diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index a8ff158..ecad05c 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -55,13 +55,18 @@ jobs:
       fail-fast: false
       matrix:
         python-version: ["3.8", "3.9", "3.10", "3.11"]
-        torch-version: ["1.9.*", "1.10.*", "1.11.*", "1.12.*", "1.13.*", "2.0.*"]
-        lightning-version: ["2.0.*"]
+        torch-version: ["1.9.*", "1.10.*", "1.11.*", "1.12.*", "1.13.*", "2.0.*", "2.1.*"]
+        lightning-version: ["2.0.*", "2.1.*"]
         exclude:
+          - python-version: "3.10"
+            torch-version: "1.8.*"
           - python-version: "3.10"
             torch-version: "1.9.*"
           - python-version: "3.10"
             torch-version: "1.10.*"
+
+          - python-version: "3.11"
+            torch-version: "1.8.*"
           - python-version: "3.11"
             torch-version: "1.9.*"
           - python-version: "3.11"
diff --git a/src/tensorneko/arch/binary_classifier.py b/src/tensorneko/arch/binary_classifier.py
index 85d70b5..0cb4e01 100644
--- a/src/tensorneko/arch/binary_classifier.py
+++ b/src/tensorneko/arch/binary_classifier.py
@@ -1,18 +1,17 @@
-from abc import ABC
-from typing import Optional, Union, Sequence, Dict
+from typing import Optional, Union, Sequence, Dict, Any
 
 from torch import Tensor
-from torch.nn import BCEWithLogitsLoss
+from torch.nn import BCEWithLogitsLoss, Module
 from torch.optim import Adam
 from torchmetrics import Accuracy, F1Score, AUROC
 
 from ..neko_model import NekoModel
 
 
-class BinaryClassifier(NekoModel, ABC):
+class BinaryClassifier(NekoModel):
 
-    def __init__(self, model=None, learning_rate: float = 1e-4, distributed: bool = False):
-        super().__init__()
+    def __init__(self, name, model: Module, learning_rate: float = 1e-4, distributed: bool = False):
+        super().__init__(name)
         self.save_hyperparameters()
         self.model = model
         self.learning_rate = learning_rate
@@ -23,8 +22,10 @@ def __init__(self, model=None, learning_rate: float = 1e-4, distributed: bool =
         self.auc_fn = AUROC(task="binary")
 
     @classmethod
-    def from_module(cls, model, learning_rate: float = 1e-4, distributed=False):
-        return cls(model, learning_rate, distributed)
+    def from_module(cls, model: Module, learning_rate: float = 1e-4, name: str = "binary_classifier",
+        distributed: bool = False
+    ):
+        return cls(name, model, learning_rate, distributed)
 
     def forward(self, x):
         return self.model(x)
@@ -49,6 +50,10 @@ def validation_step(self, batch: Optional[Union[Tensor, Sequence[Tensor]]] = Non
     ) -> Dict[str, Tensor]:
         return self.step(batch)
 
+    def predict_step(self, batch: Tensor, batch_idx: int, dataloader_idx: Optional[int] = None) -> Any:
+        x, y = batch
+        return self(x)
+
     def configure_optimizers(self):
         optimizer = Adam(self.parameters(), lr=self.learning_rate)
         return [optimizer]
diff --git a/src/tensorneko/callback/gpu_stats_logger.py b/src/tensorneko/callback/gpu_stats_logger.py
index 6018514..b78d65e 100644
--- a/src/tensorneko/callback/gpu_stats_logger.py
+++ b/src/tensorneko/callback/gpu_stats_logger.py
@@ -1,31 +1,68 @@
+from typing import Any
+
 from lightning.pytorch import Callback, Trainer, LightningModule
+from lightning.pytorch.utilities.types import STEP_OUTPUT
 
 
 class GpuStatsLogger(Callback):
     """Log GPU stats for each training epoch"""
 
-    def __init__(self, delay: float = 0.5):
+    def __init__(self, delay: float = 0.5, on_epoch: bool = True, on_step: bool = False):
         try:
             from gpumonitor.monitor import GPUStatMonitor
         except ImportError:
             raise ImportError("gpumonitor is required to use GPUStatsLogger")
 
