diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 73a69f2b04..c419164f30 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -36,7 +36,7 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.8.0
+    rev: v3.14.0
     hooks:
       - id: pyupgrade
         args: [--py38-plus]
@@ -48,20 +48,20 @@ repos:
       - id: nbstripout
 
   - repo: https://github.com/PyCQA/docformatter
-    rev: v1.7.3
+    rev: v1.7.5
     hooks:
       - id: docformatter
         additional_dependencies: [tomli]
         args: ["--in-place"]
 
   - repo: https://github.com/psf/black
-    rev: 23.3.0
+    rev: 23.9.1
     hooks:
       - id: black
         name: Format code
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.276
+    rev: v0.0.292
     hooks:
       - id: ruff
         args: ["--fix"]
diff --git a/src/flash/audio/classification/data.py b/src/flash/audio/classification/data.py
index 412713a9e0..fec824122f 100644
--- a/src/flash/audio/classification/data.py
+++ b/src/flash/audio/classification/data.py
@@ -42,8 +42,8 @@
 
 
 class AudioClassificationData(DataModule):
-    """The ``AudioClassificationData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of
-    class methods for loading data for audio classification."""
+    """The ``AudioClassificationData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of class
+    methods for loading data for audio classification."""
 
     input_transform_cls = AudioClassificationInputTransform
 
@@ -141,6 +141,7 @@ def from_files(
             >>> import os
             >>> _ = [os.remove(f"spectrogram_{i}.png") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_spectrogram_{i}.png") for i in range(1, 4)]
+
         """
 
         ds_kw = {
@@ -275,6 +276,7 @@ def from_folders(
             >>> import shutil
             >>> shutil.rmtree("train_folder")
             >>> shutil.rmtree("predict_folder")
+
         """
 
         ds_kw = {
@@ -365,6 +367,7 @@ def from_numpy(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {
@@ -453,6 +456,7 @@ def from_tensors(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {
@@ -607,6 +611,7 @@ def from_data_frame(
             >>> shutil.rmtree("predict_folder")
             >>> del train_data_frame
             >>> del predict_data_frame
+
         """
 
         ds_kw = {
@@ -854,6 +859,7 @@ def from_csv(
             >>> shutil.rmtree("predict_folder")
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
 
         ds_kw = {
diff --git a/src/flash/audio/speech_recognition/data.py b/src/flash/audio/speech_recognition/data.py
index 90117bf90b..85a5008210 100644
--- a/src/flash/audio/speech_recognition/data.py
+++ b/src/flash/audio/speech_recognition/data.py
@@ -117,6 +117,7 @@ def from_files(
             >>> import os
             >>> _ = [os.remove(f"speech_{i}.wav") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_speech_{i}.wav") for i in range(1, 4)]
+
         """
 
         ds_kw = {"sampling_rate": sampling_rate}
@@ -302,6 +303,7 @@ def from_csv(
             >>> _ = [os.remove(f"predict_speech_{i}.wav") for i in range(1, 4)]
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
 
         ds_kw = {"input_key": input_field, "sampling_rate": sampling_rate}
@@ -424,6 +426,7 @@ def from_json(
             >>> _ = [os.remove(f"predict_speech_{i}.wav") for i in range(1, 4)]
             >>> os.remove("train_data.json")
             >>> os.remove("predict_data.json")
+
         """
 
         ds_kw = {"input_key": input_field, "sampling_rate": sampling_rate, "field": field}
@@ -570,6 +573,7 @@ def from_datasets(
             >>> import os
             >>> _ = [os.remove(f"speech_{i}.wav") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_speech_{i}.wav") for i in range(1, 4)]
+
         """
 
         ds_kw = {"sampling_rate": sampling_rate}
diff --git a/src/flash/audio/speech_recognition/model.py b/src/flash/audio/speech_recognition/model.py
index a3c379c642..b88e3a02bd 100644
--- a/src/flash/audio/speech_recognition/model.py
+++ b/src/flash/audio/speech_recognition/model.py
@@ -45,6 +45,7 @@ class SpeechRecognition(Task):
         learning_rate: Learning rate to use for training, defaults to ``1e-5``.
         optimizer: Optimizer to use for training.
         lr_scheduler: The LR scheduler to use during training.
+
     """
 
     backbones: FlashRegistry = SPEECH_RECOGNITION_BACKBONES
diff --git a/src/flash/core/adapter.py b/src/flash/core/adapter.py
index 559c90ce13..71a5768ce2 100644
--- a/src/flash/core/adapter.py
+++ b/src/flash/core/adapter.py
@@ -35,6 +35,7 @@ def from_task(cls, task: "flash.Task", **kwargs) -> "Adapter":
         """Instantiate the adapter from the given :class:`~flash.core.model.Task`.
 
         This includes resolution / creation of backbones / heads and any other provider specific options.
+
         """
 
     def forward(self, x: Any) -> Any:
@@ -73,6 +74,7 @@ class AdapterTask(Task):
     Args:
         adapter: The :class:`~flash.core.adapter.Adapter` to wrap.
         kwargs: Keyword arguments to be passed to the base :class:`~flash.core.model.Task`.
+
     """
 
     def __init__(self, adapter: Adapter, **kwargs):
diff --git a/src/flash/core/data/base_viz.py b/src/flash/core/data/base_viz.py
index 9f8c90cae7..4370f00588 100644
--- a/src/flash/core/data/base_viz.py
+++ b/src/flash/core/data/base_viz.py
@@ -96,6 +96,7 @@ def show(self, batch: Dict[str, Any], running_stage: RunningStage):
         As the :class:`~flash.core.data.io.input_transform.InputTransform` hooks are injected within
         the threaded workers of the DataLoader,
         the data won't be accessible when using ``num_workers > 0``.
+
     """
 
     def _show(
diff --git a/src/flash/core/data/callback.py b/src/flash/core/data/callback.py
index c99c0ddc9a..6f6872f34f 100644
--- a/src/flash/core/data/callback.py
+++ b/src/flash/core/data/callback.py
@@ -19,6 +19,7 @@ class FlashCallback(Callback):
     Same as PyTorch Lightning, Callbacks can be provided directly to the Trainer::
 
         trainer = Trainer(callbacks=[MyCustomCallback()])
+
     """
 
     def on_per_sample_transform(self, sample: Tensor, running_stage: RunningStage) -> None:
@@ -146,6 +147,7 @@ def from_inputs(
             'val': {},
             'predict': {}
         }
+
     """
 
     batches: dict
diff --git a/src/flash/core/data/data_module.py b/src/flash/core/data/data_module.py
index 9118300c96..af30a47554 100644
--- a/src/flash/core/data/data_module.py
+++ b/src/flash/core/data/data_module.py
@@ -44,8 +44,7 @@
 
