317825	0.0454060398042202	0.009853353723883629	-0.009217869490385056	-0.07384298741817474	-0.03863298520445824	-0.00930110178887844	0.00913925375789404	-0.029738571494817734	-0.056545406579971313	0.002424028469249606	-0.048749472945928574	-0.0012868486810475588	0.011692851781845093	0.07560926675796509	-0.03548653796315193	-0.03616281971335411	0.004148614592850208	0.050662584602832794	-0.028139635920524597	-0.0886617973446846	0.04733220487833023	-0.02409905381500721	0.02966996654868126	-0.030083434656262398	-0.026568273082375526	0.012799568474292755	0.012508508749306202	0.05257260054349899	-0.013072268106043339	0.07014147937297821	-0.028495928272604942	-0.09765294194221497	-0.027897575870156288	-0.0764889344573021	-0.07166627049446106	-0.0008310305420309305	-0.024419188499450684	-0.018681880086660385	0.06614819914102554	0.04452132061123848	-0.03498907759785652	-0.07932782173156738	-0.006281064823269844	-0.03778251260519028	-0.0458807647228241	-0.026568418368697166	-0.0037505466025322676	-0.06454749405384064	-0.0069170924834907055	0.017573097720742226	-0.041839953511953354	-0.019592350348830223	0.010737994685769081	-0.03726894408464432	-0.025852065533399582	0.030858874320983887	-0.051807940006256104	-0.011001212522387505	0.01839853823184967	-0.034502580761909485	0.047021981328725815	0.0999656543135643	0.060857247561216354	0.0532912015914917	0.03735193610191345	0.045010242611169815	0.08131970465183258	0.016594063490629196	-0.01782948151230812	-0.0231779757887125	-0.038157518953084946	-0.0872567743062973	0.02596772275865078	-0.04612364247441292	-0.05020738020539284	-0.0033608581870794296	0.02218402735888958	0.07527322322130203	-0.011446462944149971	-0.03193787485361099	-0.029457688331604004	0.027955034747719765	0.011213468387722969	0.016317520290613174	0.0121716083958745	-0.01796901412308216	0.013346218504011631	-0.05026983469724655	-0.008066438138484955	-0.06672021746635437	0.04406682029366493	0.002581976354122162	0.026977237313985825	-0.026558127254247665	-0.06129206344485283	0.005601922050118446	0.03188483044505119	0.09124233573675156	-0.05201258882880211	0.03271150588989258	0.04549020156264305	0.01310017891228199	-0.08681654930114746	-0.030231652781367302	0.04961074888706207	-0.007352680899202824	-0.05658223107457161	0.022576211020350456	0.05184691399335861	-0.07433514297008514	-0.024196013808250427	0.010654167272150517	-0.061023056507110596	-0.014749246649444103	0.023614803329110146	0.0004978521028533578	-0.004269810859113932	0.0041265045292675495	-0.01889314316213131	-0.00848193559795618	0.02108495682477951	0.022576885297894478	-0.039262011647224426	0.021261099725961685	-0.032789748162031174	0.052376896142959595	-0.030345523729920387	-0.0629311203956604	-0.059945251792669296	0.07198301702737808	-0.011410478502511978	-0.0342894047498703	-0.03794778138399124	0.013549783267080784	0.001583434990607202	0.04256649687886238	-0.014718040823936462	-0.07307791709899902	-0.012735579162836075	-0.003104718867689371	-0.0659908726811409	-0.09748975932598114	-0.012846180237829685	0.002009771764278412	-0.06668756157159805	-0.009013265371322632	-0.0321209654211998	-0.004540673457086086	-0.017723236232995987	-0.027089186012744904	0.045390889048576355	0.03289976343512535	0.02407250553369522	0.00917591992765665	0.018204040825366974	0.0025288446340709925	0.026869989931583405	0.013622032478451729	-0.03225649893283844	0.0003667802666313946	0.007065822836011648	-0.04760001599788666	0.034469570964574814	-0.04869135841727257	0.003866416634991765	-0.03182389959692955	-0.016235357150435448	0.014305198565125465	-0.001543374266475439	-0.006505539175122976	-0.05021212622523308	0.03630496561527252	-0.06126599386334419	-0.005646149627864361	0.04996082931756973	-0.02750673145055771	-0.08711721003055573	-0.008128354325890541	-0.01258195098489523	0.0015078778378665447	0.034808747470378876	-0.007369241677224636	0.04443123936653137	-0.02307451330125332	-0.06876050680875778	-0.04361264035105705	-0.0785653293132782	-0.016804631799459457	0.0258890762925148	-0.00150647247210145	0.07705914974212646	-0.012030777521431446	0.0009740724344737828	-0.010205206461250782	-0.0027890114579349756	-0.005918100476264954	-0.07735243439674377	-0.006432752124965191	-0.03710796684026718	0.008142865262925625	0.02107849344611168	0.0036418954841792583	0.013318322598934174	-0.01273042056709528	-0.04075423255562782	-0.04694676026701927	0.08877082914113998	-0.03717954456806183	0.011488514021039009	0.036812275648117065	-0.013107198290526867	0.009533009491860867	0.006484562065452337	-0.034337032586336136	-0.05544818192720413	0.01272189524024725	-0.01052300725132227	0.016758186742663383	0.054225437343120575	0.031649667769670486	-0.025236638262867928	-0.05670526251196861	-0.022209901362657547	0.016839584335684776	-0.029033107683062553	-0.009697957895696163	-0.004110737703740597	-0.07602614164352417	-0.06759035587310791	-0.04162360355257988	-0.04373908415436745	0.0803217664361	-0.018998101353645325	0.02557273395359516	-0.02238454855978489	-0.0017040168168023229	0.029072418808937073	-0.028238480910658836	0.005853109527379274	0.026565128937363625	-0.056329600512981415	-0.01636124961078167	0.04840582236647606	0.003802129067480564	0.043759845197200775	0.06643834710121155	-0.03749171271920204	0.03741487115621567	0.06078055128455162	-0.018224691972136497	0.024225223809480667	0.0018170318799093366	-0.03192669153213501	0.04806649312376976	-0.07071827352046967	0.09561027586460114	-0.043130356818437576	0.07941573858261108	0.013276591897010803	-0.009736595675349236	0.08647942543029785	0.0831679254770279	-0.007885611616075039	0.022584468126296997	-0.06214706972241402
+-0.04517669603228569	-0.0833064541220665	-0.022197335958480835	0.021061573177576065	0.04917830601334572	-0.03548583760857582	-0.0014797841431573033	0.0038125021383166313	0.028514785692095757	-0.045855093747377396	-0.03395838662981987	0.006564880255609751	-0.02578931860625744	-0.04238642752170563	-0.003765606554225087	0.03985165059566498	-0.05610441043972969	0.008064305409789085	0.006410134024918079	-0.000673977134283632	0.0035867439582943916	0.08035987615585327	-0.08500710874795914	0.02173720672726631	-0.02394839935004711	-0.017219344154000282	-0.021780936047434807	0.043380651623010635	-0.017663247883319855	0.0570438876748085	0.06094621866941452	-0.021817460656166077	0.09539487212896347	-0.030794592574238777	0.02787238545715809	-0.02942565083503723	0.015035153366625309	0.04444682598114014	0.025812776759266853	-0.0034202905371785164	0.030221635475754738	-0.030529094859957695	0.05577406287193298	-0.05796732008457184	-0.01214983407407999	-0.044332146644592285	-0.018116850405931473	-0.031024903059005737	-0.03625166788697243	0.08677056431770325	0.06784673035144806	0.0015994968125596642	0.033919475972652435	-0.025358926504850388	0.022052621468901634	-0.025457143783569336	0.016872355714440346	-0.04824452847242355	-0.0412750281393528	0.005002850200980902	-0.029605526477098465	0.04511161148548126	0.015756119042634964	0.013665275648236275	-0.06494535505771637	-0.0038196872919797897	0.03233005851507187	0.052174605429172516	-0.0013460282934829593	-0.055717382580041885	0.02181909792125225	-0.00805128738284111	-0.061368249356746674	-0.014552989974617958	-0.027881426736712456	0.023483699187636375	0.08495070040225983	0.008446373045444489	-0.0029085094574838877	-0.02968318946659565	0.003879341995343566	0.00960420910269022	0.05589292570948601	-0.04108120873570442	0.03337534889578819	-0.06832296401262283	-0.006863259244710207	-0.08075617253780365	-0.003071950748562813	-0.05606634542346001	0.06964168697595596	0.016958804801106453	0.05195393040776253	0.012703221291303635	0.012326079420745373	0.05879192426800728	0.027820268645882607	-0.07936340570449829	-0.009585011750459671	0.02819632925093174	-0.0437348447740078	0.04339161515235901	0.0710005983710289	0.038789052516222	-0.010024726390838623	0.0032359501346945763	-0.028666893020272255	0.041932206600904465	0.08033487200737	0.06925128400325775	0.03241788223385811	0.06286340206861496	0.021626653149724007	0.022817032411694527	0.022769156843423843	0.01643717661499977	0.0065070162527263165	0.06398915499448776	0.0657392144203186	0.04636931046843529	0.001838038326241076	0.01615300215780735	0.00415816530585289	-0.05005021020770073	-0.00932849757373333	0.13135764002799988	-0.018998727202415466	-0.0691291019320488	-0.0022196792997419834	-0.01579490303993225	0.06958893686532974	0.025971226394176483	9.148252865998074e-05	-0.008491338230669498	-0.02079620212316513	-0.008769377134740353	-0.03480036184191704	-0.014591967687010765	0.04928077757358551	-0.04152956232428551	-0.051128655672073364	-0.005973937455564737	-0.04323520138859749	-0.013970923610031605	0.004259040113538504	0.0008025665883906186	-0.03736246004700661	-0.03461693972349167	-0.03976410627365112	-0.04646176099777222	-0.03096156008541584	-0.023923953995108604	0.008131396025419235	-0.0659148171544075	0.013585402630269527	-0.0006450186483561993	-0.006336495745927095	0.04395449906587601	0.019187266007065773	0.07255125045776367	-0.08192428201436996	-0.0649503692984581	0.029292667284607887	-0.049048349261283875	-0.02176673896610737	0.02217429131269455	0.01915634237229824	0.008891663514077663	-0.014365575276315212	-0.02247951552271843	-0.028443260118365288	0.03750894218683243	0.019823787733912468	0.0470656156539917	0.078343965113163	0.04024606943130493	0.02485004998743534	0.005713348742574453	0.005233163479715586	-0.05215964838862419	-0.024968484416604042	0.01218404620885849	-0.010636487975716591	-0.033835750073194504	-0.04303848743438721	-0.004762410186231136	-0.020244013518095016	0.013618833385407925	0.03604225069284439	0.04209910333156586	0.016542665660381317	-0.0623437836766243	0.018678035587072372	0.049858398735523224	-0.020066089928150177	0.04733588546514511	0.04834514483809471	0.15304386615753174	-0.07882963865995407	-0.02991197444498539	-0.026628868654370308	-0.024094384163618088	0.06285680830478668	-0.045330874621868134	-0.017425091937184334	-0.00997129362076521	0.06318583339452744	0.05724511295557022	-0.02200419083237648	-0.0234755240380764	0.041785456240177155	0.015941033139824867	0.02692887745797634	0.051923446357250214	0.047385070472955704	-0.02039504610002041	-0.04406098276376724	-0.014941402710974216	-0.02390238642692566	-0.08861988037824631	-0.010018364526331425	-0.014987270347774029	-0.03495291247963905	0.04665446653962135	-0.05431237816810608	-0.06795140355825424	0.056865256279706955	-0.07919967919588089	0.005293664522469044	-0.005874905269593	0.045717526227235794	0.007266710978001356	0.021198708564043045	0.00771607831120491	-0.025284430012106895	0.0017564368899911642	-0.05311598628759384	0.05710078403353691	-0.03649725019931793	-0.012972529046237469	-0.045979782938957214	0.0027315483894199133	-0.012395016849040985	-0.022510718554258347	-0.012324340641498566	-0.009436930529773235	0.1228373721241951	0.0591370090842247	-0.06816636770963669	0.01789761148393154	-0.07851602137088776	-0.0669669657945633	-0.0006987710949033499	-0.008509972132742405	0.008645028807222843	-0.0371529757976532	0.0017946668667718768	-0.11344826966524124	-0.017436916008591652	-0.020398247987031937	-0.0063074990175664425	0.01268768310546875	0.009405134245753288	-0.02951316349208355	0.023250451311469078	0.012293877080082893	-0.07941041886806488	0.06318920850753784	0.007245465647429228	-0.028516490012407303	-0.035215433686971664	0.031941041350364685	0.03397925943136215	-0.03129502013325691	0.023658297955989838	-0.0218264851719141	0.0814981758594513	-0.008542900905013084	-0.04655633121728897	-0.02959556132555008	-0.041785333305597305	-0.009093321859836578	0.05219235643744469	0.0002890188479796052	0.000695408380124718	-0.015640754252672195	0.025411274284124374	-0.019151415675878525	-0.001454535056836903	-0.021380625665187836	-0.07092046737670898	-0.024748941883444786	-0.03251577541232109	-0.01223522238433361	0.00666810292750597	0.020954104140400887	0.03442103788256645	-0.06672975420951843	0.020372681319713593	0.03457043319940567	-0.0075527457520365715	-0.010743594728410244	0.05265268683433533	0.0024151913821697235	-0.02632649429142475	0.08000590652227402	-0.07070021331310272	0.023101111873984337	0.022447291761636734	0.020754793658852577	0.04156443476676941	0.10966365039348602	0.0544048435986042	0.05136921629309654	-0.033803150057792664	0.03357939422130585	-0.03527297079563141	0.04807434603571892	-0.0004033682926092297	0.005820466671139002	0.0523175410926342	-0.012979337945580482	0.02431347407400608	-0.007978040724992752	0.06038212776184082	-0.0041523100808262825	-0.015679454430937767	-0.032301608473062515	0.0030772238969802856	-0.03558844327926636	-0.006327520124614239	0.003336545079946518	0.029535433277487755	0.03824887052178383	-0.06631138175725937	-0.004720458295196295	-0.031248493120074272	0.008895957842469215	-0.0580996610224247	0.05194057524204254	0.02225644327700138	-0.03160196542739868	0.0035584690049290657	-0.007313095964491367	0.06339907646179199	-0.08723116666078568	0.014452163130044937	-0.001027204329147935	-0.006322664674371481	-0.09858883172273636	-0.08324074000120163	0.0002613662218209356	-0.028595777228474617	-0.014334621839225292	0.024344321340322495	-0.014039728790521622	0.01792902871966362	-0.07668434083461761	0.03030403144657612	-0.011623174883425236	-0.004330970346927643	0.050266724079847336	0.019943267107009888	0.024065494537353516	-0.0013023130595684052	-0.005440481007099152	-0.13016889989376068	-0.0022202918771654367	0.024735108017921448	-0.028930528089404106	0.05112456530332565	-0.0027636883314698935	-0.07573164254426956	-0.02472751960158348	-0.06401924788951874	-0.02225920557975769	-0.0075602722354233265	-0.019451506435871124	0.05644720420241356	-0.0117472093552351	-0.018709585070610046	0.020440304651856422	0.060358595103025436	-0.0313408263027668	-0.045581620186567307	-0.010905261151492596	0.046800512820482254	-0.04081793129444122	-0.012337458319962025	-0.07917723804712296	0.0045067258179187775	0.009880613535642624	0.061398059129714966	-0.04954909160733223	0.07224715501070023	0.0012077123392373323	0.011225518770515919	0.08850572258234024	0.10532582551240921	0.0030392620246857405	0.035866767168045044	0.0003337165107950568	-0.042562052607536316	-0.011920562013983727	0.01768633909523487	-0.04606204852461815	-0.015712842345237732	-0.01594843715429306	-0.012772814370691776	-0.05551614612340927	-0.031476233154535294	-0.013056931085884571	0.03428853675723076	-0.10314701497554779	0.03945164754986763	-0.007120369467884302	0.019867204129695892	-0.019701702520251274	0.043421052396297455	-0.03203239664435387	-0.0019061823841184378	-0.028038330376148224	0.008867125026881695	0.00014758011093363166	-0.024978043511509895	-0.060226861387491226	-0.01351713202893734	0.10331891477108002	-0.06479256600141525	0.02815660834312439	-0.03375650569796562	-0.01556705404073	-0.05170265957713127	0.022972943261265755	0.06618937104940414	-0.046888332813978195	0.0880800262093544	0.010777339339256287	-0.05696061626076698	-0.029512034729123116	-0.018498796969652176	-0.10293910652399063	-0.0035388970281928778	-0.04872547462582588	-0.03119305707514286	-0.002069688169285655	0.04971225559711456	0.004979084711521864	0.03187170624732971	0.04463855177164078	-0.07153934985399246	-0.02634013630449772	-0.0595812126994133	0.039656031876802444	-0.01631517894566059	0.023406248539686203	-0.022701505571603775	-0.1305995136499405	-0.11658299714326859	0.042809031903743744	0.0041432310827076435	-0.06927081942558289	-0.0924200490117073	-0.0038569453172385693	0.058598194271326065	-0.024633392691612244	0.05735192447900772	-0.10052396357059479	-0.06997813284397125	-0.003476561512798071	0.03842482715845108	0.018223512917757034	-0.08793105185031891	0.0607488714158535	-0.07614685595035553	0.01636521704494953	0.0008473143680021167	-0.014640259556472301	0.045911937952041626	0.009274011477828026	-0.03464742749929428	-0.06944279372692108	-0.008045533671975136	0.005735071841627359	-0.05187903717160225	0.008372962474822998	-0.008740480057895184	-0.07669339329004288	-0.0961713194847107	0.06157144904136658	0.036451712250709534	0.08993728458881378	0.07006338983774185	-0.04785195738077164	0.07741186022758484	0.03630228340625763	0.02350170537829399	-0.05333169177174568	-0.058914750814437866	0.019090469926595688	0.024533428251743317	-0.022624634206295013	0.011452319100499153	-0.0469464473426342	0.12388209998607635	0.022376233711838722	-0.009833777323365211	0.0013596247881650925	0.04812192916870117	0.07783013582229614	0.011456763371825218	-0.0858767107129097
+0.06329822540283203	0.0029055378399789333	0.016986383125185966	0.06242050975561142	-0.06595514714717865	-0.02834014967083931	-0.024651428684592247	0.02333117462694645	-0.0190593171864748	-0.056589946150779724	-0.053993333131074905	-0.015316353179514408	-0.03188525140285492	0.023118777200579643	0.02964351885020733	-0.0270969457924366	-0.05255550891160965	0.043308962136507034	-0.017685238271951675	-0.028744088485836983	0.0371541790664196	-0.017337173223495483	-0.07426171004772186	0.05285562947392464	0.03282327577471733	-0.026137877255678177	-0.03693495690822601	0.0299282968044281	-0.005431260447949171	0.04162680730223656	0.03606392443180084	-0.03809253126382828	0.036961790174245834	-0.04621804133057594	-0.07183115929365158	0.039454348385334015	-0.028769653290510178	0.11435367912054062	0.012060602195560932	-0.004799525253474712	-0.05285041406750679	0.011884124018251896	-0.06250708550214767	-0.03268003463745117	0.052076421678066254	0.03481884300708771	0.008227641694247723	-0.0025175935588777065	0.04350107163190842	0.04614923149347305	0.09599708765745163	-0.02928285300731659	-0.016270538792014122	0.009445438161492348	0.020621540024876595	0.020479269325733185	0.058578163385391235	-0.02125818282365799	-0.008520694449543953	-0.01053579617291689	-0.09419805556535721	-0.038318440318107605	0.023578975349664688	-0.024354184046387672	-0.019107533618807793	0.008709719404578209	0.005911797285079956	0.0902295708656311	0.0062787593342363834	-0.039614301174879074	0.003663902636617422	0.09517280757427216	-0.07462278008460999	-0.03420255705714226	0.06432746350765228	-0.0719086304306984	-0.019467921927571297	0.04927530139684677	-0.0019756390247493982	0.022491250187158585	0.008259803988039494	-0.03172041103243828	0.15111713111400604	-0.047407813370227814	-0.07465759664773941	-0.0008533314685337245	-0.012527347542345524	-0.07300591468811035	0.04474581778049469	0.04417633265256882	0.0030247773975133896	0.06856992840766907	-0.04764025658369064	0.023236582055687904	-0.010965928435325623	-0.0023649137001484632	0.031327903270721436	-0.0759960263967514	-0.033066242933273315	0.028979865834116936	-0.005130514036864042	-0.013909072615206242	0.030693337321281433	-0.031136129051446915	0.04462054744362831	0.036963433027267456	-0.03364485129714012	-0.008079275488853455	-0.011062802746891975	0.030407095327973366	0.01851550117135048	0.06288248300552368	0.06978646665811539	0.04118172451853752	0.06793852895498276	0.023104127496480942	0.0553678534924984	-0.0635836273431778	-0.046847257763147354	-0.011916340328752995	-0.022969722747802734	0.08923252671957016	0.010230089537799358	-0.05551370978355408	0.0060832807794213295	0.01874828338623047	-0.0672578439116478	-0.052925076335668564	0.04974277317523956	-0.028178710490465164	0.10503237694501877	0.04017677903175354	-0.034085169434547424	0.0399814136326313	0.05561155825853348	0.0035306205973029137	0.006943729240447283	0.04890376701951027	0.002613560762256384	-0.05059395357966423	0.014424684457480907	0.03562313690781593	-0.013721009716391563	-0.05201857537031174	0.09470649808645248	0.08451390266418457	0.05596786364912987	-0.03403419256210327	-0.06166073679924011	-0.008224974386394024	-0.0609951876103878	-0.08175787329673767	-0.04109758883714676	-0.07463748753070831	-0.019550494849681854	0.022147616371512413	0.004359210841357708	0.025843678042292595	-0.04055265337228775	0.012027681805193424	-0.06482838839292526	-0.04023659601807594	-0.01029766071587801	-0.042927730828523636	-0.0775262638926506	0.022249503061175346	-0.03395729884505272	0.013717218302190304	-0.07441136240959167	-0.020760752260684967	-0.008311006240546703	-0.006995892617851496	0.07309629768133163	0.08424562215805054	0.08303756266832352	0.025321759283542633	0.044773850589990616	-0.04750918596982956	-0.02765297330915928	-0.021102113649249077	0.06247878819704056	-0.046182867139577866	0.03734934702515602	-0.04221334308385849	0.02929374761879444	-0.018072335049510002	0.053696487098932266	-0.013776981271803379	0.07106447219848633	0.0044114599004387856	-0.03391653671860695	-0.015379113145172596	0.05137135460972786	0.08630923926830292	-0.03256317228078842	0.05227361246943474	-0.04315933212637901	0.041116807609796524	-0.04211321100592613	-0.03657473623752594	-0.0619581900537014	0.03212821111083031	0.03613362833857536	0.07330064475536346	0.0584263913333416	-0.08620437979698181	0.0706855058670044	0.07478669285774231	0.042665328830480576	-0.04286230728030205	0.015514311380684376	0.01526184007525444	0.010954375378787518	0.05815141648054123	0.04339529573917389	0.003765401430428028	-0.007352248299866915	-0.01985800266265869	0.026955878362059593	-0.025421498343348503	-0.028853492811322212	0.0055741858668625355	-0.0901992991566658	0.021221719682216644	-0.07873817533254623	-0.024089345708489418	-0.030515726655721664	0.05458509922027588	-0.022874394431710243	-0.015320997685194016	-0.03162394464015961	0.008329748176038265	-0.014545094221830368	-0.04045276716351509	-0.002922983141615987	-0.011417298577725887	-0.07887261360883713	0.10869991034269333	0.017044400796294212	-0.04822203144431114	-0.0720389112830162	0.02690870314836502	0.03180380165576935	0.006527343299239874	0.030732836574316025	0.031615838408470154	0.07048837095499039	-0.014302394352853298	-0.003479607170447707	0.001270391047000885	-0.020879119634628296	-0.02358950488269329	-0.021793203428387642	0.012885653413832188	-0.019363263621926308	-0.014225680381059647	0.0013783222530037165	-0.01762620359659195	-0.01504504680633545	0.025917761027812958	0.011035612784326077	0.002104494720697403	-0.024887802079319954	-0.01646433211863041	0.0019623874686658382	-0.038897935301065445	-0.007378843612968922	0.016001757234334946	-0.018844671547412872	0.04779472202062607	-0.060830384492874146	-0.017293188720941544	0.0027925390750169754	-0.018698209896683693	-0.010759289376437664	-0.042874135076999664	0.06835740804672241	-0.0325692854821682	-0.0022891357075423002	-0.016992611810564995	-0.06817010045051575	-0.048380956053733826	-0.05999987572431564	-0.021743420511484146	0.0075890556909143925	0.09739330410957336	0.009399598464369774	-0.04448748007416725	-0.05695623159408569	-0.022373806685209274	-0.04181978479027748	-0.04219001904129982	0.010396606288850307	0.01113964430987835	-0.036977335810661316	-0.02899247035384178	0.003956767730414867	-0.006592909339815378	-0.003608275903388858	-0.0250715259462595	-0.019756635650992393	-0.06817881762981415	0.04390886425971985	0.006799276918172836	-0.010965220630168915	0.02051444537937641	-0.02115541324019432	0.02311011403799057	0.0720372349023819	0.06160581111907959	0.02314298041164875	0.061510708183050156	0.04986390471458435	0.13586722314357758	-0.007278834003955126	-0.028433997184038162	-0.048659518361091614	-0.06173495575785637	-0.06422962993383408	0.09376571327447891	0.0074048773385584354	-0.003915468230843544	-9.290637535741553e-05	0.018198959529399872	0.072573222219944	-0.021667003631591797	-0.022895783185958862	-0.06510888040065765	-0.00936676561832428	-0.01612096279859543	-0.002461442956700921	-0.018217336386442184	0.01908797025680542	-0.053892239928245544	-0.07406625151634216	-0.02932681329548359	-0.02010909840464592	0.026430517435073853	0.0040258062072098255	0.02482149749994278	-0.05261344462633133	-0.01947776973247528	-0.008295638486742973	0.02258877642452717	0.06981350481510162	-0.03868011012673378	-0.01108796987682581	0.08030126243829727	0.029790788888931274	-0.0746222659945488	0.015680573880672455	0.04853644222021103	-0.042014095932245255	-0.05340602621436119	-0.027817759662866592	0.04223821312189102	-0.051991697400808334	0.02091202698647976	-0.026145178824663162	-0.0308983214199543	-0.006550151854753494	0.006126812659204006	0.04139021411538124	0.054699283093214035	-0.007039657793939114	-0.031717702746391296	-0.023847222328186035	-0.005931582767516375	0.02288922294974327	-0.008250491693615913	0.013255554251372814	-0.051345307379961014	0.029201218858361244	-0.08160366117954254	-0.0442810095846653	-0.035812314599752426	0.016266457736492157	-0.042944952845573425	-0.03417206555604935	-0.016000967472791672	-0.01602877490222454	-0.033046744763851166	0.007227451540529728	0.037129782140254974	-0.06916176527738571	-0.026796892285346985	-0.04580351710319519	-0.061941958963871	-0.08997603505849838	-0.03586643561720848	-0.005157173611223698	-0.0499306358397007	0.028721515089273453	-0.020767582580447197	-0.03336089476943016	0.018311183899641037	0.020831800997257233	0.01489169243723154	0.07082673907279968	0.03715851157903671	-0.006299719680100679	0.06540513038635254	-0.010070242919027805	0.009830494411289692	0.04440591484308243	-0.06932712346315384	-0.02240455150604248	-0.013866430148482323	-0.037575315684080124	0.09932201355695724	-0.06588901579380035	-0.010240168310701847	-0.03201117366552353	6.349483010126278e-05	0.04490279778838158	-0.016339166089892387	0.018026253208518028	-0.01744053140282631	0.07626865804195404	-0.07037429511547089	-0.07260745018720627	0.0348411500453949	-0.02984958328306675	-0.08312094956636429	-0.047637905925512314	-0.026745030656456947	-0.01871263049542904	0.03482021018862724	-0.02729884348809719	0.0037655006162822247	0.039573755115270615	-0.03173588588833809	-0.0577474869787693	-0.06239180266857147	-0.019210342317819595	0.05758088454604149	-0.011766571551561356	0.039420224726200104	0.051342837512493134	-0.024217884987592697	0.01791059412062168	-0.025235014036297798	-0.009066302329301834	-0.06467722356319427	-0.022172577679157257	0.020677318796515465	0.004437293391674757	-0.003877372946590185	-0.024287458509206772	-0.008840538561344147	-0.026041174307465553	-0.004984494298696518	-0.08468842506408691	0.0792873203754425	-0.00018827947496902198	-0.005458911415189505	0.07217612862586975	-0.05169234424829483	0.02911519631743431	0.01708277314901352	-0.05912407487630844	-0.05954176187515259	-0.00872963760048151	-0.015256671234965324	0.005023044999688864	0.02347906306385994	-0.01269439421594143	0.004030297975987196	-0.019645817577838898	0.029232177883386612	0.016822492703795433	-0.06008358672261238	0.026225198060274124	0.010012187995016575	-0.05648469179868698	-0.1014041155576706	0.014341411180794239	-0.031242653727531433	0.05151350796222687	0.05680577829480171	0.018443681299686432	-0.013244048692286015	-0.0001164936984423548	-0.007595103699713945	-0.013979777693748474	0.020042812451720238	0.009878193028271198	-0.05858278274536133	0.03302319720387459	0.10351331532001495	-0.07484711706638336	0.006899505387991667	0.0373547337949276	-0.03914622217416763	-0.005108025390654802	0.03542198985815048	-0.011181010864675045	0.06608925014734268	-0.013313128612935543	0.011324986815452576	0.0728294774889946	-0.08049122244119644	0.0994265079498291	-0.062171366065740585	0.06967711448669434	0.03321409597992897	0.004036921542137861	0.09865596145391464	0.06301674991846085	0.0004978252109140158	0.01605948805809021	-0.0950840413570404
+0.02032247558236122	-0.01851820759475231	0.006900942884385586	0.055281445384025574	-0.07430485635995865	-0.03181419521570206	-0.00486111780628562	0.01777501590549946	0.02969902940094471	-0.05542105808854103	-0.013223918154835701	-0.003905088407918811	0.011795747093856335	0.019279493018984795	0.052928127348423004	-0.03845434635877609	-0.05461669713258743	0.016414906829595566	-0.011774569749832153	-0.042921289801597595	0.0032047515269368887	0.026040008291602135	-0.06198522448539734	0.03892902657389641	0.004522103816270828	0.006168357562273741	-0.06715238094329834	0.009687073528766632	-0.019069278612732887	0.037036869674921036	0.05377954617142677	-0.02136925421655178	0.049710530787706375	-0.0785667672753334	-0.05181959643959999	0.03639303520321846	-0.036210477352142334	0.11918765306472778	0.030460434034466743	-0.03581094741821289	-0.027622107416391373	-0.005356219597160816	-0.04746117442846298	-0.05382277071475983	0.020126957446336746	-0.007070367690175772	0.000392000685678795	-0.029357202351093292	0.032310884445905685	0.012817361392080784	0.10877706855535507	0.020880872383713722	-0.056899916380643845	-0.049491774290800095	-0.036435823887586594	0.00876651331782341	0.041740477085113525	-0.08554520457983017	-0.08413159102201462	0.04723464697599411	-0.09578320384025574	0.00690712034702301	0.011541204527020454	0.002136519644409418	-0.03155504912137985	-0.016250448301434517	-0.010709558613598347	0.06092333048582077	0.007585915271192789	-0.036740150302648544	0.047898489981889725	0.11885908246040344	-0.0724414587020874	-0.0820104256272316	0.05611755698919296	-0.06143578886985779	0.005912320222705603	0.02888522855937481	-0.004518298432230949	0.021517232060432434	-0.004543457180261612	-0.05521870404481888	0.09784869849681854	-0.05089612677693367	-0.013234523124992847	-0.03934859111905098	0.00438604224473238	-0.05317525565624237	0.0464186891913414	0.03870490565896034	0.012851432897150517	0.04853935167193413	-0.036212000995874405	0.059919022023677826	0.020315848290920258	-0.007588041480630636	0.017695575952529907	-0.12491849064826965	-0.04375598952174187	0.00976602640002966	0.0026844386011362076	-0.012345252558588982	0.06741443276405334	0.013474540784955025	0.06193014234304428	0.05458151549100876	-0.05960319936275482	0.033924080431461334	0.04856070131063461	0.06444788724184036	0.030705314129590988	0.06707117706537247	0.06190645322203636	0.06674368679523468	0.030175400897860527	-0.009403014555573463	0.01336219534277916	-0.033362094312906265	-0.026754233986139297	0.02814558334648609	0.0034571215510368347	0.03164052218198776	-0.027427557855844498	-0.05921463295817375	-0.02231723628938198	0.031947504729032516	-0.09323499351739883	-0.01846008002758026	0.04478411376476288	-0.020655391737818718	0.12467416375875473	0.038486696779727936	-0.004967127926647663	0.02661353163421154	0.044698286801576614	0.04972238838672638	-0.00612086895853281	0.019193975254893303	0.005884779151529074	-0.02437097392976284	-0.0219583697617054	0.06769932061433792	0.014550370164215565	-0.0490000881254673	0.04708295315504074	0.03749002516269684	0.027873599901795387	-0.027378885075449944	-0.07638418674468994	-0.012671135365962982	-0.08387473225593567	-0.08273161947727203	0.02350069396197796	-0.0792730525135994	0.027474520727992058	0.02718912810087204	0.061993565410375595	0.04148145765066147	-0.004860592540353537	0.006869633216410875	-0.04456216096878052	-0.045747339725494385	-0.026560140773653984	-0.07517842203378677	-0.01977052539587021	-0.035208895802497864	0.022682836279273033	0.015518284402787685	-0.048538606613874435	-0.02832813747227192	-0.014146055094897747	0.0304257869720459	0.04620745778083801	0.0696205422282219	0.04294106736779213	-0.0025173844769597054	0.03025832772254944	-0.06439995020627975	0.00342567334882915	-0.007662044372409582	0.025495195761322975	-0.04144665226340294	0.017149219289422035	-0.042645152658224106	-0.010221309959888458	-0.0022142028901726007	0.040523018687963486	-0.008597834035754204	-0.0049288771115243435	0.002529867459088564	-0.06039136275649071	-0.06971181929111481	0.051190584897994995	0.0743388831615448	-0.015565702691674232	0.03572230413556099	0.00451106671243906	0.04904556646943092	0.0009598106844350696	-0.0283963605761528	-0.03567187115550041	0.018472952768206596	0.01876724138855934	0.03517752140760422	-0.01074568834155798	-0.06673643738031387	0.06865804642438889	0.023568492382764816	0.032786302268505096	-0.05129408836364746	-0.005982401315122843	0.010210628621280193	-0.0028250531759113073	0.019407881423830986	0.07248356938362122	-0.04393354803323746	-0.01580912433564663	-0.0010610128520056605	0.06968595832586288	-0.05464581400156021	0.031222637742757797	0.00226618186570704	-0.07571236789226532	0.022358138114213943	-0.042365383356809616	-0.09113306552171707	-0.026238109916448593	0.0004379694873932749	-0.018880851566791534	-0.03067283146083355	-0.008458394557237625	0.01921280287206173	-0.008428496308624744	-0.021471647545695305	-0.020819449797272682	0.016523145139217377	-0.0883568599820137	0.09169928729534149	-0.010174831375479698	-0.047716982662677765	-0.06196371838450432	-0.01984143629670143	0.027991099283099174	-0.012286082841455936	0.030437443405389786	0.059906430542469025	0.08957826346158981	0.05059589445590973	-0.010710201226174831	0.009218123741447926	-0.03651187941431999	-0.055155906826257706	0.0030260770581662655	0.03761805593967438	-0.044325847178697586	-0.01198037713766098	0.01725219562649727	-0.08083118498325348	-0.006945142988115549	-0.007904455997049809	0.014936523512005806	-0.0023049027658998966	-0.02037517912685871	-0.022184889763593674	0.06163756176829338	0.010841554962098598	-0.05119677260518074	0.01858549192547798	-0.022130576893687248	0.07021599262952805	-0.04621480405330658	-0.032258640974760056	0.03983521834015846	-0.012255377136170864	0.03180088475346565	-0.027006644755601883	0.04490445554256439	-0.056249357759952545	-0.08970881998538971	-0.03131484240293503	-0.05936827510595322	-0.0488070547580719	-0.01522769220173359	-0.028471454977989197	-0.0413157120347023	0.0808463916182518	-0.013251730240881443	-0.0293879471719265	-0.04959174618124962	-0.034952111542224884	-0.014161616563796997	-0.08995791524648666	-0.007712304126471281	-0.022029977291822433	-0.06892319023609161	-0.01771640218794346	0.024615800008177757	-0.057573962956666946	-0.007913107983767986	0.010519377887248993	-0.031766921281814575	-0.05293859541416168	0.059791844338178635	-0.05814073607325554	-0.04267522320151329	-0.011017742566764355	-0.07106218487024307	0.044250745326280594	0.08173483610153198	0.025997143238782883	0.04353742301464081	0.04869887977838516	0.05079280957579613	0.08770471811294556	0.018323611468076706	-0.05463142693042755	-0.005746849346905947	-0.03657481446862221	-0.09583790600299835	0.039033010601997375	-0.0216088704764843	-0.008544950745999813	-0.0013781820889562368	0.0014946098672226071	0.077175572514534	-0.010885324329137802	-0.018263477832078934	-0.004391167312860489	0.07239632308483124	-0.007102392613887787	0.01652558520436287	0.006961785722523928	0.00844767689704895	0.015641573816537857	-0.05900009721517563	-0.0356365442276001	-0.033382970839738846	0.033873118460178375	-0.0077868918888270855	0.06551576405763626	-0.014314460568130016	-0.07042023539543152	-0.009227842092514038	0.024274248629808426	0.10730380564928055	-0.073191337287426	0.02165011130273342	0.028754331171512604	-0.009516683407127857	-0.10572618246078491	-0.004463164135813713	0.030948424711823463	-0.049079157412052155	-0.04619999974966049	0.0216684490442276	0.04476705193519592	-0.059013307094573975	-0.028227001428604126	0.0013892819406464696	-0.04804646223783493	0.0020920305978506804	0.04438651725649834	-0.00418953038752079	0.02946203015744686	-0.030570240691304207	-0.02307598479092121	-0.0073412442579865456	0.0019403249025344849	0.008849815465509892	-0.020213264971971512	0.008781632408499718	-0.05779054015874863	0.06700608134269714	-0.02382199838757515	-0.016698313876986504	-0.07803566008806229	0.05119187757372856	0.0024766556452959776	-0.031794507056474686	-0.021084658801555634	-0.01882259175181389	-0.00659561762586236	0.0024611670523881912	0.031258393079042435	-0.06589336693286896	-0.023467257618904114	-0.05254727229475975	-0.036482252180576324	-0.059168700128793716	-0.05975870415568352	0.015309692360460758	-0.08423832803964615	-0.008632847107946873	-0.047330450266599655	-0.0030359586235135794	-0.007789313793182373	-0.016426298767328262	0.006427091546356678	0.028817009180784225	0.04469939321279526	-0.04123181104660034	0.04935023561120033	-0.01361688133329153	0.04588884115219116	0.009119045920670033	-0.050647519528865814	0.03365873545408249	0.028531532734632492	-0.047939203679561615	0.012025692500174046	-0.05103122070431709	0.011260377243161201	0.022805139422416687	-0.011448291130363941	0.03420983999967575	0.0023893725592643023	-0.014648369513452053	-0.009601112455129623	0.06349893659353256	-0.07455899566411972	-0.03272896632552147	0.009119250811636448	-0.05501718074083328	-0.08153126388788223	-0.014742768369615078	-0.02503400854766369	-0.0641455426812172	0.032384391874074936	-0.015820693224668503	0.02724023349583149	0.018447648733854294	-0.0640031024813652	-0.03617219626903534	-0.007539272774010897	-0.047624774277210236	0.02095315232872963	0.007777342572808266	0.05206596106290817	-0.02689967304468155	0.001118591520935297	0.016492625698447227	0.0021379815880209208	0.02800496481359005	-0.09768421947956085	-0.012939258478581905	-0.043243657797575	-0.010216213762760162	-0.016501232981681824	-0.00682029826566577	0.04341289773583412	-0.08786831796169281	-0.020496532320976257	-0.03473297879099846	0.07539509981870651	-0.030934739857912064	-0.0210777148604393	0.04145832732319832	-0.014545364305377007	-0.024373991414904594	0.037902019917964935	-0.04695998504757881	-0.090125173330307	0.00013186110300011933	-0.03473389148712158	0.04190395399928093	0.01786254532635212	0.0046316650696098804	-0.029042426496744156	-0.05582987144589424	-0.03442419692873955	0.01252778060734272	-0.07376310229301453	0.004839090630412102	0.008668310940265656	-0.08183842897415161	-0.0890234038233757	0.0028836913406848907	-0.06085911765694618	0.06941252946853638	0.02933945693075657	0.022058775648474693	-0.024807816371321678	-0.00989496149122715	-0.010940837673842907	-0.033092088997364044	0.05028357729315758	-0.0010918615153059363	-0.07699474692344666	-0.031109875068068504	0.08841890096664429	-0.03254250809550285	0.030168287456035614	0.05810220539569855	-0.04545220732688904	0.034791551530361176	0.03162364661693573	-0.035520318895578384	0.013658060692250729	-0.03102044016122818	-0.025478925555944443	0.07206389307975769	-0.0781688466668129	0.08866022527217865	-0.05471455305814743	0.10382094234228134	0.015190022066235542	-7.28705053916201e-05	0.07767869532108307	0.09671850502490997	-0.003259680699557066	-0.0065346574410796165	-0.0779583752155304
+0.008456605486571789	-0.052731048315763474	-0.03388197720050812	-0.00866050086915493	-0.09829316288232803	-0.06822725385427475	0.005607123952358961	0.0787285566329956	-0.029302727431058884	-0.037580396980047226	-0.036214955151081085	0.03942594677209854	-0.009735713712871075	0.015108074061572552	0.020225755870342255	-0.018949249759316444	-0.0237929355353117	0.024673055857419968	-0.054461318999528885	-0.04464062303304672	0.036217667162418365	0.025882305577397346	-0.06710044294595718	0.011959651485085487	0.005432765930891037	-0.060421090573072433	-0.04995913803577423	-0.0032300155144184828	0.0213957279920578	0.050989311188459396	0.06243039667606354	-0.04871131107211113	0.07647978514432907	-0.0157233364880085	-0.06382438540458679	0.052374254912137985	-0.06964920461177826	0.08039087057113647	0.04279521480202675	0.006246368866413832	-0.04904545098543167	-0.009590868838131428	-0.02334871143102646	-0.03489242494106293	0.04382188990712166	0.021761873736977577	-0.02037615329027176	-0.04973620921373367	0.04085288196802139	0.05058249086141586	0.053171902894973755	0.03365209326148033	0.03940017893910408	0.013912925496697426	-0.023810429498553276	-0.02894718199968338	0.020055456086993217	-0.012288682162761688	-0.07346956431865692	-0.006628262810409069	-0.07508231699466705	0.016877062618732452	0.02263367362320423	0.029578883200883865	0.016420526430010796	-0.021538250148296356	-0.016107715666294098	0.08584309369325638	0.021071111783385277	-0.014212685637176037	-0.005876233801245689	0.060050565749406815	-0.04907913878560066	-0.06313131004571915	0.053422845900058746	-0.00858194101601839	0.008345214650034904	0.02031761035323143	-0.03816251829266548	-0.009564949199557304	-0.032830506563186646	-0.03238481655716896	0.1195293739438057	-0.07944568991661072	-0.027090072631835938	-0.053505994379520416	-0.013751334510743618	-0.049191467463970184	0.059396639466285706	0.003803676227107644	0.04684477299451828	0.026970824226737022	-0.06487561762332916	0.006247084122151136	-0.024444105103611946	0.013975964859127998	0.025125304237008095	-0.10004311054944992	-0.05720314383506775	0.003925171215087175	0.024450033903121948	-0.020792406052350998	0.08285413682460785	-0.02535979077219963	0.03028920479118824	0.040302760899066925	-0.004929540678858757	0.013006075285375118	0.035422250628471375	0.02480362541973591	-0.0002530133060645312	0.09745249897241592	0.0651940405368805	0.006102651357650757	0.05636901408433914	-0.00757844652980566	-0.04227453097701073	-0.03459080681204796	0.004742131102830172	0.01189331989735365	-0.023463767021894455	0.028379682451486588	-0.02319217287003994	-0.06106272712349892	-0.04197605326771736	0.06575226783752441	-0.10181824117898941	-0.02150019258260727	0.05342988297343254	-0.05358242616057396	0.08762891590595245	0.02339697629213333	-0.04003508761525154	0.041627075523138046	0.0701768696308136	0.0017582401633262634	0.012784775346517563	0.03966718912124634	-0.00901959277689457	-0.09529119729995728	-0.04355315864086151	0.0956045389175415	-0.009216848760843277	-0.022524811327457428	0.03353039547801018	0.06097014248371124	0.042141590267419815	-0.02663794346153736	-0.06940750032663345	0.046810321509838104	-0.07826551049947739	-0.09595663845539093	-0.028639990836381912	-0.05582955852150917	0.03745795041322708	0.05642199143767357	-0.01841820403933525	0.016398154199123383	0.011746590957045555	-0.006826398894190788	-0.054149527102708817	-0.051785584539175034	-0.02524210512638092	-0.03042120859026909	0.004459126386791468	0.032724082469940186	-0.007908503524959087	0.04051971435546875	-0.04237496480345726	-0.03851472586393356	-0.00712125888094306	-0.04901178181171417	0.022180259227752686	0.08415264636278152	0.014792739413678646	0.01496886182576418	-0.0005657292203977704	-0.06386595219373703	-0.02183396741747856	-0.045747060328722	0.013969050720334053	-0.0915144756436348	0.06219939887523651	-0.05084853991866112	0.016515810042619705	-0.03691021725535393	0.03917711228132248	-0.06590205430984497	-0.009114118292927742	-0.0535050667822361	-0.025695687159895897	-0.07459399104118347	0.013360099866986275	0.07846543192863464	-0.028231285512447357	0.003693496575579047	-0.0009576866286806762	0.04550205171108246	-0.02234928496181965	-0.00011836967314593494	-0.05844211205840111	0.05176765099167824	0.015735063701868057	0.003702700836583972	0.011263230815529823	-0.09460140019655228	0.07620090991258621	-0.027803152799606323	0.014443297870457172	-0.05025559291243553	0.03503798320889473	0.07663309574127197	0.0068887448869645596	0.035355664789676666	0.04152644798159599	-0.03739961236715317	0.020728321745991707	-0.002969413297250867	0.09676951915025711	-0.05463448539376259	0.001112340367399156	0.023405108600854874	-0.05918584764003754	0.012501755729317665	0.0047288136556744576	-0.12757457792758942	-0.019435686990618706	-0.01449867244809866	-0.022211305797100067	-0.04558343067765236	-0.005585025995969772	0.029207130894064903	0.045431606471538544	-0.0035895765759050846	0.015124594792723656	-0.005805204156786203	-0.007333487272262573	0.06713686138391495	-0.031304214149713516	-0.05151735246181488	-0.029332643374800682	-0.035615816712379456	0.04203423485159874	0.017941199243068695	0.032461728900671005	0.03532891720533371	0.08635622262954712	-0.02105771005153656	-0.029382582753896713	-0.04572327062487602	-0.024481305852532387	-0.03576049953699112	-0.01488669402897358	0.049932412803173065	-0.05277852341532707	-0.0787644013762474	0.05292585492134094	-0.08301877230405807	0.009795810095965862	0.0022551012225449085	0.04075879231095314	-0.030743485316634178	-0.04608496278524399	-0.04473509266972542	0.005415810737758875	-0.004276568535715342	-0.04017306864261627	-0.01082628034055233	-0.06872440874576569	0.02892915904521942	-0.002141786040738225	0.0022344072349369526	0.0021841702982783318	-0.015147121623158455	0.030170705169439316	-0.047457821667194366	0.05177547410130501	-0.039417702704668045	-0.06479084491729736	-0.032292064279317856	-0.02281031385064125	-0.10050500929355621	0.013105574063956738	-0.024935785681009293	-0.022908754646778107	0.08340290933847427	0.040409695357084274	-0.043223876506090164	-0.04842538386583328	-0.03852242976427078	-0.04970133677124977	-0.023387106135487556	-0.004986658226698637	-0.030844034627079964	-0.06272381544113159	-0.023073486983776093	0.029121408239006996	-0.037609148770570755	-0.018892554566264153	0.07937942445278168	0.04935172572731972	-0.05058063566684723	0.0360877700150013	0.0025884355418384075	0.0008559722336940467	-0.009614921174943447	-0.04834621399641037	0.01614435389637947	0.0658758282661438	0.05409206077456474	0.029703456908464432	0.0315694622695446	0.027076179161667824	0.09698300808668137	0.010343854315578938	-0.03584069386124611	0.005119684152305126	-0.06282297521829605	-0.05418141931295395	0.012189309112727642	-0.023436645045876503	-0.03334394469857216	-0.01070515625178814	-0.03188528120517731	0.045007552951574326	-0.013668205589056015	0.004213291220366955	-0.050964679569005966	0.028144916519522667	1.662573777139187e-05	0.0380510538816452	0.01800193265080452	-0.017137974500656128	0.0018712354358285666	-0.059338316321372986	0.002724330173805356	-0.06723158806562424	0.023091742768883705	-0.004565277602523565	0.02832881547510624	-0.02157326601445675	-0.040258292108774185	0.006536277011036873	0.022811369970440865	0.10720449686050415	-0.046636417508125305	0.01963084749877453	-0.010822840966284275	0.04079972952604294	-0.09304201602935791	0.002124559134244919	-0.010094230994582176	-0.01347068790346384	-0.022229641675949097	0.029541974887251854	0.06230346858501434	-0.05801442638039589	-0.004794037435203791	-0.016024842858314514	-0.010358181782066822	-0.041934579610824585	-0.022885441780090332	-0.003138965927064419	0.11310239881277084	0.042626507580280304	0.008202877826988697	0.027552863582968712	0.059061188250780106	0.037642642855644226	0.004433705937117338	0.03262399137020111	-0.007151847239583731	0.06908131390810013	-0.04979822412133217	-0.07659432291984558	-0.06368009746074677	0.030338911339640617	-0.04066576063632965	-0.017272477969527245	-0.04922506958246231	0.013575811870396137	0.013803298585116863	0.027796771377325058	-0.027143971994519234	-0.048125457018613815	0.045577432960271835	-0.01421482302248478	-0.05488889291882515	-0.08544675260782242	-0.009270886890590191	0.013530313968658447	-0.030383262783288956	-0.001856667804531753	-0.039348118007183075	-0.0069916266947984695	0.01866593398153782	-0.022448157891631126	0.0034609141293913126	0.06589338183403015	0.05189840868115425	0.05612261965870857	0.037435609847307205	0.046171512454748154	0.028313856571912766	-0.009496002458035946	-0.058729637414216995	0.02957308664917946	0.027300234884023666	-0.08932048827409744	0.003469930961728096	-0.032857708632946014	-0.0135690001770854	-0.034864261746406555	0.0015638796612620354	-0.011375614441931248	-0.04316806420683861	0.022014714777469635	-0.040849167853593826	0.07846204191446304	-0.0597829595208168	-0.05674385651946068	0.07681537419557571	0.013348439708352089	-0.07542899250984192	-0.008957307785749435	-0.038167696446180344	-0.01645250990986824	0.0934092253446579	-0.030899129807949066	0.01019855123013258	0.028286462649703026	-0.01760968379676342	-0.05664840340614319	-0.02203056775033474	-0.0038954350166022778	-0.01617027074098587	0.01965051144361496	0.046573422849178314	0.027370577678084373	-0.003912215121090412	0.017650894820690155	-0.08052058517932892	0.04423874244093895	-0.06121005490422249	-0.01091107539832592	-0.03059428185224533	0.004660924430936575	0.007446800824254751	-0.014443332329392433	0.04634758457541466	-0.036635711789131165	-0.010817687958478928	-0.06561651825904846	0.07568103075027466	-0.01854291744530201	0.00129842315800488	0.016753841191530228	-0.04234101623296738	-0.043580349534749985	0.054371535778045654	-0.047188106924295425	-0.08541004359722137	0.03190035745501518	-0.05289008095860481	0.036135394126176834	0.042873453348875046	0.06285534799098969	0.009807518683373928	-0.08192078769207001	-0.003325254423543811	0.006297369487583637	-0.026264071464538574	0.057861145585775375	0.030305638909339905	-0.05683036893606186	-0.04911930859088898	-0.07529007643461227	-0.07086094468832016	0.09605871886014938	-0.028688602149486542	0.03878708928823471	-0.05965331196784973	0.012895305640995502	-0.0209056306630373	-0.0210272129625082	0.01338373962789774	0.0004737664421554655	-0.08231192827224731	-0.011419524438679218	0.09584395587444305	-0.023246824741363525	0.03954925015568733	0.07969904690980911	-0.040964141488075256	0.03212236240506172	0.016111910343170166	0.0069672223180532455	0.05597599595785141	0.025417592376470566	0.01775471866130829	0.0178235936909914	-0.026317089796066284	0.058895863592624664	-0.021407533437013626	0.0835641622543335	0.04041486606001854	-0.009377419017255306	0.07782338559627533	0.027054352685809135	-0.013352732174098492	-0.006498945411294699	-0.06847313791513443
diff --git a/notebooks/log_dir/events.out.tfevents.1541600225.Armans-MacBook-Pro.local b/notebooks/log_dir/events.out.tfevents.1541600225.Armans-MacBook-Pro.local
new file mode 100644
index 000000000..270b3d711
Binary files /dev/null and b/notebooks/log_dir/events.out.tfevents.1541600225.Armans-MacBook-Pro.local differ
diff --git a/notebooks/log_dir/events.out.tfevents.1541602534.Armans-MacBook-Pro.local b/notebooks/log_dir/events.out.tfevents.1541602534.Armans-MacBook-Pro.local
new file mode 100644
index 000000000..a3db921fc
Binary files /dev/null and b/notebooks/log_dir/events.out.tfevents.1541602534.Armans-MacBook-Pro.local differ
diff --git a/notebooks/log_dir/projector_config.pbtxt b/notebooks/log_dir/projector_config.pbtxt
new file mode 100644
index 000000000..6ad33d9e4
--- /dev/null
+++ b/notebooks/log_dir/projector_config.pbtxt
@@ -0,0 +1,15 @@
+embeddings {
+tensor_name: "default:00000"
+tensor_path: "00000/default/tensors.tsv"
+metadata_path: "00000/default/metadata.tsv"
+}
+embeddings {
+tensor_name: "default:00000"
+tensor_path: "00000/default/tensors.tsv"
+metadata_path: "00000/default/metadata.tsv"
+sprite {
+image_path: "00000/default/sprite.png"
+single_image_dim: 64
+single_image_dim: 64
+}
+}
diff --git a/notebooks/noam-chomsky-1-aligned.jpg b/notebooks/noam-chomsky-1-aligned.jpg
new file mode 100644
index 000000000..cb79dc41c
Binary files /dev/null and b/notebooks/noam-chomsky-1-aligned.jpg differ
diff --git a/notebooks/noam-chomsky-1.jpg b/notebooks/noam-chomsky-1.jpg
new file mode 100644
index 000000000..4f4934df9
Binary files /dev/null and b/notebooks/noam-chomsky-1.jpg differ
diff --git a/notebooks/noam-chomsky-2.jpg b/notebooks/noam-chomsky-2.jpg
new file mode 100644
index 000000000..cdd8de9f1
Binary files /dev/null and b/notebooks/noam-chomsky-2.jpg differ
diff --git a/notebooks/not-noam-chomsky.jpg b/notebooks/not-noam-chomsky.jpg
new file mode 100755
index 000000000..9af04d634
Binary files /dev/null and b/notebooks/not-noam-chomsky.jpg differ
diff --git a/notebooks/people/foucault/image-10.jpeg b/notebooks/people/foucault/image-10.jpeg
new file mode 100755
index 000000000..00a6e7dc2
Binary files /dev/null and b/notebooks/people/foucault/image-10.jpeg differ
diff --git a/notebooks/people/foucault/image-11.jpeg b/notebooks/people/foucault/image-11.jpeg
new file mode 100755
index 000000000..421d312a3
Binary files /dev/null and b/notebooks/people/foucault/image-11.jpeg differ
diff --git a/notebooks/people/foucault/image-12.jpeg b/notebooks/people/foucault/image-12.jpeg
new file mode 100755
index 000000000..c8ad327ed
Binary files /dev/null and b/notebooks/people/foucault/image-12.jpeg differ
diff --git a/notebooks/people/foucault/image-13.jpeg b/notebooks/people/foucault/image-13.jpeg
new file mode 100755
index 000000000..be39f8da2
Binary files /dev/null and b/notebooks/people/foucault/image-13.jpeg differ
diff --git a/notebooks/people/foucault/image-14.jpeg b/notebooks/people/foucault/image-14.jpeg
new file mode 100755
index 000000000..97b25a0c9
Binary files /dev/null and b/notebooks/people/foucault/image-14.jpeg differ
diff --git a/notebooks/people/foucault/image-15.jpeg b/notebooks/people/foucault/image-15.jpeg
new file mode 100755
index 000000000..7a079b202
Binary files /dev/null and b/notebooks/people/foucault/image-15.jpeg differ
diff --git a/notebooks/people/foucault/image-16.jpeg b/notebooks/people/foucault/image-16.jpeg
new file mode 100755
index 000000000..ab756acae
Binary files /dev/null and b/notebooks/people/foucault/image-16.jpeg differ
diff --git a/notebooks/people/foucault/image-17.jpeg b/notebooks/people/foucault/image-17.jpeg
new file mode 100755
index 000000000..5a43ca2d7
Binary files /dev/null and b/notebooks/people/foucault/image-17.jpeg differ
diff --git a/notebooks/people/foucault/image-18.jpeg b/notebooks/people/foucault/image-18.jpeg
new file mode 100755
index 000000000..055b53974
Binary files /dev/null and b/notebooks/people/foucault/image-18.jpeg differ
diff --git a/notebooks/people/foucault/image-19.jpeg b/notebooks/people/foucault/image-19.jpeg
new file mode 100755
index 000000000..2c4cfabda
Binary files /dev/null and b/notebooks/people/foucault/image-19.jpeg differ
diff --git a/notebooks/people/foucault/image-2.jpeg b/notebooks/people/foucault/image-2.jpeg
new file mode 100755
index 000000000..9af04d634
Binary files /dev/null and b/notebooks/people/foucault/image-2.jpeg differ
diff --git a/notebooks/people/foucault/image-20.jpeg b/notebooks/people/foucault/image-20.jpeg
new file mode 100755
index 000000000..0266ac7ca
Binary files /dev/null and b/notebooks/people/foucault/image-20.jpeg differ
diff --git a/notebooks/people/foucault/image-3.jpeg b/notebooks/people/foucault/image-3.jpeg
new file mode 100755
index 000000000..83e2e7327
Binary files /dev/null and b/notebooks/people/foucault/image-3.jpeg differ
diff --git a/notebooks/people/foucault/image-4.jpeg b/notebooks/people/foucault/image-4.jpeg
new file mode 100755
index 000000000..e3163aebe
Binary files /dev/null and b/notebooks/people/foucault/image-4.jpeg differ
diff --git a/notebooks/people/foucault/image-5.jpeg b/notebooks/people/foucault/image-5.jpeg
new file mode 100755
index 000000000..0fa128f26
Binary files /dev/null and b/notebooks/people/foucault/image-5.jpeg differ
diff --git a/notebooks/people/foucault/image-6.jpeg b/notebooks/people/foucault/image-6.jpeg
new file mode 100755
index 000000000..cfc3ee8a1
Binary files /dev/null and b/notebooks/people/foucault/image-6.jpeg differ
diff --git a/notebooks/people/foucault/image-7.jpeg b/notebooks/people/foucault/image-7.jpeg
new file mode 100755
index 000000000..182896351
Binary files /dev/null and b/notebooks/people/foucault/image-7.jpeg differ
diff --git a/notebooks/people/foucault/image-8.jpeg b/notebooks/people/foucault/image-8.jpeg
new file mode 100755
index 000000000..06c6c1b17
Binary files /dev/null and b/notebooks/people/foucault/image-8.jpeg differ
diff --git a/notebooks/people/foucault/image-9.jpeg b/notebooks/people/foucault/image-9.jpeg
new file mode 100755
index 000000000..891373d08
Binary files /dev/null and b/notebooks/people/foucault/image-9.jpeg differ
diff --git a/notebooks/people/foucault/image.jpeg b/notebooks/people/foucault/image.jpeg
new file mode 100755
index 000000000..58ff93714
Binary files /dev/null and b/notebooks/people/foucault/image.jpeg differ
diff --git a/notebooks/people/foucault/images-11.jpeg b/notebooks/people/foucault/images-11.jpeg
new file mode 100755
index 000000000..8a729d8d1
Binary files /dev/null and b/notebooks/people/foucault/images-11.jpeg differ
diff --git a/notebooks/people/foucault/images-12.jpeg b/notebooks/people/foucault/images-12.jpeg
new file mode 100755
index 000000000..2ff6a4821
Binary files /dev/null and b/notebooks/people/foucault/images-12.jpeg differ
diff --git a/notebooks/people/foucault/images-13.jpeg b/notebooks/people/foucault/images-13.jpeg
new file mode 100755
index 000000000..1b05570ba
Binary files /dev/null and b/notebooks/people/foucault/images-13.jpeg differ
diff --git a/notebooks/people/foucault/images-15.jpeg b/notebooks/people/foucault/images-15.jpeg
new file mode 100755
index 000000000..b414ccd8a
Binary files /dev/null and b/notebooks/people/foucault/images-15.jpeg differ
diff --git a/notebooks/people/foucault/images-16.jpeg b/notebooks/people/foucault/images-16.jpeg
new file mode 100755
index 000000000..8b1d07583
Binary files /dev/null and b/notebooks/people/foucault/images-16.jpeg differ
diff --git a/notebooks/people/foucault/images-17.jpeg b/notebooks/people/foucault/images-17.jpeg
new file mode 100755
index 000000000..fb77c742e
Binary files /dev/null and b/notebooks/people/foucault/images-17.jpeg differ
diff --git a/notebooks/people/foucault/images-18.jpeg b/notebooks/people/foucault/images-18.jpeg
new file mode 100755
index 000000000..707118665
Binary files /dev/null and b/notebooks/people/foucault/images-18.jpeg differ
diff --git a/notebooks/people/foucault/images-19.jpeg b/notebooks/people/foucault/images-19.jpeg
new file mode 100755
index 000000000..f8a5cc63f
Binary files /dev/null and b/notebooks/people/foucault/images-19.jpeg differ
diff --git a/notebooks/people/foucault/images-2.jpeg b/notebooks/people/foucault/images-2.jpeg
new file mode 100755
index 000000000..fd8050947
Binary files /dev/null and b/notebooks/people/foucault/images-2.jpeg differ
diff --git a/notebooks/people/foucault/images-22.jpeg b/notebooks/people/foucault/images-22.jpeg
new file mode 100755
index 000000000..95642f15d
Binary files /dev/null and b/notebooks/people/foucault/images-22.jpeg differ
diff --git a/notebooks/people/foucault/images-23.jpeg b/notebooks/people/foucault/images-23.jpeg
new file mode 100755
index 000000000..bbf4ac910
Binary files /dev/null and b/notebooks/people/foucault/images-23.jpeg differ
diff --git a/notebooks/people/foucault/images-24.jpeg b/notebooks/people/foucault/images-24.jpeg
new file mode 100755
index 000000000..b1703cc84
Binary files /dev/null and b/notebooks/people/foucault/images-24.jpeg differ
diff --git a/notebooks/people/foucault/images-25.jpeg b/notebooks/people/foucault/images-25.jpeg
new file mode 100755
index 000000000..bb2351a4e
Binary files /dev/null and b/notebooks/people/foucault/images-25.jpeg differ
diff --git a/notebooks/people/foucault/images-26.jpeg b/notebooks/people/foucault/images-26.jpeg
new file mode 100755
index 000000000..e2d4da3ed
Binary files /dev/null and b/notebooks/people/foucault/images-26.jpeg differ
diff --git a/notebooks/people/foucault/images-27.jpeg b/notebooks/people/foucault/images-27.jpeg
new file mode 100755
index 000000000..c410d47d9
Binary files /dev/null and b/notebooks/people/foucault/images-27.jpeg differ
diff --git a/notebooks/people/foucault/images-3.jpeg b/notebooks/people/foucault/images-3.jpeg
new file mode 100755
index 000000000..c3583b623
Binary files /dev/null and b/notebooks/people/foucault/images-3.jpeg differ
diff --git a/notebooks/people/foucault/images-4.jpeg b/notebooks/people/foucault/images-4.jpeg
new file mode 100755
index 000000000..66924f48d
Binary files /dev/null and b/notebooks/people/foucault/images-4.jpeg differ
diff --git a/notebooks/people/foucault/images-5.jpeg b/notebooks/people/foucault/images-5.jpeg
new file mode 100755
index 000000000..f0480e001
Binary files /dev/null and b/notebooks/people/foucault/images-5.jpeg differ
diff --git a/notebooks/people/foucault/images-6.jpeg b/notebooks/people/foucault/images-6.jpeg
new file mode 100755
index 000000000..fbd7fd48b
Binary files /dev/null and b/notebooks/people/foucault/images-6.jpeg differ
diff --git a/notebooks/people/foucault/images-7.jpeg b/notebooks/people/foucault/images-7.jpeg
new file mode 100755
index 000000000..ab25564d3
Binary files /dev/null and b/notebooks/people/foucault/images-7.jpeg differ
diff --git a/notebooks/people/foucault/images-8.jpeg b/notebooks/people/foucault/images-8.jpeg
new file mode 100755
index 000000000..cce16bacb
Binary files /dev/null and b/notebooks/people/foucault/images-8.jpeg differ
diff --git a/notebooks/people/foucault/images-9.jpeg b/notebooks/people/foucault/images-9.jpeg
new file mode 100755
index 000000000..d4475fa2b
Binary files /dev/null and b/notebooks/people/foucault/images-9.jpeg differ
diff --git a/notebooks/people/foucault/images.jpeg b/notebooks/people/foucault/images.jpeg
new file mode 100755
index 000000000..f69f0dd0f
Binary files /dev/null and b/notebooks/people/foucault/images.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-11.jpeg b/notebooks/people/noam_chomsky/image-11.jpeg
new file mode 100644
index 000000000..9842a7441
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-11.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-12.jpeg b/notebooks/people/noam_chomsky/image-12.jpeg
new file mode 100644
index 000000000..5df76649e
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-12.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-13.jpeg b/notebooks/people/noam_chomsky/image-13.jpeg
new file mode 100644
index 000000000..e207dd4fb
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-13.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-14.jpeg b/notebooks/people/noam_chomsky/image-14.jpeg
new file mode 100644
index 000000000..b2005220c
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-14.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-15.jpeg b/notebooks/people/noam_chomsky/image-15.jpeg
new file mode 100644
index 000000000..d36124fcf
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-15.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-16.jpeg b/notebooks/people/noam_chomsky/image-16.jpeg
new file mode 100644
index 000000000..59d450193
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-16.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-17.jpeg b/notebooks/people/noam_chomsky/image-17.jpeg
new file mode 100644
index 000000000..c61cdeac2
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-17.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-18.jpeg b/notebooks/people/noam_chomsky/image-18.jpeg
new file mode 100644
index 000000000..82b569afe
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-18.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-19.jpeg b/notebooks/people/noam_chomsky/image-19.jpeg
new file mode 100644
index 000000000..b0d898b13
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-19.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-2.jpeg b/notebooks/people/noam_chomsky/image-2.jpeg
new file mode 100644
index 000000000..0bbda4532
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-2.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-20.jpeg b/notebooks/people/noam_chomsky/image-20.jpeg
new file mode 100644
index 000000000..3aad0b982
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-20.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-21.jpeg b/notebooks/people/noam_chomsky/image-21.jpeg
new file mode 100644
index 000000000..c54f05f17
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-21.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-22.jpeg b/notebooks/people/noam_chomsky/image-22.jpeg
new file mode 100644
index 000000000..0dc56eec2
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-22.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-23.jpeg b/notebooks/people/noam_chomsky/image-23.jpeg
new file mode 100644
index 000000000..428281b0c
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-23.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-24.jpeg b/notebooks/people/noam_chomsky/image-24.jpeg
new file mode 100644
index 000000000..101e119ec
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-24.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-26.jpeg b/notebooks/people/noam_chomsky/image-26.jpeg
new file mode 100644
index 000000000..c636307be
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-26.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-27.jpeg b/notebooks/people/noam_chomsky/image-27.jpeg
new file mode 100644
index 000000000..522c437e5
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-27.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-28.jpeg b/notebooks/people/noam_chomsky/image-28.jpeg
new file mode 100644
index 000000000..16590af3f
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-28.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-29.jpeg b/notebooks/people/noam_chomsky/image-29.jpeg
new file mode 100644
index 000000000..56d74b2b4
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-29.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-30.jpeg b/notebooks/people/noam_chomsky/image-30.jpeg
new file mode 100644
index 000000000..239ee4b45
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-30.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-31.jpeg b/notebooks/people/noam_chomsky/image-31.jpeg
new file mode 100644
index 000000000..feb237aa2
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-31.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-32.jpeg b/notebooks/people/noam_chomsky/image-32.jpeg
new file mode 100644
index 000000000..f704dfff9
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-32.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-33.jpeg b/notebooks/people/noam_chomsky/image-33.jpeg
new file mode 100644
index 000000000..3a1c55bbe
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-33.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-4.jpeg b/notebooks/people/noam_chomsky/image-4.jpeg
new file mode 100644
index 000000000..4ad2758d2
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-4.jpeg differ
diff --git a/notebooks/people/noam_chomsky/image-9.jpeg b/notebooks/people/noam_chomsky/image-9.jpeg
new file mode 100644
index 000000000..8cef87957
Binary files /dev/null and b/notebooks/people/noam_chomsky/image-9.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-100.jpeg b/notebooks/people/noam_chomsky/images-100.jpeg
new file mode 100644
index 000000000..325dcfe51
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-100.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-101.jpeg b/notebooks/people/noam_chomsky/images-101.jpeg
new file mode 100644
index 000000000..ff958e6ee
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-101.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-102.jpeg b/notebooks/people/noam_chomsky/images-102.jpeg
new file mode 100644
index 000000000..287a556ac
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-102.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-104.jpeg b/notebooks/people/noam_chomsky/images-104.jpeg
new file mode 100644
index 000000000..9d5d515ec
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-104.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-105.jpeg b/notebooks/people/noam_chomsky/images-105.jpeg
new file mode 100644
index 000000000..b86a6781f
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-105.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-106.jpeg b/notebooks/people/noam_chomsky/images-106.jpeg
new file mode 100644
index 000000000..cf9a037f5
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-106.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-107.jpeg b/notebooks/people/noam_chomsky/images-107.jpeg
new file mode 100644
index 000000000..d0735b9b9
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-107.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-108.jpeg b/notebooks/people/noam_chomsky/images-108.jpeg
new file mode 100644
index 000000000..e734525a7
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-108.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-109.jpeg b/notebooks/people/noam_chomsky/images-109.jpeg
new file mode 100644
index 000000000..758b31935
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-109.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-110.jpeg b/notebooks/people/noam_chomsky/images-110.jpeg
new file mode 100644
index 000000000..1cbb301b4
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-110.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-111.jpeg b/notebooks/people/noam_chomsky/images-111.jpeg
new file mode 100644
index 000000000..bed3b1464
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-111.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-112.jpeg b/notebooks/people/noam_chomsky/images-112.jpeg
new file mode 100644
index 000000000..8dea129b6
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-112.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-113.jpeg b/notebooks/people/noam_chomsky/images-113.jpeg
new file mode 100644
index 000000000..021892d5c
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-113.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-114.jpeg b/notebooks/people/noam_chomsky/images-114.jpeg
new file mode 100644
index 000000000..bf28be4f2
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-114.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-115.jpeg b/notebooks/people/noam_chomsky/images-115.jpeg
new file mode 100644
index 000000000..a8c7564de
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-115.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-116.jpeg b/notebooks/people/noam_chomsky/images-116.jpeg
new file mode 100644
index 000000000..d24843aec
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-116.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-117.jpeg b/notebooks/people/noam_chomsky/images-117.jpeg
new file mode 100644
index 000000000..5e8836de3
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-117.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-118.jpeg b/notebooks/people/noam_chomsky/images-118.jpeg
new file mode 100644
index 000000000..b15cce06d
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-118.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-119.jpeg b/notebooks/people/noam_chomsky/images-119.jpeg
new file mode 100644
index 000000000..6eca38a3b
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-119.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-120.jpeg b/notebooks/people/noam_chomsky/images-120.jpeg
new file mode 100644
index 000000000..814a3c3b6
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-120.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-121.jpeg b/notebooks/people/noam_chomsky/images-121.jpeg
new file mode 100644
index 000000000..2213d36f2