-        self.monitor = GPUStatMonitor(delay=delay)
+        self.monitor_epoch = GPUStatMonitor(delay=delay) if on_epoch else None
+        self.monitor_step = GPUStatMonitor(delay=delay) if on_step else None
+        self.on_epoch = on_epoch
+        self.on_step = on_step
+        assert self.on_epoch or self.on_step, "on_epoch and on_step cannot be both False"
 
     def on_train_epoch_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
-        self.monitor.reset()
+        if not self.on_epoch:
+            return
+        self.monitor_epoch.reset()
 
     def on_train_epoch_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
-        for gpu in self.monitor.average_stats.gpus:
+        if not self.on_epoch:
+            return
+        for gpu in self.monitor_epoch.average_stats.gpus:
+            logged_info = {
+                f"gpu{gpu.index}_memory_used_epoch": gpu.memory_used / 1024,
+                f"gpu{gpu.index}_memory_total_epoch": gpu.memory_total / 1024,
+                f"gpu{gpu.index}_memory_util_epoch": gpu.memory_used / gpu.memory_total,
+                f"gpu{gpu.index}_temperature_epoch": float(gpu.temperature),
+                f"gpu{gpu.index}_utilization_epoch": gpu.utilization / 100,
+                f"gpu{gpu.index}_power_draw_epoch": float(gpu.power_draw),
+                f"gpu{gpu.index}_power_percentage_epoch": gpu.power_draw / gpu.power_limit,
+                f"gpu{gpu.index}_fan_speed_epoch": float(gpu.fan_speed) if gpu.fan_speed is not None else 0.,
+            }
+            pl_module.logger.log_metrics(logged_info, step=trainer.global_step)
+            pl_module.log_dict(logged_info, logger=False, sync_dist=pl_module.distributed)
+
+    def on_train_batch_start(
+        self, trainer: Trainer, pl_module: LightningModule, batch: Any, batch_idx: int
+    ) -> None:
+        if not self.on_step:
+            return
+        self.monitor_step.reset()
+
+    def on_train_batch_end(
+        self, trainer: Trainer, pl_module: LightningModule, outputs: STEP_OUTPUT, batch: Any, batch_idx: int
+    ) -> None:
+        if not self.on_step:
+            return
+        for gpu in self.monitor_step.average_stats.gpus:
             logged_info = {
-                f"gpu{gpu.index}_memory_used": gpu.memory_used / 1024,
-                f"gpu{gpu.index}_memory_total": gpu.memory_total / 1024,
-                f"gpu{gpu.index}_memory_util": gpu.memory_used / gpu.memory_total,
-                f"gpu{gpu.index}_temperature": float(gpu.temperature),
-                f"gpu{gpu.index}_utilization": gpu.utilization / 100,
-                f"gpu{gpu.index}_power_draw": float(gpu.power_draw),
-                f"gpu{gpu.index}_power_percentage": gpu.power_draw / gpu.power_limit,
-                f"gpu{gpu.index}_fan_speed": float(gpu.fan_speed) if gpu.fan_speed is not None else 0.,
+                f"gpu{gpu.index}_memory_used_step": gpu.memory_used / 1024,
+                f"gpu{gpu.index}_memory_total_step": gpu.memory_total / 1024,
+                f"gpu{gpu.index}_memory_util_step": gpu.memory_used / gpu.memory_total,
+                f"gpu{gpu.index}_temperature_step": float(gpu.temperature),
+                f"gpu{gpu.index}_utilization_step": gpu.utilization / 100,
+                f"gpu{gpu.index}_power_draw_step": float(gpu.power_draw),
+                f"gpu{gpu.index}_power_percentage_step": gpu.power_draw / gpu.power_limit,
+                f"gpu{gpu.index}_fan_speed_step": float(gpu.fan_speed) if gpu.fan_speed is not None else 0.,
             }
             pl_module.logger.log_metrics(logged_info, step=trainer.global_step)
             pl_module.log_dict(logged_info, logger=False, sync_dist=pl_module.distributed)
diff --git a/src/tensorneko/callback/system_stats_logger.py b/src/tensorneko/callback/system_stats_logger.py
index f707607..5a8a116 100644
--- a/src/tensorneko/callback/system_stats_logger.py
+++ b/src/tensorneko/callback/system_stats_logger.py
@@ -1,22 +1,44 @@
+from typing import Any
+
 from lightning.pytorch import Callback, Trainer, LightningModule
+from lightning.pytorch.utilities.types import STEP_OUTPUT
 
 
 class SystemStatsLogger(Callback):
     """Log system stats for each training epoch"""
 