 class DatasetInput(Input):
     """The ``DatasetInput`` implements default behaviours for data sources which expect the input to
-    :meth:`~flash.core.data.io.input.Input.load_data` to be a :class:`torch.utils.data.dataset.Dataset`
-    """
+    :meth:`~flash.core.data.io.input.Input.load_data` to be a :class:`torch.utils.data.dataset.Dataset`"""
 
     def load_sample(self, sample: Any) -> Dict[str, Any]:
         if isinstance(sample, tuple) and len(sample) == 2:
@@ -103,6 +102,7 @@ class DataModule(pl.LightningDataModule):
         >>> datamodule = DataModule(train_input, sampler=WeightedRandomSampler([0.1, 0.5], 2), batch_size=1)
         >>> print(datamodule.train_dataloader().sampler)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
         <torch.utils.data.sampler.WeightedRandomSampler object at ...>
+
     """
 
     input_transform_cls = InputTransform
@@ -399,6 +399,7 @@ def configure_data_fetcher(*args, **kwargs) -> BaseDataFetcher:
         """This function is used to configure a :class:`~flash.core.data.callback.BaseDataFetcher`.
 
         Override with your custom one.
+
         """
         return BaseDataFetcher()
 
diff --git a/src/flash/core/data/io/classification_input.py b/src/flash/core/data/io/classification_input.py
index 31daf8273c..579696f432 100644
--- a/src/flash/core/data/io/classification_input.py
+++ b/src/flash/core/data/io/classification_input.py
@@ -25,6 +25,7 @@ class ClassificationInputMixin(Properties):
       targets and store metadata like ``labels`` and ``num_classes``.
     * In the ``load_sample`` method, use ``format_target`` to convert the target to a standard format for use with our
       tasks.
+
     """
 
     target_formatter: TargetFormatter
@@ -46,6 +47,7 @@ def load_target_metadata(
                 rather than inferring from the targets.
             add_background: If ``True``, a background class will be inserted as class zero if ``labels`` and
                 ``num_classes`` are being inferred.
+
         """
         self.target_formatter = target_formatter
         if target_formatter is None and targets is not None:
diff --git a/src/flash/core/data/io/input.py b/src/flash/core/data/io/input.py
index 199aee6f00..60e7a630e4 100644
--- a/src/flash/core/data/io/input.py
+++ b/src/flash/core/data/io/input.py
@@ -108,6 +108,7 @@ def _validate_input(input: "InputBase") -> None:
     Raises:
         RuntimeError: If the ``input`` is of type ``Input`` and it's ``data`` attribute does not support ``len``.
         RuntimeError: If the ``input`` is of type ``IterableInput`` and it's ``data`` attribute does support ``len``.
+
     """
     if input.data is not None:
         if isinstance(input, Input) and not _has_len(input.data):
@@ -122,6 +123,7 @@ def _wrap_init(class_dict: Dict[str, Any]) -> None:
 
     Args:
         class_dict: The class construction dict, optionally containing an init to wrap.
+
     """
     if "__init__" in class_dict:
         fn = class_dict["__init__"]
@@ -153,14 +155,15 @@ def __new__(mcs, name: str, bases: Tuple, class_dict: Dict[str, Any]) -> "_Itera
 
 class InputBase(Properties, metaclass=_InputMeta):
     """``InputBase`` is the base class for the :class:`~flash.core.data.io.input.Input` and
-    :class:`~flash.core.data.io.input.IterableInput` dataset implementations in Flash. These datasets are
-    constructed via the ``load_data`` and ``load_sample`` hooks, which allow a single dataset object to include custom
-    loading logic according to the running stage (e.g. train, validate, test, predict).
+    :class:`~flash.core.data.io.input.IterableInput` dataset implementations in Flash. These datasets are constructed
+    via the ``load_data`` and ``load_sample`` hooks, which allow a single dataset object to include custom loading logic
+    according to the running stage (e.g. train, validate, test, predict).
 
     Args:
         running_stage: The running stage for which the input will be used.
         *args: Any arguments that are to be passed to the ``load_data`` hook.
         **kwargs: Any additional keyword arguments to pass to the ``load_data`` hook.
+
     """
 
     def __init__(self, running_stage: RunningStage, *args: Any, **kwargs: Any) -> None:
@@ -194,6 +197,7 @@ def load_data(*args: Any, **kwargs: Any) -> Union[Sequence, Iterable]:
         Args:
             *args: Any arguments that the input requires.
             **kwargs: Any additional keyword arguments that the input requires.
+
         """
         return args[0]
 
@@ -203,6 +207,7 @@ def train_load_data(self, *args: Any, **kwargs: Any) -> Union[Sequence, Iterable
         Args:
             *args: Any arguments that the input requires.
             **kwargs: Any additional keyword arguments that the input requires.
+
         """
         return self.load_data(*args, **kwargs)
 
@@ -212,6 +217,7 @@ def val_load_data(self, *args: Any, **kwargs: Any) -> Union[Sequence, Iterable]:
         Args:
             *args: Any arguments that the input requires.
             **kwargs: Any additional keyword arguments that the input requires.
+
         """
         return self.load_data(*args, **kwargs)
 
@@ -221,6 +227,7 @@ def test_load_data(self, *args: Any, **kwargs: Any) -> Union[Sequence, Iterable]
         Args:
             *args: Any arguments that the input requires.
             **kwargs: Any additional keyword arguments that the input requires.
+
         """
         return self.load_data(*args, **kwargs)
 
@@ -230,6 +237,7 @@ def predict_load_data(self, *args: Any, **kwargs: Any) -> Union[Sequence, Iterab
         Args:
             *args: Any arguments that the input requires.
             **kwargs: Any additional keyword arguments that the input requires.
+
         """
         return self.load_data(*args, **kwargs)
 
@@ -240,6 +248,7 @@ def load_sample(sample: Dict[str, Any]) -> Any:
 
         Args:
             sample: A single sample from the output of the ``load_data`` hook.
+
         """
         return sample
 
@@ -248,6 +257,7 @@ def train_load_sample(self, sample: Dict[str, Any]) -> Any:
 
         Args:
             sample: A single sample from the output of the ``load_data`` hook.
+
         """
         return self.load_sample(sample)
 
@@ -256,6 +266,7 @@ def val_load_sample(self, sample: Dict[str, Any]) -> Any:
 
         Args:
             sample: A single sample from the output of the ``load_data`` hook.
+
         """
         return self.load_sample(sample)
 
@@ -264,6 +275,7 @@ def test_load_sample(self, sample: Dict[str, Any]) -> Any:
 
         Args:
             sample: A single sample from the output of the ``load_data`` hook.
+
         """
         return self.load_sample(sample)
 
@@ -272,6 +284,7 @@ def predict_load_sample(self, sample: Dict[str, Any]) -> Any:
 
         Args:
             sample: A single sample from the output of the ``load_data`` hook.
+
         """
         return self.load_sample(sample)
 
@@ -279,6 +292,7 @@ def __bool__(self):
         """If ``self.data`` is ``None`` then the ``InputBase`` is considered falsey.
 