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-121.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-122.jpeg b/notebooks/people/noam_chomsky/images-122.jpeg
new file mode 100644
index 000000000..6eaa1a545
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-122.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-123.jpeg b/notebooks/people/noam_chomsky/images-123.jpeg
new file mode 100644
index 000000000..7dfcbcb37
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-123.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-125.jpeg b/notebooks/people/noam_chomsky/images-125.jpeg
new file mode 100644
index 000000000..7d3db2dc4
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-125.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-126.jpeg b/notebooks/people/noam_chomsky/images-126.jpeg
new file mode 100644
index 000000000..08501a16e
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-126.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-128.jpeg b/notebooks/people/noam_chomsky/images-128.jpeg
new file mode 100644
index 000000000..9a879185f
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-128.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-130.jpeg b/notebooks/people/noam_chomsky/images-130.jpeg
new file mode 100644
index 000000000..5c41a29cb
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-130.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-131.jpeg b/notebooks/people/noam_chomsky/images-131.jpeg
new file mode 100644
index 000000000..5631e5b06
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-131.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-134.jpeg b/notebooks/people/noam_chomsky/images-134.jpeg
new file mode 100644
index 000000000..dc8a43d19
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-134.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-135.jpeg b/notebooks/people/noam_chomsky/images-135.jpeg
new file mode 100644
index 000000000..5fef1aaca
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-135.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-136.jpeg b/notebooks/people/noam_chomsky/images-136.jpeg
new file mode 100644
index 000000000..ab79e4456
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-136.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-137.jpeg b/notebooks/people/noam_chomsky/images-137.jpeg
new file mode 100644
index 000000000..71a699a74
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-137.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-138.jpeg b/notebooks/people/noam_chomsky/images-138.jpeg
new file mode 100644
index 000000000..a17facbb8
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-138.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-139.jpeg b/notebooks/people/noam_chomsky/images-139.jpeg
new file mode 100644
index 000000000..b9caa52e5
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-139.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-14.jpeg b/notebooks/people/noam_chomsky/images-14.jpeg
new file mode 100644
index 000000000..674224d27
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-14.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-140.jpeg b/notebooks/people/noam_chomsky/images-140.jpeg
new file mode 100644
index 000000000..789e924b7
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-140.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-141.jpeg b/notebooks/people/noam_chomsky/images-141.jpeg
new file mode 100644
index 000000000..3d4670f28
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-141.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-143.jpeg b/notebooks/people/noam_chomsky/images-143.jpeg
new file mode 100644
index 000000000..ddc093490
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-143.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-144.jpeg b/notebooks/people/noam_chomsky/images-144.jpeg
new file mode 100644
index 000000000..ea2362ab9
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-144.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-145.jpeg b/notebooks/people/noam_chomsky/images-145.jpeg
new file mode 100644
index 000000000..62d15481c
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-145.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-146.jpeg b/notebooks/people/noam_chomsky/images-146.jpeg
new file mode 100644
index 000000000..49b93828f
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-146.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-147.jpeg b/notebooks/people/noam_chomsky/images-147.jpeg
new file mode 100644
index 000000000..5126fe5dc
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-147.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-148.jpeg b/notebooks/people/noam_chomsky/images-148.jpeg
new file mode 100644
index 000000000..962a41e4b
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-148.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-149.jpeg b/notebooks/people/noam_chomsky/images-149.jpeg
new file mode 100644
index 000000000..a97d4fba8
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-149.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-15.jpeg b/notebooks/people/noam_chomsky/images-15.jpeg
new file mode 100644
index 000000000..de91890c5
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-15.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-150.jpeg b/notebooks/people/noam_chomsky/images-150.jpeg
new file mode 100644
index 000000000..6a2ac4af6
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-150.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-151.jpeg b/notebooks/people/noam_chomsky/images-151.jpeg
new file mode 100644
index 000000000..45ec99542
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-151.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-152.jpeg b/notebooks/people/noam_chomsky/images-152.jpeg
new file mode 100644
index 000000000..1f0aa67eb
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-152.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-153.jpeg b/notebooks/people/noam_chomsky/images-153.jpeg
new file mode 100644
index 000000000..70a6d96bb
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-153.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-156.jpeg b/notebooks/people/noam_chomsky/images-156.jpeg
new file mode 100644
index 000000000..1497eea01
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-156.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-157.jpeg b/notebooks/people/noam_chomsky/images-157.jpeg
new file mode 100644
index 000000000..a28dbf2ae
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-157.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-158.jpeg b/notebooks/people/noam_chomsky/images-158.jpeg
new file mode 100644
index 000000000..e1909b618
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-158.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-159.jpeg b/notebooks/people/noam_chomsky/images-159.jpeg
new file mode 100644
index 000000000..af6a83468
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-159.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-16.jpeg b/notebooks/people/noam_chomsky/images-16.jpeg
new file mode 100644
index 000000000..e8e2ccf17
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-16.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-160.jpeg b/notebooks/people/noam_chomsky/images-160.jpeg
new file mode 100644
index 000000000..dcf166bb9
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-160.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-163.jpeg b/notebooks/people/noam_chomsky/images-163.jpeg
new file mode 100644
index 000000000..41542fd69
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-163.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-164.jpeg b/notebooks/people/noam_chomsky/images-164.jpeg
new file mode 100644
index 000000000..9011be189
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-164.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-165.jpeg b/notebooks/people/noam_chomsky/images-165.jpeg
new file mode 100644
index 000000000..0afbfec2b
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-165.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-166.jpeg b/notebooks/people/noam_chomsky/images-166.jpeg
new file mode 100644
index 000000000..8a58e660c
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-166.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-167.jpeg b/notebooks/people/noam_chomsky/images-167.jpeg
new file mode 100644
index 000000000..f254d4a6f
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-167.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-168.jpeg b/notebooks/people/noam_chomsky/images-168.jpeg
new file mode 100644
index 000000000..fa89e3632
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-168.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-17.jpeg b/notebooks/people/noam_chomsky/images-17.jpeg
new file mode 100644
index 000000000..fba8af2f2
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-17.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-170.jpeg b/notebooks/people/noam_chomsky/images-170.jpeg
new file mode 100644
index 000000000..bc5768c09
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-170.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-171.jpeg b/notebooks/people/noam_chomsky/images-171.jpeg
new file mode 100644
index 000000000..3acc06916
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-171.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-172.jpeg b/notebooks/people/noam_chomsky/images-172.jpeg
new file mode 100644
index 000000000..bc1b355e7
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-172.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-173.jpeg b/notebooks/people/noam_chomsky/images-173.jpeg
new file mode 100644
index 000000000..82f9e90fa
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-173.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-174.jpeg b/notebooks/people/noam_chomsky/images-174.jpeg
new file mode 100644
index 000000000..c6e388194
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-174.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-175.jpeg b/notebooks/people/noam_chomsky/images-175.jpeg
new file mode 100644
index 000000000..ff5f7d322
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-175.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-176.jpeg b/notebooks/people/noam_chomsky/images-176.jpeg
new file mode 100644
index 000000000..353b43248
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-176.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-177.jpeg b/notebooks/people/noam_chomsky/images-177.jpeg
new file mode 100644
index 000000000..882179276
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-177.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-179.jpeg b/notebooks/people/noam_chomsky/images-179.jpeg
new file mode 100644
index 000000000..3a208d9e9
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-179.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-18.jpeg b/notebooks/people/noam_chomsky/images-18.jpeg
new file mode 100644
index 000000000..ab5ac80eb
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-18.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-181.jpeg b/notebooks/people/noam_chomsky/images-181.jpeg
new file mode 100644
index 000000000..eb2b86f87
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-181.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-182.jpeg b/notebooks/people/noam_chomsky/images-182.jpeg
new file mode 100644
index 000000000..ec8321133
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-182.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-183.jpeg b/notebooks/people/noam_chomsky/images-183.jpeg
new file mode 100644
index 000000000..2e3db4d29
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-183.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-185.jpeg b/notebooks/people/noam_chomsky/images-185.jpeg
new file mode 100644
index 000000000..4f1e9be34
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-185.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-186.jpeg b/notebooks/people/noam_chomsky/images-186.jpeg
new file mode 100644
index 000000000..15d2f00de
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-186.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-188.jpeg b/notebooks/people/noam_chomsky/images-188.jpeg
new file mode 100644
index 000000000..0d1626ef8
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-188.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-189.jpeg b/notebooks/people/noam_chomsky/images-189.jpeg
new file mode 100644
index 000000000..d3295bd42
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-189.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-190.jpeg b/notebooks/people/noam_chomsky/images-190.jpeg
new file mode 100644
index 000000000..3b5391897
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-190.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-20.jpeg b/notebooks/people/noam_chomsky/images-20.jpeg
new file mode 100644
index 000000000..bf522bc47
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-20.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-21.jpeg b/notebooks/people/noam_chomsky/images-21.jpeg
new file mode 100644
index 000000000..bf0a6614d
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-21.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-22.jpeg b/notebooks/people/noam_chomsky/images-22.jpeg
new file mode 100644
index 000000000..7748e2e2f
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-22.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-23.jpeg b/notebooks/people/noam_chomsky/images-23.jpeg
new file mode 100644
index 000000000..54e54ba2f
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-23.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-24.jpeg b/notebooks/people/noam_chomsky/images-24.jpeg
new file mode 100644
index 000000000..8270706fe
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-24.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-25.jpeg b/notebooks/people/noam_chomsky/images-25.jpeg
new file mode 100644
index 000000000..48fe27fea
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-25.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-26.jpeg b/notebooks/people/noam_chomsky/images-26.jpeg
new file mode 100644
index 000000000..9df1ca167
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-26.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-27.jpeg b/notebooks/people/noam_chomsky/images-27.jpeg
new file mode 100644
index 000000000..5b5ed9a24
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-27.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-28.jpeg b/notebooks/people/noam_chomsky/images-28.jpeg
new file mode 100644
index 000000000..e7bde00bf
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-28.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-30.jpeg b/notebooks/people/noam_chomsky/images-30.jpeg
new file mode 100644
index 000000000..62575ba6c
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-30.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-31.jpeg b/notebooks/people/noam_chomsky/images-31.jpeg
new file mode 100644
index 000000000..dce84f85d
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-31.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-33.jpeg b/notebooks/people/noam_chomsky/images-33.jpeg
new file mode 100644
index 000000000..d15d4b950
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-33.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-34.jpeg b/notebooks/people/noam_chomsky/images-34.jpeg
new file mode 100644
index 000000000..dd24aa1ad
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-34.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-35.jpeg b/notebooks/people/noam_chomsky/images-35.jpeg
new file mode 100644
index 000000000..0f2c232a6
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-35.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-36.jpeg b/notebooks/people/noam_chomsky/images-36.jpeg
new file mode 100644
index 000000000..b454ee9c9
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-36.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-37.jpeg b/notebooks/people/noam_chomsky/images-37.jpeg
new file mode 100644
index 000000000..f08f6b257
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-37.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-38.jpeg b/notebooks/people/noam_chomsky/images-38.jpeg
new file mode 100644
index 000000000..5779d5be7
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-38.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-39.jpeg b/notebooks/people/noam_chomsky/images-39.jpeg
new file mode 100644
index 000000000..379f021b5
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-39.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-4.jpeg b/notebooks/people/noam_chomsky/images-4.jpeg
new file mode 100644
index 000000000..e1dfc1fa8
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-4.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-41.jpeg b/notebooks/people/noam_chomsky/images-41.jpeg
new file mode 100644
index 000000000..a1101481d
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-41.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-42.jpeg b/notebooks/people/noam_chomsky/images-42.jpeg
new file mode 100644
index 000000000..bbaf25d04
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-42.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-43.jpeg b/notebooks/people/noam_chomsky/images-43.jpeg
new file mode 100644
index 000000000..1de2dd576
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-43.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-44.jpeg b/notebooks/people/noam_chomsky/images-44.jpeg
new file mode 100644
index 000000000..3bb0502d4
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-44.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-45.jpeg b/notebooks/people/noam_chomsky/images-45.jpeg
new file mode 100644
index 000000000..08f25efc0
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-45.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-46.jpeg b/notebooks/people/noam_chomsky/images-46.jpeg
new file mode 100644
index 000000000..521f65818
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-46.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-47.jpeg b/notebooks/people/noam_chomsky/images-47.jpeg
new file mode 100644
index 000000000..48e4f2595
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-47.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-48.jpeg b/notebooks/people/noam_chomsky/images-48.jpeg
new file mode 100644
index 000000000..88e449b23
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-48.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-49.jpeg b/notebooks/people/noam_chomsky/images-49.jpeg
new file mode 100644
index 000000000..b313fb2dc
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-49.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-50.jpeg b/notebooks/people/noam_chomsky/images-50.jpeg
new file mode 100644
index 000000000..680f08684
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-50.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-52.jpeg b/notebooks/people/noam_chomsky/images-52.jpeg
new file mode 100644
index 000000000..453121185
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-52.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-53.jpeg b/notebooks/people/noam_chomsky/images-53.jpeg
new file mode 100644
index 000000000..e8ad709f8
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-53.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-55.jpeg b/notebooks/people/noam_chomsky/images-55.jpeg
new file mode 100644
index 000000000..7252ac86d
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-55.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-56.jpeg b/notebooks/people/noam_chomsky/images-56.jpeg
new file mode 100644
index 000000000..d1fa32588
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-56.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-57.jpeg b/notebooks/people/noam_chomsky/images-57.jpeg
new file mode 100644
index 000000000..e7f4721bf
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-57.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-58.jpeg b/notebooks/people/noam_chomsky/images-58.jpeg
new file mode 100644
index 000000000..5b2b85005
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-58.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-59.jpeg b/notebooks/people/noam_chomsky/images-59.jpeg
new file mode 100644
index 000000000..224f73f5e
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-59.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-60.jpeg b/notebooks/people/noam_chomsky/images-60.jpeg
new file mode 100644
index 000000000..9607d7439
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-60.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-61.jpeg b/notebooks/people/noam_chomsky/images-61.jpeg
new file mode 100644
index 000000000..e44f2e7fd
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-61.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-62.jpeg b/notebooks/people/noam_chomsky/images-62.jpeg
new file mode 100644
index 000000000..496c33fe0
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-62.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-64.jpeg b/notebooks/people/noam_chomsky/images-64.jpeg
new file mode 100644
index 000000000..b6b28adac
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-64.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-65.jpeg b/notebooks/people/noam_chomsky/images-65.jpeg
new file mode 100644
index 000000000..cc7fb9bb6
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-65.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-66.jpeg b/notebooks/people/noam_chomsky/images-66.jpeg
new file mode 100644
index 000000000..426548afd
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-66.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-67.jpeg b/notebooks/people/noam_chomsky/images-67.jpeg
new file mode 100644
index 000000000..5694e9196
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-67.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-68.jpeg b/notebooks/people/noam_chomsky/images-68.jpeg
new file mode 100644
index 000000000..547615a14
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-68.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-69.jpeg b/notebooks/people/noam_chomsky/images-69.jpeg
new file mode 100644
index 000000000..bf3d639af
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-69.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-70.jpeg b/notebooks/people/noam_chomsky/images-70.jpeg
new file mode 100644
index 000000000..8724224eb
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-70.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-71.jpeg b/notebooks/people/noam_chomsky/images-71.jpeg
new file mode 100644
index 000000000..d00388a90
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-71.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-72.jpeg b/notebooks/people/noam_chomsky/images-72.jpeg
new file mode 100644
index 000000000..fc5ca8bb8
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-72.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-73.jpeg b/notebooks/people/noam_chomsky/images-73.jpeg
new file mode 100644
index 000000000..e8e148686
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-73.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-74.jpeg b/notebooks/people/noam_chomsky/images-74.jpeg
new file mode 100644
index 000000000..eef9db73b
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-74.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-75.jpeg b/notebooks/people/noam_chomsky/images-75.jpeg
new file mode 100644
index 000000000..c5fe8b00d
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-75.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-76.jpeg b/notebooks/people/noam_chomsky/images-76.jpeg
new file mode 100644
index 000000000..65856333a
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-76.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-77.jpeg b/notebooks/people/noam_chomsky/images-77.jpeg
new file mode 100644
index 000000000..55d5e4bcb
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-77.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-78.jpeg b/notebooks/people/noam_chomsky/images-78.jpeg
new file mode 100644
index 000000000..f87858d50
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-78.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-79.jpeg b/notebooks/people/noam_chomsky/images-79.jpeg
new file mode 100644
index 000000000..e8dd8cb57
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-79.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-80.jpeg b/notebooks/people/noam_chomsky/images-80.jpeg
new file mode 100644
index 000000000..1e2ff7c8a
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-80.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-81.jpeg b/notebooks/people/noam_chomsky/images-81.jpeg
new file mode 100644
index 000000000..fd33d77fe
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-81.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-82.jpeg b/notebooks/people/noam_chomsky/images-82.jpeg
new file mode 100644
index 000000000..a604fa226
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-82.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-83.jpeg b/notebooks/people/noam_chomsky/images-83.jpeg
new file mode 100644
index 000000000..2d24e6981
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-83.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-84.jpeg b/notebooks/people/noam_chomsky/images-84.jpeg
new file mode 100644
index 000000000..4cc354894
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-84.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-85.jpeg b/notebooks/people/noam_chomsky/images-85.jpeg
new file mode 100644
index 000000000..946e42e74
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-85.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-86.jpeg b/notebooks/people/noam_chomsky/images-86.jpeg
new file mode 100644
index 000000000..8533a027d
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-86.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-87.jpeg b/notebooks/people/noam_chomsky/images-87.jpeg
new file mode 100644
index 000000000..643c5cecd
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-87.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-88.jpeg b/notebooks/people/noam_chomsky/images-88.jpeg
new file mode 100644
index 000000000..0caf19bea
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-88.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-89.jpeg b/notebooks/people/noam_chomsky/images-89.jpeg
new file mode 100644
index 000000000..e7b5c41f5
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-89.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-90.jpeg b/notebooks/people/noam_chomsky/images-90.jpeg
new file mode 100644
index 000000000..7b211c01d
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-90.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-91.jpeg b/notebooks/people/noam_chomsky/images-91.jpeg
new file mode 100644
index 000000000..d825b3610
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-91.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-92.jpeg b/notebooks/people/noam_chomsky/images-92.jpeg
new file mode 100644
index 000000000..58c25dfcf
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-92.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-93.jpeg b/notebooks/people/noam_chomsky/images-93.jpeg
new file mode 100644
index 000000000..a6fd9e9a9
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-93.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-94.jpeg b/notebooks/people/noam_chomsky/images-94.jpeg
new file mode 100644
index 000000000..8f2d9e220
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-94.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-96.jpeg b/notebooks/people/noam_chomsky/images-96.jpeg
new file mode 100644
index 000000000..ec9e5e387
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-96.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-97.jpeg b/notebooks/people/noam_chomsky/images-97.jpeg
new file mode 100644
index 000000000..5b2ff1265
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-97.jpeg differ
diff --git a/notebooks/people/noam_chomsky/images-98.jpeg b/notebooks/people/noam_chomsky/images-98.jpeg
new file mode 100644
index 000000000..1c54b81fc
Binary files /dev/null and b/notebooks/people/noam_chomsky/images-98.jpeg differ
diff --git a/notebooks/tsne.jpg b/notebooks/tsne.jpg
new file mode 100644
index 000000000..cf4bfd756
Binary files /dev/null and b/notebooks/tsne.jpg differ
diff --git a/requirements.txt b/requirements.txt
index b7418c9ac..b6bb4abeb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,5 @@
 tensorflow==1.7
+tensorlayer==1.7
 scipy
 scikit-learn
 opencv-python
@@ -7,3 +8,12 @@ matplotlib
 Pillow
 requests
 psutil
+progressbar2
+mtcnn
+pathos
+dask
+tinytag
+docker
+torch
+torchvision
+tensorboardX
\ No newline at end of file
diff --git a/setup.py b/setup.py
new file mode 100644
index 000000000..4b07537aa
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,32 @@
+from setuptools import find_packages, setup
+
+setup(
+    name='facenet_sandberg',
+    version='0.0.2',
+    description="Face recognition using TensorFlow",
+    long_description="Face recognition with Google's FaceNet deep neural network & TensorFlow. Mirror of https://github.com/davidsandberg/facenet.",
+    url='https://github.com/armanrahman22/facenet',
+    packages=find_packages(),
+    maintainer='Arman Rahman',
+    maintainer_email='armanrahman22@gmail.com',
+    include_package_data=True,
+    license='MIT',
+    install_requires=[
+        'tensorflow',
+        'scipy',
+        'docker',
+        'scikit-learn',
+        'opencv-python',
+        'h5py',
+        'matplotlib',
+        'Pillow',
+        'requests',
+        'psutil',
+        'progressbar2',
+        'mtcnn',
+        'pathos',
+        'dask',
+        'tensorlayer',
+        'torch',
+        'torchvision',
+        'tensorboardX'])
diff --git a/src/align/align_dataset_mtcnn.py b/src/align/align_dataset_mtcnn.py
deleted file mode 100644
index 7d5e735e6..000000000
--- a/src/align/align_dataset_mtcnn.py
+++ /dev/null
@@ -1,159 +0,0 @@
-"""Performs face alignment and stores face thumbnails in the output directory."""
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from scipy import misc
-import sys
-import os
-import argparse
-import tensorflow as tf
-import numpy as np
-import facenet
-import align.detect_face
-import random
-from time import sleep
-
-def main(args):
-    sleep(random.random())
-    output_dir = os.path.expanduser(args.output_dir)
-    if not os.path.exists(output_dir):
-        os.makedirs(output_dir)
-    # Store some git revision info in a text file in the log directory
-    src_path,_ = os.path.split(os.path.realpath(__file__))
-    facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
-    dataset = facenet.get_dataset(args.input_dir)
-    
-    print('Creating networks and loading parameters')
-    
-    with tf.Graph().as_default():
-        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
-        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
-        with sess.as_default():
-            pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
-    
-    minsize = 20 # minimum size of face
-    threshold = [ 0.6, 0.7, 0.7 ]  # three steps's threshold
-    factor = 0.709 # scale factor
-
-    # Add a random key to the filename to allow alignment using multiple processes
-    random_key = np.random.randint(0, high=99999)
-    bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
-    
-    with open(bounding_boxes_filename, "w") as text_file:
-        nrof_images_total = 0
-        nrof_successfully_aligned = 0
-        if args.random_order:
-            random.shuffle(dataset)
-        for cls in dataset:
-            output_class_dir = os.path.join(output_dir, cls.name)
-            if not os.path.exists(output_class_dir):
-                os.makedirs(output_class_dir)
-                if args.random_order:
-                    random.shuffle(cls.image_paths)
-            for image_path in cls.image_paths:
-                nrof_images_total += 1
-                filename = os.path.splitext(os.path.split(image_path)[1])[0]
-                output_filename = os.path.join(output_class_dir, filename+'.png')
-                print(image_path)
-                if not os.path.exists(output_filename):
-                    try:
-                        img = misc.imread(image_path)
-                    except (IOError, ValueError, IndexError) as e:
-                        errorMessage = '{}: {}'.format(image_path, e)
-                        print(errorMessage)
-                    else:
-                        if img.ndim<2:
-                            print('Unable to align "%s"' % image_path)
-                            text_file.write('%s\n' % (output_filename))
-                            continue
-                        if img.ndim == 2:
-                            img = facenet.to_rgb(img)
-                        img = img[:,:,0:3]
-    
-                        bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
-                        nrof_faces = bounding_boxes.shape[0]
-                        if nrof_faces>0:
-                            det = bounding_boxes[:,0:4]
-                            det_arr = []
-                            img_size = np.asarray(img.shape)[0:2]
-                            if nrof_faces>1:
-                                if args.detect_multiple_faces:
-                                    for i in range(nrof_faces):
-                                        det_arr.append(np.squeeze(det[i]))
-                                else:
-                                    bounding_box_size = (det[:,2]-det[:,0])*(det[:,3]-det[:,1])
-                                    img_center = img_size / 2
-                                    offsets = np.vstack([ (det[:,0]+det[:,2])/2-img_center[1], (det[:,1]+det[:,3])/2-img_center[0] ])
-                                    offset_dist_squared = np.sum(np.power(offsets,2.0),0)
-                                    index = np.argmax(bounding_box_size-offset_dist_squared*2.0) # some extra weight on the centering
-                                    det_arr.append(det[index,:])
-                            else:
-                                det_arr.append(np.squeeze(det))
-
-                            for i, det in enumerate(det_arr):
-                                det = np.squeeze(det)
-                                bb = np.zeros(4, dtype=np.int32)
-                                bb[0] = np.maximum(det[0]-args.margin/2, 0)
-                                bb[1] = np.maximum(det[1]-args.margin/2, 0)
-                                bb[2] = np.minimum(det[2]+args.margin/2, img_size[1])
-                                bb[3] = np.minimum(det[3]+args.margin/2, img_size[0])
-                                cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
-                                scaled = misc.imresize(cropped, (args.image_size, args.image_size), interp='bilinear')
-                                nrof_successfully_aligned += 1
-                                filename_base, file_extension = os.path.splitext(output_filename)
-                                if args.detect_multiple_faces:
-                                    output_filename_n = "{}_{}{}".format(filename_base, i, file_extension)
-                                else:
-                                    output_filename_n = "{}{}".format(filename_base, file_extension)
-                                misc.imsave(output_filename_n, scaled)
-                                text_file.write('%s %d %d %d %d\n' % (output_filename_n, bb[0], bb[1], bb[2], bb[3]))
-                        else:
-                            print('Unable to align "%s"' % image_path)
-                            text_file.write('%s\n' % (output_filename))
-                            
-    print('Total number of images: %d' % nrof_images_total)
-    print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
-            
-
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    
-    parser.add_argument('input_dir', type=str, help='Directory with unaligned images.')
-    parser.add_argument('output_dir', type=str, help='Directory with aligned face thumbnails.')
-    parser.add_argument('--image_size', type=int,
-        help='Image size (height, width) in pixels.', default=182)
-    parser.add_argument('--margin', type=int,
-        help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
-    parser.add_argument('--random_order', 
-        help='Shuffles the order of images to enable alignment using multiple processes.', action='store_true')
-    parser.add_argument('--gpu_memory_fraction', type=float,
-        help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
-    parser.add_argument('--detect_multiple_faces', type=bool,
-                        help='Detect and align multiple faces per image.', default=False)
-    return parser.parse_args(argv)
-
-if __name__ == '__main__':
-    main(parse_arguments(sys.argv[1:]))
diff --git a/src/align/det1.npy b/src/align/det1.npy
deleted file mode 100644
index 7c05a2c56..000000000
Binary files a/src/align/det1.npy and /dev/null differ
diff --git a/src/align/det2.npy b/src/align/det2.npy
deleted file mode 100644
index 85d5bf09c..000000000
Binary files a/src/align/det2.npy and /dev/null differ
diff --git a/src/align/det3.npy b/src/align/det3.npy
deleted file mode 100644
index 90d5ba975..000000000
Binary files a/src/align/det3.npy and /dev/null differ
diff --git a/src/align/detect_face.py b/src/align/detect_face.py
deleted file mode 100644
index 7f98ca7fb..000000000
--- a/src/align/detect_face.py
+++ /dev/null
@@ -1,781 +0,0 @@
-""" Tensorflow implementation of the face detection / alignment algorithm found at
-https://github.com/kpzhang93/MTCNN_face_detection_alignment
-"""
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from six import string_types, iteritems
-
-import numpy as np
-import tensorflow as tf
-#from math import floor
-import cv2
-import os
-
-def layer(op):
-    """Decorator for composable network layers."""
-
-    def layer_decorated(self, *args, **kwargs):
-        # Automatically set a name if not provided.
-        name = kwargs.setdefault('name', self.get_unique_name(op.__name__))
-        # Figure out the layer inputs.
-        if len(self.terminals) == 0:
-            raise RuntimeError('No input variables found for layer %s.' % name)
-        elif len(self.terminals) == 1:
-            layer_input = self.terminals[0]
-        else:
-            layer_input = list(self.terminals)
-        # Perform the operation and get the output.
-        layer_output = op(self, layer_input, *args, **kwargs)
-        # Add to layer LUT.
-        self.layers[name] = layer_output
-        # This output is now the input for the next layer.
-        self.feed(layer_output)
-        # Return self for chained calls.
-        return self
-
-    return layer_decorated
-
-class Network(object):
-
-    def __init__(self, inputs, trainable=True):
-        # The input nodes for this network
-        self.inputs = inputs
-        # The current list of terminal nodes
-        self.terminals = []
-        # Mapping from layer names to layers
-        self.layers = dict(inputs)
-        # If true, the resulting variables are set as trainable
-        self.trainable = trainable
-
-        self.setup()
-
-    def setup(self):
-        """Construct the network. """
-        raise NotImplementedError('Must be implemented by the subclass.')
-
-    def load(self, data_path, session, ignore_missing=False):
-        """Load network weights.
-        data_path: The path to the numpy-serialized network weights
-        session: The current TensorFlow session
-        ignore_missing: If true, serialized weights for missing layers are ignored.
-        """
-        data_dict = np.load(data_path, encoding='latin1').item() #pylint: disable=no-member
-
-        for op_name in data_dict:
-            with tf.variable_scope(op_name, reuse=True):
-                for param_name, data in iteritems(data_dict[op_name]):
-                    try:
-                        var = tf.get_variable(param_name)
-                        session.run(var.assign(data))
-                    except ValueError:
-                        if not ignore_missing:
-                            raise
-
-    def feed(self, *args):
-        """Set the input(s) for the next operation by replacing the terminal nodes.
-        The arguments can be either layer names or the actual layers.
-        """
-        assert len(args) != 0
-        self.terminals = []
-        for fed_layer in args:
-            if isinstance(fed_layer, string_types):
-                try:
-                    fed_layer = self.layers[fed_layer]
-                except KeyError:
-                    raise KeyError('Unknown layer name fed: %s' % fed_layer)
-            self.terminals.append(fed_layer)
-        return self
-
-    def get_output(self):
-        """Returns the current network output."""
-        return self.terminals[-1]
-
-    def get_unique_name(self, prefix):
-        """Returns an index-suffixed unique name for the given prefix.
-        This is used for auto-generating layer names based on the type-prefix.
-        """
-        ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) + 1
-        return '%s_%d' % (prefix, ident)
-
-    def make_var(self, name, shape):
-        """Creates a new TensorFlow variable."""
-        return tf.get_variable(name, shape, trainable=self.trainable)
-
-    def validate_padding(self, padding):
-        """Verifies that the padding is one of the supported ones."""
-        assert padding in ('SAME', 'VALID')
-
-    @layer
-    def conv(self,
-             inp,
-             k_h,
-             k_w,
-             c_o,
-             s_h,
-             s_w,
-             name,
-             relu=True,
-             padding='SAME',
-             group=1,
-             biased=True):
-        # Verify that the padding is acceptable
-        self.validate_padding(padding)
-        # Get the number of channels in the input
-        c_i = int(inp.get_shape()[-1])
-        # Verify that the grouping parameter is valid
-        assert c_i % group == 0
-        assert c_o % group == 0
-        # Convolution for a given input and kernel
-        convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
-        with tf.variable_scope(name) as scope:
-            kernel = self.make_var('weights', shape=[k_h, k_w, c_i // group, c_o])
-            # This is the common-case. Convolve the input without any further complications.
-            output = convolve(inp, kernel)
-            # Add the biases
-            if biased:
-                biases = self.make_var('biases', [c_o])
-                output = tf.nn.bias_add(output, biases)
-            if relu:
-                # ReLU non-linearity
-                output = tf.nn.relu(output, name=scope.name)
-            return output
-
-    @layer
-    def prelu(self, inp, name):
-        with tf.variable_scope(name):
-            i = int(inp.get_shape()[-1])
-            alpha = self.make_var('alpha', shape=(i,))
-            output = tf.nn.relu(inp) + tf.multiply(alpha, -tf.nn.relu(-inp))
-        return output
-
-    @layer
-    def max_pool(self, inp, k_h, k_w, s_h, s_w, name, padding='SAME'):
-        self.validate_padding(padding)
-        return tf.nn.max_pool(inp,
-                              ksize=[1, k_h, k_w, 1],
-                              strides=[1, s_h, s_w, 1],
-                              padding=padding,
-                              name=name)
-
-    @layer
-    def fc(self, inp, num_out, name, relu=True):
-        with tf.variable_scope(name):
-            input_shape = inp.get_shape()
-            if input_shape.ndims == 4:
-                # The input is spatial. Vectorize it first.
-                dim = 1
-                for d in input_shape[1:].as_list():
-                    dim *= int(d)
-                feed_in = tf.reshape(inp, [-1, dim])
-            else:
-                feed_in, dim = (inp, input_shape[-1].value)
-            weights = self.make_var('weights', shape=[dim, num_out])
-            biases = self.make_var('biases', [num_out])
-            op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b
-            fc = op(feed_in, weights, biases, name=name)
-            return fc
-
-
-    """
-    Multi dimensional softmax,
-    refer to https://github.com/tensorflow/tensorflow/issues/210
-    compute softmax along the dimension of target
-    the native softmax only supports batch_size x dimension
-    """
-    @layer
-    def softmax(self, target, axis, name=None):
-        max_axis = tf.reduce_max(target, axis, keepdims=True)
-        target_exp = tf.exp(target-max_axis)
-        normalize = tf.reduce_sum(target_exp, axis, keepdims=True)
-        softmax = tf.div(target_exp, normalize, name)
-        return softmax
-    
-class PNet(Network):
-    def setup(self):
-        (self.feed('data') #pylint: disable=no-value-for-parameter, no-member
-             .conv(3, 3, 10, 1, 1, padding='VALID', relu=False, name='conv1')
-             .prelu(name='PReLU1')
-             .max_pool(2, 2, 2, 2, name='pool1')
-             .conv(3, 3, 16, 1, 1, padding='VALID', relu=False, name='conv2')
-             .prelu(name='PReLU2')
-             .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv3')
-             .prelu(name='PReLU3')
-             .conv(1, 1, 2, 1, 1, relu=False, name='conv4-1')
-             .softmax(3,name='prob1'))
-
-        (self.feed('PReLU3') #pylint: disable=no-value-for-parameter
-             .conv(1, 1, 4, 1, 1, relu=False, name='conv4-2'))
-        
-class RNet(Network):
-    def setup(self):
-        (self.feed('data') #pylint: disable=no-value-for-parameter, no-member
-             .conv(3, 3, 28, 1, 1, padding='VALID', relu=False, name='conv1')
-             .prelu(name='prelu1')
-             .max_pool(3, 3, 2, 2, name='pool1')
-             .conv(3, 3, 48, 1, 1, padding='VALID', relu=False, name='conv2')
-             .prelu(name='prelu2')
-             .max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
-             .conv(2, 2, 64, 1, 1, padding='VALID', relu=False, name='conv3')
-             .prelu(name='prelu3')
-             .fc(128, relu=False, name='conv4')
-             .prelu(name='prelu4')
-             .fc(2, relu=False, name='conv5-1')
-             .softmax(1,name='prob1'))
-
-        (self.feed('prelu4') #pylint: disable=no-value-for-parameter
-             .fc(4, relu=False, name='conv5-2'))
-
-class ONet(Network):
-    def setup(self):
-        (self.feed('data') #pylint: disable=no-value-for-parameter, no-member
-             .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv1')
-             .prelu(name='prelu1')
-             .max_pool(3, 3, 2, 2, name='pool1')
-             .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv2')
-             .prelu(name='prelu2')
-             .max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
-             .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv3')
-             .prelu(name='prelu3')
-             .max_pool(2, 2, 2, 2, name='pool3')
-             .conv(2, 2, 128, 1, 1, padding='VALID', relu=False, name='conv4')
-             .prelu(name='prelu4')
-             .fc(256, relu=False, name='conv5')
-             .prelu(name='prelu5')
-             .fc(2, relu=False, name='conv6-1')
-             .softmax(1, name='prob1'))
-
-        (self.feed('prelu5') #pylint: disable=no-value-for-parameter
-             .fc(4, relu=False, name='conv6-2'))
-
-        (self.feed('prelu5') #pylint: disable=no-value-for-parameter
-             .fc(10, relu=False, name='conv6-3'))
-
-def create_mtcnn(sess, model_path):
-    if not model_path:
-        model_path,_ = os.path.split(os.path.realpath(__file__))
-
-    with tf.variable_scope('pnet'):
-        data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
-        pnet = PNet({'data':data})
-        pnet.load(os.path.join(model_path, 'det1.npy'), sess)
-    with tf.variable_scope('rnet'):
-        data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
-        rnet = RNet({'data':data})
-        rnet.load(os.path.join(model_path, 'det2.npy'), sess)
-    with tf.variable_scope('onet'):
-        data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
-        onet = ONet({'data':data})
-        onet.load(os.path.join(model_path, 'det3.npy'), sess)
-        
-    pnet_fun = lambda img : sess.run(('pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0'), feed_dict={'pnet/input:0':img})
-    rnet_fun = lambda img : sess.run(('rnet/conv5-2/conv5-2:0', 'rnet/prob1:0'), feed_dict={'rnet/input:0':img})
-    onet_fun = lambda img : sess.run(('onet/conv6-2/conv6-2:0', 'onet/conv6-3/conv6-3:0', 'onet/prob1:0'), feed_dict={'onet/input:0':img})
-    return pnet_fun, rnet_fun, onet_fun
-
-def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):
-    """Detects faces in an image, and returns bounding boxes and points for them.
-    img: input image
-    minsize: minimum faces' size
-    pnet, rnet, onet: caffemodel
-    threshold: threshold=[th1, th2, th3], th1-3 are three steps's threshold
-    factor: the factor used to create a scaling pyramid of face sizes to detect in the image.
-    """
-    factor_count=0
-    total_boxes=np.empty((0,9))
-    points=np.empty(0)
-    h=img.shape[0]
-    w=img.shape[1]
-    minl=np.amin([h, w])
-    m=12.0/minsize
-    minl=minl*m
-    # create scale pyramid
-    scales=[]
-    while minl>=12:
-        scales += [m*np.power(factor, factor_count)]
-        minl = minl*factor
-        factor_count += 1
-
-    # first stage
-    for scale in scales:
-        hs=int(np.ceil(h*scale))
-        ws=int(np.ceil(w*scale))
-        im_data = imresample(img, (hs, ws))
-        im_data = (im_data-127.5)*0.0078125
-        img_x = np.expand_dims(im_data, 0)
-        img_y = np.transpose(img_x, (0,2,1,3))
-        out = pnet(img_y)
-        out0 = np.transpose(out[0], (0,2,1,3))
-        out1 = np.transpose(out[1], (0,2,1,3))
-        
-        boxes, _ = generateBoundingBox(out1[0,:,:,1].copy(), out0[0,:,:,:].copy(), scale, threshold[0])
-        
-        # inter-scale nms
-        pick = nms(boxes.copy(), 0.5, 'Union')
-        if boxes.size>0 and pick.size>0:
-            boxes = boxes[pick,:]
-            total_boxes = np.append(total_boxes, boxes, axis=0)
-
-    numbox = total_boxes.shape[0]
-    if numbox>0:
-        pick = nms(total_boxes.copy(), 0.7, 'Union')
-        total_boxes = total_boxes[pick,:]
-        regw = total_boxes[:,2]-total_boxes[:,0]
-        regh = total_boxes[:,3]-total_boxes[:,1]
-        qq1 = total_boxes[:,0]+total_boxes[:,5]*regw
-        qq2 = total_boxes[:,1]+total_boxes[:,6]*regh
-        qq3 = total_boxes[:,2]+total_boxes[:,7]*regw
-        qq4 = total_boxes[:,3]+total_boxes[:,8]*regh
-        total_boxes = np.transpose(np.vstack([qq1, qq2, qq3, qq4, total_boxes[:,4]]))
-        total_boxes = rerec(total_boxes.copy())
-        total_boxes[:,0:4] = np.fix(total_boxes[:,0:4]).astype(np.int32)
-        dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
-
-    numbox = total_boxes.shape[0]
-    if numbox>0:
-        # second stage
-        tempimg = np.zeros((24,24,3,numbox))
-        for k in range(0,numbox):
-            tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))
-            tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]
-            if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:
-                tempimg[:,:,:,k] = imresample(tmp, (24, 24))
-            else:
-                return np.empty()
-        tempimg = (tempimg-127.5)*0.0078125
-        tempimg1 = np.transpose(tempimg, (3,1,0,2))
-        out = rnet(tempimg1)
-        out0 = np.transpose(out[0])
-        out1 = np.transpose(out[1])
-        score = out1[1,:]
-        ipass = np.where(score>threshold[1])
-        total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])
-        mv = out0[:,ipass[0]]
-        if total_boxes.shape[0]>0:
-            pick = nms(total_boxes, 0.7, 'Union')
-            total_boxes = total_boxes[pick,:]
-            total_boxes = bbreg(total_boxes.copy(), np.transpose(mv[:,pick]))
-            total_boxes = rerec(total_boxes.copy())
-
-    numbox = total_boxes.shape[0]
-    if numbox>0:
-        # third stage
-        total_boxes = np.fix(total_boxes).astype(np.int32)
-        dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
-        tempimg = np.zeros((48,48,3,numbox))
-        for k in range(0,numbox):
-            tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))
-            tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]
-            if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:
-                tempimg[:,:,:,k] = imresample(tmp, (48, 48))
-            else:
-                return np.empty()
-        tempimg = (tempimg-127.5)*0.0078125
-        tempimg1 = np.transpose(tempimg, (3,1,0,2))
-        out = onet(tempimg1)
-        out0 = np.transpose(out[0])
-        out1 = np.transpose(out[1])
-        out2 = np.transpose(out[2])
-        score = out2[1,:]
-        points = out1
-        ipass = np.where(score>threshold[2])
-        points = points[:,ipass[0]]
-        total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])
-        mv = out0[:,ipass[0]]
-
-        w = total_boxes[:,2]-total_boxes[:,0]+1
-        h = total_boxes[:,3]-total_boxes[:,1]+1
-        points[0:5,:] = np.tile(w,(5, 1))*points[0:5,:] + np.tile(total_boxes[:,0],(5, 1))-1
-        points[5:10,:] = np.tile(h,(5, 1))*points[5:10,:] + np.tile(total_boxes[:,1],(5, 1))-1
-        if total_boxes.shape[0]>0:
-            total_boxes = bbreg(total_boxes.copy(), np.transpose(mv))
-            pick = nms(total_boxes.copy(), 0.7, 'Min')
-            total_boxes = total_boxes[pick,:]
-            points = points[:,pick]
-                
-    return total_boxes, points
-
-
-def bulk_detect_face(images, detection_window_size_ratio, pnet, rnet, onet, threshold, factor):
-    """Detects faces in a list of images
-    images: list containing input images
-    detection_window_size_ratio: ratio of minimum face size to smallest image dimension
-    pnet, rnet, onet: caffemodel
-    threshold: threshold=[th1 th2 th3], th1-3 are three steps's threshold [0-1]
-    factor: the factor used to create a scaling pyramid of face sizes to detect in the image.
-    """
-    all_scales = [None] * len(images)
-    images_with_boxes = [None] * len(images)
-
-    for i in range(len(images)):
-        images_with_boxes[i] = {'total_boxes': np.empty((0, 9))}
-
-    # create scale pyramid
-    for index, img in enumerate(images):
-        all_scales[index] = []
-        h = img.shape[0]
-        w = img.shape[1]
-        minsize = int(detection_window_size_ratio * np.minimum(w, h))
-        factor_count = 0
-        minl = np.amin([h, w])
-        if minsize <= 12:
-            minsize = 12
-
-        m = 12.0 / minsize
-        minl = minl * m
-        while minl >= 12:
-            all_scales[index].append(m * np.power(factor, factor_count))
-            minl = minl * factor
-            factor_count += 1
-
-    # # # # # # # # # # # # #
-    # first stage - fast proposal network (pnet) to obtain face candidates
-    # # # # # # # # # # # # #
-
-    images_obj_per_resolution = {}
-
-    # TODO: use some type of rounding to number module 8 to increase probability that pyramid images will have the same resolution across input images
-
-    for index, scales in enumerate(all_scales):
-        h = images[index].shape[0]
-        w = images[index].shape[1]
-
-        for scale in scales:
-            hs = int(np.ceil(h * scale))
-            ws = int(np.ceil(w * scale))
-
-            if (ws, hs) not in images_obj_per_resolution:
-                images_obj_per_resolution[(ws, hs)] = []
-
-            im_data = imresample(images[index], (hs, ws))
-            im_data = (im_data - 127.5) * 0.0078125
-            img_y = np.transpose(im_data, (1, 0, 2))  # caffe uses different dimensions ordering
-            images_obj_per_resolution[(ws, hs)].append({'scale': scale, 'image': img_y, 'index': index})
-
-    for resolution in images_obj_per_resolution:
-        images_per_resolution = [i['image'] for i in images_obj_per_resolution[resolution]]
-        outs = pnet(images_per_resolution)
-
-        for index in range(len(outs[0])):
-            scale = images_obj_per_resolution[resolution][index]['scale']
-            image_index = images_obj_per_resolution[resolution][index]['index']
-            out0 = np.transpose(outs[0][index], (1, 0, 2))
-            out1 = np.transpose(outs[1][index], (1, 0, 2))
-
-            boxes, _ = generateBoundingBox(out1[:, :, 1].copy(), out0[:, :, :].copy(), scale, threshold[0])
-
-            # inter-scale nms
-            pick = nms(boxes.copy(), 0.5, 'Union')
-            if boxes.size > 0 and pick.size > 0:
-                boxes = boxes[pick, :]
-                images_with_boxes[image_index]['total_boxes'] = np.append(images_with_boxes[image_index]['total_boxes'],
-                                                                          boxes,
-                                                                          axis=0)
-
-    for index, image_obj in enumerate(images_with_boxes):
-        numbox = image_obj['total_boxes'].shape[0]
-        if numbox > 0:
-            h = images[index].shape[0]
-            w = images[index].shape[1]
-            pick = nms(image_obj['total_boxes'].copy(), 0.7, 'Union')
-            image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
-            regw = image_obj['total_boxes'][:, 2] - image_obj['total_boxes'][:, 0]
-            regh = image_obj['total_boxes'][:, 3] - image_obj['total_boxes'][:, 1]
-            qq1 = image_obj['total_boxes'][:, 0] + image_obj['total_boxes'][:, 5] * regw
-            qq2 = image_obj['total_boxes'][:, 1] + image_obj['total_boxes'][:, 6] * regh
-            qq3 = image_obj['total_boxes'][:, 2] + image_obj['total_boxes'][:, 7] * regw
-            qq4 = image_obj['total_boxes'][:, 3] + image_obj['total_boxes'][:, 8] * regh
-            image_obj['total_boxes'] = np.transpose(np.vstack([qq1, qq2, qq3, qq4, image_obj['total_boxes'][:, 4]]))
-            image_obj['total_boxes'] = rerec(image_obj['total_boxes'].copy())
-            image_obj['total_boxes'][:, 0:4] = np.fix(image_obj['total_boxes'][:, 0:4]).astype(np.int32)
-            dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(image_obj['total_boxes'].copy(), w, h)
-
-            numbox = image_obj['total_boxes'].shape[0]
-            tempimg = np.zeros((24, 24, 3, numbox))
-
-            if numbox > 0:
-                for k in range(0, numbox):
-                    tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
-                    tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = images[index][y[k] - 1:ey[k], x[k] - 1:ex[k], :]
-                    if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
-                        tempimg[:, :, :, k] = imresample(tmp, (24, 24))
-                    else:
-                        return np.empty()
-
-                tempimg = (tempimg - 127.5) * 0.0078125
-                image_obj['rnet_input'] = np.transpose(tempimg, (3, 1, 0, 2))
-
-    # # # # # # # # # # # # #
-    # second stage - refinement of face candidates with rnet
-    # # # # # # # # # # # # #
-
-    bulk_rnet_input = np.empty((0, 24, 24, 3))
-    for index, image_obj in enumerate(images_with_boxes):
-        if 'rnet_input' in image_obj:
-            bulk_rnet_input = np.append(bulk_rnet_input, image_obj['rnet_input'], axis=0)
-
-    out = rnet(bulk_rnet_input)
-    out0 = np.transpose(out[0])
-    out1 = np.transpose(out[1])
-    score = out1[1, :]
-
-    i = 0
-    for index, image_obj in enumerate(images_with_boxes):
-        if 'rnet_input' not in image_obj:
-            continue
-
-        rnet_input_count = image_obj['rnet_input'].shape[0]
-        score_per_image = score[i:i + rnet_input_count]
-        out0_per_image = out0[:, i:i + rnet_input_count]
-
-        ipass = np.where(score_per_image > threshold[1])
-        image_obj['total_boxes'] = np.hstack([image_obj['total_boxes'][ipass[0], 0:4].copy(),
-                                              np.expand_dims(score_per_image[ipass].copy(), 1)])
-
-        mv = out0_per_image[:, ipass[0]]
-
-        if image_obj['total_boxes'].shape[0] > 0:
-            h = images[index].shape[0]
-            w = images[index].shape[1]
-            pick = nms(image_obj['total_boxes'], 0.7, 'Union')
-            image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
-            image_obj['total_boxes'] = bbreg(image_obj['total_boxes'].copy(), np.transpose(mv[:, pick]))
-            image_obj['total_boxes'] = rerec(image_obj['total_boxes'].copy())
-
-            numbox = image_obj['total_boxes'].shape[0]
-
-            if numbox > 0:
-                tempimg = np.zeros((48, 48, 3, numbox))
-                image_obj['total_boxes'] = np.fix(image_obj['total_boxes']).astype(np.int32)
-                dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(image_obj['total_boxes'].copy(), w, h)
-
-                for k in range(0, numbox):
-                    tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
-                    tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = images[index][y[k] - 1:ey[k], x[k] - 1:ex[k], :]
-                    if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
-                        tempimg[:, :, :, k] = imresample(tmp, (48, 48))
-                    else:
-                        return np.empty()
-                tempimg = (tempimg - 127.5) * 0.0078125
-                image_obj['onet_input'] = np.transpose(tempimg, (3, 1, 0, 2))
-
-        i += rnet_input_count
-
-    # # # # # # # # # # # # #
-    # third stage - further refinement and facial landmarks positions with onet
-    # # # # # # # # # # # # #
-
-    bulk_onet_input = np.empty((0, 48, 48, 3))
-    for index, image_obj in enumerate(images_with_boxes):
-        if 'onet_input' in image_obj:
-            bulk_onet_input = np.append(bulk_onet_input, image_obj['onet_input'], axis=0)
-
-    out = onet(bulk_onet_input)
-
-    out0 = np.transpose(out[0])
-    out1 = np.transpose(out[1])
-    out2 = np.transpose(out[2])
-    score = out2[1, :]
-    points = out1
-
-    i = 0
-    ret = []
-    for index, image_obj in enumerate(images_with_boxes):
-        if 'onet_input' not in image_obj:
-            ret.append(None)
-            continue
-
-        onet_input_count = image_obj['onet_input'].shape[0]
-
-        out0_per_image = out0[:, i:i + onet_input_count]
-        score_per_image = score[i:i + onet_input_count]
-        points_per_image = points[:, i:i + onet_input_count]
-
-        ipass = np.where(score_per_image > threshold[2])
-        points_per_image = points_per_image[:, ipass[0]]
-
-        image_obj['total_boxes'] = np.hstack([image_obj['total_boxes'][ipass[0], 0:4].copy(),
-                                              np.expand_dims(score_per_image[ipass].copy(), 1)])
-        mv = out0_per_image[:, ipass[0]]
-
-        w = image_obj['total_boxes'][:, 2] - image_obj['total_boxes'][:, 0] + 1
-        h = image_obj['total_boxes'][:, 3] - image_obj['total_boxes'][:, 1] + 1
-        points_per_image[0:5, :] = np.tile(w, (5, 1)) * points_per_image[0:5, :] + np.tile(
-            image_obj['total_boxes'][:, 0], (5, 1)) - 1
-        points_per_image[5:10, :] = np.tile(h, (5, 1)) * points_per_image[5:10, :] + np.tile(
-            image_obj['total_boxes'][:, 1], (5, 1)) - 1
-
-        if image_obj['total_boxes'].shape[0] > 0:
-            image_obj['total_boxes'] = bbreg(image_obj['total_boxes'].copy(), np.transpose(mv))
-            pick = nms(image_obj['total_boxes'].copy(), 0.7, 'Min')
-            image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
-            points_per_image = points_per_image[:, pick]
-
-            ret.append((image_obj['total_boxes'], points_per_image))
-        else:
-            ret.append(None)
-
-        i += onet_input_count
-
-    return ret
-
-
-# function [boundingbox] = bbreg(boundingbox,reg)
-def bbreg(boundingbox,reg):
-    """Calibrate bounding boxes"""
-    if reg.shape[1]==1:
-        reg = np.reshape(reg, (reg.shape[2], reg.shape[3]))
-
-    w = boundingbox[:,2]-boundingbox[:,0]+1
-    h = boundingbox[:,3]-boundingbox[:,1]+1
-    b1 = boundingbox[:,0]+reg[:,0]*w
-    b2 = boundingbox[:,1]+reg[:,1]*h
-    b3 = boundingbox[:,2]+reg[:,2]*w
-    b4 = boundingbox[:,3]+reg[:,3]*h
-    boundingbox[:,0:4] = np.transpose(np.vstack([b1, b2, b3, b4 ]))
-    return boundingbox
- 
-def generateBoundingBox(imap, reg, scale, t):
-    """Use heatmap to generate bounding boxes"""
-    stride=2
-    cellsize=12
-
-    imap = np.transpose(imap)
-    dx1 = np.transpose(reg[:,:,0])
-    dy1 = np.transpose(reg[:,:,1])
-    dx2 = np.transpose(reg[:,:,2])
-    dy2 = np.transpose(reg[:,:,3])
-    y, x = np.where(imap >= t)
-    if y.shape[0]==1:
-        dx1 = np.flipud(dx1)
-        dy1 = np.flipud(dy1)
-        dx2 = np.flipud(dx2)
-        dy2 = np.flipud(dy2)
-    score = imap[(y,x)]
-    reg = np.transpose(np.vstack([ dx1[(y,x)], dy1[(y,x)], dx2[(y,x)], dy2[(y,x)] ]))
-    if reg.size==0:
-        reg = np.empty((0,3))
-    bb = np.transpose(np.vstack([y,x]))
-    q1 = np.fix((stride*bb+1)/scale)
-    q2 = np.fix((stride*bb+cellsize-1+1)/scale)
-    boundingbox = np.hstack([q1, q2, np.expand_dims(score,1), reg])
-    return boundingbox, reg
- 
-# function pick = nms(boxes,threshold,type)
-def nms(boxes, threshold, method):
-    if boxes.size==0:
-        return np.empty((0,3))
-    x1 = boxes[:,0]
-    y1 = boxes[:,1]
-    x2 = boxes[:,2]
-    y2 = boxes[:,3]
-    s = boxes[:,4]
-    area = (x2-x1+1) * (y2-y1+1)
-    I = np.argsort(s)
-    pick = np.zeros_like(s, dtype=np.int16)
-    counter = 0
-    while I.size>0:
-        i = I[-1]
-        pick[counter] = i
-        counter += 1
-        idx = I[0:-1]
-        xx1 = np.maximum(x1[i], x1[idx])
-        yy1 = np.maximum(y1[i], y1[idx])
-        xx2 = np.minimum(x2[i], x2[idx])
-        yy2 = np.minimum(y2[i], y2[idx])
-        w = np.maximum(0.0, xx2-xx1+1)
-        h = np.maximum(0.0, yy2-yy1+1)
-        inter = w * h
-        if method is 'Min':
-            o = inter / np.minimum(area[i], area[idx])
-        else:
-            o = inter / (area[i] + area[idx] - inter)
-        I = I[np.where(o<=threshold)]
-    pick = pick[0:counter]
-    return pick
-
-# function [dy edy dx edx y ey x ex tmpw tmph] = pad(total_boxes,w,h)
-def pad(total_boxes, w, h):
-    """Compute the padding coordinates (pad the bounding boxes to square)"""
-    tmpw = (total_boxes[:,2]-total_boxes[:,0]+1).astype(np.int32)
-    tmph = (total_boxes[:,3]-total_boxes[:,1]+1).astype(np.int32)
-    numbox = total_boxes.shape[0]
-
-    dx = np.ones((numbox), dtype=np.int32)
-    dy = np.ones((numbox), dtype=np.int32)
-    edx = tmpw.copy().astype(np.int32)
-    edy = tmph.copy().astype(np.int32)
-
-    x = total_boxes[:,0].copy().astype(np.int32)
-    y = total_boxes[:,1].copy().astype(np.int32)
-    ex = total_boxes[:,2].copy().astype(np.int32)
-    ey = total_boxes[:,3].copy().astype(np.int32)
-
-    tmp = np.where(ex>w)
-    edx.flat[tmp] = np.expand_dims(-ex[tmp]+w+tmpw[tmp],1)
-    ex[tmp] = w
-    
-    tmp = np.where(ey>h)
-    edy.flat[tmp] = np.expand_dims(-ey[tmp]+h+tmph[tmp],1)
-    ey[tmp] = h
-
-    tmp = np.where(x<1)
-    dx.flat[tmp] = np.expand_dims(2-x[tmp],1)
-    x[tmp] = 1
-
-    tmp = np.where(y<1)
-    dy.flat[tmp] = np.expand_dims(2-y[tmp],1)
-    y[tmp] = 1
-    
-    return dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph
-
-# function [bboxA] = rerec(bboxA)
-def rerec(bboxA):
-    """Convert bboxA to square."""
-    h = bboxA[:,3]-bboxA[:,1]
-    w = bboxA[:,2]-bboxA[:,0]
-    l = np.maximum(w, h)
-    bboxA[:,0] = bboxA[:,0]+w*0.5-l*0.5
-    bboxA[:,1] = bboxA[:,1]+h*0.5-l*0.5
-    bboxA[:,2:4] = bboxA[:,0:2] + np.transpose(np.tile(l,(2,1)))
-    return bboxA
-
-def imresample(img, sz):
-    im_data = cv2.resize(img, (sz[1], sz[0]), interpolation=cv2.INTER_AREA) #@UndefinedVariable
-    return im_data
-
-    # This method is kept for debugging purpose
-#     h=img.shape[0]
-#     w=img.shape[1]
-#     hs, ws = sz
-#     dx = float(w) / ws
-#     dy = float(h) / hs
-#     im_data = np.zeros((hs,ws,3))
-#     for a1 in range(0,hs):
-#         for a2 in range(0,ws):
-#             for a3 in range(0,3):
-#                 im_data[a1,a2,a3] = img[int(floor(a1*dy)),int(floor(a2*dx)),a3]
-#     return im_data
-
diff --git a/src/compare.py b/src/compare.py
deleted file mode 100644
index bc53cc421..000000000
--- a/src/compare.py
+++ /dev/null
@@ -1,130 +0,0 @@
-"""Performs face alignment and calculates L2 distance between the embeddings of images."""
-
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from scipy import misc
-import tensorflow as tf
-import numpy as np
-import sys
-import os
-import copy
-import argparse
-import facenet
-import align.detect_face
-
-def main(args):
-
-    images = load_and_align_data(args.image_files, args.image_size, args.margin, args.gpu_memory_fraction)
-    with tf.Graph().as_default():
-
-        with tf.Session() as sess:
-      
-            # Load the model
-            facenet.load_model(args.model)
-    
-            # Get input and output tensors
-            images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
-            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
-            phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
-