-    def __init__(self):
+    def __init__(self, on_epoch: bool = True, on_step: bool = False):
         try:
             import psutil
         except ImportError:
             raise ImportError("psutil is required to use SystemStatsLogger")
         self.psutil = psutil
+        self.on_epoch = on_epoch
+        self.on_step = on_step
+        assert self.on_epoch or self.on_step, "on_epoch and on_step cannot be both False"
 
     def on_train_epoch_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
+        if not self.on_epoch:
+            return
+        cpu_usage = self.psutil.cpu_percent()
+        memory_usage = self.psutil.virtual_memory().percent
+        logged_info = {
+            "cpu_usage_epoch": cpu_usage,
+            "memory_usage_epoch": memory_usage
+        }
+        pl_module.logger.log_metrics(logged_info, step=trainer.global_step)
+        pl_module.log_dict(logged_info, logger=False, sync_dist=pl_module.distributed)
+
+    def on_train_batch_end(
+        self, trainer: Trainer, pl_module: LightningModule, outputs: STEP_OUTPUT, batch: Any, batch_idx: int
+    ) -> None:
+        if not self.on_step:
+            return
         cpu_usage = self.psutil.cpu_percent()
         memory_usage = self.psutil.virtual_memory().percent
         logged_info = {
-            "cpu_usage": cpu_usage,
-            "memory_usage": memory_usage
+            "cpu_usage_step": cpu_usage,
+            "memory_usage_step": memory_usage
         }
         pl_module.logger.log_metrics(logged_info, step=trainer.global_step)
         pl_module.log_dict(logged_info, logger=False, sync_dist=pl_module.distributed)
diff --git a/src/tensorneko/evaluation/fid.py b/src/tensorneko/evaluation/fid.py
index b1f0b95..cb105aa 100644
--- a/src/tensorneko/evaluation/fid.py
+++ b/src/tensorneko/evaluation/fid.py
@@ -90,8 +90,8 @@ def compute(self, batch_size=128, num_workers=0, progress_bar: bool = False) ->
 
         if progress_bar:
             tqdm = import_tqdm_auto().tqdm
-            pred = tqdm(total=len(pred), desc="Forward predicted features")
-            true = tqdm(total=len(true), desc="Forward ground truth features")
+            pred = tqdm(pred, total=len(pred), desc="Forward predicted features")
+            true = tqdm(true, total=len(true), desc="Forward ground truth features")
 
         for batch in pred:
             self.fid.update(batch.to(self.device), real=False)
diff --git a/src/tensorneko/neko_trainer.py b/src/tensorneko/neko_trainer.py
index 9d889bb..f7ada1c 100644
--- a/src/tensorneko/neko_trainer.py
+++ b/src/tensorneko/neko_trainer.py
@@ -3,16 +3,20 @@
 from time import time
 from typing import Optional, Union, List, Dict
 
+from lightning.fabric.plugins.precision.precision import _PRECISION_INPUT
+from lightning.fabric.utilities.types import _PATH
 from lightning.pytorch import Trainer, Callback
 from lightning.pytorch.accelerators import Accelerator
 from lightning.pytorch.callbacks import ModelCheckpoint, Checkpoint
 from lightning.pytorch.loggers import Logger, TensorBoardLogger
-from lightning.pytorch.plugins import PLUGIN_INPUT
 from lightning.pytorch.profilers import Profiler
 from lightning.pytorch.strategies import Strategy
 from lightning.pytorch.trainer.connectors.accelerator_connector import _LITERAL_WARN
-from lightning.fabric.plugins.precision.precision import _PRECISION_INPUT
-from lightning.fabric.utilities.types import _PATH
+
+try:
+    from lightning.pytorch.plugins import PLUGIN_INPUT
+except ImportError:
+    from lightning.pytorch.plugins import _PLUGIN_INPUT as PLUGIN_INPUT
 