         This allows for quickly checking whether or not the ``InputBase`` is populated with data.
+
         """
         return self.data is not None
 
diff --git a/src/flash/core/data/io/transform_predictions.py b/src/flash/core/data/io/transform_predictions.py
index a486c1659e..fc4588898f 100644
--- a/src/flash/core/data/io/transform_predictions.py
+++ b/src/flash/core/data/io/transform_predictions.py
@@ -28,6 +28,7 @@ class TransformPredictions(Callback):
     Args:
         output_transform: The :class:`~flash.core.data.io.output_transform.OutputTransform` to apply.
         output: The :class:`~flash.core.data.io.output.Output` to apply.
+
     """
 
     def __init__(self, output_transform: OutputTransform, output: Output):
diff --git a/src/flash/core/data/transforms.py b/src/flash/core/data/transforms.py
index 6aa428e8c0..9825103522 100644
--- a/src/flash/core/data/transforms.py
+++ b/src/flash/core/data/transforms.py
@@ -66,6 +66,7 @@ class ApplyToKeys(nn.Sequential):
     Args:
         keys: The key (``str``) or sequence of keys (``Sequence[str]``) to extract and forward to the transforms.
         args: The transforms, passed to the ``nn.Sequential`` super constructor.
+
     """
 
     def __init__(self, keys: Union[str, Sequence[str]], *args):
diff --git a/src/flash/core/data/utilities/classification.py b/src/flash/core/data/utilities/classification.py
index 6bd6992a5a..0492bfa3ff 100644
--- a/src/flash/core/data/utilities/classification.py
+++ b/src/flash/core/data/utilities/classification.py
@@ -73,6 +73,7 @@ class TargetFormatter:
         >>> formatter = CustomStringTargetFormatter()
         >>> formatter("#1")
         1
+
     """
 
     multi_label: ClassVar[Optional[bool]] = None
@@ -109,6 +110,7 @@ class SingleNumericTargetFormatter(TargetFormatter):
         5
         >>> formatter(torch.tensor(5))
         5
+
     """
 
     multi_label: ClassVar[Optional[bool]] = False
@@ -137,6 +139,7 @@ class SingleLabelTargetFormatter(TargetFormatter):
         0
         >>> formatter(["dog"])
         1
+
     """
 
     multi_label: ClassVar[Optional[bool]] = False
@@ -167,6 +170,7 @@ class SingleBinaryTargetFormatter(TargetFormatter):
         0
         >>> formatter(torch.tensor([0, 1]))
         1
+
     """
 
     multi_label: ClassVar[Optional[bool]] = False
@@ -196,6 +200,7 @@ class MultiNumericTargetFormatter(TargetFormatter):
         [0, 0, 1, 0, 0, 1, 0, 0, 0, 0]
         >>> formatter(torch.tensor([2, 5]))
         [0, 0, 1, 0, 0, 1, 0, 0, 0, 0]
+
     """
 
     multi_label: ClassVar[Optional[bool]] = True
@@ -224,6 +229,7 @@ class MultiLabelTargetFormatter(SingleLabelTargetFormatter):
         [0, 1, 1]
         >>> formatter(["bird"])
         [1, 0, 0]
+
     """
 
     multi_label: ClassVar[Optional[bool]] = True
@@ -253,6 +259,7 @@ class CommaDelimitedMultiLabelTargetFormatter(MultiLabelTargetFormatter):
         [0, 1, 1]
         >>> formatter("bird")
         [1, 0, 0]
+
     """
 
     multi_label: ClassVar[Optional[bool]] = True
@@ -278,6 +285,7 @@ class SpaceDelimitedTargetFormatter(MultiLabelTargetFormatter):
         [0, 1, 1]
         >>> formatter("bird")
         [1, 0, 0]
+
     """
 
     multi_label: ClassVar[Optional[bool]] = True
@@ -304,6 +312,7 @@ class MultiBinaryTargetFormatter(TargetFormatter):
         [0, 1, 1]
         >>> formatter(torch.tensor([1, 0, 0]))
         [1, 0, 0]
+
     """
 
     multi_label: ClassVar[Optional[bool]] = True
@@ -330,6 +339,7 @@ class MultiSoftTargetFormatter(MultiBinaryTargetFormatter):
         [0.1, 0.9, 0.6]
         >>> formatter(torch.tensor([0.9, 0.6, 0.7]))  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
         [0..., 0..., 0...]
+
     """
 
     binary: ClassVar[Optional[bool]] = False
@@ -353,6 +363,7 @@ def _get_target_formatter_type(target: Any) -> Type[TargetFormatter]:
 
     Args:
         target: A target that is one of: a single target, a list of targets, a comma delimited string.
+
     """
     if isinstance(target, str):
         target = _strip(target)
@@ -392,6 +403,7 @@ def _resolve_target_formatter(a: Type[TargetFormatter], b: Type[TargetFormatter]
 
     Raises:
         ValueError: If the two target formatters could not be resolved.
+
     """
     if a is b:
         return a
@@ -423,6 +435,7 @@ def _get_target_details(
     Returns:
         (labels, num_classes): Tuple containing the inferred ``labels`` (or ``None`` if no labels could be inferred)
         and ``num_classes``.
+
     """
     targets = _as_list(targets)
     if target_formatter_type.numeric:
@@ -481,6 +494,7 @@ def get_target_formatter(
 
     Returns:
         The target formatter to use when formatting targets.
+
     """
     targets = _as_list(targets)
     target_formatter_type: Type[TargetFormatter] = reduce(
diff --git a/src/flash/core/data/utilities/collate.py b/src/flash/core/data/utilities/collate.py
index 54f4a75b72..5e82c7434a 100644
--- a/src/flash/core/data/utilities/collate.py
+++ b/src/flash/core/data/utilities/collate.py
@@ -44,6 +44,7 @@ def wrap_collate(collate):
 
     Returns:
         The wrapped collate function.
+
     """
     return functools.partial(_wrap_collate, collate)
 
@@ -61,5 +62,6 @@ def default_collate(batch: List[Any]) -> Any:
 
     Returns:
         The collated batch.
+
     """
     return _default_collate(batch)
diff --git a/src/flash/core/data/utilities/sort.py b/src/flash/core/data/utilities/sort.py
index 521b2550f5..060d37e1a8 100644
--- a/src/flash/core/data/utilities/sort.py
+++ b/src/flash/core/data/utilities/sort.py
@@ -28,5 +28,6 @@ def sorted_alphanumeric(iterable: Iterable[str]) -> Iterable[str]:
     this returns ``["class_1", "class_2", "class_11"]``.
 