-            # Run forward pass to calculate embeddings
-            feed_dict = { images_placeholder: images, phase_train_placeholder:False }
-            emb = sess.run(embeddings, feed_dict=feed_dict)
-            
-            nrof_images = len(args.image_files)
-
-            print('Images:')
-            for i in range(nrof_images):
-                print('%1d: %s' % (i, args.image_files[i]))
-            print('')
-            
-            # Print distance matrix
-            print('Distance matrix')
-            print('    ', end='')
-            for i in range(nrof_images):
-                print('    %1d     ' % i, end='')
-            print('')
-            for i in range(nrof_images):
-                print('%1d  ' % i, end='')
-                for j in range(nrof_images):
-                    dist = np.sqrt(np.sum(np.square(np.subtract(emb[i,:], emb[j,:]))))
-                    print('  %1.4f  ' % dist, end='')
-                print('')
-            
-            
-def load_and_align_data(image_paths, image_size, margin, gpu_memory_fraction):
-
-    minsize = 20 # minimum size of face
-    threshold = [ 0.6, 0.7, 0.7 ]  # three steps's threshold
-    factor = 0.709 # scale factor
-    
-    print('Creating networks and loading parameters')
-    with tf.Graph().as_default():
-        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
-        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
-        with sess.as_default():
-            pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
-  
-    tmp_image_paths=copy.copy(image_paths)
-    img_list = []
-    for image in tmp_image_paths:
-        img = misc.imread(os.path.expanduser(image), mode='RGB')
-        img_size = np.asarray(img.shape)[0:2]
-        bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
-        if len(bounding_boxes) < 1:
-          image_paths.remove(image)
-          print("can't detect face, remove ", image)
-          continue
-        det = np.squeeze(bounding_boxes[0,0:4])
-        bb = np.zeros(4, dtype=np.int32)
-        bb[0] = np.maximum(det[0]-margin/2, 0)
-        bb[1] = np.maximum(det[1]-margin/2, 0)
-        bb[2] = np.minimum(det[2]+margin/2, img_size[1])
-        bb[3] = np.minimum(det[3]+margin/2, img_size[0])
-        cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
-        aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
-        prewhitened = facenet.prewhiten(aligned)
-        img_list.append(prewhitened)
-    images = np.stack(img_list)
-    return images
-
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    
-    parser.add_argument('model', type=str, 
-        help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
-    parser.add_argument('image_files', type=str, nargs='+', help='Images to compare')
-    parser.add_argument('--image_size', type=int,
-        help='Image size (height, width) in pixels.', default=160)
-    parser.add_argument('--margin', type=int,
-        help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
-    parser.add_argument('--gpu_memory_fraction', type=float,
-        help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
-    return parser.parse_args(argv)
-
-if __name__ == '__main__':
-    main(parse_arguments(sys.argv[1:]))
diff --git a/src/lfw.py b/src/lfw.py
deleted file mode 100644
index 91944332d..000000000
--- a/src/lfw.py
+++ /dev/null
@@ -1,86 +0,0 @@
-"""Helper for evaluation on the Labeled Faces in the Wild dataset 
-"""
-
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import numpy as np
-import facenet
-
-def evaluate(embeddings, actual_issame, nrof_folds=10, distance_metric=0, subtract_mean=False):
-    # Calculate evaluation metrics
-    thresholds = np.arange(0, 4, 0.01)
-    embeddings1 = embeddings[0::2]
-    embeddings2 = embeddings[1::2]
-    tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2,
-        np.asarray(actual_issame), nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)
-    thresholds = np.arange(0, 4, 0.001)
-    val, val_std, far = facenet.calculate_val(thresholds, embeddings1, embeddings2,
-        np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)
-    return tpr, fpr, accuracy, val, val_std, far
-
-def get_paths(lfw_dir, pairs):
-    nrof_skipped_pairs = 0
-    path_list = []
-    issame_list = []
-    for pair in pairs:
-        if len(pair) == 3:
-            path0 = add_extension(os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])))
-            path1 = add_extension(os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[2])))
-            issame = True
-        elif len(pair) == 4:
-            path0 = add_extension(os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])))
-            path1 = add_extension(os.path.join(lfw_dir, pair[2], pair[2] + '_' + '%04d' % int(pair[3])))
-            issame = False
-        if os.path.exists(path0) and os.path.exists(path1):    # Only add the pair if both paths exist
-            path_list += (path0,path1)
-            issame_list.append(issame)
-        else:
-            nrof_skipped_pairs += 1
-    if nrof_skipped_pairs>0:
-        print('Skipped %d image pairs' % nrof_skipped_pairs)
-    
-    return path_list, issame_list
-  
-def add_extension(path):
-    if os.path.exists(path+'.jpg'):
-        return path+'.jpg'
-    elif os.path.exists(path+'.png'):
-        return path+'.png'
-    else:
-        raise RuntimeError('No file "%s" with extension png or jpg.' % path)
-
-def read_pairs(pairs_filename):
-    pairs = []
-    with open(pairs_filename, 'r') as f:
-        for line in f.readlines()[1:]:
-            pair = line.strip().split()
-            pairs.append(pair)
-    return np.array(pairs)
-
-
-
diff --git a/src/models/__init__.py b/src/models/__init__.py
deleted file mode 100644
index efa625274..000000000
--- a/src/models/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# flake8: noqa
-
diff --git a/src/train_softmax.py b/src/train_softmax.py
deleted file mode 100644
index 6b0b28b58..000000000
--- a/src/train_softmax.py
+++ /dev/null
@@ -1,580 +0,0 @@
-"""Training a face recognizer with TensorFlow using softmax cross entropy loss
-"""
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from datetime import datetime
-import os.path
-import time
-import sys
-import random
-import tensorflow as tf
-import numpy as np
-import importlib
-import argparse
-import facenet
-import lfw
-import h5py
-import math
-import tensorflow.contrib.slim as slim
-from tensorflow.python.ops import data_flow_ops
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import array_ops
-
-def main(args):
-  
-    network = importlib.import_module(args.model_def)
-    image_size = (args.image_size, args.image_size)
-
-    subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
-    log_dir = os.path.join(os.path.expanduser(args.logs_base_dir), subdir)
-    if not os.path.isdir(log_dir):  # Create the log directory if it doesn't exist
-        os.makedirs(log_dir)
-    model_dir = os.path.join(os.path.expanduser(args.models_base_dir), subdir)
-    if not os.path.isdir(model_dir):  # Create the model directory if it doesn't exist
-        os.makedirs(model_dir)
-
-    stat_file_name = os.path.join(log_dir, 'stat.h5')
-
-    # Write arguments to a text file
-    facenet.write_arguments_to_file(args, os.path.join(log_dir, 'arguments.txt'))
-        
-    # Store some git revision info in a text file in the log directory
-    src_path,_ = os.path.split(os.path.realpath(__file__))
-    facenet.store_revision_info(src_path, log_dir, ' '.join(sys.argv))
-
-    np.random.seed(seed=args.seed)
-    random.seed(args.seed)
-    dataset = facenet.get_dataset(args.data_dir)
-    if args.filter_filename:
-        dataset = filter_dataset(dataset, os.path.expanduser(args.filter_filename), 
-            args.filter_percentile, args.filter_min_nrof_images_per_class)
-        
-    if args.validation_set_split_ratio>0.0:
-        train_set, val_set = facenet.split_dataset(dataset, args.validation_set_split_ratio, args.min_nrof_val_images_per_class, 'SPLIT_IMAGES')
-    else:
-        train_set, val_set = dataset, []
-        
-    nrof_classes = len(train_set)
-    
-    print('Model directory: %s' % model_dir)
-    print('Log directory: %s' % log_dir)
-    pretrained_model = None
-    if args.pretrained_model:
-        pretrained_model = os.path.expanduser(args.pretrained_model)
-        print('Pre-trained model: %s' % pretrained_model)
-    
-    if args.lfw_dir:
-        print('LFW directory: %s' % args.lfw_dir)
-        # Read the file containing the pairs used for testing
-        pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
-        # Get the paths for the corresponding images
-        lfw_paths, actual_issame = lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs)
-    
-    with tf.Graph().as_default():
-        tf.set_random_seed(args.seed)
-        global_step = tf.Variable(0, trainable=False)
-        
-        # Get a list of image paths and their labels
-        image_list, label_list = facenet.get_image_paths_and_labels(train_set)
-        assert len(image_list)>0, 'The training set should not be empty'
-        
-        val_image_list, val_label_list = facenet.get_image_paths_and_labels(val_set)
-
-        # Create a queue that produces indices into the image_list and label_list 
-        labels = ops.convert_to_tensor(label_list, dtype=tf.int32)
-        range_size = array_ops.shape(labels)[0]
-        index_queue = tf.train.range_input_producer(range_size, num_epochs=None,
-                             shuffle=True, seed=None, capacity=32)
-        
-        index_dequeue_op = index_queue.dequeue_many(args.batch_size*args.epoch_size, 'index_dequeue')
-        
-        learning_rate_placeholder = tf.placeholder(tf.float32, name='learning_rate')
-        batch_size_placeholder = tf.placeholder(tf.int32, name='batch_size')
-        phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
-        image_paths_placeholder = tf.placeholder(tf.string, shape=(None,1), name='image_paths')
-        labels_placeholder = tf.placeholder(tf.int32, shape=(None,1), name='labels')
-        control_placeholder = tf.placeholder(tf.int32, shape=(None,1), name='control')
-        
-        nrof_preprocess_threads = 4
-        input_queue = data_flow_ops.FIFOQueue(capacity=2000000,
-                                    dtypes=[tf.string, tf.int32, tf.int32],
-                                    shapes=[(1,), (1,), (1,)],
-                                    shared_name=None, name=None)
-        enqueue_op = input_queue.enqueue_many([image_paths_placeholder, labels_placeholder, control_placeholder], name='enqueue_op')
-        image_batch, label_batch = facenet.create_input_pipeline(input_queue, image_size, nrof_preprocess_threads, batch_size_placeholder)
-
-        image_batch = tf.identity(image_batch, 'image_batch')
-        image_batch = tf.identity(image_batch, 'input')
-        label_batch = tf.identity(label_batch, 'label_batch')
-        
-        print('Number of classes in training set: %d' % nrof_classes)
-        print('Number of examples in training set: %d' % len(image_list))
-
-        print('Number of classes in validation set: %d' % len(val_set))
-        print('Number of examples in validation set: %d' % len(val_image_list))
-        
-        print('Building training graph')
-        
-        # Build the inference graph
-        prelogits, _ = network.inference(image_batch, args.keep_probability, 
-            phase_train=phase_train_placeholder, bottleneck_layer_size=args.embedding_size, 
-            weight_decay=args.weight_decay)
-        logits = slim.fully_connected(prelogits, len(train_set), activation_fn=None, 
-                weights_initializer=slim.initializers.xavier_initializer(), 
-                weights_regularizer=slim.l2_regularizer(args.weight_decay),
-                scope='Logits', reuse=False)
-
-        embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')
-
-        # Norm for the prelogits
-        eps = 1e-4
-        prelogits_norm = tf.reduce_mean(tf.norm(tf.abs(prelogits)+eps, ord=args.prelogits_norm_p, axis=1))
-        tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, prelogits_norm * args.prelogits_norm_loss_factor)
-
-        # Add center loss
-        prelogits_center_loss, _ = facenet.center_loss(prelogits, label_batch, args.center_loss_alfa, nrof_classes)
-        tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, prelogits_center_loss * args.center_loss_factor)
-
-        learning_rate = tf.train.exponential_decay(learning_rate_placeholder, global_step,
-            args.learning_rate_decay_epochs*args.epoch_size, args.learning_rate_decay_factor, staircase=True)
-        tf.summary.scalar('learning_rate', learning_rate)
-
-        # Calculate the average cross entropy loss across the batch
-        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
-            labels=label_batch, logits=logits, name='cross_entropy_per_example')
-        cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
-        tf.add_to_collection('losses', cross_entropy_mean)
-        
-        correct_prediction = tf.cast(tf.equal(tf.argmax(logits, 1), tf.cast(label_batch, tf.int64)), tf.float32)
-        accuracy = tf.reduce_mean(correct_prediction)
-        
-        # Calculate the total losses
-        regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
-        total_loss = tf.add_n([cross_entropy_mean] + regularization_losses, name='total_loss')
-
-        # Build a Graph that trains the model with one batch of examples and updates the model parameters
-        train_op = facenet.train(total_loss, global_step, args.optimizer, 
-            learning_rate, args.moving_average_decay, tf.global_variables(), args.log_histograms)
-        
-        # Create a saver
-        saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=3)
-
-        # Build the summary operation based on the TF collection of Summaries.
-        summary_op = tf.summary.merge_all()
-
-        # Start running operations on the Graph.
-        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
-        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
-        sess.run(tf.global_variables_initializer())
-        sess.run(tf.local_variables_initializer())
-        summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
-        coord = tf.train.Coordinator()
-        tf.train.start_queue_runners(coord=coord, sess=sess)
-
-        with sess.as_default():
-
-            if pretrained_model:
-                print('Restoring pretrained model: %s' % pretrained_model)
-                saver.restore(sess, pretrained_model)
-
-            # Training and validation loop
-            print('Running training')
-            nrof_steps = args.max_nrof_epochs*args.epoch_size
-            nrof_val_samples = int(math.ceil(args.max_nrof_epochs / args.validate_every_n_epochs))   # Validate every validate_every_n_epochs as well as in the last epoch
-            stat = {
-                'loss': np.zeros((nrof_steps,), np.float32),
-                'center_loss': np.zeros((nrof_steps,), np.float32),
-                'reg_loss': np.zeros((nrof_steps,), np.float32),
-                'xent_loss': np.zeros((nrof_steps,), np.float32),
-                'prelogits_norm': np.zeros((nrof_steps,), np.float32),
-                'accuracy': np.zeros((nrof_steps,), np.float32),
-                'val_loss': np.zeros((nrof_val_samples,), np.float32),
-                'val_xent_loss': np.zeros((nrof_val_samples,), np.float32),
-                'val_accuracy': np.zeros((nrof_val_samples,), np.float32),
-                'lfw_accuracy': np.zeros((args.max_nrof_epochs,), np.float32),
-                'lfw_valrate': np.zeros((args.max_nrof_epochs,), np.float32),
-                'learning_rate': np.zeros((args.max_nrof_epochs,), np.float32),
-                'time_train': np.zeros((args.max_nrof_epochs,), np.float32),
-                'time_validate': np.zeros((args.max_nrof_epochs,), np.float32),
-                'time_evaluate': np.zeros((args.max_nrof_epochs,), np.float32),
-                'prelogits_hist': np.zeros((args.max_nrof_epochs, 1000), np.float32),
-              }
-            for epoch in range(1,args.max_nrof_epochs+1):
-                step = sess.run(global_step, feed_dict=None)
-                # Train for one epoch
-                t = time.time()
-                cont = train(args, sess, epoch, image_list, label_list, index_dequeue_op, enqueue_op, image_paths_placeholder, labels_placeholder,
-                    learning_rate_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder, global_step, 
-                    total_loss, train_op, summary_op, summary_writer, regularization_losses, args.learning_rate_schedule_file,
-                    stat, cross_entropy_mean, accuracy, learning_rate,
-                    prelogits, prelogits_center_loss, args.random_rotate, args.random_crop, args.random_flip, prelogits_norm, args.prelogits_hist_max, args.use_fixed_image_standardization)
-                stat['time_train'][epoch-1] = time.time() - t
-                
-                if not cont:
-                    break
-                  
-                t = time.time()
-                if len(val_image_list)>0 and ((epoch-1) % args.validate_every_n_epochs == args.validate_every_n_epochs-1 or epoch==args.max_nrof_epochs):
-                    validate(args, sess, epoch, val_image_list, val_label_list, enqueue_op, image_paths_placeholder, labels_placeholder, control_placeholder,
-                        phase_train_placeholder, batch_size_placeholder, 
-                        stat, total_loss, regularization_losses, cross_entropy_mean, accuracy, args.validate_every_n_epochs, args.use_fixed_image_standardization)
-                stat['time_validate'][epoch-1] = time.time() - t
-
-                # Save variables and the metagraph if it doesn't exist already
-                save_variables_and_metagraph(sess, saver, summary_writer, model_dir, subdir, epoch)
-
-                # Evaluate on LFW
-                t = time.time()
-                if args.lfw_dir:
-                    evaluate(sess, enqueue_op, image_paths_placeholder, labels_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder, 
-                        embeddings, label_batch, lfw_paths, actual_issame, args.lfw_batch_size, args.lfw_nrof_folds, log_dir, step, summary_writer, stat, epoch, 
-                        args.lfw_distance_metric, args.lfw_subtract_mean, args.lfw_use_flipped_images, args.use_fixed_image_standardization)
-                stat['time_evaluate'][epoch-1] = time.time() - t
-
-                print('Saving statistics')
-                with h5py.File(stat_file_name, 'w') as f:
-                    for key, value in stat.iteritems():
-                        f.create_dataset(key, data=value)
-    
-    return model_dir
-  
-def find_threshold(var, percentile):
-    hist, bin_edges = np.histogram(var, 100)
-    cdf = np.float32(np.cumsum(hist)) / np.sum(hist)
-    bin_centers = (bin_edges[:-1]+bin_edges[1:])/2
-    #plt.plot(bin_centers, cdf)
-    threshold = np.interp(percentile*0.01, cdf, bin_centers)
-    return threshold
-  
-def filter_dataset(dataset, data_filename, percentile, min_nrof_images_per_class):
-    with h5py.File(data_filename,'r') as f:
-        distance_to_center = np.array(f.get('distance_to_center'))
-        label_list = np.array(f.get('label_list'))
-        image_list = np.array(f.get('image_list'))
-        distance_to_center_threshold = find_threshold(distance_to_center, percentile)
-        indices = np.where(distance_to_center>=distance_to_center_threshold)[0]
-        filtered_dataset = dataset
-        removelist = []
-        for i in indices:
-            label = label_list[i]
-            image = image_list[i]
-            if image in filtered_dataset[label].image_paths:
-                filtered_dataset[label].image_paths.remove(image)
-            if len(filtered_dataset[label].image_paths)<min_nrof_images_per_class:
-                removelist.append(label)
-
-        ix = sorted(list(set(removelist)), reverse=True)
-        for i in ix:
-            del(filtered_dataset[i])
-
-    return filtered_dataset
-  
-def train(args, sess, epoch, image_list, label_list, index_dequeue_op, enqueue_op, image_paths_placeholder, labels_placeholder, 
-      learning_rate_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder, step, 
-      loss, train_op, summary_op, summary_writer, reg_losses, learning_rate_schedule_file, 
-      stat, cross_entropy_mean, accuracy, 
-      learning_rate, prelogits, prelogits_center_loss, random_rotate, random_crop, random_flip, prelogits_norm, prelogits_hist_max, use_fixed_image_standardization):
-    batch_number = 0
-    
-    if args.learning_rate>0.0:
-        lr = args.learning_rate
-    else:
-        lr = facenet.get_learning_rate_from_file(learning_rate_schedule_file, epoch)
-        
-    if lr<=0:
-        return False 
-
-    index_epoch = sess.run(index_dequeue_op)
-    label_epoch = np.array(label_list)[index_epoch]
-    image_epoch = np.array(image_list)[index_epoch]
-    
-    # Enqueue one epoch of image paths and labels
-    labels_array = np.expand_dims(np.array(label_epoch),1)
-    image_paths_array = np.expand_dims(np.array(image_epoch),1)
-    control_value = facenet.RANDOM_ROTATE * random_rotate + facenet.RANDOM_CROP * random_crop + facenet.RANDOM_FLIP * random_flip + facenet.FIXED_STANDARDIZATION * use_fixed_image_standardization
-    control_array = np.ones_like(labels_array) * control_value
-    sess.run(enqueue_op, {image_paths_placeholder: image_paths_array, labels_placeholder: labels_array, control_placeholder: control_array})
-
-    # Training loop
-    train_time = 0
-    while batch_number < args.epoch_size:
-        start_time = time.time()
-        feed_dict = {learning_rate_placeholder: lr, phase_train_placeholder:True, batch_size_placeholder:args.batch_size}
-        tensor_list = [loss, train_op, step, reg_losses, prelogits, cross_entropy_mean, learning_rate, prelogits_norm, accuracy, prelogits_center_loss]
-        if batch_number % 100 == 0:
-            loss_, _, step_, reg_losses_, prelogits_, cross_entropy_mean_, lr_, prelogits_norm_, accuracy_, center_loss_, summary_str = sess.run(tensor_list + [summary_op], feed_dict=feed_dict)
-            summary_writer.add_summary(summary_str, global_step=step_)
-        else:
-            loss_, _, step_, reg_losses_, prelogits_, cross_entropy_mean_, lr_, prelogits_norm_, accuracy_, center_loss_ = sess.run(tensor_list, feed_dict=feed_dict)
-         
-        duration = time.time() - start_time
-        stat['loss'][step_-1] = loss_
-        stat['center_loss'][step_-1] = center_loss_
-        stat['reg_loss'][step_-1] = np.sum(reg_losses_)
-        stat['xent_loss'][step_-1] = cross_entropy_mean_
-        stat['prelogits_norm'][step_-1] = prelogits_norm_
-        stat['learning_rate'][epoch-1] = lr_
-        stat['accuracy'][step_-1] = accuracy_
-        stat['prelogits_hist'][epoch-1,:] += np.histogram(np.minimum(np.abs(prelogits_), prelogits_hist_max), bins=1000, range=(0.0, prelogits_hist_max))[0]
-        
-        duration = time.time() - start_time
-        print('Epoch: [%d][%d/%d]\tTime %.3f\tLoss %2.3f\tXent %2.3f\tRegLoss %2.3f\tAccuracy %2.3f\tLr %2.5f\tCl %2.3f' %
-              (epoch, batch_number+1, args.epoch_size, duration, loss_, cross_entropy_mean_, np.sum(reg_losses_), accuracy_, lr_, center_loss_))
-        batch_number += 1
-        train_time += duration
-    # Add validation loss and accuracy to summary
-    summary = tf.Summary()
-    #pylint: disable=maybe-no-member
-    summary.value.add(tag='time/total', simple_value=train_time)
-    summary_writer.add_summary(summary, global_step=step_)
-    return True
-
-def validate(args, sess, epoch, image_list, label_list, enqueue_op, image_paths_placeholder, labels_placeholder, control_placeholder,
-             phase_train_placeholder, batch_size_placeholder, 
-             stat, loss, regularization_losses, cross_entropy_mean, accuracy, validate_every_n_epochs, use_fixed_image_standardization):
-  
-    print('Running forward pass on validation set')
-
-    nrof_batches = len(label_list) // args.lfw_batch_size
-    nrof_images = nrof_batches * args.lfw_batch_size
-    
-    # Enqueue one epoch of image paths and labels
-    labels_array = np.expand_dims(np.array(label_list[:nrof_images]),1)
-    image_paths_array = np.expand_dims(np.array(image_list[:nrof_images]),1)
-    control_array = np.ones_like(labels_array, np.int32)*facenet.FIXED_STANDARDIZATION * use_fixed_image_standardization
-    sess.run(enqueue_op, {image_paths_placeholder: image_paths_array, labels_placeholder: labels_array, control_placeholder: control_array})
-
-    loss_array = np.zeros((nrof_batches,), np.float32)
-    xent_array = np.zeros((nrof_batches,), np.float32)
-    accuracy_array = np.zeros((nrof_batches,), np.float32)
-
-    # Training loop
-    start_time = time.time()
-    for i in range(nrof_batches):
-        feed_dict = {phase_train_placeholder:False, batch_size_placeholder:args.lfw_batch_size}
-        loss_, cross_entropy_mean_, accuracy_ = sess.run([loss, cross_entropy_mean, accuracy], feed_dict=feed_dict)
-        loss_array[i], xent_array[i], accuracy_array[i] = (loss_, cross_entropy_mean_, accuracy_)
-        if i % 10 == 9:
-            print('.', end='')
-            sys.stdout.flush()
-    print('')
-
-    duration = time.time() - start_time
-
-    val_index = (epoch-1)//validate_every_n_epochs
-    stat['val_loss'][val_index] = np.mean(loss_array)
-    stat['val_xent_loss'][val_index] = np.mean(xent_array)
-    stat['val_accuracy'][val_index] = np.mean(accuracy_array)
-
-    print('Validation Epoch: %d\tTime %.3f\tLoss %2.3f\tXent %2.3f\tAccuracy %2.3f' %
-          (epoch, duration, np.mean(loss_array), np.mean(xent_array), np.mean(accuracy_array)))
-
-
-def evaluate(sess, enqueue_op, image_paths_placeholder, labels_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder, 
-        embeddings, labels, image_paths, actual_issame, batch_size, nrof_folds, log_dir, step, summary_writer, stat, epoch, distance_metric, subtract_mean, use_flipped_images, use_fixed_image_standardization):
-    start_time = time.time()
-    # Run forward pass to calculate embeddings
-    print('Runnning forward pass on LFW images')
-    
-    # Enqueue one epoch of image paths and labels
-    nrof_embeddings = len(actual_issame)*2  # nrof_pairs * nrof_images_per_pair
-    nrof_flips = 2 if use_flipped_images else 1
-    nrof_images = nrof_embeddings * nrof_flips
-    labels_array = np.expand_dims(np.arange(0,nrof_images),1)
-    image_paths_array = np.expand_dims(np.repeat(np.array(image_paths),nrof_flips),1)
-    control_array = np.zeros_like(labels_array, np.int32)
-    if use_fixed_image_standardization:
-        control_array += np.ones_like(labels_array)*facenet.FIXED_STANDARDIZATION
-    if use_flipped_images:
-        # Flip every second image
-        control_array += (labels_array % 2)*facenet.FLIP
-    sess.run(enqueue_op, {image_paths_placeholder: image_paths_array, labels_placeholder: labels_array, control_placeholder: control_array})
-    
-    embedding_size = int(embeddings.get_shape()[1])
-    assert nrof_images % batch_size == 0, 'The number of LFW images must be an integer multiple of the LFW batch size'
-    nrof_batches = nrof_images // batch_size
-    emb_array = np.zeros((nrof_images, embedding_size))
-    lab_array = np.zeros((nrof_images,))
-    for i in range(nrof_batches):
-        feed_dict = {phase_train_placeholder:False, batch_size_placeholder:batch_size}
-        emb, lab = sess.run([embeddings, labels], feed_dict=feed_dict)
-        lab_array[lab] = lab
-        emb_array[lab, :] = emb
-        if i % 10 == 9:
-            print('.', end='')
-            sys.stdout.flush()
-    print('')
-    embeddings = np.zeros((nrof_embeddings, embedding_size*nrof_flips))
-    if use_flipped_images:
-        # Concatenate embeddings for flipped and non flipped version of the images
-        embeddings[:,:embedding_size] = emb_array[0::2,:]
-        embeddings[:,embedding_size:] = emb_array[1::2,:]
-    else:
-        embeddings = emb_array
-
-    assert np.array_equal(lab_array, np.arange(nrof_images))==True, 'Wrong labels used for evaluation, possibly caused by training examples left in the input pipeline'
-    _, _, accuracy, val, val_std, far = lfw.evaluate(embeddings, actual_issame, nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)
-    
-    print('Accuracy: %2.5f+-%2.5f' % (np.mean(accuracy), np.std(accuracy)))
-    print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
-    lfw_time = time.time() - start_time
-    # Add validation loss and accuracy to summary
-    summary = tf.Summary()
-    #pylint: disable=maybe-no-member
-    summary.value.add(tag='lfw/accuracy', simple_value=np.mean(accuracy))
-    summary.value.add(tag='lfw/val_rate', simple_value=val)
-    summary.value.add(tag='time/lfw', simple_value=lfw_time)
-    summary_writer.add_summary(summary, step)
-    with open(os.path.join(log_dir,'lfw_result.txt'),'at') as f:
-        f.write('%d\t%.5f\t%.5f\n' % (step, np.mean(accuracy), val))
-    stat['lfw_accuracy'][epoch-1] = np.mean(accuracy)
-    stat['lfw_valrate'][epoch-1] = val
-
-def save_variables_and_metagraph(sess, saver, summary_writer, model_dir, model_name, step):
-    # Save the model checkpoint
-    print('Saving variables')
-    start_time = time.time()
-    checkpoint_path = os.path.join(model_dir, 'model-%s.ckpt' % model_name)
-    saver.save(sess, checkpoint_path, global_step=step, write_meta_graph=False)
-    save_time_variables = time.time() - start_time
-    print('Variables saved in %.2f seconds' % save_time_variables)
-    metagraph_filename = os.path.join(model_dir, 'model-%s.meta' % model_name)
-    save_time_metagraph = 0  
-    if not os.path.exists(metagraph_filename):
-        print('Saving metagraph')
-        start_time = time.time()
-        saver.export_meta_graph(metagraph_filename)
-        save_time_metagraph = time.time() - start_time
-        print('Metagraph saved in %.2f seconds' % save_time_metagraph)
-    summary = tf.Summary()
-    #pylint: disable=maybe-no-member
-    summary.value.add(tag='time/save_variables', simple_value=save_time_variables)
-    summary.value.add(tag='time/save_metagraph', simple_value=save_time_metagraph)
-    summary_writer.add_summary(summary, step)
-  
-
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    
-    parser.add_argument('--logs_base_dir', type=str, 
-        help='Directory where to write event logs.', default='~/logs/facenet')
-    parser.add_argument('--models_base_dir', type=str,
-        help='Directory where to write trained models and checkpoints.', default='~/models/facenet')
-    parser.add_argument('--gpu_memory_fraction', type=float,
-        help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
-    parser.add_argument('--pretrained_model', type=str,
-        help='Load a pretrained model before training starts.')
-    parser.add_argument('--data_dir', type=str,
-        help='Path to the data directory containing aligned face patches.',
-        default='~/datasets/casia/casia_maxpy_mtcnnalign_182_160')
-    parser.add_argument('--model_def', type=str,
-        help='Model definition. Points to a module containing the definition of the inference graph.', default='models.inception_resnet_v1')
-    parser.add_argument('--max_nrof_epochs', type=int,
-        help='Number of epochs to run.', default=500)
-    parser.add_argument('--batch_size', type=int,
-        help='Number of images to process in a batch.', default=90)
-    parser.add_argument('--image_size', type=int,
-        help='Image size (height, width) in pixels.', default=160)
-    parser.add_argument('--epoch_size', type=int,
-        help='Number of batches per epoch.', default=1000)
-    parser.add_argument('--embedding_size', type=int,
-        help='Dimensionality of the embedding.', default=128)
-    parser.add_argument('--random_crop', 
-        help='Performs random cropping of training images. If false, the center image_size pixels from the training images are used. ' +
-         'If the size of the images in the data directory is equal to image_size no cropping is performed', action='store_true')
-    parser.add_argument('--random_flip', 
-        help='Performs random horizontal flipping of training images.', action='store_true')
-    parser.add_argument('--random_rotate', 
-        help='Performs random rotations of training images.', action='store_true')
-    parser.add_argument('--use_fixed_image_standardization', 
-        help='Performs fixed standardization of images.', action='store_true')
-    parser.add_argument('--keep_probability', type=float,
-        help='Keep probability of dropout for the fully connected layer(s).', default=1.0)
-    parser.add_argument('--weight_decay', type=float,
-        help='L2 weight regularization.', default=0.0)
-    parser.add_argument('--center_loss_factor', type=float,
-        help='Center loss factor.', default=0.0)
-    parser.add_argument('--center_loss_alfa', type=float,
-        help='Center update rate for center loss.', default=0.95)
-    parser.add_argument('--prelogits_norm_loss_factor', type=float,
-        help='Loss based on the norm of the activations in the prelogits layer.', default=0.0)
-    parser.add_argument('--prelogits_norm_p', type=float,
-        help='Norm to use for prelogits norm loss.', default=1.0)
-    parser.add_argument('--prelogits_hist_max', type=float,
-        help='The max value for the prelogits histogram.', default=10.0)
-    parser.add_argument('--optimizer', type=str, choices=['ADAGRAD', 'ADADELTA', 'ADAM', 'RMSPROP', 'MOM'],
-        help='The optimization algorithm to use', default='ADAGRAD')
-    parser.add_argument('--learning_rate', type=float,
-        help='Initial learning rate. If set to a negative value a learning rate ' +
-        'schedule can be specified in the file "learning_rate_schedule.txt"', default=0.1)
-    parser.add_argument('--learning_rate_decay_epochs', type=int,
-        help='Number of epochs between learning rate decay.', default=100)
-    parser.add_argument('--learning_rate_decay_factor', type=float,
-        help='Learning rate decay factor.', default=1.0)
-    parser.add_argument('--moving_average_decay', type=float,
-        help='Exponential decay for tracking of training parameters.', default=0.9999)
-    parser.add_argument('--seed', type=int,
-        help='Random seed.', default=666)
-    parser.add_argument('--nrof_preprocess_threads', type=int,
-        help='Number of preprocessing (data loading and augmentation) threads.', default=4)
-    parser.add_argument('--log_histograms', 
-        help='Enables logging of weight/bias histograms in tensorboard.', action='store_true')
-    parser.add_argument('--learning_rate_schedule_file', type=str,
-        help='File containing the learning rate schedule that is used when learning_rate is set to to -1.', default='data/learning_rate_schedule.txt')
-    parser.add_argument('--filter_filename', type=str,
-        help='File containing image data used for dataset filtering', default='')
-    parser.add_argument('--filter_percentile', type=float,
-        help='Keep only the percentile images closed to its class center', default=100.0)
-    parser.add_argument('--filter_min_nrof_images_per_class', type=int,
-        help='Keep only the classes with this number of examples or more', default=0)
-    parser.add_argument('--validate_every_n_epochs', type=int,
-        help='Number of epoch between validation', default=5)
-    parser.add_argument('--validation_set_split_ratio', type=float,
-        help='The ratio of the total dataset to use for validation', default=0.0)
-    parser.add_argument('--min_nrof_val_images_per_class', type=float,
-        help='Classes with fewer images will be removed from the validation set', default=0)
- 
-    # Parameters for validation on LFW
-    parser.add_argument('--lfw_pairs', type=str,
-        help='The file containing the pairs to use for validation.', default='data/pairs.txt')
-    parser.add_argument('--lfw_dir', type=str,
-        help='Path to the data directory containing aligned face patches.', default='')
-    parser.add_argument('--lfw_batch_size', type=int,
-        help='Number of images to process in a batch in the LFW test set.', default=100)
-    parser.add_argument('--lfw_nrof_folds', type=int,
-        help='Number of folds to use for cross validation. Mainly used for testing.', default=10)
-    parser.add_argument('--lfw_distance_metric', type=int,
-        help='Type of distance metric to use. 0: Euclidian, 1:Cosine similarity distance.', default=0)
-    parser.add_argument('--lfw_use_flipped_images', 
-        help='Concatenates embeddings for the image and its horizontally flipped counterpart.', action='store_true')
-    parser.add_argument('--lfw_subtract_mean', 
-        help='Subtract feature mean before calculating distance.', action='store_true')
-    return parser.parse_args(argv)
-  
-
-if __name__ == '__main__':
-    main(parse_arguments(sys.argv[1:]))
diff --git a/src/train_tripletloss.py b/src/train_tripletloss.py
deleted file mode 100644
index d6df19a4d..000000000
--- a/src/train_tripletloss.py
+++ /dev/null
@@ -1,486 +0,0 @@
-"""Training a face recognizer with TensorFlow based on the FaceNet paper
-FaceNet: A Unified Embedding for Face Recognition and Clustering: http://arxiv.org/abs/1503.03832
-"""
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from datetime import datetime
-import os.path
-import time
-import sys
-import tensorflow as tf
-import numpy as np
-import importlib
-import itertools
-import argparse
-import facenet
-import lfw
-
-from tensorflow.python.ops import data_flow_ops
-
-from six.moves import xrange  # @UnresolvedImport
-
-def main(args):
-  
-    network = importlib.import_module(args.model_def)
-
-    subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
-    log_dir = os.path.join(os.path.expanduser(args.logs_base_dir), subdir)
-    if not os.path.isdir(log_dir):  # Create the log directory if it doesn't exist
-        os.makedirs(log_dir)
-    model_dir = os.path.join(os.path.expanduser(args.models_base_dir), subdir)
-    if not os.path.isdir(model_dir):  # Create the model directory if it doesn't exist
-        os.makedirs(model_dir)
-
-    # Write arguments to a text file
-    facenet.write_arguments_to_file(args, os.path.join(log_dir, 'arguments.txt'))
-        
-    # Store some git revision info in a text file in the log directory
-    src_path,_ = os.path.split(os.path.realpath(__file__))
-    facenet.store_revision_info(src_path, log_dir, ' '.join(sys.argv))
-
-    np.random.seed(seed=args.seed)
-    train_set = facenet.get_dataset(args.data_dir)
-    
-    print('Model directory: %s' % model_dir)
-    print('Log directory: %s' % log_dir)
-    if args.pretrained_model:
-        print('Pre-trained model: %s' % os.path.expanduser(args.pretrained_model))
-    
-    if args.lfw_dir:
-        print('LFW directory: %s' % args.lfw_dir)
-        # Read the file containing the pairs used for testing
-        pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
-        # Get the paths for the corresponding images
-        lfw_paths, actual_issame = lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs)
-        
-    
-    with tf.Graph().as_default():
-        tf.set_random_seed(args.seed)
-        global_step = tf.Variable(0, trainable=False)
-
-        # Placeholder for the learning rate
-        learning_rate_placeholder = tf.placeholder(tf.float32, name='learning_rate')
-        
-        batch_size_placeholder = tf.placeholder(tf.int32, name='batch_size')
-        
-        phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
-        
-        image_paths_placeholder = tf.placeholder(tf.string, shape=(None,3), name='image_paths')
-        labels_placeholder = tf.placeholder(tf.int64, shape=(None,3), name='labels')
-        
-        input_queue = data_flow_ops.FIFOQueue(capacity=100000,
-                                    dtypes=[tf.string, tf.int64],
-                                    shapes=[(3,), (3,)],
-                                    shared_name=None, name=None)
-        enqueue_op = input_queue.enqueue_many([image_paths_placeholder, labels_placeholder])
-        
-        nrof_preprocess_threads = 4
-        images_and_labels = []
-        for _ in range(nrof_preprocess_threads):
-            filenames, label = input_queue.dequeue()
-            images = []
-            for filename in tf.unstack(filenames):
-                file_contents = tf.read_file(filename)
-                image = tf.image.decode_image(file_contents, channels=3)
-                
-                if args.random_crop:
-                    image = tf.random_crop(image, [args.image_size, args.image_size, 3])
-                else:
-                    image = tf.image.resize_image_with_crop_or_pad(image, args.image_size, args.image_size)
-                if args.random_flip:
-                    image = tf.image.random_flip_left_right(image)
-    
-                #pylint: disable=no-member
-                image.set_shape((args.image_size, args.image_size, 3))
-                images.append(tf.image.per_image_standardization(image))
-            images_and_labels.append([images, label])
-    
-        image_batch, labels_batch = tf.train.batch_join(
-            images_and_labels, batch_size=batch_size_placeholder, 
-            shapes=[(args.image_size, args.image_size, 3), ()], enqueue_many=True,
-            capacity=4 * nrof_preprocess_threads * args.batch_size,
-            allow_smaller_final_batch=True)
-        image_batch = tf.identity(image_batch, 'image_batch')
-        image_batch = tf.identity(image_batch, 'input')
-        labels_batch = tf.identity(labels_batch, 'label_batch')
-
-        # Build the inference graph
-        prelogits, _ = network.inference(image_batch, args.keep_probability, 
-            phase_train=phase_train_placeholder, bottleneck_layer_size=args.embedding_size,
-            weight_decay=args.weight_decay)
-        
-        embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')
-        # Split embeddings into anchor, positive and negative and calculate triplet loss
-        anchor, positive, negative = tf.unstack(tf.reshape(embeddings, [-1,3,args.embedding_size]), 3, 1)
-        triplet_loss = facenet.triplet_loss(anchor, positive, negative, args.alpha)
-        
-        learning_rate = tf.train.exponential_decay(learning_rate_placeholder, global_step,
-            args.learning_rate_decay_epochs*args.epoch_size, args.learning_rate_decay_factor, staircase=True)
-        tf.summary.scalar('learning_rate', learning_rate)
-
-        # Calculate the total losses
-        regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
-        total_loss = tf.add_n([triplet_loss] + regularization_losses, name='total_loss')
-
-        # Build a Graph that trains the model with one batch of examples and updates the model parameters
-        train_op = facenet.train(total_loss, global_step, args.optimizer, 
-            learning_rate, args.moving_average_decay, tf.global_variables())
-        
-        # Create a saver
-        saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=3)
-
-        # Build the summary operation based on the TF collection of Summaries.
-        summary_op = tf.summary.merge_all()
-
-        # Start running operations on the Graph.
-        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
-        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))        
-
-        # Initialize variables
-        sess.run(tf.global_variables_initializer(), feed_dict={phase_train_placeholder:True})
-        sess.run(tf.local_variables_initializer(), feed_dict={phase_train_placeholder:True})
-
-        summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
-        coord = tf.train.Coordinator()
-        tf.train.start_queue_runners(coord=coord, sess=sess)
-
-        with sess.as_default():
-
-            if args.pretrained_model:
-                print('Restoring pretrained model: %s' % args.pretrained_model)
-                saver.restore(sess, os.path.expanduser(args.pretrained_model))
-
-            # Training and validation loop
-            epoch = 0
-            while epoch < args.max_nrof_epochs:
-                step = sess.run(global_step, feed_dict=None)
-                epoch = step // args.epoch_size
-                # Train for one epoch
-                train(args, sess, train_set, epoch, image_paths_placeholder, labels_placeholder, labels_batch,
-                    batch_size_placeholder, learning_rate_placeholder, phase_train_placeholder, enqueue_op, input_queue, global_step, 
-                    embeddings, total_loss, train_op, summary_op, summary_writer, args.learning_rate_schedule_file,
-                    args.embedding_size, anchor, positive, negative, triplet_loss)
-
-                # Save variables and the metagraph if it doesn't exist already
-                save_variables_and_metagraph(sess, saver, summary_writer, model_dir, subdir, step)
-
-                # Evaluate on LFW
-                if args.lfw_dir:
-                    evaluate(sess, lfw_paths, embeddings, labels_batch, image_paths_placeholder, labels_placeholder, 
-                            batch_size_placeholder, learning_rate_placeholder, phase_train_placeholder, enqueue_op, actual_issame, args.batch_size, 
-                            args.lfw_nrof_folds, log_dir, step, summary_writer, args.embedding_size)
-
-    return model_dir
-
-
-def train(args, sess, dataset, epoch, image_paths_placeholder, labels_placeholder, labels_batch,
-          batch_size_placeholder, learning_rate_placeholder, phase_train_placeholder, enqueue_op, input_queue, global_step, 
-          embeddings, loss, train_op, summary_op, summary_writer, learning_rate_schedule_file,
-          embedding_size, anchor, positive, negative, triplet_loss):
-    batch_number = 0
-    
-    if args.learning_rate>0.0:
-        lr = args.learning_rate
-    else:
-        lr = facenet.get_learning_rate_from_file(learning_rate_schedule_file, epoch)
-    while batch_number < args.epoch_size:
-        # Sample people randomly from the dataset
-        image_paths, num_per_class = sample_people(dataset, args.people_per_batch, args.images_per_person)
-        
-        print('Running forward pass on sampled images: ', end='')
-        start_time = time.time()
-        nrof_examples = args.people_per_batch * args.images_per_person
-        labels_array = np.reshape(np.arange(nrof_examples),(-1,3))
-        image_paths_array = np.reshape(np.expand_dims(np.array(image_paths),1), (-1,3))
-        sess.run(enqueue_op, {image_paths_placeholder: image_paths_array, labels_placeholder: labels_array})
-        emb_array = np.zeros((nrof_examples, embedding_size))
-        nrof_batches = int(np.ceil(nrof_examples / args.batch_size))
-        for i in range(nrof_batches):
-            batch_size = min(nrof_examples-i*args.batch_size, args.batch_size)
-            emb, lab = sess.run([embeddings, labels_batch], feed_dict={batch_size_placeholder: batch_size, 
-                learning_rate_placeholder: lr, phase_train_placeholder: True})
-            emb_array[lab,:] = emb
-        print('%.3f' % (time.time()-start_time))
-
-        # Select triplets based on the embeddings
-        print('Selecting suitable triplets for training')
-        triplets, nrof_random_negs, nrof_triplets = select_triplets(emb_array, num_per_class, 
-            image_paths, args.people_per_batch, args.alpha)
-        selection_time = time.time() - start_time
-        print('(nrof_random_negs, nrof_triplets) = (%d, %d): time=%.3f seconds' % 
-            (nrof_random_negs, nrof_triplets, selection_time))
-
-        # Perform training on the selected triplets
-        nrof_batches = int(np.ceil(nrof_triplets*3/args.batch_size))
-        triplet_paths = list(itertools.chain(*triplets))
-        labels_array = np.reshape(np.arange(len(triplet_paths)),(-1,3))
-        triplet_paths_array = np.reshape(np.expand_dims(np.array(triplet_paths),1), (-1,3))
-        sess.run(enqueue_op, {image_paths_placeholder: triplet_paths_array, labels_placeholder: labels_array})
-        nrof_examples = len(triplet_paths)
-        train_time = 0
-        i = 0
-        emb_array = np.zeros((nrof_examples, embedding_size))
-        loss_array = np.zeros((nrof_triplets,))
-        summary = tf.Summary()
-        step = 0
-        while i < nrof_batches:
-            start_time = time.time()
-            batch_size = min(nrof_examples-i*args.batch_size, args.batch_size)
-            feed_dict = {batch_size_placeholder: batch_size, learning_rate_placeholder: lr, phase_train_placeholder: True}
-            err, _, step, emb, lab = sess.run([loss, train_op, global_step, embeddings, labels_batch], feed_dict=feed_dict)
-            emb_array[lab,:] = emb
-            loss_array[i] = err
-            duration = time.time() - start_time
-            print('Epoch: [%d][%d/%d]\tTime %.3f\tLoss %2.3f' %
-                  (epoch, batch_number+1, args.epoch_size, duration, err))
-            batch_number += 1
-            i += 1
-            train_time += duration
-            summary.value.add(tag='loss', simple_value=err)
-            
-        # Add validation loss and accuracy to summary
-        #pylint: disable=maybe-no-member
-        summary.value.add(tag='time/selection', simple_value=selection_time)
-        summary_writer.add_summary(summary, step)
-    return step
-  
-def select_triplets(embeddings, nrof_images_per_class, image_paths, people_per_batch, alpha):
-    """ Select the triplets for training
-    """
-    trip_idx = 0
-    emb_start_idx = 0
-    num_trips = 0
-    triplets = []
-    
-    # VGG Face: Choosing good triplets is crucial and should strike a balance between
-    #  selecting informative (i.e. challenging) examples and swamping training with examples that
-    #  are too hard. This is achieve by extending each pair (a, p) to a triplet (a, p, n) by sampling
-    #  the image n at random, but only between the ones that violate the triplet loss margin. The
-    #  latter is a form of hard-negative mining, but it is not as aggressive (and much cheaper) than
-    #  choosing the maximally violating example, as often done in structured output learning.
-
-    for i in xrange(people_per_batch):
-        nrof_images = int(nrof_images_per_class[i])
-        for j in xrange(1,nrof_images):
-            a_idx = emb_start_idx + j - 1
-            neg_dists_sqr = np.sum(np.square(embeddings[a_idx] - embeddings), 1)
-            for pair in xrange(j, nrof_images): # For every possible positive pair.
-                p_idx = emb_start_idx + pair
-                pos_dist_sqr = np.sum(np.square(embeddings[a_idx]-embeddings[p_idx]))
-                neg_dists_sqr[emb_start_idx:emb_start_idx+nrof_images] = np.NaN
-                #all_neg = np.where(np.logical_and(neg_dists_sqr-pos_dist_sqr<alpha, pos_dist_sqr<neg_dists_sqr))[0]  # FaceNet selection
-                all_neg = np.where(neg_dists_sqr-pos_dist_sqr<alpha)[0] # VGG Face selecction
-                nrof_random_negs = all_neg.shape[0]
-                if nrof_random_negs>0:
-                    rnd_idx = np.random.randint(nrof_random_negs)
-                    n_idx = all_neg[rnd_idx]
-                    triplets.append((image_paths[a_idx], image_paths[p_idx], image_paths[n_idx]))
-                    #print('Triplet %d: (%d, %d, %d), pos_dist=%2.6f, neg_dist=%2.6f (%d, %d, %d, %d, %d)' % 
-                    #    (trip_idx, a_idx, p_idx, n_idx, pos_dist_sqr, neg_dists_sqr[n_idx], nrof_random_negs, rnd_idx, i, j, emb_start_idx))
-                    trip_idx += 1
-
-                num_trips += 1
-
-        emb_start_idx += nrof_images
-
-    np.random.shuffle(triplets)
-    return triplets, num_trips, len(triplets)
-
-def sample_people(dataset, people_per_batch, images_per_person):
-    nrof_images = people_per_batch * images_per_person
-  
-    # Sample classes from the dataset
-    nrof_classes = len(dataset)
-    class_indices = np.arange(nrof_classes)
-    np.random.shuffle(class_indices)
-    
-    i = 0
-    image_paths = []
-    num_per_class = []
-    sampled_class_indices = []
-    # Sample images from these classes until we have enough
-    while len(image_paths)<nrof_images:
-        class_index = class_indices[i]
-        nrof_images_in_class = len(dataset[class_index])
-        image_indices = np.arange(nrof_images_in_class)
-        np.random.shuffle(image_indices)
-        nrof_images_from_class = min(nrof_images_in_class, images_per_person, nrof_images-len(image_paths))
-        idx = image_indices[0:nrof_images_from_class]
-        image_paths_for_class = [dataset[class_index].image_paths[j] for j in idx]
-        sampled_class_indices += [class_index]*nrof_images_from_class
-        image_paths += image_paths_for_class
-        num_per_class.append(nrof_images_from_class)
-        i+=1
-  
-    return image_paths, num_per_class
-
-def evaluate(sess, image_paths, embeddings, labels_batch, image_paths_placeholder, labels_placeholder, 
-        batch_size_placeholder, learning_rate_placeholder, phase_train_placeholder, enqueue_op, actual_issame, batch_size, 
-        nrof_folds, log_dir, step, summary_writer, embedding_size):
-    start_time = time.time()
-    # Run forward pass to calculate embeddings
-    print('Running forward pass on LFW images: ', end='')
-    
-    nrof_images = len(actual_issame)*2
-    assert(len(image_paths)==nrof_images)
-    labels_array = np.reshape(np.arange(nrof_images),(-1,3))
-    image_paths_array = np.reshape(np.expand_dims(np.array(image_paths),1), (-1,3))
-    sess.run(enqueue_op, {image_paths_placeholder: image_paths_array, labels_placeholder: labels_array})
-    emb_array = np.zeros((nrof_images, embedding_size))
-    nrof_batches = int(np.ceil(nrof_images / batch_size))
-    label_check_array = np.zeros((nrof_images,))
-    for i in xrange(nrof_batches):
-        batch_size = min(nrof_images-i*batch_size, batch_size)
-        emb, lab = sess.run([embeddings, labels_batch], feed_dict={batch_size_placeholder: batch_size,
-            learning_rate_placeholder: 0.0, phase_train_placeholder: False})
-        emb_array[lab,:] = emb
-        label_check_array[lab] = 1
-    print('%.3f' % (time.time()-start_time))
-    
-    assert(np.all(label_check_array==1))
-    
-    _, _, accuracy, val, val_std, far = lfw.evaluate(emb_array, actual_issame, nrof_folds=nrof_folds)
-    
-    print('Accuracy: %1.3f+-%1.3f' % (np.mean(accuracy), np.std(accuracy)))
-    print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
-    lfw_time = time.time() - start_time
-    # Add validation loss and accuracy to summary
-    summary = tf.Summary()
-    #pylint: disable=maybe-no-member
-    summary.value.add(tag='lfw/accuracy', simple_value=np.mean(accuracy))
-    summary.value.add(tag='lfw/val_rate', simple_value=val)
-    summary.value.add(tag='time/lfw', simple_value=lfw_time)
-    summary_writer.add_summary(summary, step)
-    with open(os.path.join(log_dir,'lfw_result.txt'),'at') as f:
-        f.write('%d\t%.5f\t%.5f\n' % (step, np.mean(accuracy), val))
-
-def save_variables_and_metagraph(sess, saver, summary_writer, model_dir, model_name, step):
-    # Save the model checkpoint
-    print('Saving variables')
-    start_time = time.time()
-    checkpoint_path = os.path.join(model_dir, 'model-%s.ckpt' % model_name)
-    saver.save(sess, checkpoint_path, global_step=step, write_meta_graph=False)
-    save_time_variables = time.time() - start_time
-    print('Variables saved in %.2f seconds' % save_time_variables)
-    metagraph_filename = os.path.join(model_dir, 'model-%s.meta' % model_name)
-    save_time_metagraph = 0  
-    if not os.path.exists(metagraph_filename):
-        print('Saving metagraph')
-        start_time = time.time()
-        saver.export_meta_graph(metagraph_filename)
-        save_time_metagraph = time.time() - start_time
-        print('Metagraph saved in %.2f seconds' % save_time_metagraph)
-    summary = tf.Summary()
-    #pylint: disable=maybe-no-member
-    summary.value.add(tag='time/save_variables', simple_value=save_time_variables)
-    summary.value.add(tag='time/save_metagraph', simple_value=save_time_metagraph)
-    summary_writer.add_summary(summary, step)
-  
-  
-def get_learning_rate_from_file(filename, epoch):
-    with open(filename, 'r') as f:
-        for line in f.readlines():
-            line = line.split('#', 1)[0]
-            if line:
-                par = line.strip().split(':')
-                e = int(par[0])
-                lr = float(par[1])
-                if e <= epoch:
-                    learning_rate = lr
-                else:
-                    return learning_rate
-    
-
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    
-    parser.add_argument('--logs_base_dir', type=str, 
-        help='Directory where to write event logs.', default='~/logs/facenet')
-    parser.add_argument('--models_base_dir', type=str,
-        help='Directory where to write trained models and checkpoints.', default='~/models/facenet')
-    parser.add_argument('--gpu_memory_fraction', type=float,
-        help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
-    parser.add_argument('--pretrained_model', type=str,
-        help='Load a pretrained model before training starts.')
-    parser.add_argument('--data_dir', type=str,
-        help='Path to the data directory containing aligned face patches.',
-        default='~/datasets/casia/casia_maxpy_mtcnnalign_182_160')
-    parser.add_argument('--model_def', type=str,
-        help='Model definition. Points to a module containing the definition of the inference graph.', default='models.inception_resnet_v1')
-    parser.add_argument('--max_nrof_epochs', type=int,
-        help='Number of epochs to run.', default=500)
-    parser.add_argument('--batch_size', type=int,
-        help='Number of images to process in a batch.', default=90)
-    parser.add_argument('--image_size', type=int,
-        help='Image size (height, width) in pixels.', default=160)
-    parser.add_argument('--people_per_batch', type=int,
-        help='Number of people per batch.', default=45)
-    parser.add_argument('--images_per_person', type=int,
-        help='Number of images per person.', default=40)
-    parser.add_argument('--epoch_size', type=int,
-        help='Number of batches per epoch.', default=1000)
-    parser.add_argument('--alpha', type=float,
-        help='Positive to negative triplet distance margin.', default=0.2)
-    parser.add_argument('--embedding_size', type=int,
-        help='Dimensionality of the embedding.', default=128)
-    parser.add_argument('--random_crop', 
-        help='Performs random cropping of training images. If false, the center image_size pixels from the training images are used. ' +
-         'If the size of the images in the data directory is equal to image_size no cropping is performed', action='store_true')
-    parser.add_argument('--random_flip', 
-        help='Performs random horizontal flipping of training images.', action='store_true')
-    parser.add_argument('--keep_probability', type=float,
-        help='Keep probability of dropout for the fully connected layer(s).', default=1.0)
-    parser.add_argument('--weight_decay', type=float,
-        help='L2 weight regularization.', default=0.0)
-    parser.add_argument('--optimizer', type=str, choices=['ADAGRAD', 'ADADELTA', 'ADAM', 'RMSPROP', 'MOM'],
-        help='The optimization algorithm to use', default='ADAGRAD')
-    parser.add_argument('--learning_rate', type=float,
-        help='Initial learning rate. If set to a negative value a learning rate ' +
-        'schedule can be specified in the file "learning_rate_schedule.txt"', default=0.1)
-    parser.add_argument('--learning_rate_decay_epochs', type=int,
-        help='Number of epochs between learning rate decay.', default=100)
-    parser.add_argument('--learning_rate_decay_factor', type=float,
-        help='Learning rate decay factor.', default=1.0)
-    parser.add_argument('--moving_average_decay', type=float,
-        help='Exponential decay for tracking of training parameters.', default=0.9999)
-    parser.add_argument('--seed', type=int,
-        help='Random seed.', default=666)
-    parser.add_argument('--learning_rate_schedule_file', type=str,
-        help='File containing the learning rate schedule that is used when learning_rate is set to to -1.', default='data/learning_rate_schedule.txt')
-
-    # Parameters for validation on LFW
-    parser.add_argument('--lfw_pairs', type=str,
-        help='The file containing the pairs to use for validation.', default='data/pairs.txt')
-    parser.add_argument('--lfw_dir', type=str,
-        help='Path to the data directory containing aligned face patches.', default='')
-    parser.add_argument('--lfw_nrof_folds', type=int,
-        help='Number of folds to use for cross validation. Mainly used for testing.', default=10)
-    return parser.parse_args(argv)
-  
-
-if __name__ == '__main__':
-    main(parse_arguments(sys.argv[1:]))
diff --git a/src/validate_on_lfw.py b/src/validate_on_lfw.py
deleted file mode 100644
index ac456c5f6..000000000
--- a/src/validate_on_lfw.py
+++ /dev/null
@@ -1,164 +0,0 @@
-"""Validate a face recognizer on the "Labeled Faces in the Wild" dataset (http://vis-www.cs.umass.edu/lfw/).
-Embeddings are calculated using the pairs from http://vis-www.cs.umass.edu/lfw/pairs.txt and the ROC curve
-is calculated and plotted. Both the model metagraph and the model parameters need to exist
-in the same directory, and the metagraph should have the extension '.meta'.
-"""
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-import numpy as np
-import argparse
-import facenet
-import lfw
-import os
-import sys
-from tensorflow.python.ops import data_flow_ops
-from sklearn import metrics
-from scipy.optimize import brentq
-from scipy import interpolate
-
-def main(args):
-  
-    with tf.Graph().as_default():
-      
-        with tf.Session() as sess:
-            
-            # Read the file containing the pairs used for testing
-            pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
-
-            # Get the paths for the corresponding images
-            paths, actual_issame = lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs)
-            
-            image_paths_placeholder = tf.placeholder(tf.string, shape=(None,1), name='image_paths')
-            labels_placeholder = tf.placeholder(tf.int32, shape=(None,1), name='labels')
-            batch_size_placeholder = tf.placeholder(tf.int32, name='batch_size')
-            control_placeholder = tf.placeholder(tf.int32, shape=(None,1), name='control')
-            phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
- 
-            nrof_preprocess_threads = 4
-            image_size = (args.image_size, args.image_size)
-            eval_input_queue = data_flow_ops.FIFOQueue(capacity=2000000,
-                                        dtypes=[tf.string, tf.int32, tf.int32],
-                                        shapes=[(1,), (1,), (1,)],
-                                        shared_name=None, name=None)
-            eval_enqueue_op = eval_input_queue.enqueue_many([image_paths_placeholder, labels_placeholder, control_placeholder], name='eval_enqueue_op')
-            image_batch, label_batch = facenet.create_input_pipeline(eval_input_queue, image_size, nrof_preprocess_threads, batch_size_placeholder)
-     
-            # Load the model
-            input_map = {'image_batch': image_batch, 'label_batch': label_batch, 'phase_train': phase_train_placeholder}
-            facenet.load_model(args.model, input_map=input_map)
-
-            # Get output tensor
-            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
-#              
-            coord = tf.train.Coordinator()
-            tf.train.start_queue_runners(coord=coord, sess=sess)
-
-            evaluate(sess, eval_enqueue_op, image_paths_placeholder, labels_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder,
-                embeddings, label_batch, paths, actual_issame, args.lfw_batch_size, args.lfw_nrof_folds, args.distance_metric, args.subtract_mean,
-                args.use_flipped_images, args.use_fixed_image_standardization)
-
-              
-def evaluate(sess, enqueue_op, image_paths_placeholder, labels_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder,
-        embeddings, labels, image_paths, actual_issame, batch_size, nrof_folds, distance_metric, subtract_mean, use_flipped_images, use_fixed_image_standardization):
-    # Run forward pass to calculate embeddings
-    print('Runnning forward pass on LFW images')
-    
-    # Enqueue one epoch of image paths and labels
-    nrof_embeddings = len(actual_issame)*2  # nrof_pairs * nrof_images_per_pair
-    nrof_flips = 2 if use_flipped_images else 1
-    nrof_images = nrof_embeddings * nrof_flips
-    labels_array = np.expand_dims(np.arange(0,nrof_images),1)
-    image_paths_array = np.expand_dims(np.repeat(np.array(image_paths),nrof_flips),1)
-    control_array = np.zeros_like(labels_array, np.int32)
-    if use_fixed_image_standardization:
-        control_array += np.ones_like(labels_array)*facenet.FIXED_STANDARDIZATION
-    if use_flipped_images:
-        # Flip every second image
-        control_array += (labels_array % 2)*facenet.FLIP
-    sess.run(enqueue_op, {image_paths_placeholder: image_paths_array, labels_placeholder: labels_array, control_placeholder: control_array})
-    
-    embedding_size = int(embeddings.get_shape()[1])
-    assert nrof_images % batch_size == 0, 'The number of LFW images must be an integer multiple of the LFW batch size'
-    nrof_batches = nrof_images // batch_size
-    emb_array = np.zeros((nrof_images, embedding_size))
-    lab_array = np.zeros((nrof_images,))
-    for i in range(nrof_batches):
-        feed_dict = {phase_train_placeholder:False, batch_size_placeholder:batch_size}
-        emb, lab = sess.run([embeddings, labels], feed_dict=feed_dict)
-        lab_array[lab] = lab
-        emb_array[lab, :] = emb
-        if i % 10 == 9:
-            print('.', end='')
-            sys.stdout.flush()
-    print('')
-    embeddings = np.zeros((nrof_embeddings, embedding_size*nrof_flips))
-    if use_flipped_images:
-        # Concatenate embeddings for flipped and non flipped version of the images
-        embeddings[:,:embedding_size] = emb_array[0::2,:]
-        embeddings[:,embedding_size:] = emb_array[1::2,:]
-    else:
-        embeddings = emb_array
-
-    assert np.array_equal(lab_array, np.arange(nrof_images))==True, 'Wrong labels used for evaluation, possibly caused by training examples left in the input pipeline'
-    tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(embeddings, actual_issame, nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)
-    
-    print('Accuracy: %2.5f+-%2.5f' % (np.mean(accuracy), np.std(accuracy)))
-    print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
-    
-    auc = metrics.auc(fpr, tpr)
-    print('Area Under Curve (AUC): %1.3f' % auc)
-    eer = brentq(lambda x: 1. - x - interpolate.interp1d(fpr, tpr)(x), 0., 1.)
-    print('Equal Error Rate (EER): %1.3f' % eer)
-    
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    
-    parser.add_argument('lfw_dir', type=str,
-        help='Path to the data directory containing aligned LFW face patches.')
-    parser.add_argument('--lfw_batch_size', type=int,
-        help='Number of images to process in a batch in the LFW test set.', default=100)
-    parser.add_argument('model', type=str, 
-        help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
-    parser.add_argument('--image_size', type=int,
-        help='Image size (height, width) in pixels.', default=160)
-    parser.add_argument('--lfw_pairs', type=str,
-        help='The file containing the pairs to use for validation.', default='data/pairs.txt')
-    parser.add_argument('--lfw_nrof_folds', type=int,
-        help='Number of folds to use for cross validation. Mainly used for testing.', default=10)
-    parser.add_argument('--distance_metric', type=int,
-        help='Distance metric  0:euclidian, 1:cosine similarity.', default=0)
-    parser.add_argument('--use_flipped_images', 
-        help='Concatenates embeddings for the image and its horizontally flipped counterpart.', action='store_true')
-    parser.add_argument('--subtract_mean', 
-        help='Subtract feature mean before calculating distance.', action='store_true')
-    parser.add_argument('--use_fixed_image_standardization', 
-        help='Performs fixed standardization of images.', action='store_true')
-    return parser.parse_args(argv)
-
-if __name__ == '__main__':
-    main(parse_arguments(sys.argv[1:]))
diff --git a/test/batch_norm_test.py b/test/batch_norm_test.py
index 48cfd555f..1aa84f3fc 100644
--- a/test/batch_norm_test.py
+++ b/test/batch_norm_test.py
@@ -1,17 +1,17 @@
 # MIT License
-# 
+#
 # Copyright (c) 2016 David Sandberg
-# 
+#
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
 # in the Software without restriction, including without limitation the rights
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
 # furnished to do so, subject to the following conditions:
-# 
+#
 # The above copyright notice and this permission notice shall be included in all
 # copies or substantial portions of the Software.
-# 
+#
 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
@@ -21,46 +21,51 @@
 # SOFTWARE.
 