 from .callback import NilCallback, LrLogger, EpochNumLogger, EpochTimeLogger, GpuStatsLogger, SystemStatsLogger
 
diff --git a/src/tensorneko/util/__init__.py b/src/tensorneko/util/__init__.py
index fa3929b..d0f168b 100644
--- a/src/tensorneko/util/__init__.py
+++ b/src/tensorneko/util/__init__.py
@@ -9,7 +9,7 @@
 from .configuration import Configuration
 from .misc import reduce_dict_by, summarize_dict_by, with_printed_shape, is_bad_num, count_parameters, compose, \
     generate_inf_seq, listdir, with_printed, ifelse, dict_add, as_list, identity, list_to_dict, circular_pad, \
-    load_py, try_until_success
+    load_py, try_until_success, sample_indexes
 from .misc import get_tensorneko_path
 from .dispatched_misc import sparse2binary, binary2sparse
 from .reproducibility import Seed
@@ -71,6 +71,7 @@
     "circular_pad",
     "load_py",
     "try_until_success",
+    "sample_indexes",
     "download_file",
     "WindowMerger",
 ]
diff --git a/src/tensorneko/util/configuration.py b/src/tensorneko/util/configuration.py
index 49c0d1b..5c787da 100644
--- a/src/tensorneko/util/configuration.py
+++ b/src/tensorneko/util/configuration.py
@@ -1,10 +1,12 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Any
+from typing import Generic
 
+from tensorneko_util.util.type import T
 
-class Configuration(ABC):
+
+class Configuration(ABC, Generic[T]):
     """
     Configuration base abstract class.
 
@@ -55,7 +57,7 @@ def __iter__(self):
         return iter((*self.args, *self.kwargs.values()))
 
     @abstractmethod
-    def build(self) -> Any:
+    def build(self) -> T:
         """
         A method to build an object.
 
diff --git a/src/tensorneko/util/misc.py b/src/tensorneko/util/misc.py
index 2f7169d..d6807fb 100644
--- a/src/tensorneko/util/misc.py
+++ b/src/tensorneko/util/misc.py
@@ -9,7 +9,7 @@
 from torch.nn import Module
 
 from tensorneko_util.util.misc import generate_inf_seq, listdir, with_printed, ifelse, dict_add, as_list, \
-    identity, list_to_dict, compose, circular_pad, load_py, try_until_success
+    identity, list_to_dict, compose, circular_pad, load_py, try_until_success, sample_indexes
 from .type import T, A
 
 
@@ -165,3 +165,4 @@ def get_tensorneko_path() -> str:
 circular_pad = circular_pad
 load_py = load_py
 try_until_success = try_until_success
+sample_indexes = sample_indexes
diff --git a/src/tensorneko/util/type.py b/src/tensorneko/util/type.py
index 4a51759..643fb4e 100644
--- a/src/tensorneko/util/type.py
+++ b/src/tensorneko/util/type.py
@@ -1,4 +1,3 @@
-from enum import Enum
 from typing import Callable, Union, List, Tuple, TypeVar
 
 import numpy as np
diff --git a/src/tensorneko_util/backend/_tqdm.py b/src/tensorneko_util/backend/_tqdm.py
index 6ccfb00..516e394 100644
--- a/src/tensorneko_util/backend/_tqdm.py
+++ b/src/tensorneko_util/backend/_tqdm.py
@@ -1,4 +1,5 @@
 _is_tqdm_available = None
+_is_mita_tqdm_available = None
 
 
 def import_tqdm():
@@ -35,3 +36,21 @@ def import_tqdm_auto():
             return auto
         else:
             raise ImportError("tqdm is not installed. Please install it by `pip install tqdm`")
+
+
+def import_mita_tqdm():
+    global _is_mita_tqdm_available
+    if _is_mita_tqdm_available is None:
+        try:
+            from mita_client import mita_tqdm
+            _is_mita_tqdm_available = True
+            return mita_tqdm
+        except ImportError:
+            _is_mita_tqdm_available = False
+            raise ImportError("mita_client is not installed. Please install it by `pip install mita_client`")
+    else:
+        if _is_mita_tqdm_available:
+            from mita_client import mita_tqdm
+            return mita_tqdm
+        else:
+            raise ImportError("mita_client is not installed. Please install it by `pip install mita_client`")
diff --git a/src/tensorneko_util/preprocess/crop.py b/src/tensorneko_util/preprocess/crop.py
index c4835f4..6bc83a8 100644
--- a/src/tensorneko_util/preprocess/crop.py
+++ b/src/tensorneko_util/preprocess/crop.py
@@ -60,7 +60,7 @@ def crop_with_padding(image: ndarray, x1: int, x2: int, y1: int, y2: int, pad_va
 