     Copied from: https://blog.codinghorror.com/sorting-for-humans-natural-sort-order/
+
     """
     return sorted(iterable, key=_alphanumeric_key)
diff --git a/src/flash/core/integrations/fiftyone/utils.py b/src/flash/core/integrations/fiftyone/utils.py
index 8a14dd21a5..5470079ea6 100644
--- a/src/flash/core/integrations/fiftyone/utils.py
+++ b/src/flash/core/integrations/fiftyone/utils.py
@@ -96,6 +96,7 @@ class FiftyOneLabelUtilities:
         label_field: The field in the ``SampleCollection`` containing the ground truth labels.
         label_cls: The ``FiftyOne.Label`` subclass to expect ground truth labels to be instances of. If ``None``,
             defaults to ``FiftyOne.Label``.
+
     """
 
     def __init__(self, label_field: str = "ground_truth", label_cls: Optional[Type[Label]] = None):
diff --git a/src/flash/core/integrations/labelstudio/input.py b/src/flash/core/integrations/labelstudio/input.py
index ed2aa63bdb..b2a720d859 100644
--- a/src/flash/core/integrations/labelstudio/input.py
+++ b/src/flash/core/integrations/labelstudio/input.py
@@ -222,19 +222,22 @@ def _split_train_val_data(data: Dict, split: float = 0) -> List[Dict]:
 
 
 class LabelStudioInput(BaseLabelStudioInput, Input):
-    """The ``LabelStudioInput`` expects the input to
-    :meth:`~flash.core.data.io.input.Input.load_data` to be a json export from label studio."""
+    """The ``LabelStudioInput`` expects the input to :meth:`~flash.core.data.io.input.Input.load_data` to be a json
+    export from label studio."""
 
 
 class LabelStudioIterableInput(BaseLabelStudioInput, IterableInput):
-    """The ``LabelStudioInput`` expects the input to
-    :meth:`~flash.core.data.io.input.Input.load_data` to be a json export from label studio."""
+    """The ``LabelStudioInput`` expects the input to :meth:`~flash.core.data.io.input.Input.load_data` to be a json
+    export from label studio."""
 
 
 class LabelStudioImageClassificationInput(LabelStudioInput):
-    """The ``LabelStudioImageInput`` expects the input to
-    :meth:`~flash.core.data.io.input.Input.load_data` to be a json export from label studio.
-    Export data should point to image files"""
+    """The ``LabelStudioImageInput`` expects the input to :meth:`~flash.core.data.io.input.Input.load_data` to be a json
+    export from label studio.
+
+    Export data should point to image files
+
+    """
 
     def load_sample(self, sample: Mapping[str, Any] = None) -> Any:
         """Load 1 sample from dataset."""
@@ -248,9 +251,11 @@ def load_sample(self, sample: Mapping[str, Any] = None) -> Any:
 
 
 class LabelStudioTextClassificationInput(LabelStudioInput):
-    """The ``LabelStudioTextInput`` expects the input to
-    :meth:`~flash.core.data.io.input.Input.load_data` to be a json export from label studio.
+    """The ``LabelStudioTextInput`` expects the input to :meth:`~flash.core.data.io.input.Input.load_data` to be a json
+    export from label studio.
+
     Export data should point to text data
+
     """
 
     def load_sample(self, sample: Mapping[str, Any] = None) -> Any:
@@ -265,9 +270,12 @@ def load_sample(self, sample: Mapping[str, Any] = None) -> Any:
 
 
 class LabelStudioVideoClassificationInput(LabelStudioIterableInput):
-    """The ``LabelStudioVideoInput`` expects the input to
-    :meth:`~flash.core.data.io.input.Input.load_data` to be a json export from label studio.
-    Export data should point to video files"""
+    """The ``LabelStudioVideoInput`` expects the input to :meth:`~flash.core.data.io.input.Input.load_data` to be a json
+    export from label studio.
+
+    Export data should point to video files
+
+    """
 
     def __init__(
         self,
diff --git a/src/flash/core/model.py b/src/flash/core/model.py
index df1c92399c..617f7d8943 100644
--- a/src/flash/core/model.py
+++ b/src/flash/core/model.py
@@ -73,6 +73,7 @@ class ModuleWrapperBase:
     ``LightningModule`` instances so that nested calls to ``.log`` are handled correctly. The ``ModuleWrapperBase`` is
     also stateful, meaning that a :class:`~flash.core.data.data_pipeline.DataPipelineState` can be attached. Attached
     state will be forwarded to any nested ``ModuleWrapperBase`` instances.
+
     """
 
     def __init__(self):
@@ -485,6 +486,7 @@ def modules_to_freeze(self) -> Optional[nn.Module]:
 
         Returns:
             The backbone ``Module`` to freeze or ``None`` if this task does not have a ``backbone`` attribute.
+
         """
         return getattr(self, "backbone", None)
 
@@ -579,6 +581,7 @@ def as_embedder(self, layer: str):
 
         Args:
             layer: The layer to embed to. This should be one of the :meth:`~flash.core.model.Task.available_layers`.
+
         """
         from flash.core.utilities.embedder import Embedder  # Avoid circular import
 
@@ -672,6 +675,7 @@ def available_outputs(cls) -> List[str]:
 
             >>> print(Task.available_outputs())
             ['preds', 'raw']
+
         """
         return cls.outputs.available_keys()
 
@@ -861,6 +865,7 @@ def serve(
             input_cls: The ``ServeInput`` type to use.
             transform: The transform to use when serving.
             transform_kwargs: Keyword arguments used to instantiate the transform.
+
         """
         from flash.core.serve.flash_components import build_flash_serve_model_component
 
diff --git a/src/flash/core/registry.py b/src/flash/core/registry.py
index 924cce8219..a1d830f55e 100644
--- a/src/flash/core/registry.py
+++ b/src/flash/core/registry.py
@@ -187,6 +187,7 @@ class ExternalRegistry(FlashRegistry):
     Args:
         getter: A function whose first argument is a key that can optionally take additional args and kwargs.
         providers: The provider(/s) of entries in this registry.
+
     """
 
     # Prevent users from trying to remove or register items
diff --git a/src/flash/core/serve/dag/optimization.py b/src/flash/core/serve/dag/optimization.py
index 5b8de1196a..967607daf3 100644
--- a/src/flash/core/serve/dag/optimization.py
+++ b/src/flash/core/serve/dag/optimization.py
@@ -368,6 +368,7 @@ def default_fused_keys_renamer(keys, max_fused_key_length=120):
 