 import unittest
-import tensorflow as tf
-import models
+
 import numpy as np
 import numpy.testing as testing
+import tensorflow as tf
+
+import models
 
-class BatchNormTest(unittest.TestCase):
 
+class BatchNormTest(unittest.TestCase):
 
     @unittest.skip("Skip batch norm test case")
     def testBatchNorm(self):
-      
+
         tf.set_random_seed(123)
-  
+
         x = tf.placeholder(tf.float32, [None, 20, 20, 10], name='input')
         phase_train = tf.placeholder(tf.bool, name='phase_train')
-        
+
         # generate random noise to pass into batch norm
-        #x_gen = tf.random_normal([50,20,20,10])
-        
+        # x_gen = tf.random_normal([50,20,20,10])
+
         bn = models.network.batch_norm(x, phase_train)
-        
+
         init = tf.global_variables_initializer()
         sess = tf.Session(config=tf.ConfigProto())
         sess.run(init)
-  
+
         with sess.as_default():
-        
-            #generate a constant variable to pass into batch norm
-            y = np.random.normal(0, 1, size=(50,20,20,10))
-            
+
+            # generate a constant variable to pass into batch norm
+            y = np.random.normal(0, 1, size=(50, 20, 20, 10))
+
             feed_dict = {x: y, phase_train: True}
             sess.run(bn, feed_dict=feed_dict)
-            
+
             feed_dict = {x: y, phase_train: False}
             y1 = sess.run(bn, feed_dict=feed_dict)
             y2 = sess.run(bn, feed_dict=feed_dict)
-            
-            testing.assert_almost_equal(y1, y2, 10, 'Output from two forward passes with phase_train==false should be equal')
+
+            testing.assert_almost_equal(
+                y1,
+                y2,
+                10,
+                'Output from two forward passes with phase_train==false should be equal')
 
 
 if __name__ == "__main__":
     unittest.main()
-    
\ No newline at end of file
diff --git a/test/center_loss_test.py b/test/center_loss_test.py
index 196cd1143..88e28cfaf 100644
--- a/test/center_loss_test.py
+++ b/test/center_loss_test.py
@@ -1,17 +1,17 @@
 # MIT License
-# 
+#
 # Copyright (c) 2016 David Sandberg
-# 
+#
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
 # in the Software without restriction, including without limitation the rights
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
 # furnished to do so, subject to the following conditions:
-# 
+#
 # The above copyright notice and this permission notice shall be included in all
 # copies or substantial portions of the Software.
-# 
+#
 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
@@ -23,65 +23,83 @@
 import unittest
 import tensorflow as tf
 import numpy as np
-import facenet
+from facenet_sandberg import facenet
 
-class CenterLossTest(unittest.TestCase):
-  
 
+class CenterLossTest(unittest.TestCase):
 
     def testCenterLoss(self):
         batch_size = 16
         nrof_features = 2
         nrof_classes = 16
         alfa = 0.5
-        
+
         with tf.Graph().as_default():
-        
-            features = tf.placeholder(tf.float32, shape=(batch_size, nrof_features), name='features')
-            labels = tf.placeholder(tf.int32, shape=(batch_size,), name='labels')
+
+            features = tf.placeholder(
+                tf.float32,
+                shape=(
+                    batch_size,
+                    nrof_features),
+                name='features')
+            labels = tf.placeholder(
+                tf.int32, shape=(
+                    batch_size,), name='labels')
 
             # Define center loss
-            center_loss, centers = facenet.center_loss(features, labels, alfa, nrof_classes)
-            
-            label_to_center = np.array( [ 
-                 [-3,-3],  [-3,-1],  [-3,1],  [-3,3],
-                 [-1,-3],  [-1,-1],  [-1,1],  [-1,3],
-                 [ 1,-3],  [ 1,-1],  [ 1,1],  [ 1,3],
-                 [ 3,-3],  [ 3,-1],  [ 3,1],  [ 3,3] 
-                 ])
-                
+            center_loss, centers = facenet.center_loss(
+                features, labels, alfa, nrof_classes)
+
+            label_to_center = np.array([
+                [-3, -3], [-3, -1], [-3, 1], [-3, 3],
+                [-1, -3], [-1, -1], [-1, 1], [-1, 3],
+                [1, -3], [1, -1], [1, 1], [1, 3],
+                [3, -3], [3, -1], [3, 1], [3, 3]
+            ])
+
             sess = tf.Session()
             with sess.as_default():
                 sess.run(tf.global_variables_initializer())
                 np.random.seed(seed=666)
-                
-                for _ in range(0,100):
+
+                for _ in range(0, 100):
                     # Create array of random labels
-                    lbls = np.random.randint(low=0, high=nrof_classes, size=(batch_size,))
-                    feats = create_features(label_to_center, batch_size, nrof_features, lbls)
+                    lbls = np.random.randint(
+                        low=0, high=nrof_classes, size=(
+                            batch_size,))
+                    feats = create_features(
+                        label_to_center, batch_size, nrof_features, lbls)
+
+                    center_loss_, centers_ = sess.run([center_loss, centers], feed_dict={
+                                                      features: feats, labels: lbls})
+
+                # After a large number of updates the estimated centers should
+                # be close to the true ones
+                np.testing.assert_almost_equal(
+                    centers_,
+                    label_to_center,
+                    decimal=5,
+                    err_msg='Incorrect estimated centers')
+                np.testing.assert_almost_equal(
+                    center_loss_, 0.0, decimal=5, err_msg='Incorrect center loss')
 
-                    center_loss_, centers_ = sess.run([center_loss, centers], feed_dict={features:feats, labels:lbls})
-                    
-                # After a large number of updates the estimated centers should be close to the true ones
-                np.testing.assert_almost_equal(centers_, label_to_center, decimal=5, err_msg='Incorrect estimated centers')
-                np.testing.assert_almost_equal(center_loss_, 0.0, decimal=5, err_msg='Incorrect center loss')
-                
 
 def create_features(label_to_center, batch_size, nrof_features, labels):
     # Map label to center
-#     label_to_center_dict = { 
-#          0:(-3,-3),  1:(-3,-1),  2:(-3,1),  3:(-3,3),
-#          4:(-1,-3),  5:(-1,-1),  6:(-1,1),  7:(-1,3),
-#          8:( 1,-3),  9:( 1,-1), 10:( 1,1), 11:( 1,3),
-#         12:( 3,-3), 13:( 3,-1), 14:( 3,1), 15:( 3,3),
-#         }
+    #     label_to_center_dict = {
+    #          0:(-3,-3),  1:(-3,-1),  2:(-3,1),  3:(-3,3),
+    #          4:(-1,-3),  5:(-1,-1),  6:(-1,1),  7:(-1,3),
+    #          8:( 1,-3),  9:( 1,-1), 10:( 1,1), 11:( 1,3),
+    #         12:( 3,-3), 13:( 3,-1), 14:( 3,1), 15:( 3,3),
+    #         }
     # Create array of features corresponding to the labels
     feats = np.zeros((batch_size, nrof_features))
     for i in range(batch_size):
-        cntr =  label_to_center[labels[i]]
+        cntr = label_to_center[labels[i]]
         for j in range(nrof_features):
-            feats[i,j] = cntr[j]
+            feats[i, j] = cntr[j]
     return feats
-                      
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/test/restore_test.py b/test/restore_test.py
index befb04dc6..704744c2f 100644
--- a/test/restore_test.py
+++ b/test/restore_test.py
@@ -1,17 +1,17 @@
 # MIT License
-# 
+#
 # Copyright (c) 2016 David Sandberg
-# 
+#
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
 # in the Software without restriction, including without limitation the rights
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
 # furnished to do so, subject to the following conditions:
-# 
+#
 # The above copyright notice and this permission notice shall be included in all
 # copies or substantial portions of the Software.
-# 
+#
 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
@@ -27,80 +27,87 @@
 import tensorflow as tf
 import numpy as np
 
+
 class TrainTest(unittest.TestCase):
-  
+
     @classmethod
     def setUpClass(self):
         self.tmp_dir = tempfile.mkdtemp()
-        
+
     @classmethod
     def tearDownClass(self):
         # Recursively remove the temporary directory
         shutil.rmtree(self.tmp_dir)
 
     def test_restore_noema(self):
-        
+
         # Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
         x_data = np.random.rand(100).astype(np.float32)
         y_data = x_data * 0.1 + 0.3
-        
+
         # Try to find values for W and b that compute y_data = W * x_data + b
         # (We know that W should be 0.1 and b 0.3, but TensorFlow will
         # figure that out for us.)
         W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W')
         b = tf.Variable(tf.zeros([1]), name='b')
         y = W * x_data + b
-        
+
         # Minimize the mean squared errors.
         loss = tf.reduce_mean(tf.square(y - y_data))
         optimizer = tf.train.GradientDescentOptimizer(0.5)
         train = optimizer.minimize(loss)
-        
+
         # Before starting, initialize the variables.  We will 'run' this first.
         init = tf.global_variables_initializer()
 
         saver = tf.train.Saver(tf.trainable_variables())
-        
+
         # Launch the graph.
         sess = tf.Session()
         sess.run(init)
-        
+
         # Fit the line.
         for _ in range(201):
             sess.run(train)
-        
+
         w_reference = sess.run('W:0')
         b_reference = sess.run('b:0')
-        
+
         saver.save(sess, os.path.join(self.tmp_dir, "model_ex1"))
-        
+
         tf.reset_default_graph()
 