 
 @dispatch
-def crop_with_padding(image: ndarray, x1: np.int32, x2: np.int32, y1: np.int32, y2: np.int32,
+def crop_with_padding(image: ndarray, x1: ndarray, x2: ndarray, y1: ndarray, y2: ndarray,
     pad_value: Union[int, float] = 0., batch: bool = False
 ) -> ndarray:
     return crop_with_padding(image, int(x1), int(x2), int(y1), int(y2), pad_value, batch)
diff --git a/src/tensorneko_util/preprocess/crop.pyi b/src/tensorneko_util/preprocess/crop.pyi
index 87a07a1..24d17d5 100644
--- a/src/tensorneko_util/preprocess/crop.pyi
+++ b/src/tensorneko_util/preprocess/crop.pyi
@@ -7,3 +7,9 @@ from numpy import ndarray
 def crop_with_padding(image: ndarray, x1: int, x2: int, y1: int, y2: int, pad_value: Union[int, float] = 0.,
     batch: bool = False
 ) -> ndarray: ...
+
+
+@overload
+def crop_with_padding(image: ndarray, x1: ndarray, x2: ndarray, y1: ndarray, y2: ndarray,
+    pad_value: Union[int, float] = 0., batch: bool = False
+) -> ndarray: ...
diff --git a/src/tensorneko_util/util/__init__.py b/src/tensorneko_util/util/__init__.py
index af9a28f..4c1be41 100644
--- a/src/tensorneko_util/util/__init__.py
+++ b/src/tensorneko_util/util/__init__.py
@@ -3,7 +3,7 @@
 from .dispatcher import dispatch
 from .fp import __, F, _, Stream, return_option, Option, Monad, Eval, Seq, AbstractSeq, curry
 from .misc import generate_inf_seq, compose, listdir, with_printed, ifelse, dict_add, as_list, identity, list_to_dict, \
-    get_tensorneko_util_path, circular_pad, load_py, try_until_success
+    get_tensorneko_util_path, circular_pad, load_py, try_until_success, sample_indexes
 from .dispatched_misc import sparse2binary, binary2sparse
 from .ref import ref
 from .timer import Timer
@@ -50,6 +50,7 @@
     "circular_pad",
     "load_py",
     "try_until_success",
+    "sample_indexes",
     "download_file",
     "WindowMerger",
 ]
diff --git a/src/tensorneko_util/util/dispatcher.py b/src/tensorneko_util/util/dispatcher.py
index 45b3cf0..1f202f4 100644
--- a/src/tensorneko_util/util/dispatcher.py
+++ b/src/tensorneko_util/util/dispatcher.py
@@ -2,7 +2,7 @@
 
 import inspect
 import warnings
-from typing import Callable, Dict, List, Generic, Sequence, Optional
+from typing import Callable, Dict, List, Generic, Sequence, Optional, TYPE_CHECKING, overload
 
 from .type import T
 
@@ -223,3 +223,7 @@ def add(x: str, y: str) -> str:
 
 
 dispatch = DispatcherDecorator()
+
+# used for type hint, only `typing.overload` can be used for type hint
+if TYPE_CHECKING:
+    dispatch = overload
diff --git a/src/tensorneko_util/util/eventbus/event.py b/src/tensorneko_util/util/eventbus/event.py
index 342de91..7d96749 100644
--- a/src/tensorneko_util/util/eventbus/event.py
+++ b/src/tensorneko_util/util/eventbus/event.py
@@ -6,9 +6,10 @@
 
 class EventMeta(type):
 