     The optional parameter `max_fused_key_length` is used to limit the maximum string length for each renamed key. If
     this parameter is set to `None`, there is no limit.
+
     """
     it = reversed(keys)
     first_key = next(it)
@@ -476,6 +477,7 @@ def fuse(
     dependencies
         dict mapping dependencies after fusion.  Useful side effect to accelerate other
         downstream optimizations.
+
     """
 
     if keys is not None and not isinstance(keys, set):
diff --git a/src/flash/core/serve/dag/order.py b/src/flash/core/serve/dag/order.py
index 69447fad22..07dab3ed48 100644
--- a/src/flash/core/serve/dag/order.py
+++ b/src/flash/core/serve/dag/order.py
@@ -557,6 +557,7 @@ def graph_metrics(dependencies, dependents, total_dependencies):
     Returns
     -------
     metrics: Dict[key, Tuple[int, int, int, int, int]]
+
     """
     result = {}
     num_needed = {k: len(v) for k, v in dependents.items() if v}
diff --git a/src/flash/core/serve/dag/rewrite.py b/src/flash/core/serve/dag/rewrite.py
index aebe058ba6..c742961233 100644
--- a/src/flash/core/serve/dag/rewrite.py
+++ b/src/flash/core/serve/dag/rewrite.py
@@ -248,6 +248,7 @@ def __init__(self, *rules):
         ----------
         rules
             One or more instances of RewriteRule
+
         """
         self._net = Node()
         self.rules = []
@@ -358,6 +359,7 @@ def rewrite(self, task, strategy="bottom_up"):
 
         >>> rs.rewrite(term)  # doctest: +ELLIPSIS
         (<function double at ...>, (<function double at ...>, 2))
+
         """
         return strategies[strategy](self, task)
 
diff --git a/src/flash/core/serve/dag/task.py b/src/flash/core/serve/dag/task.py
index 59bb7875f8..57d49f7a67 100644
--- a/src/flash/core/serve/dag/task.py
+++ b/src/flash/core/serve/dag/task.py
@@ -392,6 +392,7 @@ def getcycle(d, keys):
     See Also
     --------
     isdag
+
     """
     return _toposort(d, keys=keys, returncycle=True)
 
@@ -410,6 +411,7 @@ def isdag(d, keys):
     See Also
     --------
     getcycle
+
     """
     return not getcycle(d, keys)
 
diff --git a/src/flash/core/serve/interfaces/models.py b/src/flash/core/serve/interfaces/models.py
index 2177640b14..11f7266b96 100644
--- a/src/flash/core/serve/interfaces/models.py
+++ b/src/flash/core/serve/interfaces/models.py
@@ -99,6 +99,7 @@ def request_model(self) -> RequestModel:
           the key "image" -> the raw data... The field names are NOT altered,
           and therefore this workaround should pose very little issue for
           our end users).
+
         """
         attrib_dict = {}
         inputs = self._endpoint.inputs
@@ -164,6 +165,7 @@ def response_model(self) -> ResponseModel:
           the key "image" -> the raw data... The field names are NOT altered,
           and therefore this workaround should pose very little issue for
           our end users).
+
         """
         attrib_dict = {}
         outputs = self._endpoint.outputs
diff --git a/src/flash/core/trainer.py b/src/flash/core/trainer.py
index fe7e39b83a..36d58651b9 100644
--- a/src/flash/core/trainer.py
+++ b/src/flash/core/trainer.py
@@ -53,6 +53,7 @@ def _defaults_from_env_vars(fn: Callable) -> Callable:
     """Copy of ``pytorch_lightning.trainer.connectors.env_vars_connector._defaults_from_env_vars``.
 
     Required to fix build error in readthedocs.
+
     """
 
     @wraps(fn)
@@ -116,6 +117,7 @@ def fit(
 
             val_dataloaders: Either a single Pytorch Dataloader or a list of them, specifying validation samples.
                 If the model has a predefined val_dataloaders method this will be skipped
+
         """
         if any(isinstance(c, BaseFinetuning) for c in self.callbacks):
             # TODO: if we find a finetuning callback in the trainer should we remove it? or just warn the user?
@@ -160,6 +162,7 @@ def finetune(
                 By default, ``no_freeze`` strategy will be used.
 
             train_bn: Whether to train Batch Norm layer
+
         """
         self._resolve_callbacks(model, strategy, train_bn=train_bn)
         return super().fit(model, train_dataloader, val_dataloaders, datamodule)
diff --git a/src/flash/core/utilities/flash_cli.py b/src/flash/core/utilities/flash_cli.py
index 132fc85479..87a4985101 100644
--- a/src/flash/core/utilities/flash_cli.py
+++ b/src/flash/core/utilities/flash_cli.py
@@ -152,6 +152,7 @@ def __init__(
                     the corresponding ``DataModule.from_*`` method.
                 - ``Callable``. A custom method.
             kwargs: See the parent arguments
+
         """
         if datamodule_attributes is None:
             datamodule_attributes = {"num_classes"}
diff --git a/src/flash/core/utilities/lightning_cli.py b/src/flash/core/utilities/lightning_cli.py
index 898df68854..9f8e293f24 100644
--- a/src/flash/core/utilities/lightning_cli.py
+++ b/src/flash/core/utilities/lightning_cli.py
@@ -261,6 +261,7 @@ def __init__(
             subclass_mode_data: Whether datamodule can be any `subclass
                 <https://jsonargparse.readthedocs.io/en/stable/#class-type-and-sub-classes>`_
                 of the given class.
+
         """
         self.model_class = model_class
         self.datamodule_class = datamodule_class
diff --git a/src/flash/core/utilities/stability.py b/src/flash/core/utilities/stability.py
index 8dd553a8a8..34c9ecfe73 100644
--- a/src/flash/core/utilities/stability.py
+++ b/src/flash/core/utilities/stability.py
@@ -68,6 +68,7 @@ def beta(message: str = "This feature is currently in Beta."):
         ...     MyBetaFeatureWithCustomMessage()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
         ...
         <...>
+
     """
 
     def decorator(callable: Union[Callable, Type]):
diff --git a/src/flash/graph/classification/data.py b/src/flash/graph/classification/data.py
index 457d7b45c2..b52730788f 100644
--- a/src/flash/graph/classification/data.py
+++ b/src/flash/graph/classification/data.py
@@ -164,6 +164,7 @@ def from_datasets(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {
diff --git a/src/flash/graph/classification/input_transform.py b/src/flash/graph/classification/input_transform.py
index 66a0134f00..78543bedeb 100644
--- a/src/flash/graph/classification/input_transform.py
+++ b/src/flash/graph/classification/input_transform.py
@@ -33,6 +33,7 @@ class PyGTransformAdapter:
 