-        saver = tf.train.import_meta_graph(os.path.join(self.tmp_dir, "model_ex1.meta"))
+        saver = tf.train.import_meta_graph(
+            os.path.join(self.tmp_dir, "model_ex1.meta"))
         sess = tf.Session()
         saver.restore(sess, os.path.join(self.tmp_dir, "model_ex1"))
-        
+
         w_restored = sess.run('W:0')
         b_restored = sess.run('b:0')
-        
-        self.assertAlmostEqual(w_reference, w_restored, 'Restored model use different weight than the original model')
-        self.assertAlmostEqual(b_reference, b_restored, 'Restored model use different weight than the original model')
 
+        self.assertAlmostEqual(
+            w_reference,
+            w_restored,
+            'Restored model use different weight than the original model')
+        self.assertAlmostEqual(
+            b_reference,
+            b_restored,
+            'Restored model use different weight than the original model')
 
     @unittest.skip("Skip restore EMA test case for now")
     def test_restore_ema(self):
-        
+
         # Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
         x_data = np.random.rand(100).astype(np.float32)
         y_data = x_data * 0.1 + 0.3
-        
+
         # Try to find values for W and b that compute y_data = W * x_data + b
         # (We know that W should be 0.1 and b 0.3, but TensorFlow will
         # figure that out for us.)
         W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W')
         b = tf.Variable(tf.zeros([1]), name='b')
         y = W * x_data + b
-        
+
         # Minimize the mean squared errors.
         loss = tf.reduce_mean(tf.square(y - y_data))
         optimizer = tf.train.GradientDescentOptimizer(0.5)
@@ -111,30 +118,33 @@ def test_restore_ema(self):
         averages_op = ema.apply(tf.trainable_variables())
         with tf.control_dependencies([opt_op]):
             train_op = tf.group(averages_op)
-  
+
         # Before starting, initialize the variables.  We will 'run' this first.
         init = tf.global_variables_initializer()
 
         saver = tf.train.Saver(tf.trainable_variables())
-        
+
         # Launch the graph.
         sess = tf.Session()
         sess.run(init)
-        
+
         # Fit the line.
         for _ in range(201):
             sess.run(train_op)
-        
+
         w_reference = sess.run('W/ExponentialMovingAverage:0')
         b_reference = sess.run('b/ExponentialMovingAverage:0')
-        
+
         saver.save(sess, os.path.join(self.tmp_dir, "model_ex1"))
-                
+
         tf.reset_default_graph()
 
-        tf.train.import_meta_graph(os.path.join(self.tmp_dir, "model_ex1.meta"))
+        tf.train.import_meta_graph(
+            os.path.join(
+                self.tmp_dir,
+                "model_ex1.meta"))
         sess = tf.Session()
-        
+
         print('------------------------------------------------------')
         for var in tf.global_variables():
             print('all variables: ' + var.op.name)
@@ -156,18 +166,24 @@ def test_restore_ema(self):
                 ema_name = var.op.name + '/ExponentialMovingAverage'
                 print('%s: %s' % (ema_name, var.op.name))
                 restore_vars[ema_name] = var
-            
+
         saver = tf.train.Saver(restore_vars, name='ema_restore')
-        
+
         saver.restore(sess, os.path.join(self.tmp_dir, "model_ex1"))
-        
+
         w_restored = sess.run('W:0')
         b_restored = sess.run('b:0')
-        
-        self.assertAlmostEqual(w_reference, w_restored, 'Restored model modes not use the EMA filtered weight')
-        self.assertAlmostEqual(b_reference, b_restored, 'Restored model modes not use the EMA filtered bias')
 
-        
+        self.assertAlmostEqual(
+            w_reference,
+            w_restored,
+            'Restored model modes not use the EMA filtered weight')
+        self.assertAlmostEqual(
+            b_reference,
+            b_restored,
+            'Restored model modes not use the EMA filtered bias')
+
+
 # Create a checkpoint file pointing to the model
 def create_checkpoint_file(model_dir, model_file):
     checkpoint_filename = os.path.join(model_dir, 'checkpoint')
@@ -175,7 +191,7 @@ def create_checkpoint_file(model_dir, model_file):
     with open(checkpoint_filename, 'w') as f:
         f.write('model_checkpoint_path: "%s"\n' % full_model_filename)
         f.write('all_model_checkpoint_paths: "%s"\n' % full_model_filename)
-        
+
+
 if __name__ == "__main__":
     unittest.main()
-    
\ No newline at end of file
diff --git a/test/train_test.py b/test/train_test.py
index 12cd6638a..f1986366a 100644
--- a/test/train_test.py
+++ b/test/train_test.py
@@ -1,17 +1,17 @@
 # MIT License
-# 
+#
 # Copyright (c) 2016 David Sandberg
-# 
+#
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
 # in the Software without restriction, including without limitation the rights
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
 # furnished to do so, subject to the following conditions:
-# 
+#
 # The above copyright notice and this permission notice shall be included in all
 # copies or substantial portions of the Software.
-# 
+#
 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
@@ -26,9 +26,10 @@
 import cv2
 import os
 import shutil
-import download_and_extract  # @UnresolvedImport
+from facenet_sandberg import download_and_extract  # @UnresolvedImport
 import subprocess
 
+
 def memory_usage_psutil():
     # return the memory usage in MB
     import psutil
@@ -36,6 +37,7 @@ def memory_usage_psutil():
     mem = process.memory_info()[0] / float(2 ** 20)
     return mem
 
+
 def align_dataset_if_needed(self):
     if not os.path.exists('data/lfw_aligned'):
         argv = ['python',
@@ -43,12 +45,12 @@ def align_dataset_if_needed(self):
                 'data/lfw',
                 'data/lfw_aligned',
                 '--image_size', '160',
-                '--margin', '32' ]
+                '--margin', '32']
         subprocess.call(argv)
-        
-        
+
+
 class TrainTest(unittest.TestCase):
-  
+
     @classmethod
     def setUpClass(self):
         self.tmp_dir = tempfile.mkdtemp()
@@ -57,12 +59,21 @@ def setUpClass(self):
         self.lfw_pairs_file = create_mock_lfw_pairs(self.tmp_dir)
         print(self.lfw_pairs_file)
         self.pretrained_model_name = '20180402-114759'
-        download_and_extract.download_and_extract_file(self.pretrained_model_name, 'data/')
+        download_and_extract.download_and_extract_file(
+            self.pretrained_model_name, 'data/')
         download_and_extract.download_and_extract_file('lfw-subset', 'data/')
-        self.model_file = os.path.join('data', self.pretrained_model_name, 'model-%s.ckpt-275' % self.pretrained_model_name)
-        self.pretrained_model = os.path.join('data', self.pretrained_model_name)
-        self.frozen_graph_filename = os.path.join('data', self.pretrained_model_name+'.pb')
-        print('Memory utilization (SetUpClass): %.3f MB' % memory_usage_psutil())
+        self.model_file = os.path.join(
+            'data',
+            self.pretrained_model_name,
+            'model-%s.ckpt-275' %
+            self.pretrained_model_name)
+        self.pretrained_model = os.path.join(
+            'data', self.pretrained_model_name)
+        self.frozen_graph_filename = os.path.join(
+            'data', self.pretrained_model_name + '.pb')
+        print(
+            'Memory utilization (SetUpClass): %.3f MB' %
+            memory_usage_psutil())
 
     @classmethod
     def tearDownClass(self):
@@ -87,7 +98,7 @@ def test_training_classifier_inception_resnet_v1(self):
                 '--lfw_dir', self.dataset_dir,
                 '--lfw_nrof_folds', '2',
                 '--lfw_batch_size', '1',
-                '--nrof_preprocess_threads', '1' ]
+                '--nrof_preprocess_threads', '1']
         subprocess.call(argv)
 
     def test_training_classifier_inception_resnet_v2(self):
@@ -104,9 +115,9 @@ def test_training_classifier_inception_resnet_v2(self):
                 '--lfw_pairs', self.lfw_pairs_file,
                 '--lfw_dir', self.dataset_dir,
                 '--lfw_nrof_folds', '2',
-                '--lfw_batch_size', '1' ]
+                '--lfw_batch_size', '1']
         subprocess.call(argv)
-  
+
     def test_training_classifier_squeezenet(self):
         print('test_training_classifier_squeezenet')
         argv = ['python',
@@ -122,9 +133,9 @@ def test_training_classifier_squeezenet(self):
                 '--lfw_dir', self.dataset_dir,
                 '--lfw_nrof_folds', '2',
                 '--lfw_batch_size', '1',
-                '--nrof_preprocess_threads', '1' ]
+                '--nrof_preprocess_threads', '1']
         subprocess.call(argv)
- 
+
     def test_train_tripletloss_inception_resnet_v1(self):
         print('test_train_tripletloss_inception_resnet_v1')
         argv = ['python',
@@ -140,9 +151,9 @@ def test_train_tripletloss_inception_resnet_v1(self):
                 '--images_per_person', '3',
                 '--lfw_pairs', self.lfw_pairs_file,
                 '--lfw_dir', self.dataset_dir,
-                '--lfw_nrof_folds', '2' ]
+                '--lfw_nrof_folds', '2']
         subprocess.call(argv)
-  
+
     def test_finetune_tripletloss_inception_resnet_v1(self):
         print('test_finetune_tripletloss_inception_resnet_v1')
         argv = ['python',
@@ -160,34 +171,37 @@ def test_finetune_tripletloss_inception_resnet_v1(self):
                 '--images_per_person', '3',
                 '--lfw_pairs', self.lfw_pairs_file,
                 '--lfw_dir', self.dataset_dir,
-                '--lfw_nrof_folds', '2' ]
+                '--lfw_nrof_folds', '2']
         subprocess.call(argv)
-  
+
     def test_compare(self):
         print('test_compare')
         argv = ['python',
                 'src/compare.py',
                 os.path.join('data/', self.pretrained_model_name),
                 'data/images/Anthony_Hopkins_0001.jpg',
-                'data/images/Anthony_Hopkins_0002.jpg' ]
+                'data/images/Anthony_Hopkins_0002.jpg']
         subprocess.call(argv)
-         
+
     def test_validate_on_lfw(self):
         print('test_validate_on_lfw')
         align_dataset_if_needed(self)
         argv = ['python',
-                'src/validate_on_lfw.py', 
+                'src/validate_on_lfw.py',
                 'data/lfw_aligned',
                 self.pretrained_model,
                 '--lfw_pairs', 'data/lfw/pairs_small.txt',
                 '--lfw_nrof_folds', '2',
                 '--lfw_batch_size', '6']
         subprocess.call(argv)
- 
+
     def test_validate_on_lfw_frozen_graph(self):
         print('test_validate_on_lfw_frozen_graph')
-        self.pretrained_model = os.path.join('data', self.pretrained_model_name)
-        frozen_model = os.path.join(self.pretrained_model, self.pretrained_model_name+'.pb')
+        self.pretrained_model = os.path.join(
+            'data', self.pretrained_model_name)
+        frozen_model = os.path.join(
+            self.pretrained_model,
+            self.pretrained_model_name + '.pb')
         argv = ['python',
                 'src/validate_on_lfw.py',
                 self.dataset_dir,
@@ -196,33 +210,40 @@ def test_validate_on_lfw_frozen_graph(self):
                 '--lfw_nrof_folds', '2',
                 '--lfw_batch_size', '6']
         subprocess.call(argv)
- 
+
     def test_freeze_graph(self):
         print('test_freeze_graph')
         argv = ['python',
                 'src/freeze_graph.py',
                 self.pretrained_model,
-                self.frozen_graph_filename ]
+                self.frozen_graph_filename]
         subprocess.call(argv)
 
 # Create a mock dataset with random pixel images
+
+
 def create_mock_dataset(dataset_dir, image_size):
-   
+
     nrof_persons = 3
     nrof_images_per_person = 2
     np.random.seed(seed=666)
     os.mkdir(dataset_dir)
     for i in range(nrof_persons):
-        class_name = '%04d' % (i+1)
+        class_name = '%04d' % (i + 1)
         class_dir = os.path.join(dataset_dir, class_name)
         os.mkdir(class_dir)
         for j in range(nrof_images_per_person):
-            img_name = '%04d' % (j+1)
-            img_path = os.path.join(class_dir, class_name+'_'+img_name + '.png')
-            img = np.random.uniform(low=0.0, high=255.0, size=(image_size,image_size,3))
-            cv2.imwrite(img_path, img) #@UndefinedVariable
+            img_name = '%04d' % (j + 1)
+            img_path = os.path.join(
+                class_dir, class_name + '_' + img_name + '.png')
+            img = np.random.uniform(
+                low=0.0, high=255.0, size=(
+                    image_size, image_size, 3))
+            cv2.imwrite(img_path, img)  # @UndefinedVariable
 
 # Create a mock LFW pairs file
+
+
 def create_mock_lfw_pairs(tmp_dir):
     pairs_filename = os.path.join(tmp_dir, 'pairs_mock.txt')
     with open(pairs_filename, 'w') as f:
@@ -241,6 +262,6 @@ def create_mock_lfw_pairs(tmp_dir):
         f.write('0001 1 0003 2\n')
     return pairs_filename
 
+
 if __name__ == "__main__":
     unittest.main()
-    
\ No newline at end of file
diff --git a/test/triplet_loss_test.py b/test/triplet_loss_test.py
index 2648b3061..a2f701bb1 100644
--- a/test/triplet_loss_test.py
+++ b/test/triplet_loss_test.py
@@ -1,17 +1,17 @@
 # MIT License
-# 
+#
 # Copyright (c) 2016 David Sandberg
-# 
+#
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
 # in the Software without restriction, including without limitation the rights
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
 # furnished to do so, subject to the following conditions:
-# 
+#
 # The above copyright notice and this permission notice shall be included in all
 # copies or substantial portions of the Software.
-# 
+#
 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
@@ -23,32 +23,54 @@
 import unittest
 import tensorflow as tf
 import numpy as np
-import facenet
+from facenet_sandberg import facenet
+
 