-    def __call__(cls, *args, bus=EventBus.default, **kwargs):
+    def __call__(cls, *args, **kwargs):
         event = super().__call__(*args, **kwargs)
-        bus.emit(event, blocking=_blocking_flag)
+        event.bus = kwargs.get("bus", EventBus.default)
+        event.bus.emit(event, blocking=_blocking_flag)
         return event
 
 
@@ -29,5 +30,9 @@ def no_blocking():
 class Event(metaclass=EventMeta):
     bus: EventBus
 
+    def __init_subclass__(cls, **kwargs):
+        if "bus" in cls.__init__.__annotations__:
+            raise TypeError("`bus` parameter is preserved. It should not be annotated in the __init__ method")
+
 
 E = TypeVar("E", bound=Event)
diff --git a/src/tensorneko_util/util/misc.py b/src/tensorneko_util/util/misc.py
index d3680d9..3d4d520 100644
--- a/src/tensorneko_util/util/misc.py
+++ b/src/tensorneko_util/util/misc.py
@@ -3,11 +3,13 @@
 import time
 from functools import reduce
 from os.path import dirname, abspath
-from typing import Callable, List, Dict, Iterable, Sequence, Any, Optional, Type, TypeVar, Union, Tuple
+from types import ModuleType
+from typing import Callable, List, Dict, Iterable, Sequence, Any, Optional, Type, Union, Tuple
+
 import numpy as np
 
 from .fp import F, _, Stream
-from .type import T, R
+from .type import T, R, T_E
 
 
 def identity(*args, **kwargs):
@@ -21,15 +23,15 @@ def identity(*args, **kwargs):
         return args
 
 
-def generate_inf_seq(items: Iterable[Any]) -> Stream:
+def generate_inf_seq(items: Iterable[T]) -> Stream[T]:
     """
     Generate an infinity late-evaluate sequence.
 
     Args:
-        items [``Iterable[Any]``]: Original items.
+        items [``Iterable[T]``]: Original items.
 
     Returns:
-        :class:`~fp.stream.Stream`: The infinity sequence.
+        :class:`~fp.stream.Stream[T]`: The infinity sequence.
 
     Examples::
 
@@ -103,16 +105,16 @@ def listdir(path: str, filter_func: Callable[[str], bool] = lambda arg: True) ->
     return list(map(F(os.path.join, path), files))
 
 
-def with_printed(x: Any, func: Callable = identity) -> Any:
+def with_printed(x: T, func: Callable[[T], Any] = identity) -> T:
     """
     An identity function but with printed to console with some transform.
 
     Args:
-        x (``Any``): Input.
-        func (``Callable``, optional): A function used to apply the input for printing.
+        x (``T``): Input.
+        func (``(T) -> Any``, optional): A function used to apply the input for printing. Default: identity function.
 
     Returns:
-        ``Any``: Identity output.
+        ``T``: Identity output.
 
     Examples::
 
@@ -179,15 +181,15 @@ def as_list(*args, **kwargs) -> list:
     return list(args) + list(kwargs.values())
 
 
-def list_to_dict(l: List[T], key: Callable[[T], Any]) -> Dict[Any, T]:
+def list_to_dict(l: List[T], key: Callable[[T], R]) -> Dict[R, T]:
     """
     Convert the list as a dictionary by given key.
     Args:
         l (``List[T]``): Input list.
-        key (``(T) -> Any``): The key getter function.
+        key (``(T) -> R``): The key getter function.
 
     Returns:
-        ``Dict[Any, T]``: The output dictionary.
+        ``Dict[R, T]``: The output dictionary.
     """
     return {key(x): x for x in l}
 
@@ -202,16 +204,16 @@ def get_tensorneko_util_path() -> str:
     return dirname(dirname(abspath(__file__)))
 
 
-def circular_pad(x: List, target: int) -> List:
+def circular_pad(x: List[T], target: int) -> List[T]:
     """
     Circular padding a list to the target length.
 
     Args:
-        x(``List``): Input list.
+        x(``List[T]``): Input list.
         target(``int``): Target length.
 