     Args:
         transform: Transform to apply.
+
     """
 
     transform: Callable[[Data], Data]
diff --git a/src/flash/graph/classification/model.py b/src/flash/graph/classification/model.py
index de7abbf07c..4eff4d15f2 100644
--- a/src/flash/graph/classification/model.py
+++ b/src/flash/graph/classification/model.py
@@ -50,6 +50,7 @@ class GraphClassifier(ClassificationTask):
         optimizer: Optimizer to use for training.
         lr_scheduler: The LR scheduler to use during training.
         metrics: Metrics to compute for training and evaluation.
+
     """
 
     backbones: FlashRegistry = GRAPH_BACKBONES
diff --git a/src/flash/graph/embedding/model.py b/src/flash/graph/embedding/model.py
index e214c25cf1..22ce1842f7 100644
--- a/src/flash/graph/embedding/model.py
+++ b/src/flash/graph/embedding/model.py
@@ -29,6 +29,7 @@ class GraphEmbedder(Task):
     Args:
         backbone: A model to use to extract image features.
         pooling_fn: The global pooling operation to use (one of: "max", "max", "add" or a callable).
+
     """
 
     required_extras: str = "graph"
diff --git a/src/flash/image/classification/data.py b/src/flash/image/classification/data.py
index 3bf13bb91e..d5d6c60c42 100644
--- a/src/flash/image/classification/data.py
+++ b/src/flash/image/classification/data.py
@@ -149,6 +149,7 @@ def from_files(
             >>> import os
             >>> _ = [os.remove(f"image_{i}.png") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_image_{i}.png") for i in range(1, 4)]
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -269,6 +270,7 @@ def from_folders(
             >>> import shutil
             >>> shutil.rmtree("train_folder")
             >>> shutil.rmtree("predict_folder")
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -355,6 +357,7 @@ def from_numpy(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -446,6 +449,7 @@ def from_images(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -532,6 +536,7 @@ def from_tensors(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -671,6 +676,7 @@ def from_data_frame(
             >>> shutil.rmtree("predict_folder")
             >>> del train_data_frame
             >>> del predict_data_frame
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -902,6 +908,7 @@ def from_csv(
             >>> shutil.rmtree("predict_folder")
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -1021,6 +1028,7 @@ def from_fiftyone(
             >>> _ = [os.remove(f"predict_image_{i}.png") for i in range(1, 4)]
             >>> del train_dataset
             >>> del predict_dataset
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -1092,6 +1100,7 @@ def from_labelstudio(
                 data_folder='label-studio/media/upload',
                 val_split=0.8,
             )
+
         """
 
         train_data, val_data, test_data, predict_data = _parse_labelstudio_arguments(
@@ -1158,6 +1167,7 @@ def from_datasets(
             data_module = DataModule.from_datasets(
                 train_dataset=train_dataset,
             )
+
         """
         ds_kw = {}
 
diff --git a/src/flash/image/classification/integrations/baal/data.py b/src/flash/image/classification/integrations/baal/data.py
index 7fe1a5fe5a..fe1b913f2a 100644
--- a/src/flash/image/classification/integrations/baal/data.py
+++ b/src/flash/image/classification/integrations/baal/data.py
@@ -75,6 +75,7 @@ def __init__(
             initial_num_labels: Number of samples to randomly label to start the training with.
             query_size: Number of samples to be labelled at each Active Learning loop based on the fed heuristic.
             val_split: Float to split train dataset into train and validation set.
+
         """
         super().__init__(batch_size=1)
         self.labelled = labelled
diff --git a/src/flash/image/classification/integrations/baal/loop.py b/src/flash/image/classification/integrations/baal/loop.py
index 61785b668a..256a3b4179 100644
--- a/src/flash/image/classification/integrations/baal/loop.py
+++ b/src/flash/image/classification/integrations/baal/loop.py
@@ -58,6 +58,7 @@ def __init__(self, label_epoch_frequency: int, inference_iteration: int = 2, sho
         Args:
             label_epoch_frequency: Number of epoch to train on before requesting labellisation.
             inference_iteration: Number of inference to perform to compute uncertainty.
+
         """
         super().__init__()
         self.label_epoch_frequency = label_epoch_frequency
diff --git a/src/flash/image/classification/model.py b/src/flash/image/classification/model.py
index 2b28ecc35b..7b6d422abb 100644
--- a/src/flash/image/classification/model.py
+++ b/src/flash/image/classification/model.py
@@ -77,6 +77,7 @@ def fn_resnet(pretrained: bool = True):
         training_strategy: string indicating the training strategy. Adjust if you want to use `learn2learn`
             for doing meta-learning research
         training_strategy_kwargs: Additional kwargs for setting the training strategy
+
     """
 
     backbones: FlashRegistry = IMAGE_CLASSIFIER_BACKBONES
diff --git a/src/flash/image/detection/data.py b/src/flash/image/detection/data.py
index b6c8135675..d5e1c2e1b2 100644
--- a/src/flash/image/detection/data.py
+++ b/src/flash/image/detection/data.py
@@ -163,6 +163,7 @@ def from_files(
             >>> import os
             >>> _ = [os.remove(f"image_{i}.png") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_image_{i}.png") for i in range(1, 4)]
+
         """
 
         ds_kw = {
@@ -281,6 +282,7 @@ def from_numpy(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {
@@ -404,6 +406,7 @@ def from_images(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {
@@ -522,6 +525,7 @@ def from_tensors(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {
@@ -893,6 +897,7 @@ def from_voc(
             >>> shutil.rmtree("train_annotations")
 
         .. _PASCAL: http://host.robots.ox.ac.uk/pascal/VOC/
+
         """
         return cls.from_icedata(
             train_folder=train_folder,
@@ -1162,6 +1167,7 @@ def from_fiftyone(
             >>> import os
             >>> _ = [os.remove(f"image_{i}.png") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_image_{i}.png") for i in range(1, 4)]
+
         """
 
         ds_kw = {}
@@ -1195,6 +1201,7 @@ def from_folders(
 
         Returns:
             The constructed data module.
+
         """
         return cls(
             predict_input=input_cls(RunningStage.PREDICTING, predict_folder),
diff --git a/src/flash/image/detection/model.py b/src/flash/image/detection/model.py
index 15def3da8f..f58cfe6923 100644
--- a/src/flash/image/detection/model.py
+++ b/src/flash/image/detection/model.py
@@ -41,6 +41,7 @@ class ObjectDetector(AdapterTask):
         learning_rate: The learning rate to use for training.
         predict_kwargs: dictionary containing parameters that will be used during the prediction phase.
         kwargs: additional kwargs nessesary for initializing the backbone task
+
     """
 
     heads: FlashRegistry = OBJECT_DETECTION_HEADS
diff --git a/src/flash/image/embedding/model.py b/src/flash/image/embedding/model.py
index 2f02beafee..cd1796d58d 100644
--- a/src/flash/image/embedding/model.py
+++ b/src/flash/image/embedding/model.py
@@ -66,6 +66,7 @@ class ImageEmbedder(AdapterTask):
         backbone_kwargs: arguments to be passed to VISSL backbones, i.e. ``vision_transformer`` and ``resnet``.
         training_strategy_kwargs: arguments passed to VISSL loss function, projection head and training hooks.
         pretraining_transform_kwargs: arguments passed to VISSL transforms.
+
     """
 
     training_strategies: FlashRegistry = IMAGE_EMBEDDER_STRATEGIES
@@ -173,6 +174,7 @@ def available_training_strategies(cls) -> List[str]:
             >>> from flash.image import ImageEmbedder
             >>> ImageEmbedder.available_training_strategies()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             ['barlow_twins', ..., 'swav']
+
         """
         registry: Optional[FlashRegistry] = getattr(cls, "training_strategies", None)
         if registry is None:
diff --git a/src/flash/image/embedding/vissl/adapter.py b/src/flash/image/embedding/vissl/adapter.py
index ad901f7ed1..9633aeffb2 100644
--- a/src/flash/image/embedding/vissl/adapter.py
+++ b/src/flash/image/embedding/vissl/adapter.py
@@ -38,6 +38,7 @@ class _VISSLBackboneWrapper(nn.Module):
     """VISSL backbones take additional arguments in ``forward`` that are not needed for our integration.
 