 class DemuxEmbeddingsTest(unittest.TestCase):
-  
+
     def testDemuxEmbeddings(self):
-        batch_size = 3*12
+        batch_size = 3 * 12
         embedding_size = 16
         alpha = 0.2
-        
+
         with tf.Graph().as_default():
-        
-            embeddings = tf.placeholder(tf.float64, shape=(batch_size, embedding_size), name='embeddings')
-            anchor, positive, negative = tf.unstack(tf.reshape(embeddings, [-1,3,embedding_size]), 3, 1)
-            triplet_loss = facenet.triplet_loss(anchor, positive, negative, alpha)
-                
+
+            embeddings = tf.placeholder(
+                tf.float64,
+                shape=(
+                    batch_size,
+                    embedding_size),
+                name='embeddings')
+            anchor, positive, negative = tf.unstack(
+                tf.reshape(embeddings, [-1, 3, embedding_size]), 3, 1)
+            triplet_loss = facenet.triplet_loss(
+                anchor, positive, negative, alpha)
+
             sess = tf.Session()
             with sess.as_default():
                 np.random.seed(seed=666)
                 emb = np.random.uniform(size=(batch_size, embedding_size))
-                tf_triplet_loss = sess.run(triplet_loss, feed_dict={embeddings:emb})
-
-                pos_dist_sqr = np.sum(np.square(emb[0::3,:]-emb[1::3,:]),1)
-                neg_dist_sqr = np.sum(np.square(emb[0::3,:]-emb[2::3,:]),1)
-                np_triplet_loss = np.mean(np.maximum(0.0, pos_dist_sqr - neg_dist_sqr + alpha))
-                
-                np.testing.assert_almost_equal(tf_triplet_loss, np_triplet_loss, decimal=5, err_msg='Triplet loss is incorrect')
-                      
+                tf_triplet_loss = sess.run(
+                    triplet_loss, feed_dict={
+                        embeddings: emb})
+
+                pos_dist_sqr = np.sum(
+                    np.square(emb[0::3, :] - emb[1::3, :]), 1)
+                neg_dist_sqr = np.sum(
+                    np.square(emb[0::3, :] - emb[2::3, :]), 1)
+                np_triplet_loss = np.mean(
+                    np.maximum(
+                        0.0,
+                        pos_dist_sqr -
+                        neg_dist_sqr +
+                        alpha))
+
+                np.testing.assert_almost_equal(
+                    tf_triplet_loss,
+                    np_triplet_loss,
+                    decimal=5,
+                    err_msg='Triplet loss is incorrect')
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/tmp/__init__.py b/tmp/__init__.py
deleted file mode 100644
index efa625274..000000000
--- a/tmp/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# flake8: noqa
-
diff --git a/tmp/align_dataset.m b/tmp/align_dataset.m
deleted file mode 100644
index 1e1fce089..000000000
--- a/tmp/align_dataset.m
+++ /dev/null
@@ -1,178 +0,0 @@
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-% LFW
-% source_path = '/home/david/datasets/lfw/raw';
-% target_path = '/home/david/datasets/lfw/lfw_mtcnnalign_160';
-% image_size = 160 + 0;
-% margin = round(image_size*0.2) + 0;
-
-% FaceScrub
-% source_path = '/home/david/datasets/facescrub/facescrub/';
-% target_path = '/home/david/datasets/facescrub/facescrub_mtcnnalign_182_160';
-% failed_images_list = '/home/david/datasets/facescrub/facescrub_mtcnnalign_182_160/failed_images.txt';
-% image_size = 160 + 12;
-% margin = round(image_size*0.2) + 12;
-
-source_path = '/home/david/datasets/casia/CASIA-maxpy-clean/';
-target_path = '/home/david/datasets/casia/casia_maxpy_mtcnnalign_182_160';
-failed_images_list = '/home/david/datasets/casia/casia_maxpy_mtcnnalign_182_160/failed_images.txt';
-image_size = 160 + 12;
-margin = round(image_size*0.2) + 12;
-
-image_extension = 'png';
-minsize=20; %minimum size of face
-use_new = 0;
-
-caffe_path='/home/david/repo2/caffe/matlab';
-pdollar_toolbox_path='/home/david/repo2/toolbox';
-if use_new
-    caffe_model_path='/home/david/repo2/MTCNN_face_detection_alignment/code/codes/MTCNNv2/model';
-else
-    caffe_model_path='/home/david/repo2/MTCNN_face_detection_alignment/code/codes/MTCNNv1/model';
-end;
-addpath(genpath(caffe_path));
-addpath(genpath(pdollar_toolbox_path));
-
-caffe.set_mode_gpu();
-caffe.set_device(0);
-
-%three steps's threshold
-threshold=[0.6 0.7 0.7];
-
-%scale factor
-factor=0.709;
-
-%load caffe models
-if use_new
-    prototxt_dir =  strcat(caffe_model_path,'/det4.prototxt');
-    model_dir =  strcat(caffe_model_path,'/det4.caffemodel');
-end;
-%faces=cell(0);
-
-k = 0;
-classes = dir(source_path);
-%classes = classes(randperm(length(classes)));
-for i=1:length(classes),
-    if classes(i).name(1)~='.'
-        source_class_path = sprintf('%s/%s', source_path, classes(i).name);
-        target_class_path = sprintf('%s/%s', target_path, classes(i).name);
-        imgs = dir(source_class_path);
-        %imgs = imgs(randperm(length(imgs)));
-        if ~exist(target_class_path, 'dir'),
-            mkdir(target_class_path);
-        end;
-        for j=1:length(imgs),
-            if imgs(j).isdir==0
-                [pathstr,name,ext] = fileparts(imgs(j).name);
-                target_img_path = sprintf('%s/%s.%s', target_class_path, name, image_extension);
-                if ~exist(target_img_path,'file') && any([ strcmpi(ext,'.jpg') strcmpi(ext,'.jpeg') strcmpi(ext,'.png') strcmpi(ext,'.gif') ])
-                    if mod(k,1000)==0
-                        fprintf('Resetting GPU\n');
-                        caffe.reset_all();
-                        caffe.set_mode_gpu();
-                        caffe.set_device(0);
-                        prototxt_dir = strcat(caffe_model_path,'/det1.prototxt');
-                        model_dir = strcat(caffe_model_path,'/det1.caffemodel');
-                        PNet=caffe.Net(prototxt_dir,model_dir,'test');
-                        prototxt_dir = strcat(caffe_model_path,'/det2.prototxt');
-                        model_dir = strcat(caffe_model_path,'/det2.caffemodel');
-                        RNet=caffe.Net(prototxt_dir,model_dir,'test');
-                        prototxt_dir = strcat(caffe_model_path,'/det3.prototxt');
-                        model_dir = strcat(caffe_model_path,'/det3.caffemodel');
-                        ONet=caffe.Net(prototxt_dir,model_dir,'test');
-                        if use_new
-                            prototxt_dir = strcat(caffe_model_path,'/det4.prototxt');
-                            model_dir = strcat(caffe_model_path,'/det4.caffemodel');
-                            LNet=caffe.Net(prototxt_dir,model_dir,'test');
-                        end;
-                    end;
-                        
-                    source_img_path = sprintf('%s/%s', source_class_path, imgs(j).name);
-                    % source_img_path = '/home/david/datasets/facescrub/facescrub//Billy_Zane/095f83fefdf1dc493c013edb1ef860001193e8d9.jpg'
-                    try
-                        img = imread(source_img_path);
-                    catch exception
-                        fprintf('Unexpected error (%s): %s\n', exception.identifier, exception.message);
-                        continue;
-                    end;
-                    fprintf('%6d: %s\n', k, source_img_path);
-                    if length(size(img))<3
-                        img = repmat(img,[1,1,3]);
-                    end;
-                    img_size = size(img); % [height, width, channels]
-                    img_size = fliplr(img_size(1:2));  % [x,y]
-                    if use_new
-                        [boundingboxes, points]=detect_face_v2(img,minsize,PNet,RNet,ONet,LNet,threshold,false,factor);
-                    else
-                        [boundingboxes, points]=detect_face_v1(img,minsize,PNet,RNet,ONet,threshold,false,factor);
-                    end;
-                    nrof_faces = size(boundingboxes,1);
-                    det = boundingboxes;
-                    if nrof_faces>0
-                        if nrof_faces>1
-                            % select the faces with the largest bounding box
-                            %  closest to the image center
-                            bounding_box_size = (det(:,3)-det(:,1)).*(det(:,4)-det(:,2));
-                            img_center = img_size / 2;
-                            offsets = [ (det(:,1)+det(:,3))/2 (det(:,2)+det(:,4))/2 ] - ones(nrof_faces,1)*img_center;
-                            offset_dist_squared = sum(offsets.^2,2);
-                            [a, index] = max(bounding_box_size-offset_dist_squared*2.0); % some extra weight on the centering
-                            det = det(index,:);
-                            points = points(:,index);
-                        end;
-%                         if nrof_faces>0
-%                             figure(1); clf;
-%                             imshow(img);
-%                             hold on;
-%                             plot(points(1:5,1),points(6:10,1),'g.','MarkerSize',10);
-%                             bb = round(det(1:4));
-%                             rectangle('Position',[bb(1) bb(2) bb(3)-bb(1) bb(4)-bb(2)],'LineWidth',2,'LineStyle','-')
-%                             xxx = 1;
-%                         end;
-                        det(1) = max(det(1)-margin/2, 1);
-                        det(2) = max(det(2)-margin/2, 1);
-                        det(3) = min(det(3)+margin/2, img_size(1));
-                        det(4) = min(det(4)+margin/2, img_size(2));
-                        det(1:4) = round(det(1:4));
-                        
-                        img = img(det(2):det(4),det(1):det(3),:);
-                        img = imresize(img, [image_size, image_size]);
-                        
-                        imwrite(img, target_img_path);
-                        k = k + 1;
-                    else
-                        fprintf('Detection failed: %s\n', source_img_path);
-                        fid = fopen(failed_images_list,'at');
-                        if fid>=0
-                            fprintf(fid, '%s\n', source_img_path);
-                            fclose(fid);
-                        end;
-                    end;
-                    if mod(k,100)==0
-                        xxx = 1;
-                    end;
-                end;
-            end;
-        end;
-    end;
-end;
diff --git a/tmp/align_dataset.py b/tmp/align_dataset.py
deleted file mode 100644
index e74224a1d..000000000
--- a/tmp/align_dataset.py
+++ /dev/null
@@ -1,137 +0,0 @@
-"""Performs face alignment and stores face thumbnails in the output directory."""
-
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from scipy import misc
-import sys
-import os
-import argparse
-import random
-import align_dlib  # @UnresolvedImport
-import facenet
-
-def main(args):
-    align = align_dlib.AlignDlib(os.path.expanduser(args.dlib_face_predictor))
-    landmarkIndices = align_dlib.AlignDlib.OUTER_EYES_AND_NOSE
-    output_dir = os.path.expanduser(args.output_dir)
-    if not os.path.exists(output_dir):
-        os.makedirs(output_dir)
-    # Store some git revision info in a text file in the log directory
-    src_path,_ = os.path.split(os.path.realpath(__file__))
-    facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
-    dataset = facenet.get_dataset(args.input_dir)
-    random.shuffle(dataset)
-    # Scale the image such that the face fills the frame when cropped to crop_size
-    scale = float(args.face_size) / args.image_size
-    nrof_images_total = 0
-    nrof_prealigned_images = 0
-    nrof_successfully_aligned = 0
-    for cls in dataset:
-        output_class_dir = os.path.join(output_dir, cls.name)
-        if not os.path.exists(output_class_dir):
-            os.makedirs(output_class_dir)
-        random.shuffle(cls.image_paths)
-        for image_path in cls.image_paths:
-            nrof_images_total += 1
-            filename = os.path.splitext(os.path.split(image_path)[1])[0]
-            output_filename = os.path.join(output_class_dir, filename+'.png')
-            if not os.path.exists(output_filename):
-                try:
-                    img = misc.imread(image_path)
-                except (IOError, ValueError, IndexError) as e:
-                    errorMessage = '{}: {}'.format(image_path, e)
-                    print(errorMessage)
-                else:
-                    if img.ndim == 2:
-                        img = facenet.to_rgb(img)
-                    if args.use_center_crop:
-                        scaled = misc.imresize(img, args.prealigned_scale, interp='bilinear')
-                        sz1 = scaled.shape[1]/2
-                        sz2 = args.image_size/2
-                        aligned = scaled[(sz1-sz2):(sz1+sz2),(sz1-sz2):(sz1+sz2),:]
-                    else:
-                        aligned = align.align(args.image_size, img, landmarkIndices=landmarkIndices, 
-                                              skipMulti=False, scale=scale)
-                    if aligned is not None:
-                        print(image_path)
-                        nrof_successfully_aligned += 1
-                        misc.imsave(output_filename, aligned)
-                    elif args.prealigned_dir:
-                        # Face detection failed. Use center crop from pre-aligned dataset
-                        class_name = os.path.split(output_class_dir)[1]
-                        image_path_without_ext = os.path.join(os.path.expanduser(args.prealigned_dir), 
-                                                              class_name, filename)
-                        # Find the extension of the image
-                        exts = ('jpg', 'png')
-                        for ext in exts:
-                            temp_path = image_path_without_ext + '.' + ext
-                            image_path = ''
-                            if os.path.exists(temp_path):
-                                image_path = temp_path
-                                break
-                        try:
-                            img = misc.imread(image_path)
-                        except (IOError, ValueError, IndexError) as e:
-                            errorMessage = '{}: {}'.format(image_path, e)
-                            print(errorMessage)
-                        else:
-                            scaled = misc.imresize(img, args.prealigned_scale, interp='bilinear')
-                            sz1 = scaled.shape[1]/2
-                            sz2 = args.image_size/2
-                            cropped = scaled[(sz1-sz2):(sz1+sz2),(sz1-sz2):(sz1+sz2),:]
-                            print(image_path)
-                            nrof_prealigned_images += 1
-                            misc.imsave(output_filename, cropped)
-                    else:
-                        print('Unable to align "%s"' % image_path)
-                            
-    print('Total number of images: %d' % nrof_images_total)
-    print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
-    print('Number of pre-aligned images: %d' % nrof_prealigned_images)
-            
-
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    
-    parser.add_argument('input_dir', type=str, help='Directory with unaligned images.')
-    parser.add_argument('output_dir', type=str, help='Directory with aligned face thumbnails.')
-    parser.add_argument('--dlib_face_predictor', type=str,
-        help='File containing the dlib face predictor.', default='../data/shape_predictor_68_face_landmarks.dat')
-    parser.add_argument('--image_size', type=int,
-        help='Image size (height, width) in pixels.', default=110)
-    parser.add_argument('--face_size', type=int,
-        help='Size of the face thumbnail (height, width) in pixels.', default=96)
-    parser.add_argument('--use_center_crop', 
-        help='Use the center crop of the original image after scaling the image using prealigned_scale.', action='store_true')
-    parser.add_argument('--prealigned_dir', type=str,
-        help='Replace image with a pre-aligned version when face detection fails.', default='')
-    parser.add_argument('--prealigned_scale', type=float,
-        help='The amount of scaling to apply to prealigned images before taking the center crop.', default=0.87)
-    return parser.parse_args(argv)
-
-if __name__ == '__main__':
-    main(parse_arguments(sys.argv[1:]))
diff --git a/tmp/align_dlib.py b/tmp/align_dlib.py
deleted file mode 100644
index e5e133743..000000000
--- a/tmp/align_dlib.py
+++ /dev/null
@@ -1,204 +0,0 @@
-# Copyright 2015-2016 Carnegie Mellon University
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Module for dlib-based alignment."""
-
-# NOTE: This file has been copied from the openface project.
-#  https://github.com/cmusatyalab/openface/blob/master/openface/align_dlib.py
-
-import cv2
-import dlib
-import numpy as np
-
-TEMPLATE = np.float32([
-    (0.0792396913815, 0.339223741112), (0.0829219487236, 0.456955367943),
-    (0.0967927109165, 0.575648016728), (0.122141515615, 0.691921601066),
-    (0.168687863544, 0.800341263616), (0.239789390707, 0.895732504778),
-    (0.325662452515, 0.977068762493), (0.422318282013, 1.04329000149),
-    (0.531777802068, 1.06080371126), (0.641296298053, 1.03981924107),
-    (0.738105872266, 0.972268833998), (0.824444363295, 0.889624082279),
-    (0.894792677532, 0.792494155836), (0.939395486253, 0.681546643421),
-    (0.96111933829, 0.562238253072), (0.970579841181, 0.441758925744),
-    (0.971193274221, 0.322118743967), (0.163846223133, 0.249151738053),
-    (0.21780354657, 0.204255863861), (0.291299351124, 0.192367318323),
-    (0.367460241458, 0.203582210627), (0.4392945113, 0.233135599851),
-    (0.586445962425, 0.228141644834), (0.660152671635, 0.195923841854),
-    (0.737466449096, 0.182360984545), (0.813236546239, 0.192828009114),
-    (0.8707571886, 0.235293377042), (0.51534533827, 0.31863546193),
-    (0.516221448289, 0.396200446263), (0.517118861835, 0.473797687758),
-    (0.51816430343, 0.553157797772), (0.433701156035, 0.604054457668),
-    (0.475501237769, 0.62076344024), (0.520712933176, 0.634268222208),
-    (0.565874114041, 0.618796581487), (0.607054002672, 0.60157671656),
-    (0.252418718401, 0.331052263829), (0.298663015648, 0.302646354002),
-    (0.355749724218, 0.303020650651), (0.403718978315, 0.33867711083),
-    (0.352507175597, 0.349987615384), (0.296791759886, 0.350478978225),
-    (0.631326076346, 0.334136672344), (0.679073381078, 0.29645404267),
-    (0.73597236153, 0.294721285802), (0.782865376271, 0.321305281656),
-    (0.740312274764, 0.341849376713), (0.68499850091, 0.343734332172),
-    (0.353167761422, 0.746189164237), (0.414587777921, 0.719053835073),
-    (0.477677654595, 0.706835892494), (0.522732900812, 0.717092275768),
-    (0.569832064287, 0.705414478982), (0.635195811927, 0.71565572516),
-    (0.69951672331, 0.739419187253), (0.639447159575, 0.805236879972),
-    (0.576410514055, 0.835436670169), (0.525398405766, 0.841706377792),
-    (0.47641545769, 0.837505914975), (0.41379548902, 0.810045601727),
-    (0.380084785646, 0.749979603086), (0.477955996282, 0.74513234612),
-    (0.523389793327, 0.748924302636), (0.571057789237, 0.74332894691),
-    (0.672409137852, 0.744177032192), (0.572539621444, 0.776609286626),
-    (0.5240106503, 0.783370783245), (0.477561227414, 0.778476346951)])
-
-INV_TEMPLATE = np.float32([
-                            (-0.04099179660567834, -0.008425234314031194, 2.575498465013183),
-                            (0.04062510634554352, -0.009678089746831375, -1.2534351452524177),
-                            (0.0003666902601348179, 0.01810332406086298, -0.32206331976076663)])
-
-TPL_MIN, TPL_MAX = np.min(TEMPLATE, axis=0), np.max(TEMPLATE, axis=0)
-MINMAX_TEMPLATE = (TEMPLATE - TPL_MIN) / (TPL_MAX - TPL_MIN)
-
-
-class AlignDlib:
-    """
-    Use `dlib's landmark estimation <http://blog.dlib.net/2014/08/real-time-face-pose-estimation.html>`_ to align faces.
-
-    The alignment preprocess faces for input into a neural network.
-    Faces are resized to the same size (such as 96x96) and transformed
-    to make landmarks (such as the eyes and nose) appear at the same
-    location on every image.
-
-    Normalized landmarks:
-
-    .. image:: ../images/dlib-landmark-mean.png
-    """
-
-    #: Landmark indices corresponding to the inner eyes and bottom lip.
-    INNER_EYES_AND_BOTTOM_LIP = [39, 42, 57]
-
-    #: Landmark indices corresponding to the outer eyes and nose.
-    OUTER_EYES_AND_NOSE = [36, 45, 33]
-
-    def __init__(self, facePredictor):
-        """
-        Instantiate an 'AlignDlib' object.
-
-        :param facePredictor: The path to dlib's
-        :type facePredictor: str
-        """
-        assert facePredictor is not None
-
-        #pylint: disable=no-member
-        self.detector = dlib.get_frontal_face_detector()
-        self.predictor = dlib.shape_predictor(facePredictor)
-
-    def getAllFaceBoundingBoxes(self, rgbImg):
-        """
-        Find all face bounding boxes in an image.
-
-        :param rgbImg: RGB image to process. Shape: (height, width, 3)
-        :type rgbImg: numpy.ndarray
-        :return: All face bounding boxes in an image.
-        :rtype: dlib.rectangles
-        """
-        assert rgbImg is not None
-
-        try:
-            return self.detector(rgbImg, 1)
-        except Exception as e: #pylint: disable=broad-except
-            print("Warning: {}".format(e))
-            # In rare cases, exceptions are thrown.
-            return []
-
-    def getLargestFaceBoundingBox(self, rgbImg, skipMulti=False):
-        """
-        Find the largest face bounding box in an image.
-
-        :param rgbImg: RGB image to process. Shape: (height, width, 3)
-        :type rgbImg: numpy.ndarray
-        :param skipMulti: Skip image if more than one face detected.
-        :type skipMulti: bool
-        :return: The largest face bounding box in an image, or None.
-        :rtype: dlib.rectangle
-        """
-        assert rgbImg is not None
-
-        faces = self.getAllFaceBoundingBoxes(rgbImg)
-        if (not skipMulti and len(faces) > 0) or len(faces) == 1:
-            return max(faces, key=lambda rect: rect.width() * rect.height())
-        else:
-            return None
-
-    def findLandmarks(self, rgbImg, bb):
-        """
-        Find the landmarks of a face.
-
-        :param rgbImg: RGB image to process. Shape: (height, width, 3)
-        :type rgbImg: numpy.ndarray
-        :param bb: Bounding box around the face to find landmarks for.
-        :type bb: dlib.rectangle
-        :return: Detected landmark locations.
-        :rtype: list of (x,y) tuples
-        """
-        assert rgbImg is not None
-        assert bb is not None
-
-        points = self.predictor(rgbImg, bb)
-        #return list(map(lambda p: (p.x, p.y), points.parts()))
-        return [(p.x, p.y) for p in points.parts()]
-
-    #pylint: disable=dangerous-default-value
-    def align(self, imgDim, rgbImg, bb=None,
-              landmarks=None, landmarkIndices=INNER_EYES_AND_BOTTOM_LIP,
-              skipMulti=False, scale=1.0):
-        r"""align(imgDim, rgbImg, bb=None, landmarks=None, landmarkIndices=INNER_EYES_AND_BOTTOM_LIP)
-
-        Transform and align a face in an image.
-
-        :param imgDim: The edge length in pixels of the square the image is resized to.
-        :type imgDim: int
-        :param rgbImg: RGB image to process. Shape: (height, width, 3)
-        :type rgbImg: numpy.ndarray
-        :param bb: Bounding box around the face to align. \
-                   Defaults to the largest face.
-        :type bb: dlib.rectangle
-        :param landmarks: Detected landmark locations. \
-                          Landmarks found on `bb` if not provided.
-        :type landmarks: list of (x,y) tuples
-        :param landmarkIndices: The indices to transform to.
-        :type landmarkIndices: list of ints
-        :param skipMulti: Skip image if more than one face detected.
-        :type skipMulti: bool
-        :param scale: Scale image before cropping to the size given by imgDim.
-        :type scale: float
-        :return: The aligned RGB image. Shape: (imgDim, imgDim, 3)
-        :rtype: numpy.ndarray
-        """
-        assert imgDim is not None
-        assert rgbImg is not None
-        assert landmarkIndices is not None
-
-        if bb is None:
-            bb = self.getLargestFaceBoundingBox(rgbImg, skipMulti)
-            if bb is None:
-                return
-
-        if landmarks is None:
-            landmarks = self.findLandmarks(rgbImg, bb)
-
-        npLandmarks = np.float32(landmarks)
-        npLandmarkIndices = np.array(landmarkIndices)
-
-        #pylint: disable=maybe-no-member
-        H = cv2.getAffineTransform(npLandmarks[npLandmarkIndices],
-                                   imgDim * MINMAX_TEMPLATE[npLandmarkIndices]*scale + imgDim*(1-scale)/2)
-        thumbnail = cv2.warpAffine(rgbImg, H, (imgDim, imgDim))
-        
-        return thumbnail
diff --git a/tmp/cacd2000_split_identities.py b/tmp/cacd2000_split_identities.py
deleted file mode 100644
index cc0f1b4d5..000000000
--- a/tmp/cacd2000_split_identities.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import shutil
-import argparse
-import os
-import sys
-
-def main(args):
-    src_path_exp = os.path.expanduser(args.src_path)
-    dst_path_exp = os.path.expanduser(args.dst_path)
-    if not os.path.exists(dst_path_exp):
-        os.makedirs(dst_path_exp)
-    files = os.listdir(src_path_exp)
-    for f in files:
-        file_name = '.'.join(f.split('.')[0:-1])
-        x = file_name.split('_')
-        dir_name = '_'.join(x[1:-1])
-        class_dst_path = os.path.join(dst_path_exp, dir_name)
-        if not os.path.exists(class_dst_path):
-            os.makedirs(class_dst_path)
-        src_file_path = os.path.join(src_path_exp, f)
-        dst_file = os.path.join(class_dst_path, f)
-        print('%s -> %s' % (src_file_path, dst_file))
-        shutil.copyfile(src_file_path, dst_file)
-
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    
-    parser.add_argument('src_path', type=str, help='Path to the source directory.')
-    parser.add_argument('dst_path', type=str, help='Path to the destination directory.')
-    return parser.parse_args(argv)
-
-if __name__ == '__main__':
-    main(parse_arguments(sys.argv[1:]))
diff --git a/tmp/dataset_read_speed.py b/tmp/dataset_read_speed.py
deleted file mode 100644
index 1f7f7444a..000000000
--- a/tmp/dataset_read_speed.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import facenet
-import argparse
-import sys
-import time
-import numpy as np
-
-def main(args):
-
-    dataset = facenet.get_dataset(args.dir)
-    paths, _ = facenet.get_image_paths_and_labels(dataset)
-    t = np.zeros((len(paths)))
-    x = time.time()
-    for i, path in enumerate(paths):
-        start_time = time.time()
-        with open(path, mode='rb') as f:
-            _ = f.read()
-        duration = time.time() - start_time
-        t[i] = duration
-        if i % 1000 == 0 or i==len(paths)-1:
-            print('File %d/%d  Total time: %.2f  Avg: %.3f  Std: %.3f' % (i, len(paths), time.time()-x, np.mean(t[0:i])*1000, np.std(t[0:i])*1000))
-
-
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('dir', type=str, 
-        help='Directory with dataset to test')
-    return parser.parse_args(argv)
-
-
-if __name__ == '__main__':
-    main(parse_arguments(sys.argv[1:]))
diff --git a/tmp/deepdream.py b/tmp/deepdream.py
deleted file mode 100644
index 604636bc2..000000000
--- a/tmp/deepdream.py
+++ /dev/null
@@ -1,265 +0,0 @@
-# boilerplate code
-import numpy as np
-from functools import partial
-import PIL.Image
-
-import tensorflow as tf
-import matplotlib.pyplot as plt
-import urllib2
-import os
-import zipfile
-
-def main():
-    # download pre-trained model by running the command below in a shell
-    #  wget https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip && unzip inception5h.zip
-    url = 'https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip'
-    data_dir = '../data/'
-    model_name = os.path.split(url)[-1]
-    local_zip_file = os.path.join(data_dir, model_name)
-    if not os.path.exists(local_zip_file):
-        # Download
-        model_url = urllib2.urlopen(url)
-        with open(local_zip_file, 'wb') as output:
-            output.write(model_url.read())
-        # Extract
-        with zipfile.ZipFile(local_zip_file, 'r') as zip_ref:
-            zip_ref.extractall(data_dir)
-  
-    # start with a gray image with a little noise
-    img_noise = np.random.uniform(size=(224,224,3)) + 100.0
-  
-    model_fn = 'tensorflow_inception_graph.pb'
-    
-    # creating TensorFlow session and loading the model
-    graph = tf.Graph()
-    sess = tf.InteractiveSession(graph=graph)
-    with tf.gfile.FastGFile(os.path.join(data_dir, model_fn), 'rb') as f:
-        graph_def = tf.GraphDef()
-        graph_def.ParseFromString(f.read())
-    t_input = tf.placeholder(np.float32, name='input') # define the input tensor
-    imagenet_mean = 117.0
-    t_preprocessed = tf.expand_dims(t_input-imagenet_mean, 0)
-    tf.import_graph_def(graph_def, {'input':t_preprocessed})
-    
-    layers = [op.name for op in graph.get_operations() if op.type=='Conv2D' and 'import/' in op.name]
-    feature_nums = [int(graph.get_tensor_by_name(name+':0').get_shape()[-1]) for name in layers]
-    
-    print('Number of layers', len(layers))
-    print('Total number of feature channels:', sum(feature_nums))
-  
-  
-    # Helper functions for TF Graph visualization
-    #pylint: disable=unused-variable
-    def strip_consts(graph_def, max_const_size=32):
-        """Strip large constant values from graph_def."""
-        strip_def = tf.GraphDef()
-        for n0 in graph_def.node:
-            n = strip_def.node.add() #pylint: disable=maybe-no-member
-            n.MergeFrom(n0)
-            if n.op == 'Const':
-                tensor = n.attr['value'].tensor
-                size = len(tensor.tensor_content)
-                if size > max_const_size:
-                    tensor.tensor_content = "<stripped %d bytes>"%size
-        return strip_def
-      
-    def rename_nodes(graph_def, rename_func):
-        res_def = tf.GraphDef()
-        for n0 in graph_def.node:
-            n = res_def.node.add() #pylint: disable=maybe-no-member
-            n.MergeFrom(n0)
-            n.name = rename_func(n.name)
-            for i, s in enumerate(n.input):
-                n.input[i] = rename_func(s) if s[0]!='^' else '^'+rename_func(s[1:])
-        return res_def
-      
-    def showarray(a):
-        a = np.uint8(np.clip(a, 0, 1)*255)
-        plt.imshow(a)
-        plt.show()
-        
-    def visstd(a, s=0.1):
-        '''Normalize the image range for visualization'''
-        return (a-a.mean())/max(a.std(), 1e-4)*s + 0.5
-    
-    def T(layer):
-        '''Helper for getting layer output tensor'''
-        return graph.get_tensor_by_name("import/%s:0"%layer)
-    
-    def render_naive(t_obj, img0=img_noise, iter_n=20, step=1.0):
-        t_score = tf.reduce_mean(t_obj) # defining the optimization objective
-        t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!
-        
-        img = img0.copy()
-        for _ in range(iter_n):
-            g, _ = sess.run([t_grad, t_score], {t_input:img})
-            # normalizing the gradient, so the same step size should work 
-            g /= g.std()+1e-8         # for different layers and networks
-            img += g*step
-        showarray(visstd(img))
-        
-    def tffunc(*argtypes):
-        '''Helper that transforms TF-graph generating function into a regular one.
-        See "resize" function below.
-        '''
-        placeholders = list(map(tf.placeholder, argtypes))
-        def wrap(f):
-            out = f(*placeholders)
-            def wrapper(*args, **kw):
-                return out.eval(dict(zip(placeholders, args)), session=kw.get('session'))
-            return wrapper
-        return wrap
-    
-    # Helper function that uses TF to resize an image
-    def resize(img, size):
-        img = tf.expand_dims(img, 0)
-        return tf.image.resize_bilinear(img, size)[0,:,:,:]
-    resize = tffunc(np.float32, np.int32)(resize)
-    
-    
-    def calc_grad_tiled(img, t_grad, tile_size=512):
-        '''Compute the value of tensor t_grad over the image in a tiled way.
-        Random shifts are applied to the image to blur tile boundaries over 
-        multiple iterations.'''
-        sz = tile_size
-        h, w = img.shape[:2]
-        sx, sy = np.random.randint(sz, size=2)
-        img_shift = np.roll(np.roll(img, sx, 1), sy, 0)
-        grad = np.zeros_like(img)
-        for y in range(0, max(h-sz//2, sz),sz):
-            for x in range(0, max(w-sz//2, sz),sz):
-                sub = img_shift[y:y+sz,x:x+sz]
-                g = sess.run(t_grad, {t_input:sub})
-                grad[y:y+sz,x:x+sz] = g
-        return np.roll(np.roll(grad, -sx, 1), -sy, 0)    
-      
-    def render_multiscale(t_obj, img0=img_noise, iter_n=10, step=1.0, octave_n=3, octave_scale=1.4):
-        t_score = tf.reduce_mean(t_obj) # defining the optimization objective
-        t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!
-        
-        img = img0.copy()
-        for octave in range(octave_n):
-            if octave>0:
-                hw = np.float32(img.shape[:2])*octave_scale
-                img = resize(img, np.int32(hw))
-            for _ in range(iter_n):
-                g = calc_grad_tiled(img, t_grad)
-                # normalizing the gradient, so the same step size should work 
-                g /= g.std()+1e-8         # for different layers and networks
-                img += g*step
-            showarray(visstd(img))
-            
-    def lap_split(img):
-        '''Split the image into lo and hi frequency components'''
-        with tf.name_scope('split'):
-            lo = tf.nn.conv2d(img, k5x5, [1,2,2,1], 'SAME')
-            lo2 = tf.nn.conv2d_transpose(lo, k5x5*4, tf.shape(img), [1,2,2,1])
-            hi = img-lo2
-        return lo, hi
-    
-    def lap_split_n(img, n):
-        '''Build Laplacian pyramid with n splits'''
-        levels = []
-        for _ in range(n):
-            img, hi = lap_split(img)
-            levels.append(hi)
-        levels.append(img)
-        return levels[::-1]
-    
-    def lap_merge(levels):
-        '''Merge Laplacian pyramid'''
-        img = levels[0]
-        for hi in levels[1:]:
-            with tf.name_scope('merge'):
-                img = tf.nn.conv2d_transpose(img, k5x5*4, tf.shape(hi), [1,2,2,1]) + hi
-        return img
-    
-    def normalize_std(img, eps=1e-10):
-        '''Normalize image by making its standard deviation = 1.0'''
-        with tf.name_scope('normalize'):
-            std = tf.sqrt(tf.reduce_mean(tf.square(img)))
-            return img/tf.maximum(std, eps)
-    
-    def lap_normalize(img, scale_n=4):
-        '''Perform the Laplacian pyramid normalization.'''
-        img = tf.expand_dims(img,0)
-        tlevels = lap_split_n(img, scale_n)
-        tlevels = list(map(normalize_std, tlevels))
-        out = lap_merge(tlevels)
-        return out[0,:,:,:]
-  
-    def render_lapnorm(t_obj, img0=img_noise, visfunc=visstd,
-                       iter_n=10, step=1.0, octave_n=3, octave_scale=1.4, lap_n=4):
-        t_score = tf.reduce_mean(t_obj) # defining the optimization objective
-        t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!
-        # build the laplacian normalization graph
-        lap_norm_func = tffunc(np.float32)(partial(lap_normalize, scale_n=lap_n))
-    
-        img = img0.copy()
-        for octave in range(octave_n):
-            if octave>0:
-                hw = np.float32(img.shape[:2])*octave_scale
-                img = resize(img, np.int32(hw))
-            for _ in range(iter_n):
-                g = calc_grad_tiled(img, t_grad)
-                g = lap_norm_func(g)
-                img += g*step
-            showarray(visfunc(img))
-  
-    def render_deepdream(t_obj, img0=img_noise,
-                         iter_n=10, step=1.5, octave_n=4, octave_scale=1.4):
-        t_score = tf.reduce_mean(t_obj) # defining the optimization objective
-        t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!
-    
-        # split the image into a number of octaves
-        img = img0
-        octaves = []
-        for _ in range(octave_n-1):
-            hw = img.shape[:2]
-            lo = resize(img, np.int32(np.float32(hw)/octave_scale))
-            hi = img-resize(lo, hw)
-            img = lo
-            octaves.append(hi)
-        
-        # generate details octave by octave
-        for octave in range(octave_n):
-            if octave>0:
-                hi = octaves[-octave]
-                img = resize(img, hi.shape[:2])+hi
-            for _ in range(iter_n):
-                g = calc_grad_tiled(img, t_grad)
-                img += g*(step / (np.abs(g).mean()+1e-7))
-            showarray(img/255.0)
-  
-    # Picking some internal layer. Note that we use outputs before applying the ReLU nonlinearity
-    # to have non-zero gradients for features with negative initial activations.
-    layer = 'mixed4d_3x3_bottleneck_pre_relu'
-    channel = 139 # picking some feature channel to visualize
-    render_naive(T(layer)[:,:,:,channel])
-    
-    render_multiscale(T(layer)[:,:,:,channel])
-   
-    k = np.float32([1,4,6,4,1])
-    k = np.outer(k, k)
-    k5x5 = k[:,:,None,None]/k.sum()*np.eye(3, dtype=np.float32)
-     
-    render_lapnorm(T(layer)[:,:,:,channel])
-     
-    render_lapnorm(T(layer)[:,:,:,65])
-     
-    render_lapnorm(T('mixed3b_1x1_pre_relu')[:,:,:,101])
-     
-    render_lapnorm(T(layer)[:,:,:,65]+T(layer)[:,:,:,139], octave_n=4)
-     
-     
-    img0 = PIL.Image.open('pilatus800.jpg')
-    img0 = np.float32(img0)
-    showarray(img0/255.0)
-     
-    render_deepdream(tf.square(T('mixed4c')), img0)
-     
-    render_deepdream(T(layer)[:,:,:,139], img0)
-    
-  
-if __name__ == '__main__':
-    main()
diff --git a/tmp/detect_face_v1.m b/tmp/detect_face_v1.m
deleted file mode 100644
index 4aeb66239..000000000
--- a/tmp/detect_face_v1.m
+++ /dev/null
@@ -1,253 +0,0 @@
-% MIT License
-% 
-% Copyright (c) 2016 Kaipeng Zhang
-% 
-% Permission is hereby granted, free of charge, to any person obtaining a copy
-% of this software and associated documentation files (the "Software"), to deal
-% in the Software without restriction, including without limitation the rights
-% to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-% copies of the Software, and to permit persons to whom the Software is
-% furnished to do so, subject to the following conditions:
-% 
-% The above copyright notice and this permission notice shall be included in all
-% copies or substantial portions of the Software.
-% 
-% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-% IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-% AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-% OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-% SOFTWARE.
-
-function [total_boxes, points] = detect_face_v1(img,minsize,PNet,RNet,ONet,threshold,fastresize,factor)
-	%im: input image
-	%minsize: minimum of faces' size
-	%pnet, rnet, onet: caffemodel
-	%threshold: threshold=[th1 th2 th3], th1-3 are three steps's threshold
-	%fastresize: resize img from last scale (using in high-resolution images) if fastresize==true
-	factor_count=0;
-	total_boxes=[];
-	points=[];
-	h=size(img,1);
-	w=size(img,2);
-	minl=min([w h]);
-    img=single(img);
-	if fastresize
-		im_data=(single(img)-127.5)*0.0078125;
-    end
-    m=12/minsize;
-	minl=minl*m;
-	%creat scale pyramid
-    scales=[];
-	while (minl>=12)
-		scales=[scales m*factor^(factor_count)];
-		minl=minl*factor;
-		factor_count=factor_count+1;
-	end
-	%first stage
-	for j = 1:size(scales,2)
-		scale=scales(j);
-		hs=ceil(h*scale);
-		ws=ceil(w*scale);
-		if fastresize
-			im_data=imResample(im_data,[hs ws],'bilinear');
-		else 
-			im_data=(imResample(img,[hs ws],'bilinear')-127.5)*0.0078125;
-		end
-		PNet.blobs('data').reshape([hs ws 3 1]);
-		out=PNet.forward({im_data});
-		boxes=generateBoundingBox(out{2}(:,:,2),out{1},scale,threshold(1));
-		%inter-scale nms
-		pick=nms(boxes,0.5,'Union');
-		boxes=boxes(pick,:);
-		if ~isempty(boxes)
-			total_boxes=[total_boxes;boxes];
-		end
-	end
-	numbox=size(total_boxes,1);
-	if ~isempty(total_boxes)
-		pick=nms(total_boxes,0.7,'Union');
-		total_boxes=total_boxes(pick,:);
-		regw=total_boxes(:,3)-total_boxes(:,1);
-		regh=total_boxes(:,4)-total_boxes(:,2);
-		total_boxes=[total_boxes(:,1)+total_boxes(:,6).*regw total_boxes(:,2)+total_boxes(:,7).*regh total_boxes(:,3)+total_boxes(:,8).*regw total_boxes(:,4)+total_boxes(:,9).*regh total_boxes(:,5)];	
-		total_boxes=rerec(total_boxes);
-		total_boxes(:,1:4)=fix(total_boxes(:,1:4));
-		[dy edy dx edx y ey x ex tmpw tmph]=pad(total_boxes,w,h);
-	end
-	numbox=size(total_boxes,1);
-	if numbox>0
-		%second stage
- 		tempimg=zeros(24,24,3,numbox);
-		for k=1:numbox
-			tmp=zeros(tmph(k),tmpw(k),3);
-			tmp(dy(k):edy(k),dx(k):edx(k),:)=img(y(k):ey(k),x(k):ex(k),:);
-            if size(tmp,1)>0 && size(tmp,2)>0 || size(tmp,1)==0 && size(tmp,2)==0
-                tempimg(:,:,:,k)=imResample(tmp,[24 24],'bilinear');
-            else
-                total_boxes = [];
-                return;
-            end;
-		end
-        tempimg=(tempimg-127.5)*0.0078125;
-		RNet.blobs('data').reshape([24 24 3 numbox]);
-		out=RNet.forward({tempimg});
-		score=squeeze(out{2}(2,:));
-		pass=find(score>threshold(2));
-		total_boxes=[total_boxes(pass,1:4) score(pass)'];
-		mv=out{1}(:,pass);
-		if size(total_boxes,1)>0		
-			pick=nms(total_boxes,0.7,'Union');
-			total_boxes=total_boxes(pick,:);     
-            total_boxes=bbreg(total_boxes,mv(:,pick)');	
-            total_boxes=rerec(total_boxes);
-		end
-		numbox=size(total_boxes,1);
-		if numbox>0
-			%third stage
-			total_boxes=fix(total_boxes);
-			[dy edy dx edx y ey x ex tmpw tmph]=pad(total_boxes,w,h);
-            tempimg=zeros(48,48,3,numbox);
-			for k=1:numbox
-				tmp=zeros(tmph(k),tmpw(k),3);
-				tmp(dy(k):edy(k),dx(k):edx(k),:)=img(y(k):ey(k),x(k):ex(k),:);
-                if size(tmp,1)>0 && size(tmp,2)>0 || size(tmp,1)==0 && size(tmp,2)==0
-    				tempimg(:,:,:,k)=imResample(tmp,[48 48],'bilinear');
-                else
-                    total_boxes = [];
-                    return;
-                end;
-			end
-			tempimg=(tempimg-127.5)*0.0078125;
-			ONet.blobs('data').reshape([48 48 3 numbox]);
-			out=ONet.forward({tempimg});
-			score=squeeze(out{3}(2,:));
-			points=out{2};
-			pass=find(score>threshold(3));
-			points=points(:,pass);
-			total_boxes=[total_boxes(pass,1:4) score(pass)'];
-			mv=out{1}(:,pass);
-			w=total_boxes(:,3)-total_boxes(:,1)+1;
-            h=total_boxes(:,4)-total_boxes(:,2)+1;
-            points(1:5,:)=repmat(w',[5 1]).*points(1:5,:)+repmat(total_boxes(:,1)',[5 1])-1;
-            points(6:10,:)=repmat(h',[5 1]).*points(6:10,:)+repmat(total_boxes(:,2)',[5 1])-1;
-			if size(total_boxes,1)>0				
-				total_boxes=bbreg(total_boxes,mv(:,:)');	
-                pick=nms(total_boxes,0.7,'Min');
-				total_boxes=total_boxes(pick,:);  				
-                points=points(:,pick);
-			end
-		end
-    end 	
-end
-
-function [boundingbox] = bbreg(boundingbox,reg)
-	%calibrate bouding boxes
-	if size(reg,2)==1
-		reg=reshape(reg,[size(reg,3) size(reg,4)])';
-	end
-	w=[boundingbox(:,3)-boundingbox(:,1)]+1;
-	h=[boundingbox(:,4)-boundingbox(:,2)]+1;
-	boundingbox(:,1:4)=[boundingbox(:,1)+reg(:,1).*w boundingbox(:,2)+reg(:,2).*h boundingbox(:,3)+reg(:,3).*w boundingbox(:,4)+reg(:,4).*h];
-end
-
-function [boundingbox reg] = generateBoundingBox(map,reg,scale,t)
-	%use heatmap to generate bounding boxes
-    stride=2;
-    cellsize=12;
-    boundingbox=[];
-	map=map';
-	dx1=reg(:,:,1)';
-	dy1=reg(:,:,2)';
-	dx2=reg(:,:,3)';
-	dy2=reg(:,:,4)';
-    [y x]=find(map>=t);
-	a=find(map>=t); 
-    if size(y,1)==1
-		y=y';x=x';score=map(a)';dx1=dx1';dy1=dy1';dx2=dx2';dy2=dy2';
-	else
-		score=map(a);
-    end   
-	reg=[dx1(a) dy1(a) dx2(a) dy2(a)];
-	if isempty(reg)
-		reg=reshape([],[0 3]);
-	end
-    boundingbox=[y x];
-    boundingbox=[fix((stride*(boundingbox-1)+1)/scale) fix((stride*(boundingbox-1)+cellsize-1+1)/scale) score reg];
-end
-
-function pick = nms(boxes,threshold,type)
-	%NMS
-	if isempty(boxes)
-	  pick = [];
-	  return;
-	end
-	x1 = boxes(:,1);
-	y1 = boxes(:,2);
-	x2 = boxes(:,3);
-	y2 = boxes(:,4);
-	s = boxes(:,5);
-	area = (x2-x1+1) .* (y2-y1+1);
-	[vals, I] = sort(s);
-	pick = s*0;
-	counter = 1;
-	while ~isempty(I)
-		last = length(I);
-		i = I(last);
-		pick(counter) = i;
-		counter = counter + 1;  
-		xx1 = max(x1(i), x1(I(1:last-1)));
-		yy1 = max(y1(i), y1(I(1:last-1)));
-		xx2 = min(x2(i), x2(I(1:last-1)));
-		yy2 = min(y2(i), y2(I(1:last-1)));  
-		w = max(0.0, xx2-xx1+1);
-		h = max(0.0, yy2-yy1+1); 
-		inter = w.*h;
-		if strcmp(type,'Min')
-			o = inter ./ min(area(i),area(I(1:last-1)));
-		else
-			o = inter ./ (area(i) + area(I(1:last-1)) - inter);
-		end
-		I = I(find(o<=threshold));
-	end
-	pick = pick(1:(counter-1));
-end
-
-function [dy edy dx edx y ey x ex tmpw tmph] = pad(total_boxes,w,h)
-	%compute the padding coordinates (pad the bounding boxes to square)
-	tmpw=total_boxes(:,3)-total_boxes(:,1)+1;
-	tmph=total_boxes(:,4)-total_boxes(:,2)+1;
-	numbox=size(total_boxes,1);
-	
-    dx=ones(numbox,1);dy=ones(numbox,1);
-	edx=tmpw;edy=tmph;
-	
-	x=total_boxes(:,1);y=total_boxes(:,2);
-	ex=total_boxes(:,3);ey=total_boxes(:,4);		
-	
-	tmp=find(ex>w);
-	edx(tmp)=-ex(tmp)+w+tmpw(tmp);ex(tmp)=w;		
-	
-	tmp=find(ey>h);
-	edy(tmp)=-ey(tmp)+h+tmph(tmp);ey(tmp)=h;	
-	
-	tmp=find(x<1);
-	dx(tmp)=2-x(tmp);x(tmp)=1;	
-	
-	tmp=find(y<1);
-	dy(tmp)=2-y(tmp);y(tmp)=1;
-end
-
-function [bboxA] = rerec(bboxA)
-	%convert bboxA to square
-	bboxB=bboxA(:,1:4);
-    h=bboxA(:,4)-bboxA(:,2);
-	w=bboxA(:,3)-bboxA(:,1);
-    l=max([w h]')';
-    bboxA(:,1)=bboxA(:,1)+w.*0.5-l.*0.5;
-    bboxA(:,2)=bboxA(:,2)+h.*0.5-l.*0.5;
-    bboxA(:,3:4)=bboxA(:,1:2)+repmat(l,[1 2]);
-end
-
-
diff --git a/tmp/detect_face_v2.m b/tmp/detect_face_v2.m
deleted file mode 100644
index 3ed07b17e..000000000
--- a/tmp/detect_face_v2.m
+++ /dev/null
@@ -1,288 +0,0 @@
-% MIT License
-% 
-% Copyright (c) 2016 Kaipeng Zhang
-% 
-% Permission is hereby granted, free of charge, to any person obtaining a copy
-% of this software and associated documentation files (the "Software"), to deal
-% in the Software without restriction, including without limitation the rights
-% to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-% copies of the Software, and to permit persons to whom the Software is
-% furnished to do so, subject to the following conditions:
-% 
-% The above copyright notice and this permission notice shall be included in all
-% copies or substantial portions of the Software.
-% 
-% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-% IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-% AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-% OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-% SOFTWARE.
-
-function [total_boxes, points] = detect_face_v2(img,minsize,PNet,RNet,ONet,LNet,threshold,fastresize,factor)
-	%im: input image
-	%minsize: minimum of faces' size
-	%pnet, rnet, onet: caffemodel
-	%threshold: threshold=[th1 th2 th3], th1-3 are three steps's threshold
-	%fastresize: resize img from last scale (using in high-resolution images) if fastresize==true
-	factor_count=0;
-	total_boxes=[];
-	points=[];
-	h=size(img,1);
-	w=size(img,2);
-	minl=min([w h]);
-    img=single(img);
-	if fastresize
-		im_data=(single(img)-127.5)*0.0078125;
-    end
-    m=12/minsize;
-	minl=minl*m;
-	%creat scale pyramid
-    scales=[];
-	while (minl>=12)
-		scales=[scales m*factor^(factor_count)];
-		minl=minl*factor;
-		factor_count=factor_count+1;
-	end
-	%first stage
-	for j = 1:size(scales,2)
-		scale=scales(j);
-		hs=ceil(h*scale);
-		ws=ceil(w*scale);
-		if fastresize
-			im_data=imResample(im_data,[hs ws],'bilinear');
-		else 
-			im_data=(imResample(img,[hs ws],'bilinear')-127.5)*0.0078125;
-		end
-		PNet.blobs('data').reshape([hs ws 3 1]);
-		out=PNet.forward({im_data});
-		boxes=generateBoundingBox(out{2}(:,:,2),out{1},scale,threshold(1));
-		%inter-scale nms
-		pick=nms(boxes,0.5,'Union');
-		boxes=boxes(pick,:);
-		if ~isempty(boxes)
-			total_boxes=[total_boxes;boxes];
-		end
-	end
-	numbox=size(total_boxes,1);
-	if ~isempty(total_boxes)
-		pick=nms(total_boxes,0.7,'Union');
-		total_boxes=total_boxes(pick,:);
-		bbw=total_boxes(:,3)-total_boxes(:,1);
-		bbh=total_boxes(:,4)-total_boxes(:,2);
-		total_boxes=[total_boxes(:,1)+total_boxes(:,6).*bbw total_boxes(:,2)+total_boxes(:,7).*bbh total_boxes(:,3)+total_boxes(:,8).*bbw total_boxes(:,4)+total_boxes(:,9).*bbh total_boxes(:,5)];	
-		total_boxes=rerec(total_boxes);
-		total_boxes(:,1:4)=fix(total_boxes(:,1:4));
-		[dy edy dx edx y ey x ex tmpw tmph]=pad(total_boxes,w,h);
-	end
-	numbox=size(total_boxes,1);
-	if numbox>0
-		%second stage
- 		tempimg=zeros(24,24,3,numbox);
-		for k=1:numbox
-			tmp=zeros(tmph(k),tmpw(k),3);
-			tmp(dy(k):edy(k),dx(k):edx(k),:)=img(y(k):ey(k),x(k):ex(k),:);
-			tempimg(:,:,:,k)=imResample(tmp,[24 24],'bilinear');
-		end
-        tempimg=(tempimg-127.5)*0.0078125;
-		RNet.blobs('data').reshape([24 24 3 numbox]);
-		out=RNet.forward({tempimg});
-		score=squeeze(out{2}(2,:));
-		pass=find(score>threshold(2));
-		total_boxes=[total_boxes(pass,1:4) score(pass)'];
-		mv=out{1}(:,pass);
-		if size(total_boxes,1)>0		
-			pick=nms(total_boxes,0.7,'Union');
-			total_boxes=total_boxes(pick,:);     
-            total_boxes=bbreg(total_boxes,mv(:,pick)');	
-            total_boxes=rerec(total_boxes);
-		end
-		numbox=size(total_boxes,1);
-		if numbox>0
-			%third stage
-			total_boxes=fix(total_boxes);
-			[dy edy dx edx y ey x ex tmpw tmph]=pad(total_boxes,w,h);
-            tempimg=zeros(48,48,3,numbox);
-			for k=1:numbox
-				tmp=zeros(tmph(k),tmpw(k),3);
-				tmp(dy(k):edy(k),dx(k):edx(k),:)=img(y(k):ey(k),x(k):ex(k),:);
-				tempimg(:,:,:,k)=imResample(tmp,[48 48],'bilinear');
-			end
-			tempimg=(tempimg-127.5)*0.0078125;
-			ONet.blobs('data').reshape([48 48 3 numbox]);
-			out=ONet.forward({tempimg});
-			score=squeeze(out{3}(2,:));
-			points=out{2};
-			pass=find(score>threshold(3));
-			points=points(:,pass);
-			total_boxes=[total_boxes(pass,1:4) score(pass)'];
-			mv=out{1}(:,pass);
-			bbw=total_boxes(:,3)-total_boxes(:,1)+1;
-            bbh=total_boxes(:,4)-total_boxes(:,2)+1;
-            points(1:5,:)=repmat(bbw',[5 1]).*points(1:5,:)+repmat(total_boxes(:,1)',[5 1])-1;
-            points(6:10,:)=repmat(bbh',[5 1]).*points(6:10,:)+repmat(total_boxes(:,2)',[5 1])-1;
-			if size(total_boxes,1)>0				
-				total_boxes=bbreg(total_boxes,mv(:,:)');	
-                pick=nms(total_boxes,0.7,'Min');
-				total_boxes=total_boxes(pick,:);  				
-                points=points(:,pick);
-			end
-		end
-		numbox=size(total_boxes,1);
-		%extended stage
-		if numbox>0 
-			tempimg=zeros(24,24,15,numbox);
-			patchw=max([total_boxes(:,3)-total_boxes(:,1)+1 total_boxes(:,4)-total_boxes(:,2)+1]');
-			patchw=fix(0.25*patchw);	
-			tmp=find(mod(patchw,2)==1);
-			patchw(tmp)=patchw(tmp)+1;
-			pointx=ones(numbox,5);
-			pointy=ones(numbox,5);
-			for k=1:5
-				tmp=[points(k,:);points(k+5,:)];
-				x=fix(tmp(1,:)-0.5*patchw);
-				y=fix(tmp(2,:)-0.5*patchw);
-				[dy edy dx edx y ey x ex tmpw tmph]=pad([x' y' x'+patchw' y'+patchw'],w,h);
-				for j=1:numbox
-					tmpim=zeros(tmpw(j),tmpw(j),3);
-					tmpim(dy(j):edy(j),dx(j):edx(j),:)=img(y(j):ey(j),x(j):ex(j),:);
-					tempimg(:,:,(k-1)*3+1:(k-1)*3+3,j)=imResample(tmpim,[24 24],'bilinear');
-				end
-			end
-			LNet.blobs('data').reshape([24 24 15 numbox]);
-			tempimg=(tempimg-127.5)*0.0078125;
-			out=LNet.forward({tempimg});
-			score=squeeze(out{3}(2,:));
-			for k=1:5
-				tmp=[points(k,:);points(k+5,:)];
-				%do not make a large movement
-				temp=find(abs(out{k}(1,:)-0.5)>0.35);
-				if ~isempty(temp)
-					l=length(temp);
-					out{k}(:,temp)=ones(2,l)*0.5;
-				end
-				temp=find(abs(out{k}(2,:)-0.5)>0.35);  
-				if ~isempty(temp)
-					l=length(temp);
-					out{k}(:,temp)=ones(2,l)*0.5;
-				end
-				pointx(:,k)=(tmp(1,:)-0.5*patchw+out{k}(1,:).*patchw)';
-				pointy(:,k)=(tmp(2,:)-0.5*patchw+out{k}(2,:).*patchw)';
-			end
-			for j=1:numbox
-				points(:,j)=[pointx(j,:)';pointy(j,:)'];
-			end
-		end
-    end 	
-end
-
-function [boundingbox] = bbreg(boundingbox,reg)
-	%calibrate bouding boxes
-	if size(reg,2)==1
-		reg=reshape(reg,[size(reg,3) size(reg,4)])';
-	end
-	w=[boundingbox(:,3)-boundingbox(:,1)]+1;
-	h=[boundingbox(:,4)-boundingbox(:,2)]+1;
-	boundingbox(:,1:4)=[boundingbox(:,1)+reg(:,1).*w boundingbox(:,2)+reg(:,2).*h boundingbox(:,3)+reg(:,3).*w boundingbox(:,4)+reg(:,4).*h];
-end
-
-function [boundingbox reg] = generateBoundingBox(map,reg,scale,t)
-	%use heatmap to generate bounding boxes
-    stride=2;
-    cellsize=12;
-    boundingbox=[];
-	map=map';
-	dx1=reg(:,:,1)';
-	dy1=reg(:,:,2)';
-	dx2=reg(:,:,3)';
-	dy2=reg(:,:,4)';
-    [y x]=find(map>=t);
-	a=find(map>=t); 
-    if size(y,1)==1
-		y=y';x=x';score=map(a)';dx1=dx1';dy1=dy1';dx2=dx2';dy2=dy2';
-	else
-		score=map(a);
-    end   
-	reg=[dx1(a) dy1(a) dx2(a) dy2(a)];
-	if isempty(reg)
-		reg=reshape([],[0 3]);
-	end
-    boundingbox=[y x];
-    boundingbox=[fix((stride*(boundingbox-1)+1)/scale) fix((stride*(boundingbox-1)+cellsize-1+1)/scale) score reg];
-end
-
-function pick = nms(boxes,threshold,type)
-	%NMS
-	if isempty(boxes)
-	  pick = [];
-	  return;
-	end
-	x1 = boxes(:,1);
-	y1 = boxes(:,2);
-	x2 = boxes(:,3);
-	y2 = boxes(:,4);
-	s = boxes(:,5);
-	area = (x2-x1+1) .* (y2-y1+1);
-	[vals, I] = sort(s);
-	pick = s*0;
-	counter = 1;
-	while ~isempty(I)
-		last = length(I);
-		i = I(last);
-		pick(counter) = i;
-		counter = counter + 1;  
-		xx1 = max(x1(i), x1(I(1:last-1)));
-		yy1 = max(y1(i), y1(I(1:last-1)));
-		xx2 = min(x2(i), x2(I(1:last-1)));
-		yy2 = min(y2(i), y2(I(1:last-1)));  
-		w = max(0.0, xx2-xx1+1);
-		h = max(0.0, yy2-yy1+1); 
-		inter = w.*h;
-		if strcmp(type,'Min')
-			o = inter ./ min(area(i),area(I(1:last-1)));
-		else
-			o = inter ./ (area(i) + area(I(1:last-1)) - inter);
-		end
-		I = I(find(o<=threshold));
-	end
-	pick = pick(1:(counter-1));
-end
-
-function [dy edy dx edx y ey x ex tmpw tmph] = pad(total_boxes,w,h)
-	%compute the padding coordinates (pad the bounding boxes to square)
-	tmpw=total_boxes(:,3)-total_boxes(:,1)+1;
-	tmph=total_boxes(:,4)-total_boxes(:,2)+1;
-	numbox=size(total_boxes,1);
-	
-    dx=ones(numbox,1);dy=ones(numbox,1);
-	edx=tmpw;edy=tmph;
-	
-	x=total_boxes(:,1);y=total_boxes(:,2);
-	ex=total_boxes(:,3);ey=total_boxes(:,4);		
-	
-	tmp=find(ex>w);
-	edx(tmp)=-ex(tmp)+w+tmpw(tmp);ex(tmp)=w;		
-	
-	tmp=find(ey>h);
-	edy(tmp)=-ey(tmp)+h+tmph(tmp);ey(tmp)=h;	
-	
-	tmp=find(x<1);
-	dx(tmp)=2-x(tmp);x(tmp)=1;	
-	
-	tmp=find(y<1);
-	dy(tmp)=2-y(tmp);y(tmp)=1;
-end
-
-function [bboxA] = rerec(bboxA)
-	%convert bboxA to square
-	bboxB=bboxA(:,1:4);
-    h=bboxA(:,4)-bboxA(:,2);
-	w=bboxA(:,3)-bboxA(:,1);
-    l=max([w h]')';
-    bboxA(:,1)=bboxA(:,1)+w.*0.5-l.*0.5;
-    bboxA(:,2)=bboxA(:,2)+h.*0.5-l.*0.5;
-    bboxA(:,3:4)=bboxA(:,1:2)+repmat(l,[1 2]);
-end
-
-
diff --git a/tmp/download_vgg_face_dataset.py b/tmp/download_vgg_face_dataset.py
deleted file mode 100644
index e85d74248..000000000
--- a/tmp/download_vgg_face_dataset.py
+++ /dev/null
@@ -1,104 +0,0 @@
-"""Download the VGG face dataset from URLs given by http://www.robots.ox.ac.uk/~vgg/data/vgg_face/vgg_face_dataset.tar.gz
-"""
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from scipy import misc
-import numpy as np
-from skimage import io
-import sys
-import argparse
-import os
-import socket
-from urllib2 import HTTPError, URLError
-from httplib import HTTPException
-
-def main(args):
-    socket.setdefaulttimeout(30)
-    textfile_names = os.listdir(args.dataset_descriptor)
-    for textfile_name in textfile_names:
-        if textfile_name.endswith('.txt'):
-            with open(os.path.join(args.dataset_descriptor, textfile_name), 'rt') as f:
-                lines = f.readlines()
-            dir_name = textfile_name.split('.')[0]
-            class_path = os.path.join(args.dataset_descriptor, dir_name)
-            if not os.path.exists(class_path):
-                os.makedirs(class_path)
-            for line in lines:
-                x = line.split(' ')
-                filename = x[0]
-                url = x[1]
-                box = np.rint(np.array(map(float, x[2:6])))  # x1,y1,x2,y2
-                image_path = os.path.join(args.dataset_descriptor, dir_name, filename+'.'+args.output_format)
-                error_path = os.path.join(args.dataset_descriptor, dir_name, filename+'.err')
-                if not os.path.exists(image_path) and not os.path.exists(error_path):
-                    try:
-                        img = io.imread(url, mode='RGB')
-                    except (HTTPException, HTTPError, URLError, IOError, ValueError, IndexError, OSError) as e:
-                        error_message = '{}: {}'.format(url, e)
-                        save_error_message_file(error_path, error_message)
-                    else:
-                        try:
-                            if img.ndim == 2:
-                                img = to_rgb(img)
-                            if img.ndim != 3:
-                                raise ValueError('Wrong number of image dimensions')
-                            hist = np.histogram(img, 255, density=True)
-                            if hist[0][0]>0.9 and hist[0][254]>0.9:
-                                raise ValueError('Image is mainly black or white')
-                            else:
-                                # Crop image according to dataset descriptor
-                                img_cropped = img[int(box[1]):int(box[3]),int(box[0]):int(box[2]),:]
-                                # Scale to 256x256
-                                img_resized = misc.imresize(img_cropped, (args.image_size,args.image_size))
-                                # Save image as .png
-                                misc.imsave(image_path, img_resized)
-                        except ValueError as e:
-                            error_message = '{}: {}'.format(url, e)
-                            save_error_message_file(error_path, error_message)
-            
-def save_error_message_file(filename, error_message):
-    print(error_message)
-    with open(filename, "w") as textfile:
-        textfile.write(error_message)
-          
-def to_rgb(img):
-    w, h = img.shape
-    ret = np.empty((w, h, 3), dtype=np.uint8)
-    ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img
-    return ret
-
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('dataset_descriptor', type=str, 
-        help='Directory containing the text files with the image URLs. Image files will also be placed in this directory.')
-    parser.add_argument('--output_format', type=str, help='Format of the output images', default='png', choices=['png', 'jpg'])
-    parser.add_argument('--image_size', type=int,
-        help='Image size (height, width) in pixels.', default=256)
-    return parser.parse_args(argv)
-
-if __name__ == '__main__':
-    main(parse_arguments(sys.argv[1:]))
diff --git a/tmp/funnel_dataset.py b/tmp/funnel_dataset.py
deleted file mode 100644
index a40ffc938..000000000
--- a/tmp/funnel_dataset.py
+++ /dev/null
@@ -1,96 +0,0 @@
-"""Performs face alignment and stores face thumbnails in the output directory."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from scipy import misc
-import sys
-import os
-import argparse
-import facenet
-import subprocess
-from contextlib import contextmanager
-import tempfile
-import shutil
-import numpy as np
-
-@contextmanager
-def TemporaryDirectory():
-    name = tempfile.mkdtemp()
-    try:
-        yield name
-    finally:
-        shutil.rmtree(name)
-
-
-def main(args):
-    funnel_cmd = 'funnelReal'
-    funnel_model = 'people.train'
-
-    output_dir = os.path.expanduser(args.output_dir)
-    if not os.path.exists(output_dir):
-        os.makedirs(output_dir)
-    # Store some git revision info in a text file in the output directory
-    src_path,_ = os.path.split(os.path.realpath(__file__))
-    facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
-    dataset = facenet.get_dataset(args.input_dir)
-    np.random.shuffle(dataset)
-    # Scale the image such that the face fills the frame when cropped to crop_size
-    #scale = float(args.face_size) / args.image_size
-    with TemporaryDirectory() as tmp_dir:
-        for cls in dataset:
-            output_class_dir = os.path.join(output_dir, cls.name)
-            tmp_output_class_dir = os.path.join(tmp_dir, cls.name)
-            if not os.path.exists(output_class_dir) and not os.path.exists(tmp_output_class_dir):
-                print('Aligning class %s:' % cls.name)
-                tmp_filenames = []
-                if not os.path.exists(tmp_output_class_dir):
-                    os.makedirs(tmp_output_class_dir)