     Returns:
-        ``List``: The padded list.
+        ``List[T]``: The padded list.
     """
     if len(x) == target:
         return x
@@ -223,7 +225,7 @@ def circular_pad(x: List, target: int) -> List:
         return circular_pad(x + x, target)
 
 
-def load_py(path: str) -> Any:
+def load_py(path: str) -> ModuleType:
     """
     Load a python file as a module.
 
@@ -239,9 +241,6 @@ def load_py(path: str) -> Any:
     return module
 
 
-T_E = TypeVar("T_E", bound=Exception)
-
-
 def try_until_success(func: Callable[[Any], T], *args, max_trials: Optional[int] = None, sleep_time: int = 0,
     exception_type: Union[Type[T_E], Tuple[T_E, ...]] = Exception, **kwargs
 ) -> T:
diff --git a/src/tensorneko_util/util/server.py b/src/tensorneko_util/util/server.py
index 44cb7ff..f56e833 100644
--- a/src/tensorneko_util/util/server.py
+++ b/src/tensorneko_util/util/server.py
@@ -1,16 +1,10 @@
 from __future__ import annotations
 
-from abc import ABC, abstractmethod
-from typing import List
-
 import http.server
-import os
-import shutil
-import subprocess
+from abc import ABC, abstractmethod
 from http.server import HTTPServer
-from pathlib import Path
 from threading import Thread
-from typing import Optional, Union, List
+from typing import Optional, List
 
 
 class AbstractServer(ABC):
diff --git a/src/tensorneko_util/util/timer.py b/src/tensorneko_util/util/timer.py
index d325a87..acbf93e 100644
--- a/src/tensorneko_util/util/timer.py
+++ b/src/tensorneko_util/util/timer.py
@@ -1,7 +1,11 @@
+from __future__ import annotations
+
 import time
 from functools import wraps
 from typing import Callable, Optional
 
+from .type import T
+
 
 class Timer:
     """
@@ -30,6 +34,12 @@ def f():
             time.sleep(1)
             print("f")
 
+        # without parentheses
+        @Timer
+        def g():
+            time.sleep(1)
+            print("f")
+
         # disable verbose and get time manually
         with Timer(verbose=False) as t:
             time.sleep(1)
@@ -50,6 +60,12 @@ def __init__(self, verbose: bool = True):
         self.total_time = None
         self.verbose = verbose
 
+    def __new__(cls, *args, **kwargs):
+        if len(args) > 0 and isinstance(args[0], Callable):
+            return cls()(args[0])
+        else:
+            return super().__new__(cls)
+
     def __enter__(self):
         self.times.append(time.perf_counter())
         return self
@@ -84,4 +100,3 @@ def wrapper(*args, **kwargs):
     @staticmethod
     def _make_str(name: str, t: float):
         return f"[Timer] {name}: {t} sec"
-
diff --git a/src/tensorneko_util/util/type.py b/src/tensorneko_util/util/type.py
index 902f81e..021b9cb 100644
--- a/src/tensorneko_util/util/type.py
+++ b/src/tensorneko_util/util/type.py
@@ -8,6 +8,7 @@
 R = TypeVar('R')  # Return type
 P = TypeVar("P", int, float, str, bool)  # Primitive type
 E = TypeVar("E", bound=Enum)
+T_E = TypeVar("T_E", bound=Exception)  # Exception type
 
 K = TypeVar("K")  # Key type
 V = TypeVar("V")  # Value type
diff --git a/src/tensorneko_util/visualization/watcher/web/tsconfig.json b/src/tensorneko_util/visualization/watcher/web/tsconfig.json
index 6217765..d092745 100644
--- a/src/tensorneko_util/visualization/watcher/web/tsconfig.json
+++ b/src/tensorneko_util/visualization/watcher/web/tsconfig.json
@@ -2,6 +2,7 @@
   "extends": "@vue/tsconfig/tsconfig.web.json",
   "include": ["env.d.ts", "src/**/*", "src/**/*.vue"],
   "compilerOptions": {
+    "noImplicitAny": false,
     "importsNotUsedAsValues": "preserve",
     "baseUrl": ".",
     "paths": {
diff --git a/version.txt b/version.txt
index 09e9157..53b61ec 100644
--- a/version.txt
+++ b/version.txt
@@ -1 +1 @@
-0.3.5
\ No newline at end of file
+0.3.6
\ No newline at end of file