     This wrapper can be applied to a Flash backbone to ignore any additional arguments to ``forward``.
+
     """
 
     def __init__(self, backbone: nn.Module):
@@ -75,6 +76,7 @@ class VISSLAdapter(Adapter, AdaptVISSLHooks):
     """The ``VISSLAdapter`` is an :class:`~flash.core.adapter.Adapter` for integrating with VISSL.
 
     Also inherits from ``AdaptVISSLHooks`` to support VISSL hooks.
+
     """
 
     required_extras: str = "image"
diff --git a/src/flash/image/face_detection/model.py b/src/flash/image/face_detection/model.py
index 50e4b7ae20..ca72d85d72 100644
--- a/src/flash/image/face_detection/model.py
+++ b/src/flash/image/face_detection/model.py
@@ -44,6 +44,7 @@ class FaceDetector(Task):
         lr_scheduler: The LR scheduler to use during training.
         learning_rate: The learning rate to use for training.
         kwargs: additional kwargs nessesary for initializing face detector backbone
+
     """
 
     required_extras: str = "image"
diff --git a/src/flash/image/instance_segmentation/data.py b/src/flash/image/instance_segmentation/data.py
index 0f9430e42a..1fc68b3f15 100644
--- a/src/flash/image/instance_segmentation/data.py
+++ b/src/flash/image/instance_segmentation/data.py
@@ -431,6 +431,7 @@ def from_voc(
             >>> shutil.rmtree("train_annotations")
 
         .. _PASCAL: http://host.robots.ox.ac.uk/pascal/VOC/
+
         """
         return cls.from_icedata(
             train_folder=train_folder,
@@ -472,6 +473,7 @@ def from_folders(
 
         Returns:
             The constructed data module.
+
         """
         return cls(
             predict_input=input_cls(RunningStage.PREDICTING, predict_folder),
@@ -502,6 +504,7 @@ def from_files(
 
         Returns:
             The constructed data module.
+
         """
         return cls(
             predict_input=input_cls(RunningStage.PREDICTING, predict_files),
diff --git a/src/flash/image/instance_segmentation/model.py b/src/flash/image/instance_segmentation/model.py
index 1d4bd681d8..81521f3f85 100644
--- a/src/flash/image/instance_segmentation/model.py
+++ b/src/flash/image/instance_segmentation/model.py
@@ -34,6 +34,7 @@ class InstanceSegmentation(AdapterTask):
         learning_rate: The learning rate to use for training.
         predict_kwargs: dictionary containing parameters that will be used during the prediction phase.
         **kwargs: additional kwargs used for initializing the task
+
     """
 
     heads: FlashRegistry = INSTANCE_SEGMENTATION_HEADS
diff --git a/src/flash/image/keypoint_detection/data.py b/src/flash/image/keypoint_detection/data.py
index 98fb0f2078..4bd614dd6b 100644
--- a/src/flash/image/keypoint_detection/data.py
+++ b/src/flash/image/keypoint_detection/data.py
@@ -290,6 +290,7 @@ def from_folders(
 
         Returns:
             The constructed data module.
+
         """
         return cls(
             predict_input=input_cls(RunningStage.PREDICTING, predict_folder),
@@ -320,6 +321,7 @@ def from_files(
 
         Returns:
             The constructed data module.
+
         """
         return cls(
             predict_input=input_cls(RunningStage.PREDICTING, predict_files),
diff --git a/src/flash/image/keypoint_detection/model.py b/src/flash/image/keypoint_detection/model.py
index 1bb65ed3dd..38ccf9f8cb 100644
--- a/src/flash/image/keypoint_detection/model.py
+++ b/src/flash/image/keypoint_detection/model.py
@@ -34,6 +34,7 @@ class KeypointDetector(AdapterTask):
         learning_rate: The learning rate to use for training.
         predict_kwargs: dictionary containing parameters that will be used during the prediction phase.
         **kwargs: additional kwargs used for initializing the task
+
     """
 
     heads: FlashRegistry = KEYPOINT_DETECTION_HEADS
diff --git a/src/flash/image/segmentation/data.py b/src/flash/image/segmentation/data.py
index c9f98b9a46..ed26ee4684 100644
--- a/src/flash/image/segmentation/data.py
+++ b/src/flash/image/segmentation/data.py
@@ -141,6 +141,7 @@ def from_files(
             >>> _ = [os.remove(f"image_{i}.png") for i in range(1, 4)]
             >>> _ = [os.remove(f"mask_{i}.npy") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_image_{i}.png") for i in range(1, 4)]
+
         """
 
         ds_kw = {
@@ -282,6 +283,7 @@ def from_folders(
             >>> shutil.rmtree("train_images")
             >>> shutil.rmtree("train_masks")
             >>> shutil.rmtree("predict_folder")
+
         """
 
         ds_kw = {
@@ -370,6 +372,7 @@ def from_numpy(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {
@@ -458,6 +461,7 @@ def from_tensors(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {
@@ -569,6 +573,7 @@ def from_fiftyone(
             >>> import os
             >>> _ = [os.remove(f"image_{i}.png") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_image_{i}.png") for i in range(1, 4)]
+
         """
 
         return cls(
diff --git a/src/flash/image/segmentation/model.py b/src/flash/image/segmentation/model.py
index 121150e60c..75fab675a1 100644
--- a/src/flash/image/segmentation/model.py
+++ b/src/flash/image/segmentation/model.py
@@ -92,6 +92,7 @@ class SemanticSegmentation(ClassificationTask):
         multi_label: Whether the targets are multi-label or not.
         output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
         output_transform: :class:`~flash.core.data.io.output_transform.OutputTransform` use for post processing samples.
+
     """
 