-                input_list_filename = os.path.join(tmp_dir, 'input_list.txt')
-                output_list_filename = os.path.join(tmp_dir, 'output_list.txt')
-                input_file = open(input_list_filename, 'w')
-                output_file = open(output_list_filename,'w')
-                for image_path in cls.image_paths:
-                    filename = os.path.split(image_path)[1]
-                    input_file.write(image_path+'\n')
-                    output_filename = os.path.join(tmp_output_class_dir, filename)
-                    output_file.write(output_filename+'\n')
-                    tmp_filenames.append(output_filename)
-                input_file.close()
-                output_file.close()
-                cmd = args.funnel_dir+funnel_cmd + ' ' + input_list_filename + ' ' + args.funnel_dir+funnel_model + ' ' + output_list_filename
-                subprocess.call(cmd, shell=True)
-                
-                # Resize and crop images
-                if not os.path.exists(output_class_dir):
-                    os.makedirs(output_class_dir)
-                scale = 1.0
-                for tmp_filename in tmp_filenames:
-                    img = misc.imread(tmp_filename)
-                    img_scale = misc.imresize(img, scale)
-                    sz1 = img.shape[1]/2
-                    sz2 = args.image_size/2
-                    img_crop = img_scale[int(sz1-sz2):int(sz1+sz2),int(sz1-sz2):int(sz1+sz2),:]
-                    filename = os.path.splitext(os.path.split(tmp_filename)[1])[0]
-                    output_filename = os.path.join(output_class_dir, filename+'.png')
-                    print('Saving image %s' % output_filename)
-                    misc.imsave(output_filename, img_crop)
-                    
-                # Remove tmp directory with images
-                shutil.rmtree(tmp_output_class_dir)
-                
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    
-    parser.add_argument('input_dir', type=str, help='Directory with unaligned images.')
-    parser.add_argument('output_dir', type=str, help='Directory with aligned face thumbnails.')
-    parser.add_argument('funnel_dir', type=str, help='Directory containing the funnelReal binary and the people.train model file')
-    parser.add_argument('--image_size', type=int,
-        help='Image size (height, width) in pixels.', default=110)
-    parser.add_argument('--face_size', type=int,
-        help='Size of the face thumbnail (height, width) in pixels.', default=96)
-    return parser.parse_args(argv)
-
-if __name__ == '__main__':
-    main(parse_arguments(sys.argv[1:]))
diff --git a/tmp/invariance_test.txt b/tmp/invariance_test.txt
deleted file mode 100644
index 3ab0616e8..000000000
--- a/tmp/invariance_test.txt
+++ /dev/null
@@ -1,86 +0,0 @@
-Accuracy: 0.860±0.009
-Accuracy: 0.861±0.008
-Accuracy: 0.870±0.011
-Accuracy: 0.885±0.012
-Accuracy: 0.896±0.013
-Accuracy: 0.899±0.015
-Accuracy: 0.887±0.011
-Accuracy: 0.885±0.011
-Accuracy: 0.890±0.011
-Accuracy: 0.910±0.014
-Accuracy: 0.918±0.012
-Accuracy: 0.904±0.013
-Accuracy: 0.895±0.012
-Accuracy: 0.884±0.018
-Accuracy: 0.891±0.012
-Accuracy: 0.891±0.008
-Accuracy: 0.889±0.009
-Accuracy: 0.871±0.012
-Accuracy: 0.844±0.012
-Accuracy: 0.835±0.016
-Accuracy: 0.823±0.015
-Hoffset:	Accuracy:
--30.0000	0.8600
--27.0000	0.8607
--24.0000	0.8697
--21.0000	0.8848
--18.0000	0.8963
--15.0000	0.8992
--12.0000	0.8865
--9.0000	0.8853
--6.0000	0.8900
--3.0000	0.9097
-0.0000	0.9182
-3.0000	0.9040
-6.0000	0.8953
-9.0000	0.8843
-12.0000	0.8905
-15.0000	0.8913
-18.0000	0.8888
-21.0000	0.8708
-24.0000	0.8440
-27.0000	0.8348
-30.0000	0.8233
-Accuracy: 0.823±0.014
-Accuracy: 0.800±0.010
-Accuracy: 0.800±0.015
-Accuracy: 0.818±0.018
-Accuracy: 0.852±0.012
-Accuracy: 0.864±0.011
-Accuracy: 0.844±0.016
-Accuracy: 0.851±0.014
-Accuracy: 0.875±0.012
-Accuracy: 0.898±0.010
-Accuracy: 0.918±0.012
-Accuracy: 0.886±0.015
-Accuracy: 0.849±0.012
-Accuracy: 0.812±0.015
-Accuracy: 0.780±0.012
-Accuracy: 0.787±0.012
-Accuracy: 0.755±0.016
-Accuracy: 0.709±0.010
-Accuracy: 0.676±0.017
-Accuracy: 0.653±0.011
-Accuracy: 0.648±0.016
-Voffset:	Accuracy:
--30.0000	0.8230
--27.0000	0.7997
--24.0000	0.7995
--21.0000	0.8183
--18.0000	0.8523
--15.0000	0.8638
--12.0000	0.8442
--9.0000	0.8507
--6.0000	0.8755
--3.0000	0.8982
-0.0000	0.9182
-3.0000	0.8862
-6.0000	0.8493
-9.0000	0.8118
-12.0000	0.7803
-15.0000	0.7868
-18.0000	0.7548
-21.0000	0.7093
-24.0000	0.6763
-27.0000	0.6533
-30.0000	0.6483
diff --git a/tmp/mnist_center_loss.py b/tmp/mnist_center_loss.py
deleted file mode 100644
index 1122f7af2..000000000
--- a/tmp/mnist_center_loss.py
+++ /dev/null
@@ -1,404 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Simple, end-to-end, LeNet-5-like convolutional MNIST model example.
-This should achieve a test error of 0.7%. Please keep this model as simple and
-linear as possible, it is meant as a tutorial for simple convolutional models.
-Run with --self_test on the command line to execute a short self-test.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import gzip
-import os
-import sys
-import time
-
-from six.moves import urllib  # @UnresolvedImport
-import tensorflow as tf
-import numpy as np
-import matplotlib.pyplot as plt
-from tensorflow.python.ops import control_flow_ops
-import facenet
-from six.moves import xrange
-
-SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
-WORK_DIRECTORY = 'data'
-IMAGE_SIZE = 28
-NUM_CHANNELS = 1
-PIXEL_DEPTH = 255
-NUM_LABELS = 10
-VALIDATION_SIZE = 5000  # Size of the validation set.
-SEED = 66478  # Set to None for random seed.
-BATCH_SIZE = 64
-NUM_EPOCHS = 10
-EVAL_BATCH_SIZE = 64
-EVAL_FREQUENCY = 100  # Number of steps between evaluations.
-
-
-tf.app.flags.DEFINE_boolean("self_test", False, "True if running a self test.")
-tf.app.flags.DEFINE_boolean('use_fp16', False,
-                            "Use half floats instead of full floats if True.")
-FLAGS = tf.app.flags.FLAGS
-
-
-def data_type():
-    """Return the type of the activations, weights, and placeholder variables."""
-    if FLAGS.use_fp16:
-        return tf.float16
-    else:
-        return tf.float32
-
-
-def maybe_download(filename):
-    """Download the data from Yann's website, unless it's already here."""
-    if not tf.gfile.Exists(WORK_DIRECTORY):
-        tf.gfile.MakeDirs(WORK_DIRECTORY)
-    filepath = os.path.join(WORK_DIRECTORY, filename)
-    if not tf.gfile.Exists(filepath):
-        filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
-        with tf.gfile.GFile(filepath) as f:
-            size = f.size()
-        print('Successfully downloaded', filename, size, 'bytes.')
-    return filepath
-
-
-def extract_data(filename, num_images):
-    """Extract the images into a 4D tensor [image index, y, x, channels].
-    Values are rescaled from [0, 255] down to [-0.5, 0.5].
-    """
-    print('Extracting', filename)
-    with gzip.open(filename) as bytestream:
-        bytestream.read(16)
-        buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS)
-        data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32)
-        data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH
-        data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)
-        return data
-
-
-def extract_labels(filename, num_images):
-    """Extract the labels into a vector of int64 label IDs."""
-    print('Extracting', filename)
-    with gzip.open(filename) as bytestream:
-        bytestream.read(8)
-        buf = bytestream.read(1 * num_images)
-        labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64)
-    return labels
-
-
-def fake_data(num_images):
-    """Generate a fake dataset that matches the dimensions of MNIST."""
-    data = np.ndarray(
-        shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),
-        dtype=np.float32)
-    labels = np.zeros(shape=(num_images,), dtype=np.int64)
-    for image in range(num_images):
-        label = image % 2
-        data[image, :, :, 0] = label - 0.5
-        labels[image] = label
-    return data, labels
-
-
-def error_rate(predictions, labels):
-    """Return the error rate based on dense predictions and sparse labels."""
-    return 100.0 - (
-        100.0 *
-        np.sum(np.argmax(predictions, 1) == labels) /
-        predictions.shape[0])
-
-
-def main(argv=None):  # pylint: disable=unused-argument
-    if FLAGS.self_test:
-        print('Running self-test.')
-        train_data, train_labels = fake_data(256)
-        validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE)
-        test_data, test_labels = fake_data(EVAL_BATCH_SIZE)
-        num_epochs = 1
-    else:
-        # Get the data.
-        train_data_filename = maybe_download('train-images-idx3-ubyte.gz')
-        train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')
-        test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')
-        test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')
-    
-        # Extract it into numpy arrays.
-        train_data = extract_data(train_data_filename, 60000)
-        train_labels = extract_labels(train_labels_filename, 60000)
-        test_data = extract_data(test_data_filename, 10000)
-        test_labels = extract_labels(test_labels_filename, 10000)
-    
-        # Generate a validation set.
-        validation_data = train_data[:VALIDATION_SIZE, ...]
-        validation_labels = train_labels[:VALIDATION_SIZE]
-        train_data = train_data[VALIDATION_SIZE:, ...]
-        train_labels = train_labels[VALIDATION_SIZE:]
-        num_epochs = NUM_EPOCHS
-    train_size = train_labels.shape[0]
-
-    # This is where training samples and labels are fed to the graph.
-    # These placeholder nodes will be fed a batch of training data at each
-    # training step using the {feed_dict} argument to the Run() call below.
-    train_data_node = tf.placeholder(
-        data_type(),
-        shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
-    train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,))
-    eval_data = tf.placeholder(
-        data_type(),
-        shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
-
-    # The variables below hold all the trainable weights. They are passed an
-    # initial value which will be assigned when we call:
-    # {tf.global_variables_initializer().run()}
-    conv1_weights = tf.Variable(
-        tf.truncated_normal([5, 5, NUM_CHANNELS, 32],  # 5x5 filter, depth 32.
-                            stddev=0.1,
-                            seed=SEED, dtype=data_type()))
-    conv1_biases = tf.Variable(tf.zeros([32], dtype=data_type()))
-    conv2_weights = tf.Variable(tf.truncated_normal(
-        [5, 5, 32, 64], stddev=0.1,
-        seed=SEED, dtype=data_type()))
-    conv2_biases = tf.Variable(tf.constant(0.1, shape=[64], dtype=data_type()))
-    fc1_weights = tf.Variable(  # fully connected, depth 512.
-        tf.truncated_normal([IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],
-                            stddev=0.1,
-                            seed=SEED,
-                            dtype=data_type()))
-    fc1_biases = tf.Variable(tf.constant(0.1, shape=[512], dtype=data_type()))
-    fc1p_weights = tf.Variable(  # fully connected, depth 512.
-        tf.truncated_normal([512, 2],
-                            stddev=0.1,
-                            seed=SEED,
-                            dtype=data_type()))
-    fc1p_biases = tf.Variable(tf.constant(0.1, shape=[2], dtype=data_type()))
-    fc2_weights = tf.Variable(tf.truncated_normal([2, NUM_LABELS],
-                                                  stddev=0.1,
-                                                  seed=SEED,
-                                                  dtype=data_type()))
-    fc2_biases = tf.Variable(tf.constant(
-        0.1, shape=[NUM_LABELS], dtype=data_type()))
-    
-    def batch_norm(x, phase_train):  #pylint: disable=unused-variable
-        """
-        Batch normalization on convolutional maps.
-        Args:
-            x:           Tensor, 4D BHWD input maps
-            n_out:       integer, depth of input maps
-            phase_train: boolean tf.Variable, true indicates training phase
-            scope:       string, variable scope
-            affn:      whether to affn-transform outputs
-        Return:
-            normed:      batch-normalized maps
-        Ref: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow/33950177
-        """
-        name = 'batch_norm'
-        with tf.variable_scope(name):
-            phase_train = tf.convert_to_tensor(phase_train, dtype=tf.bool)
-            n_out = int(x.get_shape()[-1])
-            beta = tf.Variable(tf.constant(0.0, shape=[n_out], dtype=x.dtype),
-                               name=name+'/beta', trainable=True, dtype=x.dtype)
-            gamma = tf.Variable(tf.constant(1.0, shape=[n_out], dtype=x.dtype),
-                                name=name+'/gamma', trainable=True, dtype=x.dtype)
-          
-            batch_mean, batch_var = tf.nn.moments(x, [0], name='moments')
-            ema = tf.train.ExponentialMovingAverage(decay=0.9)
-            def mean_var_with_update():
-                ema_apply_op = ema.apply([batch_mean, batch_var])
-                with tf.control_dependencies([ema_apply_op]):
-                    return tf.identity(batch_mean), tf.identity(batch_var)
-            mean, var = control_flow_ops.cond(phase_train,
-                                              mean_var_with_update,
-                                              lambda: (ema.average(batch_mean), ema.average(batch_var)))
-            normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
-        return normed
-    
-
-    # We will replicate the model structure for the training subgraph, as well
-    # as the evaluation subgraphs, while sharing the trainable parameters.
-    def model(data, train=False):
-        """The Model definition."""
-        # 2D convolution, with 'SAME' padding (i.e. the output feature map has
-        # the same size as the input). Note that {strides} is a 4D array whose
-        # shape matches the data layout: [image index, y, x, depth].
-        conv = tf.nn.conv2d(data,
-                            conv1_weights,
-                            strides=[1, 1, 1, 1],
-                            padding='SAME')
-        # Bias and rectified linear non-linearity.
-        relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))
-        # Max pooling. The kernel size spec {ksize} also follows the layout of
-        # the data. Here we have a pooling window of 2, and a stride of 2.
-        pool = tf.nn.max_pool(relu,
-                              ksize=[1, 2, 2, 1],
-                              strides=[1, 2, 2, 1],
-                              padding='SAME')
-        conv = tf.nn.conv2d(pool,
-                            conv2_weights,
-                            strides=[1, 1, 1, 1],
-                            padding='SAME')
-        relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))
-        pool = tf.nn.max_pool(relu,
-                              ksize=[1, 2, 2, 1],
-                              strides=[1, 2, 2, 1],
-                              padding='SAME')
-        # Reshape the feature map cuboid into a 2D matrix to feed it to the
-        # fully connected layers.
-        pool_shape = pool.get_shape().as_list() #pylint: disable=no-member
-        reshape = tf.reshape(
-            pool,
-            [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])
-        # Fully connected layer. Note that the '+' operation automatically
-        # broadcasts the biases.
-        hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)
-        # Add a 50% dropout during training only. Dropout also scales
-        # activations such that no rescaling is needed at evaluation time.
-        if train:
-            hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)
-
-        hidden = tf.matmul(hidden, fc1p_weights) + fc1p_biases
-
-        return tf.nn.relu(tf.matmul(hidden, fc2_weights) + fc2_biases), hidden
-
-    # Training computation: logits + cross-entropy loss.
-    logits, hidden = model(train_data_node, True)
-    #logits = batch_norm(logits, True)
-    xent_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
-        logits, train_labels_node))
-    beta = 1e-3
-    #center_loss, update_centers = center_loss_op(hidden, train_labels_node)
-    center_loss, _ = facenet.center_loss(hidden, train_labels_node, 0.95, NUM_LABELS)
-    loss = xent_loss + beta * center_loss
-  
-    # L2 regularization for the fully connected parameters.
-    regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +
-                    tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))
-    # Add the regularization term to the loss.
-    loss += 5e-4 * regularizers
-  
-    # Optimizer: set up a variable that's incremented once per batch and
-    # controls the learning rate decay.
-    batch = tf.Variable(0, dtype=data_type())
-    # Decay once per epoch, using an exponential schedule starting at 0.01.
-    learning_rate = tf.train.exponential_decay(
-        0.01,                # Base learning rate.
-        batch * BATCH_SIZE,  # Current index into the dataset.
-        train_size,          # Decay step.
-        0.95,                # Decay rate.
-        staircase=True)
-    # Use simple momentum for the optimization.
-    optimizer = tf.train.MomentumOptimizer(learning_rate,
-                                           0.9).minimize(loss,
-                                                         global_step=batch)
-  
-    # Predictions for the current training minibatch.
-    train_prediction = tf.nn.softmax(logits)
-  
-    # Predictions for the test and validation, which we'll compute less often.
-    eval_logits, eval_embeddings = model(eval_data)
-    eval_prediction = tf.nn.softmax(eval_logits)
-    
-    # Small utility function to evaluate a dataset by feeding batches of data to
-    # {eval_data} and pulling the results from {eval_predictions}.
-    # Saves memory and enables this to run on smaller GPUs.
-    def eval_in_batches(data, sess):
-        """Get all predictions for a dataset by running it in small batches."""
-        size = data.shape[0]
-        if size < EVAL_BATCH_SIZE:
-            raise ValueError("batch size for evals larger than dataset: %d" % size)
-        predictions = np.ndarray(shape=(size, NUM_LABELS), dtype=np.float32)
-        for begin in xrange(0, size, EVAL_BATCH_SIZE):
-            end = begin + EVAL_BATCH_SIZE
-            if end <= size:
-                predictions[begin:end, :] = sess.run(
-                    eval_prediction,
-                    feed_dict={eval_data: data[begin:end, ...]})
-            else:
-                batch_predictions = sess.run(
-                    eval_prediction,
-                    feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]})
-                predictions[begin:, :] = batch_predictions[begin - size:, :]
-        return predictions
-  
-    def calculate_embeddings(data, sess):
-        """Get all predictions for a dataset by running it in small batches."""
-        size = data.shape[0]
-        if size < EVAL_BATCH_SIZE:
-            raise ValueError("batch size for evals larger than dataset: %d" % size)
-        predictions = np.ndarray(shape=(size, 2), dtype=np.float32)
-        for begin in xrange(0, size, EVAL_BATCH_SIZE):
-            end = begin + EVAL_BATCH_SIZE
-            if end <= size:
-                predictions[begin:end, :] = sess.run(
-                    eval_embeddings,
-                    feed_dict={eval_data: data[begin:end, ...]})
-            else:
-                batch_predictions = sess.run(
-                    eval_embeddings,
-                    feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]})
-                predictions[begin:, :] = batch_predictions[begin - size:, :]
-        return predictions
-
-    # Create a local session to run the training.
-    start_time = time.time()
-    with tf.Session() as sess:
-        # Run all the initializers to prepare the trainable parameters.
-        tf.global_variables_initializer().run() #pylint: disable=no-member
-        print('Initialized!')
-        # Loop through training steps.
-        for step in xrange(int(num_epochs * train_size) // BATCH_SIZE):
-            # Compute the offset of the current minibatch in the data.
-            # Note that we could use better randomization across epochs.
-            offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)
-            batch_data = train_data[offset:(offset + BATCH_SIZE), ...]
-            batch_labels = train_labels[offset:(offset + BATCH_SIZE)]
-            # This dictionary maps the batch data (as a numpy array) to the
-            # node in the graph it should be fed to.
-            feed_dict = {train_data_node: batch_data,
-                         train_labels_node: batch_labels}
-            # Run the graph and fetch some of the nodes.
-            #_, l, lr, predictions = sess.run([optimizer, loss, learning_rate, train_prediction], feed_dict=feed_dict)
-            _, cl, l, lr, predictions = sess.run([optimizer, center_loss, loss, learning_rate, train_prediction], feed_dict=feed_dict)
-            if step % EVAL_FREQUENCY == 0:
-                elapsed_time = time.time() - start_time
-                start_time = time.time()
-                print('Step %d (epoch %.2f), %.1f ms' %
-                      (step, float(step) * BATCH_SIZE / train_size,
-                       1000 * elapsed_time / EVAL_FREQUENCY))
-                print('Minibatch loss: %.3f  %.3f, learning rate: %.6f' % (l, cl*beta, lr))
-                print('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels))
-                print('Validation error: %.1f%%' % error_rate(
-                    eval_in_batches(validation_data, sess), validation_labels))
-                sys.stdout.flush()
-        # Finally print the result!
-        test_error = error_rate(eval_in_batches(test_data, sess), test_labels)
-        print('Test error: %.1f%%' % test_error)
-        if FLAGS.self_test:
-            print('test_error', test_error)
-            assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (
-                test_error,)
-            
-        train_embeddings = calculate_embeddings(train_data, sess)
-        
-        color_list = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'b', 'g', 'r', 'c' ]
-        plt.figure(1)
-        for n in range(0,10):
-            idx = np.where(train_labels[0:10000]==n)
-            plt.plot(train_embeddings[idx,0], train_embeddings[idx,1], color_list[n]+'.')
-        plt.show()
-
-
-if __name__ == '__main__':
-    tf.app.run()
diff --git a/tmp/mnist_noise_labels.py b/tmp/mnist_noise_labels.py
deleted file mode 100644
index d24e9a342..000000000
--- a/tmp/mnist_noise_labels.py
+++ /dev/null
@@ -1,347 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Simple, end-to-end, LeNet-5-like convolutional MNIST model example.
-This should achieve a test error of 0.7%. Please keep this model as simple and
-linear as possible, it is meant as a tutorial for simple convolutional models.
-Run with --self_test on the command line to execute a short self-test.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import gzip
-import os
-import sys
-import time
-
-from six.moves import urllib  # @UnresolvedImport
-import tensorflow as tf
-import numpy as np
-from six.moves import xrange
-
-SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
-WORK_DIRECTORY = 'data'
-IMAGE_SIZE = 28
-NUM_CHANNELS = 1
-PIXEL_DEPTH = 255
-NUM_LABELS = 10
-VALIDATION_SIZE = 5000  # Size of the validation set.
-SEED = 66478  # Set to None for random seed.
-BATCH_SIZE = 64
-NUM_EPOCHS = 10
-EVAL_BATCH_SIZE = 64
-EVAL_FREQUENCY = 100  # Number of steps between evaluations.
-NOISE_FACTOR = 0.2
-BETA = 0.8
-
-
-tf.app.flags.DEFINE_boolean("self_test", False, "True if running a self test.")
-tf.app.flags.DEFINE_boolean('use_fp16', False,
-                            "Use half floats instead of full floats if True.")
-FLAGS = tf.app.flags.FLAGS
-
-
-def data_type():
-    """Return the type of the activations, weights, and placeholder variables."""
-    if FLAGS.use_fp16:
-        return tf.float16
-    else:
-        return tf.float32
-
-
-def maybe_download(filename):
-    """Download the data from Yann's website, unless it's already here."""
-    if not tf.gfile.Exists(WORK_DIRECTORY):
-        tf.gfile.MakeDirs(WORK_DIRECTORY)
-    filepath = os.path.join(WORK_DIRECTORY, filename)
-    if not tf.gfile.Exists(filepath):
-        filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
-        with tf.gfile.GFile(filepath) as f:
-            size = f.size()
-        print('Successfully downloaded', filename, size, 'bytes.')
-    return filepath
-
-
-def extract_data(filename, num_images):
-    """Extract the images into a 4D tensor [image index, y, x, channels].
-    Values are rescaled from [0, 255] down to [-0.5, 0.5].
-    """
-    print('Extracting', filename)
-    with gzip.open(filename) as bytestream:
-        bytestream.read(16)
-        buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS)
-        data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32)
-        data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH
-        data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)
-        return data
-
-
-def extract_labels(filename, num_images):
-    """Extract the labels into a vector of int64 label IDs."""
-    print('Extracting', filename)
-    with gzip.open(filename) as bytestream:
-        bytestream.read(8)
-        buf = bytestream.read(1 * num_images)
-        labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64)
-    return labels
-
-
-def fake_data(num_images):
-    """Generate a fake dataset that matches the dimensions of MNIST."""
-    data = np.ndarray(
-        shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),
-        dtype=np.float32)
-    labels = np.zeros(shape=(num_images,), dtype=np.int64)
-    for image in range(num_images):
-        label = image % 2
-        data[image, :, :, 0] = label - 0.5
-        labels[image] = label
-    return data, labels
-
-
-def error_rate(predictions, labels):
-    """Return the error rate based on dense predictions and sparse labels."""
-    return 100.0 - (
-        100.0 *
-        np.sum(np.argmax(predictions, 1) == labels) /
-        predictions.shape[0])
-
-
-def main(argv=None):  # pylint: disable=unused-argument
-    if FLAGS.self_test:
-        print('Running self-test.')
-        train_data, train_labels = fake_data(256)
-        validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE)
-        test_data, test_labels = fake_data(EVAL_BATCH_SIZE)
-        num_epochs = 1
-    else:
-        # Get the data.
-        train_data_filename = maybe_download('train-images-idx3-ubyte.gz')
-        train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')
-        test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')
-        test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')
-    
-        # Extract it into numpy arrays.
-        train_data = extract_data(train_data_filename, 60000)
-        train_labels = extract_labels(train_labels_filename, 60000)
-        test_data = extract_data(test_data_filename, 10000)
-        test_labels = extract_labels(test_labels_filename, 10000)
-    
-        # Generate a validation set.
-        validation_data = train_data[:VALIDATION_SIZE, ...]
-        validation_labels = train_labels[:VALIDATION_SIZE]
-        train_data = train_data[VALIDATION_SIZE:, ...]
-        train_labels = train_labels[VALIDATION_SIZE:]
-        nrof_training_examples = train_labels.shape[0]
-        nrof_changed_labels = int(nrof_training_examples*NOISE_FACTOR)
-        shuf = np.arange(0,nrof_training_examples)
-        np.random.shuffle(shuf)
-        change_idx = shuf[0:nrof_changed_labels]
-        train_labels[change_idx] = (train_labels[change_idx] + np.random.randint(1,9,size=(nrof_changed_labels,))) % NUM_LABELS
-        num_epochs = NUM_EPOCHS
-    train_size = train_labels.shape[0]
-
-    # This is where training samples and labels are fed to the graph.
-    # These placeholder nodes will be fed a batch of training data at each
-    # training step using the {feed_dict} argument to the Run() call below.
-    train_data_node = tf.placeholder(
-        data_type(),
-        shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
-    train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,))
-    eval_data = tf.placeholder(
-        data_type(),
-        shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
-
-    # The variables below hold all the trainable weights. They are passed an
-    # initial value which will be assigned when we call:
-    # {tf.global_variables_initializer().run()}
-    conv1_weights = tf.Variable(
-        tf.truncated_normal([5, 5, NUM_CHANNELS, 32],  # 5x5 filter, depth 32.
-                            stddev=0.1,
-                            seed=SEED, dtype=data_type()))
-    conv1_biases = tf.Variable(tf.zeros([32], dtype=data_type()))
-    conv2_weights = tf.Variable(tf.truncated_normal(
-        [5, 5, 32, 64], stddev=0.1,
-        seed=SEED, dtype=data_type()))
-    conv2_biases = tf.Variable(tf.constant(0.1, shape=[64], dtype=data_type()))
-    fc1_weights = tf.Variable(  # fully connected, depth 512.
-        tf.truncated_normal([IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],
-                            stddev=0.1,
-                            seed=SEED,
-                            dtype=data_type()))
-    fc1_biases = tf.Variable(tf.constant(0.1, shape=[512], dtype=data_type()))
-    fc2_weights = tf.Variable(tf.truncated_normal([512, NUM_LABELS],
-                                                  stddev=0.1,
-                                                  seed=SEED,
-                                                  dtype=data_type()))
-    fc2_biases = tf.Variable(tf.constant(
-        0.1, shape=[NUM_LABELS], dtype=data_type()))
-
-    # We will replicate the model structure for the training subgraph, as well
-    # as the evaluation subgraphs, while sharing the trainable parameters.
-    def model(data, train=False):
-        """The Model definition."""
-        # 2D convolution, with 'SAME' padding (i.e. the output feature map has
-        # the same size as the input). Note that {strides} is a 4D array whose
-        # shape matches the data layout: [image index, y, x, depth].
-        conv = tf.nn.conv2d(data,
-                            conv1_weights,
-                            strides=[1, 1, 1, 1],
-                            padding='SAME')
-        # Bias and rectified linear non-linearity.
-        relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))
-        # Max pooling. The kernel size spec {ksize} also follows the layout of
-        # the data. Here we have a pooling window of 2, and a stride of 2.
-        pool = tf.nn.max_pool(relu,
-                              ksize=[1, 2, 2, 1],
-                              strides=[1, 2, 2, 1],
-                              padding='SAME')
-        conv = tf.nn.conv2d(pool,
-                            conv2_weights,
-                            strides=[1, 1, 1, 1],
-                            padding='SAME')
-        relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))
-        pool = tf.nn.max_pool(relu,
-                              ksize=[1, 2, 2, 1],
-                              strides=[1, 2, 2, 1],
-                              padding='SAME')
-        # Reshape the feature map cuboid into a 2D matrix to feed it to the
-        # fully connected layers.
-        pool_shape = pool.get_shape().as_list() #pylint: disable=no-member
-        reshape = tf.reshape(
-            pool,
-            [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])
-        # Fully connected layer. Note that the '+' operation automatically
-        # broadcasts the biases.
-        hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)
-
-        # Add a 50% dropout during training only. Dropout also scales
-        # activations such that no rescaling is needed at evaluation time.
-        if train:
-            hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)
-        return tf.matmul(hidden, fc2_weights) + fc2_biases
-
-    # Training computation: logits + cross-entropy loss.
-    logits = model(train_data_node, True)
-    
-    # t: observed noisy labels
-    # q: estimated class probabilities (output from softmax)
-    # z: argmax of q
-
-    t = tf.one_hot(train_labels_node, NUM_LABELS)
-    q = tf.nn.softmax(logits)
-    qqq = tf.arg_max(q, dimension=1)
-    z = tf.one_hot(qqq, NUM_LABELS)
-    #cross_entropy = -tf.reduce_sum(t*tf.log(q),reduction_indices=1)
-    cross_entropy = -tf.reduce_sum((BETA*t+(1-BETA)*z)*tf.log(q),reduction_indices=1)
-    
-    loss = tf.reduce_mean(cross_entropy)
-    
-#     loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
-#         logits, train_labels_node))
-  
-    # L2 regularization for the fully connected parameters.
-    regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +
-                    tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))
-    # Add the regularization term to the loss.
-    loss += 5e-4 * regularizers
-  
-    # Optimizer: set up a variable that's incremented once per batch and
-    # controls the learning rate decay.
-    batch = tf.Variable(0, dtype=data_type())
-    # Decay once per epoch, using an exponential schedule starting at 0.01.
-    learning_rate = tf.train.exponential_decay(
-        0.01,                # Base learning rate.
-        batch * BATCH_SIZE,  # Current index into the dataset.
-        train_size,          # Decay step.
-        0.95,                # Decay rate.
-        staircase=True)
-    # Use simple momentum for the optimization.
-    optimizer = tf.train.MomentumOptimizer(learning_rate,
-                                           0.9).minimize(loss,
-                                                         global_step=batch)
-  
-    # Predictions for the current training minibatch.
-    train_prediction = tf.nn.softmax(logits)
-  
-    # Predictions for the test and validation, which we'll compute less often.
-    eval_prediction = tf.nn.softmax(model(eval_data))
-    
-    # Small utility function to evaluate a dataset by feeding batches of data to
-    # {eval_data} and pulling the results from {eval_predictions}.
-    # Saves memory and enables this to run on smaller GPUs.
-    def eval_in_batches(data, sess):
-        """Get all predictions for a dataset by running it in small batches."""
-        size = data.shape[0]
-        if size < EVAL_BATCH_SIZE:
-            raise ValueError("batch size for evals larger than dataset: %d" % size)
-        predictions = np.ndarray(shape=(size, NUM_LABELS), dtype=np.float32)
-        for begin in xrange(0, size, EVAL_BATCH_SIZE):
-            end = begin + EVAL_BATCH_SIZE
-            if end <= size:
-                predictions[begin:end, :] = sess.run(
-                    eval_prediction,
-                    feed_dict={eval_data: data[begin:end, ...]})
-            else:
-                batch_predictions = sess.run(
-                    eval_prediction,
-                    feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]})
-                predictions[begin:, :] = batch_predictions[begin - size:, :]
-        return predictions
-  
-    # Create a local session to run the training.
-    start_time = time.time()
-    with tf.Session() as sess:
-        # Run all the initializers to prepare the trainable parameters.
-        tf.global_variables_initializer().run() #pylint: disable=no-member
-        print('Initialized!')
-        # Loop through training steps.
-        for step in xrange(int(num_epochs * train_size) // BATCH_SIZE):
-            # Compute the offset of the current minibatch in the data.
-            # Note that we could use better randomization across epochs.
-            offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)
-            batch_data = train_data[offset:(offset + BATCH_SIZE), ...]
-            batch_labels = train_labels[offset:(offset + BATCH_SIZE)]
-            # This dictionary maps the batch data (as a numpy array) to the
-            # node in the graph it should be fed to.
-            feed_dict = {train_data_node: batch_data,
-                         train_labels_node: batch_labels}
-            # Run the graph and fetch some of the nodes.
-            _, l, lr, predictions = sess.run(
-                [optimizer, loss, learning_rate, train_prediction],
-                feed_dict=feed_dict)
-            if step % EVAL_FREQUENCY == 0:
-                elapsed_time = time.time() - start_time
-                start_time = time.time()
-                print('Step %d (epoch %.2f), %.1f ms' %
-                      (step, float(step) * BATCH_SIZE / train_size,
-                       1000 * elapsed_time / EVAL_FREQUENCY))
-                print('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr))
-                print('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels))
-                print('Validation error: %.1f%%' % error_rate(
-                    eval_in_batches(validation_data, sess), validation_labels))
-                sys.stdout.flush()
-        # Finally print the result!
-        test_error = error_rate(eval_in_batches(test_data, sess), test_labels)
-        print('Test error: %.1f%%' % test_error)
-        if FLAGS.self_test:
-            print('test_error', test_error)
-            assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (
-                test_error,)
-
-
-if __name__ == '__main__':
-    tf.app.run()
diff --git a/tmp/mtcnn.py b/tmp/mtcnn.py
deleted file mode 100644
index 867fe0d9d..000000000
--- a/tmp/mtcnn.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-import align.detect_face
-from scipy import misc
-
-with tf.Graph().as_default():
-  
-    sess = tf.Session()
-    with sess.as_default():
-        with tf.variable_scope('pnet'):
-            data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
-            pnet = align.detect_face.PNet({'data':data})
-            pnet.load('../../data/det1.npy', sess)
-        with tf.variable_scope('rnet'):
-            data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
-            rnet = align.detect_face.RNet({'data':data})
-            rnet.load('../../data/det2.npy', sess)
-        with tf.variable_scope('onet'):
-            data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
-            onet = align.detect_face.ONet({'data':data})
-            onet.load('../../data/det3.npy', sess)
-            
-        pnet_fun = lambda img : sess.run(('pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0'), feed_dict={'pnet/input:0':img})
-        rnet_fun = lambda img : sess.run(('rnet/conv5-2/conv5-2:0', 'rnet/prob1:0'), feed_dict={'rnet/input:0':img})
-        onet_fun = lambda img : sess.run(('onet/conv6-2/conv6-2:0', 'onet/conv6-3/conv6-3:0', 'onet/prob1:0'), feed_dict={'onet/input:0':img})
-
-minsize = 20 # minimum size of face
-threshold = [ 0.6, 0.7, 0.7 ]  # three steps's threshold
-factor = 0.709 # scale factor
-
-source_path = '/home/david/datasets/casia/CASIA-maxpy-clean/0000045/002.jpg'
-img = misc.imread(source_path)
-
-bounding_boxes, points = align.detect_face.detect_face(img, minsize, pnet_fun, rnet_fun, onet_fun, threshold, factor)
-
-print('Bounding box: %s' % bounding_boxes)
-
-
diff --git a/tmp/mtcnn_test.py b/tmp/mtcnn_test.py
deleted file mode 100644
index e02b11a5b..000000000
--- a/tmp/mtcnn_test.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-import numpy as np
-import align.detect_face
-
-g1 = tf.Graph()
-with g1.as_default():
-    data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
-    pnet = align.detect_face.PNet({'data':data})
-    sess1 = tf.Session(graph=g1)
-    pnet.load('../../data/det1.npy', sess1)
-    pnet_fun = lambda img : sess1.run(('conv4-2/BiasAdd:0', 'prob1:0'), feed_dict={'input:0':img})
-np.random.seed(666)
-img = np.random.rand(1,3,150,150)
-img = np.transpose(img, (0,2,3,1))
-      
-np.set_printoptions(formatter={'float': '{: 0.4f}'.format})
-       
-# prob1=sess1.run('prob1:0', feed_dict={data:img})
-# print(prob1[0,0,0,:])
-# conv42=sess1.run('conv4-2/BiasAdd:0', feed_dict={data:img})
-# print(conv42[0,0,0,:])
-      
-# conv42, prob1 = pnet_fun(img)
-# print(prob1[0,0,0,:])
-# print(conv42[0,0,0,:])
-
-
-# [ 0.9929  0.0071] prob1, caffe
-# [ 0.9929  0.0071] prob1, tensorflow
-  
-# [ 0.1207 -0.0116 -0.1231 -0.0463] conv4-2, caffe
-# [ 0.1207 -0.0116 -0.1231 -0.0463] conv4-2, tensorflow
-      
-
-g2 = tf.Graph()
-with g2.as_default():
-    data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
-    rnet = align.detect_face.RNet({'data':data})
-    sess2 = tf.Session(graph=g2)
-    rnet.load('../../data/det2.npy', sess2)
-    rnet_fun = lambda img : sess2.run(('conv5-2/conv5-2:0', 'prob1:0'), feed_dict={'input:0':img})
-np.random.seed(666)
-img = np.random.rand(73,3,24,24)
-img = np.transpose(img, (0,2,3,1))
- 
-# np.set_printoptions(formatter={'float': '{: 0.4f}'.format})
-#  
-# prob1=sess2.run('prob1:0', feed_dict={data:img})
-# print(prob1[0,:])
-# 
-# conv52=sess2.run('conv5-2/conv5-2:0', feed_dict={data:img})
-# print(conv52[0,:])
-  
-# [ 0.9945  0.0055] prob1, caffe
-# [ 0.1108 -0.0038 -0.1631 -0.0890] conv5-2, caffe
- 
-# [ 0.9945  0.0055] prob1, tensorflow
-# [ 0.1108 -0.0038 -0.1631 -0.0890] conv5-2, tensorflow
-
-    
-g3 = tf.Graph()
-with g3.as_default():
-    data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
-    onet = align.detect_face.ONet({'data':data})
-    sess3 = tf.Session(graph=g3)
-    onet.load('../../data/det3.npy', sess3)
-    onet_fun = lambda img : sess3.run(('conv6-2/conv6-2:0', 'conv6-3/conv6-3:0', 'prob1:0'), feed_dict={'input:0':img})
-np.random.seed(666)
-img = np.random.rand(11,3,48,48)
-img = np.transpose(img, (0,2,3,1))
- 
-# np.set_printoptions(formatter={'float': '{: 0.4f}'.format})
-#  
-# prob1=sess3.run('prob1:0', feed_dict={data:img})
-# print(prob1[0,:])
-# print('prob1, tensorflow')
-# 
-# conv62=sess3.run('conv6-2/conv6-2:0', feed_dict={data:img})
-# print(conv62[0,:])
-# print('conv6-2, tensorflow')
-#  
-# conv63=sess3.run('conv6-3/conv6-3:0', feed_dict={data:img})
-# print(conv63[0,:])
-# print('conv6-3, tensorflow')
-
-# [ 0.9988  0.0012] prob1, caffe
-# [ 0.0446 -0.0968 -0.1091 -0.0212] conv6-2, caffe
-# [ 0.2429  0.6104  0.4074  0.3104  0.5939  0.2729  0.2132  0.5462  0.7863  0.7568] conv6-3, caffe
-  
-# [ 0.9988  0.0012] prob1, tensorflow
-# [ 0.0446 -0.0968 -0.1091 -0.0212] conv6-2, tensorflow
-# [ 0.2429  0.6104  0.4074  0.3104  0.5939  0.2729  0.2132  0.5462  0.7863  0.7568] conv6-3, tensorflow
-
-#pnet_fun = lambda img : sess1.run(('conv4-2/BiasAdd:0', 'prob1:0'), feed_dict={'input:0':img})
-
diff --git a/tmp/mtcnn_test_pnet_dbg.py b/tmp/mtcnn_test_pnet_dbg.py
deleted file mode 100644
index d4fdfbb6e..000000000
--- a/tmp/mtcnn_test_pnet_dbg.py
+++ /dev/null
@@ -1,124 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-import numpy as np
-import scipy.io as io
-import align.detect_face
-
-#ref = io.loadmat('pnet_dbg.mat')
-with tf.Graph().as_default():
-    sess = tf.Session()
-    with sess.as_default():
-        with tf.variable_scope('pnet'):
-#            data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
-            data = tf.placeholder(tf.float32, (1,1610, 1901,3), 'input')
-            pnet = align.detect_face.PNet({'data':data})
-            pnet.load('../../data/det1.npy', sess)
-#         with tf.variable_scope('rnet'):
-#             data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
-#             rnet = align.detect_face.RNet({'data':data})
-#             rnet.load('../../data/det2.npy', sess)
-#         with tf.variable_scope('onet'):
-#             data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
-#             onet = align.detect_face.ONet({'data':data})
-#             onet.load('../../data/det3.npy', sess)
-            
-        pnet_fun = lambda img : sess.run(('pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0'), feed_dict={'pnet/input:0':img})
-#         rnet_fun = lambda img : sess.run(('rnet/conv5-2/conv5-2:0', 'rnet/prob1:0'), feed_dict={'rnet/input:0':img})
-#         onet_fun = lambda img : sess.run(('onet/conv6-2/conv6-2:0', 'onet/conv6-3/conv6-3:0', 'onet/prob1:0'), feed_dict={'onet/input:0':img})
-        
-        
-ref = io.loadmat('pnet_dbg.mat')
-
-img_x = np.expand_dims(ref['im_data'], 0)
-img_y = np.transpose(img_x, (0,2,1,3))
-out = pnet_fun(img_y)
-out0 = np.transpose(out[0], (0,2,1,3))
-out1 = np.transpose(out[1], (0,2,1,3))
-
-#np.where(abs(out0[0,:,:,:]-ref['out0'])>1e-18)
-qqq3 = np.where(abs(out1[0,:,:,:]-ref['out1'])>1e-7) # 3390 diffs with softmax2
-print(qqq3[0].shape)
-      
-np.set_printoptions(formatter={'float': '{: 0.4f}'.format})
-       
-# prob1=sess1.run('prob1:0', feed_dict={data:img})
-# print(prob1[0,0,0,:])
-# conv42=sess1.run('conv4-2/BiasAdd:0', feed_dict={data:img})
-# print(conv42[0,0,0,:])
-      
-# conv42, prob1 = pnet_fun(img)
-# print(prob1[0,0,0,:])
-# print(conv42[0,0,0,:])
-
-
-# [ 0.9929  0.0071] prob1, caffe
-# [ 0.9929  0.0071] prob1, tensorflow
-  
-# [ 0.1207 -0.0116 -0.1231 -0.0463] conv4-2, caffe
-# [ 0.1207 -0.0116 -0.1231 -0.0463] conv4-2, tensorflow
-      
-
-# g2 = tf.Graph()
-# with g2.as_default():
-#     data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
-#     rnet = align.detect_face.RNet({'data':data})
-#     sess2 = tf.Session(graph=g2)
-#     rnet.load('../../data/det2.npy', sess2)
-#     rnet_fun = lambda img : sess2.run(('conv5-2/conv5-2:0', 'prob1:0'), feed_dict={'input:0':img})
-# np.random.seed(666)
-# img = np.random.rand(73,3,24,24)
-# img = np.transpose(img, (0,2,3,1))
- 
-# np.set_printoptions(formatter={'float': '{: 0.4f}'.format})
-#  
-# prob1=sess2.run('prob1:0', feed_dict={data:img})
-# print(prob1[0,:])
-# 
-# conv52=sess2.run('conv5-2/conv5-2:0', feed_dict={data:img})
-# print(conv52[0,:])
-  
-# [ 0.9945  0.0055] prob1, caffe
-# [ 0.1108 -0.0038 -0.1631 -0.0890] conv5-2, caffe
- 
-# [ 0.9945  0.0055] prob1, tensorflow
-# [ 0.1108 -0.0038 -0.1631 -0.0890] conv5-2, tensorflow
-
-    
-# g3 = tf.Graph()
-# with g3.as_default():
-#     data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
-#     onet = align.detect_face.ONet({'data':data})
-#     sess3 = tf.Session(graph=g3)
-#     onet.load('../../data/det3.npy', sess3)
-#     onet_fun = lambda img : sess3.run(('conv6-2/conv6-2:0', 'conv6-3/conv6-3:0', 'prob1:0'), feed_dict={'input:0':img})
-# np.random.seed(666)
-# img = np.random.rand(11,3,48,48)
-# img = np.transpose(img, (0,2,3,1))
- 
-# np.set_printoptions(formatter={'float': '{: 0.4f}'.format})
-#  
-# prob1=sess3.run('prob1:0', feed_dict={data:img})
-# print(prob1[0,:])
-# print('prob1, tensorflow')
-# 
-# conv62=sess3.run('conv6-2/conv6-2:0', feed_dict={data:img})
-# print(conv62[0,:])
-# print('conv6-2, tensorflow')
-#  
-# conv63=sess3.run('conv6-3/conv6-3:0', feed_dict={data:img})
-# print(conv63[0,:])
-# print('conv6-3, tensorflow')
-
-# [ 0.9988  0.0012] prob1, caffe
-# [ 0.0446 -0.0968 -0.1091 -0.0212] conv6-2, caffe
-# [ 0.2429  0.6104  0.4074  0.3104  0.5939  0.2729  0.2132  0.5462  0.7863  0.7568] conv6-3, caffe
-  
-# [ 0.9988  0.0012] prob1, tensorflow
-# [ 0.0446 -0.0968 -0.1091 -0.0212] conv6-2, tensorflow
-# [ 0.2429  0.6104  0.4074  0.3104  0.5939  0.2729  0.2132  0.5462  0.7863  0.7568] conv6-3, tensorflow
-
-#pnet_fun = lambda img : sess1.run(('conv4-2/BiasAdd:0', 'prob1:0'), feed_dict={'input:0':img})
-
diff --git a/tmp/network.py b/tmp/network.py
deleted file mode 100644
index c375e43d6..000000000
--- a/tmp/network.py
+++ /dev/null
@@ -1,200 +0,0 @@
-"""Functions for building the face recognition network.
-"""
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-# pylint: disable=missing-docstring
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import control_flow_ops
-
-
-def conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType, name, phase_train=True, use_batch_norm=True, weight_decay=0.0):
-    with tf.variable_scope(name):
-        l2_regularizer = lambda t: l2_loss(t, weight=weight_decay)
-        kernel = tf.get_variable("weights", [kH, kW, nIn, nOut],
-            initializer=tf.truncated_normal_initializer(stddev=1e-1),
-            regularizer=l2_regularizer, dtype=inpOp.dtype)
-        cnv = tf.nn.conv2d(inpOp, kernel, [1, dH, dW, 1], padding=padType)
-        
-        if use_batch_norm:
-            conv_bn = batch_norm(cnv, phase_train)
-        else:
-            conv_bn = cnv
-        biases = tf.get_variable("biases", [nOut], initializer=tf.constant_initializer(), dtype=inpOp.dtype)
-        bias = tf.nn.bias_add(conv_bn, biases)
-        conv1 = tf.nn.relu(bias)
-    return conv1
-
-def affine(inpOp, nIn, nOut, name, weight_decay=0.0):
-    with tf.variable_scope(name):
-        l2_regularizer = lambda t: l2_loss(t, weight=weight_decay)
-        weights = tf.get_variable("weights", [nIn, nOut],
-            initializer=tf.truncated_normal_initializer(stddev=1e-1),
-            regularizer=l2_regularizer, dtype=inpOp.dtype)
-        biases = tf.get_variable("biases", [nOut], initializer=tf.constant_initializer(), dtype=inpOp.dtype)
-        affine1 = tf.nn.relu_layer(inpOp, weights, biases)
-    return affine1
-
-def l2_loss(tensor, weight=1.0, scope=None):
-    """Define a L2Loss, useful for regularize, i.e. weight decay.
-    Args:
-      tensor: tensor to regularize.
-      weight: an optional weight to modulate the loss.
-      scope: Optional scope for op_scope.
-    Returns:
-      the L2 loss op.
-    """
-    with tf.name_scope(scope):
-        weight = tf.convert_to_tensor(weight,
-                                      dtype=tensor.dtype.base_dtype,
-                                      name='loss_weight')
-        loss = tf.multiply(weight, tf.nn.l2_loss(tensor), name='value')
-    return loss
-
-def lppool(inpOp, pnorm, kH, kW, dH, dW, padding, name):
-    with tf.variable_scope(name):
-        if pnorm == 2:
-            pwr = tf.square(inpOp)
-        else:
-            pwr = tf.pow(inpOp, pnorm)
-          
-        subsamp = tf.nn.avg_pool(pwr,
-                              ksize=[1, kH, kW, 1],
-                              strides=[1, dH, dW, 1],
-                              padding=padding)
-        subsamp_sum = tf.multiply(subsamp, kH*kW)
-        
-        if pnorm == 2:
-            out = tf.sqrt(subsamp_sum)
-        else:
-            out = tf.pow(subsamp_sum, 1/pnorm)
-    
-    return out
-
-def mpool(inpOp, kH, kW, dH, dW, padding, name):
-    with tf.variable_scope(name):
-        maxpool = tf.nn.max_pool(inpOp,
-                       ksize=[1, kH, kW, 1],
-                       strides=[1, dH, dW, 1],
-                       padding=padding)  
-    return maxpool
-
-def apool(inpOp, kH, kW, dH, dW, padding, name):
-    with tf.variable_scope(name):
-        avgpool = tf.nn.avg_pool(inpOp,
-                              ksize=[1, kH, kW, 1],
-                              strides=[1, dH, dW, 1],
-                              padding=padding)
-    return avgpool
-
-def batch_norm(x, phase_train):
-    """
-    Batch normalization on convolutional maps.
-    Args:
-        x:           Tensor, 4D BHWD input maps
-        n_out:       integer, depth of input maps
-        phase_train: boolean tf.Variable, true indicates training phase
-        scope:       string, variable scope
-        affn:      whether to affn-transform outputs
-    Return:
-        normed:      batch-normalized maps
-    Ref: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow/33950177
-    """
-    name = 'batch_norm'
-    with tf.variable_scope(name):
-        phase_train = tf.convert_to_tensor(phase_train, dtype=tf.bool)
-        n_out = int(x.get_shape()[3])
-        beta = tf.Variable(tf.constant(0.0, shape=[n_out], dtype=x.dtype),
-                           name=name+'/beta', trainable=True, dtype=x.dtype)
-        gamma = tf.Variable(tf.constant(1.0, shape=[n_out], dtype=x.dtype),
-                            name=name+'/gamma', trainable=True, dtype=x.dtype)
-      
-        batch_mean, batch_var = tf.nn.moments(x, [0,1,2], name='moments')
-        ema = tf.train.ExponentialMovingAverage(decay=0.9)
-        def mean_var_with_update():
-            ema_apply_op = ema.apply([batch_mean, batch_var])
-            with tf.control_dependencies([ema_apply_op]):
-                return tf.identity(batch_mean), tf.identity(batch_var)
-        mean, var = control_flow_ops.cond(phase_train,
-                                          mean_var_with_update,
-                                          lambda: (ema.average(batch_mean), ema.average(batch_var)))
-        normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
-    return normed
-
-def inception(inp, inSize, ks, o1s, o2s1, o2s2, o3s1, o3s2, o4s1, o4s2, o4s3, poolType, name, 
-              phase_train=True, use_batch_norm=True, weight_decay=0.0):
-  
-    print('name = ', name)
-    print('inputSize = ', inSize)
-    print('kernelSize = {3,5}')
-    print('kernelStride = {%d,%d}' % (ks,ks))
-    print('outputSize = {%d,%d}' % (o2s2,o3s2))
-    print('reduceSize = {%d,%d,%d,%d}' % (o2s1,o3s1,o4s2,o1s))
-    print('pooling = {%s, %d, %d, %d, %d}' % (poolType, o4s1, o4s1, o4s3, o4s3))
-    if (o4s2>0):
-        o4 = o4s2
-    else:
-        o4 = inSize
-    print('outputSize = ', o1s+o2s2+o3s2+o4)
-    print()
-    
-    net = []
-    
-    with tf.variable_scope(name):
-        with tf.variable_scope('branch1_1x1'):
-            if o1s>0:
-                conv1 = conv(inp, inSize, o1s, 1, 1, 1, 1, 'SAME', 'conv1x1', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
-                net.append(conv1)
-      
-        with tf.variable_scope('branch2_3x3'):
-            if o2s1>0:
-                conv3a = conv(inp, inSize, o2s1, 1, 1, 1, 1, 'SAME', 'conv1x1', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
-                conv3 = conv(conv3a, o2s1, o2s2, 3, 3, ks, ks, 'SAME', 'conv3x3', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
-                net.append(conv3)
-      
-        with tf.variable_scope('branch3_5x5'):
-            if o3s1>0:
-                conv5a = conv(inp, inSize, o3s1, 1, 1, 1, 1, 'SAME', 'conv1x1', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
-                conv5 = conv(conv5a, o3s1, o3s2, 5, 5, ks, ks, 'SAME', 'conv5x5', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
-                net.append(conv5)
-      
-        with tf.variable_scope('branch4_pool'):
-            if poolType=='MAX':
-                pool = mpool(inp, o4s1, o4s1, o4s3, o4s3, 'SAME', 'pool')
-            elif poolType=='L2':
-                pool = lppool(inp, 2, o4s1, o4s1, o4s3, o4s3, 'SAME', 'pool')
-            else:
-                raise ValueError('Invalid pooling type "%s"' % poolType)
-            
-            if o4s2>0:
-                pool_conv = conv(pool, inSize, o4s2, 1, 1, 1, 1, 'SAME', 'conv1x1', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
-            else:
-                pool_conv = pool
-            net.append(pool_conv)
-      
-        incept = array_ops.concat(net, 3, name=name)
-    return incept
diff --git a/tmp/nn2.py b/tmp/nn2.py
deleted file mode 100644
index 736265374..000000000
--- a/tmp/nn2.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-# pylint: disable=missing-docstring
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-import models.network as network
-
-def inference(images, keep_probability, phase_train=True, weight_decay=0.0):
-    """ Define an inference network for face recognition based 
-           on inception modules using batch normalization
-    
-    Args:
-      images: The images to run inference on, dimensions batch_size x height x width x channels
-      phase_train: True if batch normalization should operate in training mode
-    """
-    endpoints = {}
-    net = network.conv(images, 3, 64, 7, 7, 2, 2, 'SAME', 'conv1_7x7', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['conv1'] = net
-    net = network.mpool(net,  3, 3, 2, 2, 'SAME', 'pool1')
-    endpoints['pool1'] = net
-    net = network.conv(net,  64, 64, 1, 1, 1, 1, 'SAME', 'conv2_1x1', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['conv2_1x1'] = net
-    net = network.conv(net,  64, 192, 3, 3, 1, 1, 'SAME', 'conv3_3x3', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['conv3_3x3'] = net
-    net = network.mpool(net,  3, 3, 2, 2, 'SAME', 'pool3')
-    endpoints['pool3'] = net
-  
-    net = network.inception(net, 192, 1, 64, 96, 128, 16, 32, 3, 32, 1, 'MAX', 'incept3a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept3a'] = net
-    net = network.inception(net, 256, 1, 64, 96, 128, 32, 64, 3, 64, 1, 'MAX', 'incept3b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept3b'] = net
-    net = network.inception(net, 320, 2, 0, 128, 256, 32, 64, 3, 0, 2, 'MAX', 'incept3c', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept3c'] = net
-    
-    net = network.inception(net, 640, 1, 256, 96, 192, 32, 64, 3, 128, 1, 'MAX', 'incept4a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4a'] = net
-    net = network.inception(net, 640, 1, 224, 112, 224, 32, 64, 3, 128, 1, 'MAX', 'incept4b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4b'] = net
-    net = network.inception(net, 640, 1, 192, 128, 256, 32, 64, 3, 128, 1, 'MAX', 'incept4c', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4c'] = net
-    net = network.inception(net, 640, 1, 160, 144, 288, 32, 64, 3, 128, 1, 'MAX', 'incept4d', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4d'] = net
-    net = network.inception(net, 640, 2, 0, 160, 256, 64, 128, 3, 0, 2, 'MAX', 'incept4e', phase_train=phase_train, use_batch_norm=True)
-    endpoints['incept4e'] = net
-    
-    net = network.inception(net, 1024, 1, 384, 192, 384, 48, 128, 3, 128, 1, 'MAX', 'incept5a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept5a'] = net
-    net = network.inception(net, 1024, 1, 384, 192, 384, 48, 128, 3, 128, 1, 'MAX', 'incept5b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept5b'] = net
-    net = network.apool(net,  7, 7, 1, 1, 'VALID', 'pool6')
-    endpoints['pool6'] = net
-    net = tf.reshape(net, [-1, 1024])
-    endpoints['prelogits'] = net
-    net = tf.nn.dropout(net, keep_probability)
-    endpoints['dropout'] = net
-    
-    return net, endpoints
diff --git a/tmp/nn3.py b/tmp/nn3.py
deleted file mode 100644
index 2e0502c8f..000000000
--- a/tmp/nn3.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-# pylint: disable=missing-docstring
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-import models.network as network
-
-def inference(images, keep_probability, phase_train=True, weight_decay=0.0):
-    """ Define an inference network for face recognition based 
-           on inception modules using batch normalization
-    
-    Args:
-      images: The images to run inference on, dimensions batch_size x height x width x channels
-      phase_train: True if batch normalization should operate in training mode
-    """
-    endpoints = {}
-    net = network.conv(images, 3, 64, 7, 7, 2, 2, 'SAME', 'conv1_7x7', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['conv1'] = net
-    net = network.mpool(net,  3, 3, 2, 2, 'SAME', 'pool1')
-    endpoints['pool1'] = net
-    net = network.conv(net,  64, 64, 1, 1, 1, 1, 'SAME', 'conv2_1x1', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['conv2_1x1'] = net
-    net = network.conv(net,  64, 192, 3, 3, 1, 1, 'SAME', 'conv3_3x3', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['conv3_3x3'] = net
-    net = network.mpool(net,  3, 3, 2, 2, 'SAME', 'pool3')
-    endpoints['pool3'] = net
-  
-    net = network.inception(net, 192, 1, 64, 96, 128, 16, 32, 3, 32, 1, 'MAX', 'incept3a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept3a'] = net
-    net = network.inception(net, 256, 1, 64, 96, 128, 32, 64, 3, 64, 1, 'MAX', 'incept3b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept3b'] = net
-    net = network.inception(net, 320, 2, 0, 128, 256, 32, 64, 3, 0, 2, 'MAX', 'incept3c', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept3c'] = net
-    
-    net = network.inception(net, 640, 1, 256, 96, 192, 32, 64, 3, 128, 1, 'MAX', 'incept4a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4a'] = net
-    net = network.inception(net, 640, 1, 224, 112, 224, 32, 64, 3, 128, 1, 'MAX', 'incept4b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4b'] = net
-    net = network.inception(net, 640, 1, 192, 128, 256, 32, 64, 3, 128, 1, 'MAX', 'incept4c', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4c'] = net
-    net = network.inception(net, 640, 1, 160, 144, 288, 32, 64, 3, 128, 1, 'MAX', 'incept4d', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4d'] = net
-    net = network.inception(net, 640, 2, 0, 160, 256, 64, 128, 3, 0, 2, 'MAX', 'incept4e', phase_train=phase_train, use_batch_norm=True)
-    endpoints['incept4e'] = net
-    
-    net = network.inception(net, 1024, 1, 384, 192, 384, 48, 128, 3, 128, 1, 'MAX', 'incept5a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept5a'] = net
-    net = network.inception(net, 1024, 1, 384, 192, 384, 48, 128, 3, 128, 1, 'MAX', 'incept5b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept5b'] = net
-    net = network.apool(net,  5, 5, 1, 1, 'VALID', 'pool6')
-    endpoints['pool6'] = net
-    net = tf.reshape(net, [-1, 1024])
-    endpoints['prelogits'] = net
-    net = tf.nn.dropout(net, keep_probability)
-    endpoints['dropout'] = net
-    
-    return net, endpoints
diff --git a/tmp/nn4.py b/tmp/nn4.py
deleted file mode 100644
index 8c3c79fd0..000000000
--- a/tmp/nn4.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-# pylint: disable=missing-docstring
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-import models.network as network
-
-def inference(images, keep_probability, phase_train=True, weight_decay=0.0):
-    """ Define an inference network for face recognition based 
-           on inception modules using batch normalization
-    
-    Args:
-      images: The images to run inference on, dimensions batch_size x height x width x channels
-      phase_train: True if batch normalization should operate in training mode
-    """
-    endpoints = {}
-    net = network.conv(images, 3, 64, 7, 7, 2, 2, 'SAME', 'conv1_7x7', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['conv1'] = net
-    net = network.mpool(net,  3, 3, 2, 2, 'SAME', 'pool1')
-    endpoints['pool1'] = net
-    net = network.conv(net,  64, 64, 1, 1, 1, 1, 'SAME', 'conv2_1x1', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['conv2_1x1'] = net
-    net = network.conv(net,  64, 192, 3, 3, 1, 1, 'SAME', 'conv3_3x3', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['conv3_3x3'] = net
-    net = network.mpool(net,  3, 3, 2, 2, 'SAME', 'pool3')
-    endpoints['pool3'] = net
-  
-    net = network.inception(net, 192, 1, 64, 96, 128, 16, 32, 3, 32, 1, 'MAX', 'incept3a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept3a'] = net
-    net = network.inception(net, 256, 1, 64, 96, 128, 32, 64, 3, 64, 1, 'MAX', 'incept3b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept3b'] = net
-    net = network.inception(net, 320, 2, 0, 128, 256, 32, 64, 3, 0, 2, 'MAX', 'incept3c', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept3c'] = net
-    
-    net = network.inception(net, 640, 1, 256, 96, 192, 32, 64, 3, 128, 1, 'MAX', 'incept4a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4a'] = net
-    net = network.inception(net, 640, 1, 224, 112, 224, 32, 64, 3, 128, 1, 'MAX', 'incept4b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4b'] = net
-    net = network.inception(net, 640, 1, 192, 128, 256, 32, 64, 3, 128, 1, 'MAX', 'incept4c', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4c'] = net
-    net = network.inception(net, 640, 1, 160, 144, 288, 32, 64, 3, 128, 1, 'MAX', 'incept4d', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4d'] = net
-    net = network.inception(net, 640, 2, 0, 160, 256, 64, 128, 3, 0, 2, 'MAX', 'incept4e', phase_train=phase_train, use_batch_norm=True)
-    endpoints['incept4e'] = net
-    
-    net = network.inception(net, 1024, 1, 384, 192, 384, 0, 0, 3, 128, 1, 'MAX', 'incept5a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept5a'] = net
-    net = network.inception(net, 896, 1, 384, 192, 384, 0, 0, 3, 128, 1, 'MAX', 'incept5b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept5b'] = net
-    net = network.apool(net,  3, 3, 1, 1, 'VALID', 'pool6')
-    endpoints['pool6'] = net
-    net = tf.reshape(net, [-1, 896])
-    endpoints['prelogits'] = net
-    net = tf.nn.dropout(net, keep_probability)
-    endpoints['dropout'] = net
-    
-    return net, endpoints
diff --git a/tmp/nn4_small2_v1.py b/tmp/nn4_small2_v1.py
deleted file mode 100644
index 780aafe20..000000000
--- a/tmp/nn4_small2_v1.py
+++ /dev/null
@@ -1,75 +0,0 @@
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-# pylint: disable=missing-docstring
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-import models.network as network
-
-def inference(images, keep_probability, phase_train=True, weight_decay=0.0):
-    """ Define an inference network for face recognition based 
-           on inception modules using batch normalization
-    
-    Args:
-      images: The images to run inference on, dimensions batch_size x height x width x channels
-      phase_train: True if batch normalization should operate in training mode
-    """
-    endpoints = {}
-    net = network.conv(images, 3, 64, 7, 7, 2, 2, 'SAME', 'conv1_7x7', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['conv1'] = net
-    net = network.mpool(net,  3, 3, 2, 2, 'SAME', 'pool1')
-    endpoints['pool1'] = net
-    net = network.conv(net,  64, 64, 1, 1, 1, 1, 'SAME', 'conv2_1x1', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['conv2_1x1'] = net
-    net = network.conv(net,  64, 192, 3, 3, 1, 1, 'SAME', 'conv3_3x3', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['conv3_3x3'] = net
-    net = network.mpool(net,  3, 3, 2, 2, 'SAME', 'pool3')
-    endpoints['pool3'] = net
-  
-    net = network.inception(net, 192, 1, 64, 96, 128, 16, 32, 3, 32, 1, 'MAX', 'incept3a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept3a'] = net
-    net = network.inception(net, 256, 1, 64, 96, 128, 32, 64, 3, 64, 1, 'MAX', 'incept3b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept3b'] = net
-    net = network.inception(net, 320, 2, 0, 128, 256, 32, 64, 3, 0, 2, 'MAX', 'incept3c', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept3c'] = net
-    
-    net = network.inception(net, 640, 1, 256, 96, 192, 32, 64, 3, 128, 1, 'MAX', 'incept4a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4a'] = net
-    net = network.inception(net, 640, 2, 0, 160, 256, 64, 128, 3, 0, 2, 'MAX', 'incept4e', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept4e'] = net
-    
-    net = network.inception(net, 1024, 1, 256, 96,  384, 0, 0, 3, 96,  1, 'MAX', 'incept5a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept5a'] = net
-    net = network.inception(net, 736, 1, 256, 96, 384, 0, 0, 3, 96, 1, 'MAX', 'incept5b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
-    endpoints['incept5b'] = net
-    net = network.apool(net,  3, 3, 1, 1, 'VALID', 'pool6')
-    endpoints['pool6'] = net
-    net = tf.reshape(net, [-1, 736])
-    endpoints['prelogits'] = net
-    net = tf.nn.dropout(net, keep_probability)
-    endpoints['dropout'] = net
-    
-    return net, endpoints
-  
\ No newline at end of file
diff --git a/tmp/pilatus800.jpg b/tmp/pilatus800.jpg
deleted file mode 100644
index 90a0f3186..000000000
Binary files a/tmp/pilatus800.jpg and /dev/null differ
diff --git a/tmp/random_test.py b/tmp/random_test.py
deleted file mode 100644
index b186cc3b1..000000000
--- a/tmp/random_test.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import tensorflow as tf
-import numpy as np
-from six.moves import xrange
-
-
-with tf.Graph().as_default():
-  tf.set_random_seed(666)
-
-
-  # Placeholder for input images
-  input_placeholder = tf.placeholder(tf.float32, shape=(9, 7), name='input')
-  
-  # Split example embeddings into anchor, positive and negative
-  #anchor, positive, negative = tf.split(0, 3, input)
-  resh1 = tf.reshape(input_placeholder, [3,3,7])
-  anchor = resh1[0,:,:]
-  positive = resh1[1,:,:]
-  negative = resh1[2,:,:]
-  
-  # Build an initialization operation to run below.
-  init = tf.global_variables_initializer()
-
-  # Start running operations on the Graph.
-  sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
-  sess.run(init)
-  
-  with sess.as_default():
-    batch = np.zeros((9,7))
-    batch[0,:] = 1.1
-    batch[1,:] = 2.1
-    batch[2,:] = 3.1
-    batch[3,:] = 1.2
-    batch[4,:] = 2.2
-    batch[5,:] = 3.2
-    batch[6,:] = 1.3
-    batch[7,:] = 2.3
-    batch[8,:] = 3.3
-    feed_dict = {input_placeholder: batch }
-    print(batch)
-    print(sess.run([anchor, positive, negative], feed_dict=feed_dict))
-
-
-
-
-#feed_dict = { images_placeholder: np.zeros((90,96,96,3)), phase_train_placeholder: True }
-#vars_eval  = sess.run(tf.global_variables(), feed_dict=feed_dict)
-#for gt in vars_eval:
-  #print('%.20f' % (np.sum(gt)))
-#for gt, gv in zip(grads_eval, grad_vars):
-  #print('%40s: %.20f' % (gv.op.name, np.sum(gt)))
-
-  
-
-#import h5py
-#myFile = h5py.File('/home/david/repo/TensorFace/network.h5', 'r')
-
-## The '...' means retrieve the whole tensor
-#data = myFile[...]
-#print(data)
-
-
-#import h5py    # HDF5 support
-
-#fileName = "/home/david/repo/TensorFace/network.h5"
-#f = h5py.File(fileName,  "r")
-##for item in f.keys():
-  ##print item
-#for item in f.values():
-  #print item
-
-
-#import tensorflow as tf
-#import numpy as np
-#import matplotlib.pyplot as plt
-#import math
-#import facenet
-#import os
-#import glob
-#from scipy import misc
-
-#def plot_triplet(apn, idx):
-    #plt.subplot(1,3,1)
-    #plt.imshow(np.multiply(apn[idx*3+0,:,:,:],1/256))
-    #plt.subplot(1,3,2)
-    #plt.imshow(np.multiply(apn[idx*3+1,:,:,:],1/256))
-    #plt.subplot(1,3,3)
-    #plt.imshow(np.multiply(apn[idx*3+2,:,:,:],1/256))
-
-
-#input_image = tf.placeholder(tf.float32, name='input_image')
-#phase_train = tf.placeholder(tf.bool, name='phase_train')
-
-#n_in, n_out = 3, 16
-#ksize = 3
-#stride = 1
-#kernel = tf.Variable(tf.truncated_normal([ksize, ksize, n_in, n_out],
-                                         #stddev=math.sqrt(2/(ksize*ksize*n_out))),
-                     #name='kernel')
-#conv = tf.nn.conv2d(input_image, kernel, [1,stride,stride,1], padding="SAME")
-#conv_bn = facenet.batch_norm(conv, n_out, phase_train)
-#relu = tf.nn.relu(conv_bn)
-
-## Build an initialization operation to run below.
-#init = tf.global_variables_initializer()
-
-## Start running operations on the Graph.
-#sess = tf.Session()
-#sess.run(init)
-
-#path = '/home/david/datasets/fs_aligned/Zooey_Deschanel/'
-#files = glob.glob(os.path.join(path, '*.png'))
-#nrof_samples = 30
-#img_list = [None] * nrof_samples
-#for i in xrange(nrof_samples):
-    #img_list[i] = misc.imread(files[i])
-#images = np.stack(img_list)
-
-#feed_dict = {
-    #input_image: images.astype(np.float32),
-    #phase_train: True
-#}
-
-#out = sess.run([relu], feed_dict=feed_dict)
-#print(out[0].shape)
-
-##print(out)
-
-#plot_triplet(images, 0)
-
-
-
-#import matplotlib.pyplot as plt
-#import numpy as np
-
-#a=[3,4,5,6]
-#b = [1,a[1:3]]
-#print(b)
-
-## Generate some data...
-#x, y = np.meshgrid(np.linspace(-2,2,200), np.linspace(-2,2,200))
-#x, y = x - x.mean(), y - y.mean()
-#z = x * np.exp(-x**2 - y**2)
-#print(z.shape)
-
-## Plot the grid
-#plt.imshow(z)
-#plt.gray()
-#plt.show()
-
-#import numpy as np
-
-#np.random.seed(123)
-#rnd = 1.0*np.random.randint(1,2**32)/2**32
-#print(rnd)
diff --git a/tmp/rename_casia_directories.py b/tmp/rename_casia_directories.py
deleted file mode 100644
index eb866be3f..000000000
--- a/tmp/rename_casia_directories.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import shutil
-import argparse
-import os
-import sys
-
-def main(args):
-  
-    identity_map = {}
-    with open(os.path.expanduser(args.map_file_name), "r") as f:
-        for line in f:
-            fields = line.split(' ')
-            dir_name = fields[0]
-            class_name = fields[1].replace('\n', '').replace('\r', '')
-            if class_name not in identity_map.values():
-                identity_map[dir_name] = class_name
-            else:
-                print('Duplicate class names: %s' % class_name)
-            
-    dataset_path_exp = os.path.expanduser(args.dataset_path)
-    dirs = os.listdir(dataset_path_exp)
-    for f in dirs:
-        old_path = os.path.join(dataset_path_exp, f)
-        if f in identity_map:
-            new_path = os.path.join(dataset_path_exp, identity_map[f])
-            if os.path.isdir(old_path):
-                print('Renaming %s to %s' % (old_path, new_path))
-                shutil.move(old_path, new_path)
-
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    
-    parser.add_argument('map_file_name', type=str, help='Name of the text file that contains the directory to class name mappings.')
-    parser.add_argument('dataset_path', type=str, help='Path to the dataset directory.')
-    return parser.parse_args(argv)
-
-if __name__ == '__main__':
-    main(parse_arguments(sys.argv[1:]))
diff --git a/tmp/seed_test.py b/tmp/seed_test.py
deleted file mode 100644
index 2077cf5ee..000000000
--- a/tmp/seed_test.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import tensorflow as tf
-import numpy as np
-import sys
-import time
-sys.path.append('../src')
-import facenet
-from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import array_ops
-
-from six.moves import xrange
-
-tf.app.flags.DEFINE_integer('batch_size', 90,
-                            """Number of images to process in a batch.""")
-tf.app.flags.DEFINE_integer('image_size', 96,
-                            """Image size (height, width) in pixels.""")
-tf.app.flags.DEFINE_float('alpha', 0.2,
-                          """Positive to negative triplet distance margin.""")
-tf.app.flags.DEFINE_float('learning_rate', 0.1,
-                          """Initial learning rate.""")
-tf.app.flags.DEFINE_float('moving_average_decay', 0.9999,
-                          """Expontential decay for tracking of training parameters.""")
-
-FLAGS = tf.app.flags.FLAGS
-
-def run_train():
-  
-  with tf.Graph().as_default():
-  
-    # Set the seed for the graph
-    tf.set_random_seed(666)
-
-    # Placeholder for input images
-    images_placeholder = tf.placeholder(tf.float32, shape=(FLAGS.batch_size, FLAGS.image_size, FLAGS.image_size, 3), name='input')
-    
-    # Build the inference graph
-    embeddings = inference_conv_test(images_placeholder)
-    #embeddings = inference_affine_test(images_placeholder)
-    
-    # Split example embeddings into anchor, positive and negative
-    anchor, positive, negative = tf.split(0, 3, embeddings)
-
-    # Alternative implementation of the split operation
-    # This produces the same error
-    #resh1 = tf.reshape(embeddings, [3,int(FLAGS.batch_size/3), 128])
-    #anchor = resh1[0,:,:]
-    #positive = resh1[1,:,:]
-    #negative = resh1[2,:,:]
-    
-    # Calculate triplet loss
-    pos_dist = tf.reduce_sum(tf.square(tf.sub(anchor, positive)), 1)
-    neg_dist = tf.reduce_sum(tf.square(tf.sub(anchor, negative)), 1)
-    basic_loss = tf.add(tf.sub(pos_dist,neg_dist), FLAGS.alpha)
-    loss = tf.reduce_mean(tf.maximum(basic_loss, 0.0), 0)
-
-    # Build a Graph that trains the model with one batch of examples and updates the model parameters
-    opt = tf.train.GradientDescentOptimizer(FLAGS.learning_rate)
-    #opt = tf.train.AdagradOptimizer(FLAGS.learning_rate)  # Optimizer does not seem to matter
-    grads = opt.compute_gradients(loss)
-    train_op = opt.apply_gradients(grads)
-    
-    # Initialize the variables
-    init = tf.global_variables_initializer()
-    
-    # Launch the graph.
-    sess = tf.Session()
-    sess.run(init)
-
-    # Set the numpy seed
-    np.random.seed(666)
-    
-    with sess.as_default():
-      grads_eval = []
-      all_vars = []
-      for step in xrange(1):
-        # Generate some random input data
-        batch = np.random.random((FLAGS.batch_size, FLAGS.image_size, FLAGS.image_size, 3))
-        feed_dict = { images_placeholder: batch }
-        # Get the variables
-        var_names = tf.global_variables()
-        all_vars  += sess.run(var_names, feed_dict=feed_dict)
-        # Get the gradients
-        grad_tensors, grad_vars = zip(*grads)
-        grads_eval  += sess.run(grad_tensors, feed_dict=feed_dict)
-        # Run training
-        sess.run(train_op, feed_dict=feed_dict)
-    
-    sess.close()
-  return (var_names, all_vars, grad_vars, grads_eval)
-
-def _conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType):
-  kernel = tf.Variable(tf.truncated_normal([kH, kW, nIn, nOut],
-                                           dtype=tf.float32,
-                                           stddev=1e-1), name='weights')
-  conv = tf.nn.conv2d(inpOp, kernel, [1, dH, dW, 1], padding=padType)
-  
-  biases = tf.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32),
-                       trainable=True, name='biases')
-  bias = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
-  conv1 = tf.nn.relu(bias)
-  return conv1
-
-def _affine(inpOp, nIn, nOut):
-  kernel = tf.Variable(tf.truncated_normal([nIn, nOut],
-                                           dtype=tf.float32,
-                                           stddev=1e-1), name='weights')
-  biases = tf.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32),
-                       trainable=True, name='biases')
-  affine1 = tf.nn.relu_layer(inpOp, kernel, biases)
-  return affine1
-  
-def inference_conv_test(images):
-  conv1 = _conv(images, 3, 64, 7, 7, 2, 2, 'SAME')
-  resh1 = tf.reshape(conv1, [-1, 147456])
-  affn = _affine(resh1, 147456, 128)  # Affine layer not needed to reproduce the error
-  return affn
-
-def inference_affine_test(images):
-  resh1 = tf.reshape(images, [-1, 27648])
-  affn1 = _affine(resh1, 27648, 1024)
-  affn2 = _affine(affn1, 1024, 1024)
-  affn3 = _affine(affn2, 1024, 1024)
-  affn4 = _affine(affn3, 1024, 128)
-  return affn4
-
-# Run two sessions with the same seed. These runs should produce the same result.
-var_names1, all_vars1, grad_names1, all_grads1 = run_train()
-var_names2, all_vars2, grad_names2, all_grads2 = run_train()
-
-all_vars_close = [None] * len(all_vars1)
-for i in range(len(all_vars1)):
-  all_vars_close[i] = np.allclose(all_vars1[i], all_vars2[i], rtol=1.e-16)
-  print('%d var %s: %s' % (i, var_names1[i].op.name, all_vars_close[i]))
-  
-all_grads_close = [None] * len(all_grads1)
-for i in range(len(all_grads1)):
-  all_grads_close[i] = np.allclose(all_grads1[i], all_grads2[i], rtol=1.e-16)
-  print('%d grad %s: %s' % (i, grad_names1[i].op.name, all_grads_close[i]))
-
-assert all(all_vars_close), 'Variable values differ between the two sessions (with the same seed)'
-assert all(all_grads_close), 'Gradient values differ between the two sessions (with the same seed)'
diff --git a/tmp/select_triplets_test.py b/tmp/select_triplets_test.py
deleted file mode 100644
index 149e262b3..000000000
--- a/tmp/select_triplets_test.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import facenet
-import numpy as np
-import tensorflow as tf
-
-FLAGS = tf.app.flags.FLAGS
-
-tf.app.flags.DEFINE_integer('people_per_batch', 45,
-                            """Number of people per batch.""")
-tf.app.flags.DEFINE_integer('alpha', 0.2,
-                            """Positive to negative triplet distance margin.""")
-
-
-embeddings = np.zeros((1800,128))
-
-np.random.seed(123)
-for ix in range(embeddings.shape[0]):
-    for jx in range(embeddings.shape[1]):
-        rnd = 1.0*np.random.randint(1,2**32)/2**32
-        embeddings[ix][jx] = rnd
-
-
-emb_array = embeddings
-image_data = np.zeros((1800,96,96,3))
-
-
-num_per_class = [40 for i in range(45)]
-
-
-np.random.seed(123)
-apn, nrof_random_negs, nrof_triplets = facenet.select_triplets(emb_array, num_per_class, image_data)
diff --git a/tmp/test1.py b/tmp/test1.py
deleted file mode 100644
index f7d178505..000000000
--- a/tmp/test1.py
+++ /dev/null
@@ -1 +0,0 @@
-print('Hello world')
diff --git a/tmp/test_align.py b/tmp/test_align.py
deleted file mode 100644
index b4f42de38..000000000
--- a/tmp/test_align.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import facenet
-import os
-import matplotlib.pyplot as plt
-import numpy as np
-
-
-def main():
-    image_size = 96
-    old_dataset = '/home/david/datasets/facescrub/fs_aligned_new_oean/'
-    new_dataset = '/home/david/datasets/facescrub/facescrub_110_96/'
-    eq = 0
-    num = 0
-    l = []
-    dataset = facenet.get_dataset(old_dataset)
-    for cls in dataset:
-        new_class_dir = os.path.join(new_dataset, cls.name)
-        for image_path in cls.image_paths:
-          try:
-            filename = os.path.splitext(os.path.split(image_path)[1])[0]
-            new_filename = os.path.join(new_class_dir, filename+'.png')
-            #print(image_path)
-            if os.path.exists(new_filename):
-                a = facenet.load_data([image_path, new_filename], False, False, image_size, do_prewhiten=False)
-                if np.array_equal(a[0], a[1]):
-                  eq+=1
-                num+=1
-                err = np.sum(np.square(np.subtract(a[0], a[1])))
-                #print(err)
-                l.append(err)
-                if err>2000:
-                  fig = plt.figure(1)
-                  p1 = fig.add_subplot(121)
-                  p1.imshow(a[0])
-                  p2 = fig.add_subplot(122)
-                  p2.imshow(a[1])
-                  print('%6.1f: %s\n' % (err, new_filename))
-                  pass
-            else:
-                pass
-                #print('File not found: %s' % new_filename)
-          except:
-            pass
-if __name__ == '__main__':
-    main()
diff --git a/tmp/test_invariance_on_lfw.py b/tmp/test_invariance_on_lfw.py
deleted file mode 100644
index 3bbbde00a..000000000
--- a/tmp/test_invariance_on_lfw.py
+++ /dev/null
@@ -1,213 +0,0 @@
-"""Test invariance to translation, scaling and rotation on the "Labeled Faces in the Wild" dataset (http://vis-www.cs.umass.edu/lfw/).
-This requires test images to be cropped a bit wider than the normal to give some room for the transformations.
-"""
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-import numpy as np
-import argparse
-import facenet
-import lfw
-import matplotlib.pyplot as plt
-from scipy import misc
-import os
-import sys
-import math
-
-def main(args):
-    
-    pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
-    paths, actual_issame = lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs)
-    result_dir = '../data/'
-    plt.ioff()  # Disable interactive plotting mode
-    
-    with tf.Graph().as_default():
-
-        with tf.Session() as sess:
-    
-            # Load the model
-            print('Loading model "%s"' % args.model_file)
-            facenet.load_model(args.model_file)
-            
-            # Get input and output tensors
-            images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
-            phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
-            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
-            image_size = int(images_placeholder.get_shape()[1])
-            
-            # Run test on LFW to check accuracy for different horizontal/vertical translations of input images
-            if args.nrof_offsets>0:
-                step = 3
-                offsets = np.asarray([x*step for x in range(-args.nrof_offsets//2+1, args.nrof_offsets//2+1)])
-                horizontal_offset_accuracy = [None] * len(offsets)
-                for idx, offset in enumerate(offsets):
-                    accuracy = evaluate_accuracy(sess, images_placeholder, phase_train_placeholder, image_size, embeddings, 
-                        paths, actual_issame, translate_images, (offset,0), 60, args.orig_image_size, args.seed)
-                    print('Hoffset: %1.3f  Accuracy: %1.3f+-%1.3f' % (offset, np.mean(accuracy), np.std(accuracy)))
-                    horizontal_offset_accuracy[idx] = np.mean(accuracy)
-                vertical_offset_accuracy = [None] * len(offsets)
-                for idx, offset in enumerate(offsets):
-                    accuracy = evaluate_accuracy(sess, images_placeholder, phase_train_placeholder, image_size, embeddings, 
-                        paths, actual_issame, translate_images, (0,offset), 60, args.orig_image_size, args.seed)
-                    print('Voffset: %1.3f  Accuracy: %1.3f+-%1.3f' % (offset, np.mean(accuracy), np.std(accuracy)))
-                    vertical_offset_accuracy[idx] = np.mean(accuracy)
-                fig = plt.figure(1)
-                plt.plot(offsets, horizontal_offset_accuracy, label='Horizontal')
-                plt.plot(offsets, vertical_offset_accuracy, label='Vertical')
-                plt.legend()
-                plt.grid(True)
-                plt.title('Translation invariance on LFW')
-                plt.xlabel('Offset [pixels]')
-                plt.ylabel('Accuracy')
-#                plt.show()
-                print('Saving results in %s' % result_dir)
-                fig.savefig(os.path.join(result_dir, 'invariance_translation.png'))
-                save_result(offsets, horizontal_offset_accuracy, os.path.join(result_dir, 'invariance_translation_horizontal.txt'))
-                save_result(offsets, vertical_offset_accuracy, os.path.join(result_dir, 'invariance_translation_vertical.txt'))
-
-            # Run test on LFW to check accuracy for different rotation of input images
-            if args.nrof_angles>0:
-                step = 3
-                angles = np.asarray([x*step for x in range(-args.nrof_offsets//2+1, args.nrof_offsets//2+1)])
-                rotation_accuracy = [None] * len(angles)
-                for idx, angle in enumerate(angles):
-                    accuracy = evaluate_accuracy(sess, images_placeholder, phase_train_placeholder, image_size, embeddings, 
-                        paths, actual_issame, rotate_images, angle, 60, args.orig_image_size, args.seed)
-                    print('Angle: %1.3f  Accuracy: %1.3f+-%1.3f' % (angle, np.mean(accuracy), np.std(accuracy)))
-                    rotation_accuracy[idx] = np.mean(accuracy)
-                fig = plt.figure(2)
-                plt.plot(angles, rotation_accuracy)
-                plt.grid(True)
-                plt.title('Rotation invariance on LFW')
-                plt.xlabel('Angle [deg]')
-                plt.ylabel('Accuracy')
-#                plt.show()
-                print('Saving results in %s' % result_dir)
-                fig.savefig(os.path.join(result_dir, 'invariance_rotation.png'))
-                save_result(angles, rotation_accuracy, os.path.join(result_dir, 'invariance_rotation.txt'))
-
-            # Run test on LFW to check accuracy for different scaling of input images
-            if args.nrof_scales>0:
-                step = 0.05
-                scales = np.asarray([x*step+1 for x in range(-args.nrof_offsets//2+1, args.nrof_offsets//2+1)])
-                scale_accuracy = [None] * len(scales)
-                for scale_idx, scale in enumerate(scales):
-                    accuracy = evaluate_accuracy(sess, images_placeholder, phase_train_placeholder, image_size, embeddings, 
-                        paths, actual_issame, scale_images, scale, 60, args.orig_image_size, args.seed)
-                    print('Scale: %1.3f  Accuracy: %1.3f+-%1.3f' % (scale, np.mean(accuracy), np.std(accuracy)))
-                    scale_accuracy[scale_idx] = np.mean(accuracy)
-                fig = plt.figure(3)
-                plt.plot(scales, scale_accuracy)
-                plt.grid(True)
-                plt.title('Scale invariance on LFW')
-                plt.xlabel('Scale')
-                plt.ylabel('Accuracy')
-#                plt.show()
-                print('Saving results in %s' % result_dir)
-                fig.savefig(os.path.join(result_dir, 'invariance_scale.png'))
-                save_result(scales, scale_accuracy, os.path.join(result_dir, 'invariance_scale.txt'))
-                
-def save_result(aug, acc, filename):
-    with open(filename, "w") as f:
-        for i in range(aug.size):
-            f.write('%6.4f %6.4f\n' % (aug[i], acc[i]))
-            
-def evaluate_accuracy(sess, images_placeholder, phase_train_placeholder, image_size, embeddings, 
-        paths, actual_issame, augment_images, aug_value, batch_size, orig_image_size, seed):
-    nrof_images = len(paths)
-    nrof_batches = int(math.ceil(1.0*nrof_images / batch_size))
-    emb_list = []
-    for i in range(nrof_batches):
-        start_index = i*batch_size
-        end_index = min((i+1)*batch_size, nrof_images)
-        paths_batch = paths[start_index:end_index]
-        images = facenet.load_data(paths_batch, False, False, orig_image_size)
-        images_aug = augment_images(images, aug_value, image_size)
-        feed_dict = { images_placeholder: images_aug, phase_train_placeholder: False }
-        emb_list += sess.run([embeddings], feed_dict=feed_dict)
-    emb_array = np.vstack(emb_list)  # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
-    
-    thresholds = np.arange(0, 4, 0.01)
-    embeddings1 = emb_array[0::2]
-    embeddings2 = emb_array[1::2]
-    _, _, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2, np.asarray(actual_issame), seed)
-    return accuracy
-
-def scale_images(images, scale, image_size):
-    images_scale_list = [None] * images.shape[0]
-    for i in range(images.shape[0]):
-        images_scale_list[i] = misc.imresize(images[i,:,:,:], scale)
-    images_scale = np.stack(images_scale_list,axis=0)
-    sz1 = images_scale.shape[1]/2
-    sz2 = image_size/2
-    images_crop = images_scale[:,(sz1-sz2):(sz1+sz2),(sz1-sz2):(sz1+sz2),:]
-    return images_crop
-
-def rotate_images(images, angle, image_size):
-    images_list = [None] * images.shape[0]
-    for i in range(images.shape[0]):
-        images_list[i] = misc.imrotate(images[i,:,:,:], angle)
-    images_rot = np.stack(images_list,axis=0)
-    sz1 = images_rot.shape[1]/2
-    sz2 = image_size/2
-    images_crop = images_rot[:,(sz1-sz2):(sz1+sz2),(sz1-sz2):(sz1+sz2),:]
-    return images_crop
-
-def translate_images(images, offset, image_size):
-    h, v = offset
-    sz1 = images.shape[1]/2
-    sz2 = image_size/2
-    images_crop = images[:,(sz1-sz2+v):(sz1+sz2+v),(sz1-sz2+h):(sz1+sz2+h),:]
-    return images_crop
-
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    
-    parser.add_argument('--model_file', type=str, 
-        help='File containing the model parameters as well as the model metagraph (with extension ".meta")', 
-        default='~/models/facenet/20160514-234418/model.ckpt-500000')
-    parser.add_argument('--nrof_offsets', type=int,
-        help='Number of horizontal and vertical offsets to evaluate.', default=21)
-    parser.add_argument('--nrof_angles', type=int,
-        help='Number of angles to evaluate.', default=21)
-    parser.add_argument('--nrof_scales', type=int,
-        help='Number of scales to evaluate.', default=21)
-    parser.add_argument('--lfw_pairs', type=str,
-        help='The file containing the pairs to use for validation.', default='../data/pairs.txt')
-    parser.add_argument('--lfw_dir', type=str,
-        help='Path to the data directory containing aligned face patches.', default='~/datasets/lfw/lfw_realigned/')
-    parser.add_argument('--orig_image_size', type=int,
-        help='Image size (height, width) in pixels of the original (uncropped/unscaled) images.', default=224)
-    parser.add_argument('--lfw_nrof_folds', type=int,
-        help='Number of folds to use for cross validation. Mainly used for testing.', default=10)
-    parser.add_argument('--seed', type=int,
-        help='Random seed.', default=666)
-    return parser.parse_args(argv)
-
-
-if __name__ == '__main__':
-    main(parse_arguments(sys.argv[1:]))
diff --git a/tmp/vggface16.py b/tmp/vggface16.py
deleted file mode 100644
index df45c53ec..000000000
--- a/tmp/vggface16.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""Load the VGG Face model into TensorFlow.
-Download the model from http://www.robots.ox.ac.uk/~vgg/software/vgg_face/
-and point to the file 'vgg_face.mat'
-"""
-import numpy as np
-from scipy import io
-import tensorflow as tf
-
-def load(filename, images):
-    #filename = '../data/vgg_face_matconvnet/data/vgg_face.mat'
-    vgg16 = io.loadmat(filename)
-    vgg16Layers = vgg16['net'][0][0]['layers']
-    
-    # A function to get the weights of the VGG layers
-    def vbbWeights(layerNumber):
-        W = vgg16Layers[0][layerNumber][0][0][2][0][0]
-        W = tf.constant(W)
-        return W
-     
-    def vbbConstants(layerNumber):
-        b = vgg16Layers[0][layerNumber][0][0][2][0][1].T
-        b = tf.constant(np.reshape(b, (b.size)))
-        return b
-    
-    modelGraph = {}
-    modelGraph['input'] = images
-    
-    modelGraph['conv1_1'] = tf.nn.conv2d(modelGraph['input'], filter = vbbWeights(0), strides = [1, 1, 1, 1], padding = 'SAME')
-    modelGraph['relu1_1'] = tf.nn.relu(modelGraph['conv1_1'] + vbbConstants(0))
-    modelGraph['conv1_2'] = tf.nn.conv2d(modelGraph['relu1_1'], filter = vbbWeights(2), strides = [1, 1, 1, 1], padding = 'SAME')
-    modelGraph['relu1_2'] = tf.nn.relu(modelGraph['conv1_2'] + vbbConstants(2))
-    modelGraph['pool1'] = tf.nn.max_pool(modelGraph['relu1_2'], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')
-    
-    modelGraph['conv2_1'] = tf.nn.conv2d(modelGraph['pool1'], filter = vbbWeights(5), strides = [1, 1, 1, 1], padding = 'SAME')
-    modelGraph['relu2_1'] = tf.nn.relu(modelGraph['conv2_1'] + vbbConstants(5))
-    modelGraph['conv2_2'] = tf.nn.conv2d(modelGraph['relu2_1'], filter = vbbWeights(7), strides = [1, 1, 1, 1], padding = 'SAME')
-    modelGraph['relu2_2'] = tf.nn.relu(modelGraph['conv2_2'] + vbbConstants(7))
-    modelGraph['pool2'] = tf.nn.max_pool(modelGraph['relu2_2'], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')
-    
-    modelGraph['conv3_1'] = tf.nn.conv2d(modelGraph['pool2'], filter = vbbWeights(10), strides = [1, 1, 1, 1], padding = 'SAME')
-    modelGraph['relu3_1'] = tf.nn.relu(modelGraph['conv3_1'] + vbbConstants(10))
-    modelGraph['conv3_2'] = tf.nn.conv2d(modelGraph['relu3_1'], filter = vbbWeights(12), strides = [1, 1, 1, 1], padding = 'SAME')
-    modelGraph['relu3_2'] = tf.nn.relu(modelGraph['conv3_2'] + vbbConstants(12))
-    modelGraph['conv3_3'] = tf.nn.conv2d(modelGraph['relu3_2'], filter = vbbWeights(14), strides = [1, 1, 1, 1], padding = 'SAME')
-    modelGraph['relu3_3'] = tf.nn.relu(modelGraph['conv3_3'] + vbbConstants(14))
-    modelGraph['pool3'] = tf.nn.max_pool(modelGraph['relu3_3'], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')
-    
-    modelGraph['conv4_1'] = tf.nn.conv2d(modelGraph['pool3'], filter = vbbWeights(17), strides = [1, 1, 1, 1], padding = 'SAME')
-    modelGraph['relu4_1'] = tf.nn.relu(modelGraph['conv4_1'] + vbbConstants(17))
-    modelGraph['conv4_2'] = tf.nn.conv2d(modelGraph['relu4_1'], filter = vbbWeights(19), strides = [1, 1, 1, 1], padding = 'SAME')
-    modelGraph['relu4_2'] = tf.nn.relu(modelGraph['conv4_2'] + vbbConstants(19))
-    modelGraph['conv4_3'] = tf.nn.conv2d(modelGraph['relu4_2'], filter = vbbWeights(21), strides = [1, 1, 1, 1], padding = 'SAME')
-    modelGraph['relu4_3'] = tf.nn.relu(modelGraph['conv4_3'] + vbbConstants(21))
-    modelGraph['pool4'] = tf.nn.max_pool(modelGraph['relu4_3'], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')
-    
-    modelGraph['conv5_1'] = tf.nn.conv2d(modelGraph['pool4'], filter = vbbWeights(24), strides = [1, 1, 1, 1], padding = 'SAME')
-    modelGraph['relu5_1'] = tf.nn.relu(modelGraph['conv5_1'] + vbbConstants(24))
-    modelGraph['conv5_2'] = tf.nn.conv2d(modelGraph['relu5_1'], filter = vbbWeights(26), strides = [1, 1, 1, 1], padding = 'SAME')
-    modelGraph['relu5_2'] = tf.nn.relu(modelGraph['conv5_2'] + vbbConstants(26))
-    modelGraph['conv5_3'] = tf.nn.conv2d(modelGraph['relu5_2'], filter = vbbWeights(28), strides = [1, 1, 1, 1], padding = 'SAME')
-    modelGraph['relu5_3'] = tf.nn.relu(modelGraph['conv5_3'] + vbbConstants(28))
-    modelGraph['pool5'] = tf.nn.max_pool(modelGraph['relu5_3'], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')
-    
-    modelGraph['resh1'] = tf.reshape(modelGraph['pool5'], [-1, 25088])
-    modelGraph['fc6'] = tf.nn.relu_layer(modelGraph['resh1'], tf.reshape(vbbWeights(31), [25088, 4096]), vbbConstants(31))
-    modelGraph['dropout1'] = tf.nn.dropout(modelGraph['fc6'], 0.5)
-    modelGraph['fc7'] = tf.nn.relu_layer(modelGraph['dropout1'], tf.squeeze(vbbWeights(34), [0, 1]), vbbConstants(34))
-    modelGraph['dropout2'] = tf.nn.dropout(modelGraph['fc7'], 0.5)
-    modelGraph['fc8'] = tf.nn.relu_layer(modelGraph['dropout2'], tf.squeeze(vbbWeights(37), [0, 1]), vbbConstants(37))
-
-    return modelGraph
diff --git a/tmp/vggverydeep19.py b/tmp/vggverydeep19.py
deleted file mode 100644
index 86f22c561..000000000
--- a/tmp/vggverydeep19.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""Load the VGG imagenet model into TensorFlow.
-Download the model from http://www.robots.ox.ac.uk/~vgg/research/very_deep/
-and point to the file 'imagenet-vgg-verydeep-19.mat'
-"""
-import numpy as np
-from scipy import io
-import tensorflow as tf
-
-def load(filename, images):
-    vgg19 = io.loadmat(filename)
-    vgg19Layers = vgg19['layers']
-    
-    # A function to get the weights of the VGG layers
-    def vbbWeights(layerNumber):
-        W = vgg19Layers[0][layerNumber][0][0][2][0][0]
-        W = tf.constant(W)
-        return W
-      
-    def vbbConstants(layerNumber):
-        b = vgg19Layers[0][layerNumber][0][0][2][0][1].T
-        b = tf.constant(np.reshape(b, (b.size)))
-        return b
-    
-    modelGraph = {}
-    modelGraph['input'] = images
-    modelGraph['conv1_1'] = tf.nn.relu(tf.nn.conv2d(modelGraph['input'], filter = vbbWeights(0), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(0))
-    modelGraph['conv1_2'] = tf.nn.relu(tf.nn.conv2d(modelGraph['conv1_1'], filter = vbbWeights(2), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(2))
-    modelGraph['avgpool1'] = tf.nn.avg_pool(modelGraph['conv1_2'], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')
-    modelGraph['conv2_1'] = tf.nn.relu(tf.nn.conv2d(modelGraph['avgpool1'], filter = vbbWeights(5), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(5))
-    modelGraph['conv2_2'] = tf.nn.relu(tf.nn.conv2d(modelGraph['conv2_1'], filter = vbbWeights(7), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(7))
-    modelGraph['avgpool2'] = tf.nn.avg_pool(modelGraph['conv2_2'], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')
-    modelGraph['conv3_1'] = tf.nn.relu(tf.nn.conv2d(modelGraph['avgpool2'], filter = vbbWeights(10), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(10))
-    modelGraph['conv3_2'] = tf.nn.relu(tf.nn.conv2d(modelGraph['conv3_1'], filter = vbbWeights(12), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(12))
-    modelGraph['conv3_3'] = tf.nn.relu(tf.nn.conv2d(modelGraph['conv3_2'], filter = vbbWeights(14), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(14))
-    modelGraph['conv3_4'] = tf.nn.relu(tf.nn.conv2d(modelGraph['conv3_3'], filter = vbbWeights(16), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(16))
-    modelGraph['avgpool3'] = tf.nn.avg_pool(modelGraph['conv3_4'], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')
-    modelGraph['conv4_1'] = tf.nn.relu(tf.nn.conv2d(modelGraph['avgpool3'], filter = vbbWeights(19), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(19))
-    modelGraph['conv4_2'] = tf.nn.relu(tf.nn.conv2d(modelGraph['conv4_1'], filter = vbbWeights(21), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(21))
-    modelGraph['conv4_3'] = tf.nn.relu(tf.nn.conv2d(modelGraph['conv4_2'], filter = vbbWeights(23), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(23))
-    modelGraph['conv4_4'] = tf.nn.relu(tf.nn.conv2d(modelGraph['conv4_3'], filter = vbbWeights(25), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(25))
-    modelGraph['avgpool4'] = tf.nn.avg_pool(modelGraph['conv4_4'], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')
-    modelGraph['conv5_1'] = tf.nn.relu(tf.nn.conv2d(modelGraph['avgpool4'], filter = vbbWeights(28), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(28))
-    modelGraph['conv5_2'] = tf.nn.relu(tf.nn.conv2d(modelGraph['conv5_1'], filter = vbbWeights(30), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(30))
-    modelGraph['conv5_3'] = tf.nn.relu(tf.nn.conv2d(modelGraph['conv5_2'], filter = vbbWeights(32), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(32))
-    modelGraph['conv5_4'] = tf.nn.relu(tf.nn.conv2d(modelGraph['conv5_3'], filter = vbbWeights(34), strides = [1, 1, 1, 1], padding = 'SAME') + vbbConstants(34))
-    modelGraph['avgpool5'] = tf.nn.avg_pool(modelGraph['conv5_4'], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')
-    
-    return modelGraph
-
diff --git a/tmp/visualize.py b/tmp/visualize.py
deleted file mode 100644
index 6e5ea6877..000000000
--- a/tmp/visualize.py
+++ /dev/null
@@ -1,121 +0,0 @@
-"""Visualize individual feature channels and their combinations to explore the space of patterns learned by the neural network
-Based on http://nbviewer.jupyter.org/github/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb
-"""
-# MIT License
-# 
-# Copyright (c) 2016 David Sandberg
-# 
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-# 
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-# 
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import numpy as np
-import sys
-import argparse
-import tensorflow as tf
-import importlib
-from scipy import misc
-
-def main(args):
-  
-    network = importlib.import_module(args.model_def, 'inference')
-
-    # Start with a gray image with a little noise
-    np.random.seed(seed=args.seed)
-    img_noise = np.random.uniform(size=(args.image_size,args.image_size,3)) + 100.0
-  
-    sess = tf.Session()
-  
-    t_input = tf.placeholder(np.float32, shape=(args.image_size,args.image_size,3), name='input') # define the input tensor
-    image_mean = 117.0
-    t_preprocessed = tf.expand_dims(t_input-image_mean, 0)
-     
-    # Build the inference graph
-    network.inference(t_preprocessed, 1.0, 
-            phase_train=True, weight_decay=0.0)
-      
-    # Create a saver for restoring variables
-    saver = tf.train.Saver(tf.global_variables())
-  
-    # Restore the parameters
-    saver.restore(sess, args.model_file)
-  
-    layers = [op.name for op in tf.get_default_graph().get_operations() if op.type=='Conv2D']
-    feature_nums = {layer: int(T(layer).get_shape()[-1]) for layer in layers}
-  
-    print('Number of layers: %d' % len(layers))
-  
-    for layer in sorted(feature_nums.keys()):
-        print('%s%d' % ((layer+': ').ljust(40), feature_nums[layer]))
-  
-    # Picking some internal layer. Note that we use outputs before applying the ReLU nonlinearity
-    # to have non-zero gradients for features with negative initial activations.
-    layer = 'InceptionResnetV1/Repeat_2/block8_3/Conv2d_1x1/Conv2D'
-    #layer = 'incept4b/in4_conv1x1_31/Conv2D'
-    result_dir = '../data/'
-    print('Number of features in layer "%s": %d' % (layer, feature_nums[layer]))
-    channels = range(feature_nums[layer])
-    np.random.shuffle(channels)
-    for i in range(32):
-        print('Rendering feature %d' % channels[i])
-        channel = channels[i]
-        img = render_naive(sess, t_input, T(layer)[:,:,:,channel], img_noise)
-        filename = '%s_%03d.png' % (layer.replace('/', '_'), channel)
-        misc.imsave(os.path.join(result_dir, filename), img)
-  
-
-def T(layer):
-    '''Helper for getting layer output tensor'''
-    return tf.get_default_graph().get_tensor_by_name('%s:0' % layer)
-
-def visstd(a, s=0.1):
-    '''Normalize the image range for visualization'''
-    return (a-a.mean())/max(a.std(), 1e-4)*s + 0.5
-
-def render_naive(sess, t_input, t_obj, img0, iter_n=20, step=1.0):
-    t_score = tf.reduce_mean(t_obj) # defining the optimization objective
-    t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!
-    
-    img = img0.copy()
-    for _ in range(iter_n):
-        g, _ = sess.run([t_grad, t_score], {t_input:img})
-        # normalizing the gradient, so the same step size should work 
-        g /= g.std()+1e-8         # for different layers and networks
-        img += g*step
-    return visstd(img)
-
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    
-    parser.add_argument('model_file', type=str, 
-        help='Directory containing the graph definition and checkpoint files.')
-    parser.add_argument('--model_def', type=str, 
-        help='Model definition. Points to a module containing the definition of the inference graph.',
-        default='models.nn4')
-    parser.add_argument('--image_size', type=int,
-        help='Image size (height, width) in pixels.', default=96)
-    parser.add_argument('--seed', type=int,
-        help='Random seed.', default=666)
-    return parser.parse_args(argv)
-
-if __name__ == '__main__':
-    main(parse_arguments(sys.argv[1:]))
diff --git a/tmp/visualize_vgg_model.py b/tmp/visualize_vgg_model.py
deleted file mode 100644
index 946893688..000000000
--- a/tmp/visualize_vgg_model.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import numpy as np
-from scipy import misc
-import tensorflow as tf
-from matplotlib import pyplot, image
-import vggverydeep19
-
-paintingStyleImage = image.imread("../data/schoolofathens.jpg")
-pyplot.imshow(paintingStyleImage)
-
-inputImage = image.imread("../data/grandcentral.jpg")
-pyplot.imshow(inputImage)
-
-outputWidth = 800
-outputHeight = 600
-
-# Beta constant 
-beta = 5
-# Alpha constant
-alpha = 100
-# Noise ratio
-noiseRatio = 0.6
-
-nodes = vggverydeep19.load('../data/imagenet-vgg-verydeep-19.mat', (600, 800))
-
-# Mean VGG-19 image
-meanImage19 = np.array([103.939, 116.779, 123.68]).reshape((1,1,1,3)) #pylint: disable=no-member
-
-
-
-# Squared-error loss of content between the two feature representations
-def sqErrorLossContent(sess, modelGraph, layer):
-    p = session.run(modelGraph[layer])
-    #pylint: disable=maybe-no-member
-    N = p.shape[3]
-    M = p.shape[1] * p.shape[2]
-    return (1 / (4 * N * M)) * tf.reduce_sum(tf.pow(modelGraph[layer] - sess.run(modelGraph[layer]), 2))
- 
-# Squared-error loss of style between the two feature representations
-styleLayers = [
-    ('conv1_1', 0.2),
-    ('conv2_1', 0.2),
-    ('conv3_1', 0.2),
-    ('conv4_1', 0.2),
-    ('conv5_1', 0.2),
-]
-def sqErrorLossStyle(sess, modelGraph):
-    def intermediateCalc(x, y):
-        N = x.shape[3]
-        M = x.shape[1] * x.shape[2]
-        A = tf.matmul(tf.transpose(tf.reshape(x, (M, N))), tf.reshape(x, (M, N)))
-        G = tf.matmul(tf.transpose(tf.reshape(y, (M, N))), tf.reshape(y, (M, N)))
-        return (1 / (4 * N**2 * M**2)) * tf.reduce_sum(tf.pow(G - A, 2))
-    E = [intermediateCalc(sess.run(modelGraph[layerName]), modelGraph[layerName]) for layerName, _ in styleLayers]
-    W = [w for _, w in styleLayers]
-    return sum([W[layerNumber] * E[layerNumber] for layerNumber in range(len(styleLayers))])
-
-session = tf.InteractiveSession()
- 
-# Addition of extra dimension to image
-inputImage = np.reshape(inputImage, ((1,) + inputImage.shape))
-inputImage = inputImage - meanImage19
-# Display image
-pyplot.imshow(inputImage[0])
-
-# Addition of extra dimension to image
-paintingStyleImage = np.reshape(paintingStyleImage, ((1,) + paintingStyleImage.shape))
-paintingStyleImage = paintingStyleImage - meanImage19
-# Display image
-pyplot.imshow(paintingStyleImage[0])
-
-imageNoise = np.random.uniform(-20, 20, (1, outputHeight, outputWidth, 3)).astype('float32')
-pyplot.imshow(imageNoise[0])
-mixedImage = imageNoise * noiseRatio + inputImage * (1 - noiseRatio)
-pyplot.imshow(inputImage[0])
-
-
-session.run(tf.global_variables_initializer())
-session.run(nodes['input'].assign(inputImage))
-contentLoss = sqErrorLossContent(session, nodes, 'conv4_2')
-session.run(nodes['input'].assign(paintingStyleImage))
-styleLoss = sqErrorLossStyle(session, nodes)
-totalLoss = beta * contentLoss + alpha * styleLoss
-
-optimizer = tf.train.AdamOptimizer(2.0)
-trainStep = optimizer.minimize(totalLoss)
-session.run(tf.global_variables_initializer())
-session.run(nodes['input'].assign(inputImage))
-# Number of iterations to run.
-iterations = 2000
-session.run(tf.global_variables_initializer())
-session.run(nodes['input'].assign(inputImage))
- 
-for iters in range(iterations):
-    session.run(trainStep)
-    if iters%50 == 0:
-        # Output every 50 iterations for animation       
-        filename = 'output%d.png' % (iters)
-        im = mixedImage + meanImage19
-        im = im[0]
-        im = np.clip(im, 0, 255).astype('uint8')
-        misc.imsave(filename, im)
- 
-im = mixedImage + meanImage19
-im = im[0]
-im = np.clip(im, 0, 255).astype('uint8')
-misc.imsave('finalImage.png', im)
-
diff --git a/tmp/visualize_vggface.py b/tmp/visualize_vggface.py
deleted file mode 100644
index c34004cdd..000000000
--- a/tmp/visualize_vggface.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import numpy as np
-import tensorflow as tf
-import matplotlib.pyplot as plt
-import tmp.vggface16
-
-def main():
-  
-    sess = tf.Session()
-  
-    t_input = tf.placeholder(np.float32, name='input') # define the input tensor
-    image_mean = 117.0
-    t_preprocessed = tf.expand_dims(t_input-image_mean, 0)
-     
-    # Build the inference graph
-    nodes = tmp.vggface16.load('data/vgg_face.mat', t_preprocessed)
-        
-    img_noise = np.random.uniform(size=(224,224,3)) + 117.0
-
-    # Picking some internal layer. Note that we use outputs before applying the ReLU nonlinearity
-    # to have non-zero gradients for features with negative initial activations.
-    layer = 'conv5_3'
-    channel = 140 # picking some feature channel to visualize
-    img = render_naive(sess, t_input, nodes[layer][:,:,:,channel], img_noise)
-    showarray(img)
-
-def showarray(a):
-    a = np.uint8(np.clip(a, 0, 1)*255)
-    plt.imshow(a)
-    plt.show()
-    
-def visstd(a, s=0.1):
-    '''Normalize the image range for visualization'''
-    return (a-a.mean())/max(a.std(), 1e-4)*s + 0.5
-
-def render_naive(sess, t_input, t_obj, img0, iter_n=20, step=1.0):
-    t_score = tf.reduce_mean(t_obj) # defining the optimization objective
-    t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!
-    
-    img = img0.copy()
-    for _ in range(iter_n):
-        g, _ = sess.run([t_grad, t_score], {t_input:img})
-        # normalizing the gradient, so the same step size should work 
-        g /= g.std()+1e-8         # for different layers and networks
-        img += g*step
-    return visstd(img)
-
-  
-if __name__ == '__main__':
-    main()
diff --git a/util/plot_learning_curves.m b/util/plot_learning_curves.m
deleted file mode 100644
index c0f24a344..000000000
--- a/util/plot_learning_curves.m
+++ /dev/null
@@ -1,300 +0,0 @@
-% Plots the lerning curves for the specified training runs from data in the
-% file "lfw_result.txt" stored in the log directory for the respective
-% model.
-
-% MIT License
-% 
-% Copyright (c) 2016 David Sandberg
-% 
-% Permission is hereby granted, free of charge, to any person obtaining a copy
-% of this software and associated documentation files (the "Software"), to deal
-% in the Software without restriction, including without limitation the rights
-% to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-% copies of the Software, and to permit persons to whom the Software is
-% furnished to do so, subject to the following conditions:
-% 
-% The above copyright notice and this permission notice shall be included in all
-% copies or substantial portions of the Software.
-% 
-% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-% IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-% AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-% OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-% SOFTWARE.
-
-%%
-addpath('/home/david/git/facenet/util/');
-log_dirs = { '/home/david/logs/facenet' };
-%%
-res = { ...
-{ '20180402-114759', 'vggface2, wd=5e-4, center crop, fixed image standardization' }, ...
-};
-
-%%
-res = { ...
-{ '20180408-102900', 'casia, wd=5e-4, pnlf=5e-4, fixed image standardization' }, ...
-};
-
-%%
-
-colors = {'b', 'g', 'r', 'c', 'm', 'y', 'k'};
-markers = {'.', 'o', 'x', '+', '*', 's', 'd' };
-lines = {'-', '-.', '--', ':' };
-fontSize = 6;
-lineWidth = 2;
-lineStyles = combineStyles(colors, markers);
-lineStyles2 = combineStyles(colors, {''}, lines);
-legends = cell(length(res),1);
-legends_accuracy = cell(length(res),1);
-legends_valrate = cell(length(res),1);
-var = cell(length(res),1);
-for i=1:length(res),
-    for k=1:length(log_dirs)
-        if exist(fullfile(log_dirs{k}, res{i}{1}), 'dir')
-            ld = log_dirs{k};
-        end
-    end
-    filename = fullfile(ld, res{i}{1}, 'stat.h5');
-    
-    var{i} = readlogs(filename,{'loss', 'reg_loss', 'xent_loss', 'lfw_accuracy', ...
-        'lfw_valrate', 'val_loss', 'val_xent_loss', 'val_accuracy', ...
-        'accuracy', 'prelogits_norm', 'learning_rate', 'center_loss', ...
-        'prelogits_hist', 'accuracy'});
-    var{i}.steps = 1:length(var{i}.loss);
-    epoch = find(var{i}.lfw_accuracy,1,'last');
-    var{i}.epochs = 1:epoch;
-    legends{i} = sprintf('%s: %s', res{i}{1}, res{i}{2});
-    start_epoch = max(1,epoch-10);
-    legends_accuracy{i} = sprintf('%s: %s (%.2f%%)', res{i}{1}, res{i}{2}, mean(var{i}.lfw_accuracy(start_epoch:epoch))*100 );
-    legends_valrate{i} = sprintf('%s: %s (%.2f%%)', res{i}{1}, res{i}{2}, mean(var{i}.lfw_valrate(start_epoch:epoch))*100 );
-    
-    arguments_filename = fullfile(ld, res{i}{1}, 'arguments.txt');
-    if exist(arguments_filename)
-        str = fileread(arguments_filename);
-        var{i}.wd = getParameter(str, 'weight_decay', '0.0');
-        var{i}.cl = getParameter(str, 'center_loss_factor', '0.0');
-        var{i}.fixed_std = getParameter(str, 'use_fixed_image_standardization', '0');
-        var{i}.data_dir = getParameter(str, 'data_dir', '');
-        var{i}.lr = getParameter(str, 'learning_rate', '0.1');
-        var{i}.epoch_size = str2double(getParameter(str, 'epoch_size', '1000'));
-        var{i}.batch_size = str2double(getParameter(str, 'batch_size', '90'));
-        var{i}.examples_per_epoch = var{i}.epoch_size*var{i}.batch_size;
-        var{i}.mnipc = getParameter(str, 'filter_min_nrof_images_per_class', '-1');
-        var{i}.val_step = str2num(getParameter(str, 'validate_every_n_epochs', '10'));
-        var{i}.pnlf = getParameter(str, 'prelogits_norm_loss_factor', '-1');
-        var{i}.emb_size = getParameter(str, 'embedding_size', '-1');
-
-        fprintf('%s: wd=%s lr=%s, pnlf=%s, data_dir=%s, emb_size=%s\n', ...
-            res{i}{1}, var{i}.wd, var{i}.lr, var{i}.pnlf, var{i}.data_dir, var{i}.emb_size);
-    end
-end;
-
-timestr = datestr(now,'yyyymmdd_HHMMSS');
-
-h = 1; figure(h); close(h); figure(h); hold on; setsize(1.5);
-title('LFW accuracy');
-xlabel('Steps');
-ylabel('Accuracy');
-grid on;
-N = 1; flt = ones(1,N)/N;
-for i=1:length(var),
-    plot(var{i}.epochs*1000, filter(flt, 1, var{i}.lfw_accuracy(var{i}.epochs)), lineStyles2{i}, 'LineWidth', lineWidth);
-end;
-legend(legends_accuracy,'Location','SouthEast','FontSize',fontSize);
-v=axis;
-v(3:4) = [ 0.95 1.0 ];
-axis(v);
-accuracy_file_name = sprintf('lfw_accuracy_%s',timestr);
-%print(accuracy_file_name,'-dpng')
-
-
-if 0
-    %%
-    %h = 2; figure(h); close(h); figure(h); hold on; setsize(1.5);
-    h = 1; figure(h); hold on;
-    title('LFW validation rate');
-    xlabel('Step');
-    ylabel('VAL @ FAR = 10^{-3}');
-    grid on;
-    for i=1:length(var),
-        plot(var{i}.epochs*1000, var{i}.lfw_valrate(var{i}.epochs), lineStyles{i}, 'LineWidth', lineWidth);
-    end;
-    legend(legends_valrate,'Location','SouthEast','FontSize',fontSize);
-    v=axis;
-    v(3:4) = [ 0.5 1.0 ];
-    axis(v);
-    valrate_file_name = sprintf('lfw_valrate_%s',timestr);
-%    print(valrate_file_name,'-dpng')
-end
-
-if 0
-    %% Plot cross-entropy loss
-    h = 3; figure(h); close(h); figure(h); hold on; setsize(1.5);
-    title('Training/validation set cross-entropy loss');
-    xlabel('Step');
-    title('Training/validation set cross-entropy loss');
-    grid on;
-    N = 500; flt = ones(1,N)/N;
-    for i=1:length(var),
-        var{i}.xent_loss(var{i}.xent_loss==0) = NaN;
-        plot(var{i}.steps, filter(flt, 1, var{i}.xent_loss), lineStyles2{i}, 'LineWidth', lineWidth);
-    end;
-    legend(legends, 'Location', 'NorthEast','FontSize',fontSize);
-
-    % Plot cross-entropy loss on validation set
-    N = 1; flt = ones(1,N)/N;
-    for i=1:length(var),
-        v = var{i}.val_xent_loss;
-        val_steps = (1:length(v))*var{i}.val_step*1000;
-        v(v==0) = NaN;
-        plot(val_steps, filter(flt, 1, v), [ lineStyles2{i} '.' ], 'LineWidth', lineWidth);
-    end;
-    legend(legends, 'Location', 'NorthEast','FontSize',fontSize);
-    hold off
-    xent_file_name = sprintf('xent_%s',timestr);
-    %print(xent_file_name,'-dpng')
-end
-
-if 0
-    %% Plot accuracy on training set
-    h = 32; figure(h); clf; hold on; 
-    title('Training/validation set accuracy');
-    xlabel('Step');
-    ylabel('Training/validation set accuracy');
-    grid on;
-    N = 500; flt = ones(1,N)/N;
-    for i=1:length(var),
-        var{i}.accuracy(var{i}.accuracy==0) = NaN;
-        plot(var{i}.steps*1000, filter(flt, 1, var{i}.accuracy), lineStyles2{i}, 'LineWidth', lineWidth);
-    end;
-    legend(legends, 'Location', 'SouthEast','FontSize',fontSize);
-
-    grid on;
-    N = 1; flt = ones(1,N)/N;
-    for i=1:length(var),
-        v = var{i}.val_accuracy;
-        val_steps = (1:length(v))*var{i}.val_step*1000;
-        v(v==0) = NaN;
-        plot(val_steps*1000, filter(flt, 1, v), [ lineStyles2{i} '.' ], 'LineWidth', lineWidth);
-    end;
-    legend(legends, 'Location', 'SouthEast','FontSize',fontSize);
-    hold off
-    acc_file_name = sprintf('accuracy_%s',timestr);
-    %print(acc_file_name,'-dpng')
-end
-
-if 0
-    %% Plot prelogits CDF
-    h = 35; figure(h); clf; hold on; 
-    title('Prelogits histogram');
-    xlabel('Epoch');
-    ylabel('Prelogits histogram');
-    grid on;
-    N = 1; flt = ones(1,N)/N;
-    for i=1:length(var),
-        epoch = var{i}.epochs(end);
-        q = cumsum(var{i}.prelogits_hist(:,epoch));
-        q2 = q / q(end);
-        plot(linspace(0,10,1000), q2, lineStyles2{i}, 'LineWidth', lineWidth);
-    end;
-    legend(legends, 'Location', 'SouthEast','FontSize',fontSize);
-    hold off
-end
-
-if 0
-    %% Plot prelogits norm
-    h = 32; figure(h); clf; hold on; 
-    title('Prelogits norm');
-    xlabel('Step');
-    ylabel('Prelogits norm');
-    grid on;
-    N = 1; flt = ones(1,N)/N;
-    for i=1:length(var),
-        plot(var{i}.steps, filter(flt, 1, var{i}.prelogits_norm), lineStyles2{i}, 'LineWidth', lineWidth);
-    end;
-    legend(legends, 'Location', 'NorthEast','FontSize',fontSize);
-    hold off
-end
-
-if 0
-    %% Plot learning rate
-    h = 42; figure(h); clf; hold on; 
-    title('Learning rate');
-    xlabel('Step');
-    ylabel('Learning rate');
-    grid on;
-    N = 1; flt = ones(1,N)/N;
-    for i=1:length(var),
-        semilogy(var{i}.epochs, filter(flt, 1, var{i}.learning_rate(var{i}.epochs)), lineStyles2{i}, 'LineWidth', lineWidth);
-    end;
-    legend(legends, 'Location', 'NorthEast','FontSize',fontSize);
-    hold off
-end
-
-if 0
-    %% Plot center loss
-    h = 9; figure(h); close(h); figure(h); hold on; setsize(1.5);
-    title('Center loss');
-    xlabel('Epochs');
-    ylabel('Center loss');
-    grid on;
-    N = 500; flt = ones(1,N)/N;
-    for i=1:length(var),
-        if isempty(var{i}.center_loss)
-            var{i}.center_loss = ones(size(var{i}.steps))*NaN;
-        end;
-        var{i}.center_loss(var{i}.center_loss==0) = NaN;
-        plot(var{i}.steps/var{i}.epoch_size, filter(flt, 1, var{i}.center_loss), lineStyles2{i}, 'LineWidth', lineWidth);
-    end;
-    legend(legends, 'Location', 'NorthEast','FontSize',fontSize);
-end
-
-if 0
-    %% Plot center loss with factor
-    h = 9; figure(h); close(h); figure(h); hold on; setsize(1.5);
-    title('Center loss with factor');
-    xlabel('Epochs');
-    ylabel('Center loss * center loss factor');
-    grid on;
-    N = 500; flt = ones(1,N)/N;
-    for i=1:length(var),
-        if isempty(var{i}.center_loss)
-            var{i}.center_loss = ones(size(var{i}.steps))*NaN;
-        end;
-        var{i}.center_loss(var{i}.center_loss==0) = NaN;
-        plot(var{i}.steps/var{i}.epoch_size, filter(flt, 1, var{i}.center_loss*str2num(var{i}.cl)), lineStyles2{i}, 'LineWidth', lineWidth);
-    end;
-    legend(legends, 'Location', 'NorthEast','FontSize',fontSize);
-end
-
-if 0
-    %% Plot total loss
-    h = 4; figure(h); close(h); figure(h); hold on; setsize(1.5);
-    title('Total loss');
-    xlabel('Epochs');
-    ylabel('Total loss');
-    grid on;
-    N = 500; flt = ones(1,N)/N;
-    for i=1:length(var),
-        var{i}.loss(var{i}.loss==0) = NaN;
-        plot(var{i}.steps/var{i}.epoch_size, filter(flt, 1, var{i}.loss), lineStyles2{i}, 'LineWidth', lineWidth);
-    end;
-    legend(legends, 'Location', 'NorthEast','FontSize',fontSize);
-end
-
-if 0
-    %% Plot regularization loss
-    h = 5; figure(h); close(h); figure(h); hold on; setsize(1.5);
-    title('Regularization loss');
-    xlabel('Epochs');
-    ylabel('Regularization loss');
-    grid on;
-    N = 500; flt = ones(1,N)/N;
-    for i=1:length(var),
-        var{i}.reg_loss(var{i}.reg_loss==0) = NaN;
-        plot(var{i}.steps/var{i}.epoch_size, filter(flt, 1, var{i}.reg_loss), lineStyles2{i}, 'LineWidth', lineWidth);
-    end;
-    legend(legends, 'Location', 'NorthEast','FontSize',fontSize);
-end