     output_transform_cls = SemanticSegmentationOutputTransform
diff --git a/src/flash/image/style_transfer/data.py b/src/flash/image/style_transfer/data.py
index 8a3bb45dd3..d40e230c1d 100644
--- a/src/flash/image/style_transfer/data.py
+++ b/src/flash/image/style_transfer/data.py
@@ -100,6 +100,7 @@ def from_files(
             >>> import os
             >>> _ = [os.remove(f"image_{i}.png") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_image_{i}.png") for i in range(1, 4)]
+
         """
 
         return cls(
@@ -185,6 +186,7 @@ def from_folders(
             >>> import shutil
             >>> shutil.rmtree("train_folder")
             >>> shutil.rmtree("predict_folder")
+
         """
 
         return cls(
@@ -243,6 +245,7 @@ def from_numpy(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         return cls(
@@ -302,6 +305,7 @@ def from_tensors(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         return cls(
diff --git a/src/flash/image/style_transfer/model.py b/src/flash/image/style_transfer/model.py
index eb67e88961..c88a00e612 100644
--- a/src/flash/image/style_transfer/model.py
+++ b/src/flash/image/style_transfer/model.py
@@ -62,6 +62,7 @@ class StyleTransfer(Task):
         optimizer: Optimizer to use for training.
         lr_scheduler: The LR scheduler to use during training.
         learning_rate: Learning rate to use for training, defaults to ``1e-3``.
+
     """
 
     backbones: FlashRegistry = STYLE_TRANSFER_BACKBONES
diff --git a/src/flash/pointcloud/detection/model.py b/src/flash/pointcloud/detection/model.py
index a74ba701aa..e67e7966ff 100644
--- a/src/flash/pointcloud/detection/model.py
+++ b/src/flash/pointcloud/detection/model.py
@@ -50,6 +50,7 @@ class PointCloudObjectDetector(Task):
         lambda_loss_cls: The value to scale the loss classification.
         lambda_loss_bbox: The value to scale the bounding boxes loss.
         lambda_loss_dir: The value to scale the bounding boxes direction loss.
+
     """
 
     backbones: FlashRegistry = POINTCLOUD_OBJECT_DETECTION_BACKBONES
diff --git a/src/flash/pointcloud/segmentation/model.py b/src/flash/pointcloud/segmentation/model.py
index 9d38c9044c..d83ea1e55a 100644
--- a/src/flash/pointcloud/segmentation/model.py
+++ b/src/flash/pointcloud/segmentation/model.py
@@ -58,6 +58,7 @@ class PointCloudSegmentation(ClassificationTask):
             by the :class:`~flash.core.classification.ClassificationTask` depending on the ``multi_label`` argument.
         learning_rate: The learning rate for the optimizer.
         multi_label: If ``True``, this will be treated as a multi-label classification problem.
+
     """
 
     backbones: FlashRegistry = POINTCLOUD_SEGMENTATION_BACKBONES
diff --git a/src/flash/tabular/classification/data.py b/src/flash/tabular/classification/data.py
index 9e5f89a6b8..da3b86c000 100644
--- a/src/flash/tabular/classification/data.py
+++ b/src/flash/tabular/classification/data.py
@@ -156,6 +156,7 @@ def from_data_frame(
 
             >>> del train_data
             >>> del predict_data
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -358,6 +359,7 @@ def from_csv(
             >>> import os
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -494,6 +496,7 @@ def from_dicts(
 
             >>> del train_data
             >>> del predict_data
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -632,6 +635,7 @@ def from_lists(
 
             >>> del train_data
             >>> del predict_data
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
diff --git a/src/flash/tabular/classification/model.py b/src/flash/tabular/classification/model.py
index 4c36eac568..5c9fb44968 100644
--- a/src/flash/tabular/classification/model.py
+++ b/src/flash/tabular/classification/model.py
@@ -53,6 +53,7 @@ class TabularClassifier(ClassificationAdapterTask):
             `metric(preds,target)` and return a single scalar tensor. Defaults to :class:`torchmetrics.Accuracy`.
         learning_rate: Learning rate to use for training.
         **backbone_kwargs: Optional additional arguments for the model.
+
     """
 
     required_extras: str = "tabular"
@@ -127,6 +128,7 @@ def data_parameters(self) -> Dict[str, Any]:
                 output="classes",
             )
             Predicting...
+
         """
         return self._data_parameters
 
diff --git a/src/flash/tabular/forecasting/data.py b/src/flash/tabular/forecasting/data.py
index 36e1fc2f01..dc782e4c40 100644
--- a/src/flash/tabular/forecasting/data.py
+++ b/src/flash/tabular/forecasting/data.py
@@ -155,6 +155,7 @@ def from_data_frame(
         .. testcleanup::
 
             >>> del data
+
         """
 
         ds_kw = dict(
diff --git a/src/flash/tabular/forecasting/model.py b/src/flash/tabular/forecasting/model.py
index 1fe2144205..9777316c84 100644
--- a/src/flash/tabular/forecasting/model.py
+++ b/src/flash/tabular/forecasting/model.py
@@ -68,6 +68,7 @@ def pytorch_forecasting_model(self) -> LightningModule:
         This can be used with
         :func:`~flash.core.integrations.pytorch_forecasting.transforms.convert_predictions` to access the visualization
         features built in to PyTorch Forecasting.
+
         """
         if not isinstance(self.adapter, PyTorchForecastingAdapter):
             raise AttributeError(
diff --git a/src/flash/tabular/regression/data.py b/src/flash/tabular/regression/data.py
index 9e1dd0d01c..2cffadff94 100644
--- a/src/flash/tabular/regression/data.py
+++ b/src/flash/tabular/regression/data.py
@@ -146,6 +146,7 @@ def from_data_frame(
 
             >>> del train_data
             >>> del predict_data
+
         """
         ds_kw = {
             "categorical_fields": categorical_fields,
@@ -333,6 +334,7 @@ def from_csv(
             >>> import os
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
         ds_kw = {
             "categorical_fields": categorical_fields,
@@ -459,6 +461,7 @@ def from_dicts(
 
             >>> del train_data
             >>> del predict_data
+
         """
         ds_kw = {
             "categorical_fields": categorical_fields,
@@ -588,6 +591,7 @@ def from_lists(
 
             >>> del train_data
             >>> del predict_data
+
         """
         ds_kw = {
             "categorical_fields": categorical_fields,
diff --git a/src/flash/tabular/regression/model.py b/src/flash/tabular/regression/model.py
index 9bae8f76b3..96f5bdad18 100644
--- a/src/flash/tabular/regression/model.py
+++ b/src/flash/tabular/regression/model.py
@@ -52,6 +52,7 @@ class TabularRegressor(RegressionAdapterTask):
             `metric(preds,target)` and return a single scalar tensor. Defaults to :class:`torchmetrics.Accuracy`.
         learning_rate: Learning rate to use for training.
         **backbone_kwargs: Optional additional arguments for the model.
+
     """
 
     required_extras: str = "tabular"
@@ -121,6 +122,7 @@ def data_parameters(self) -> Dict[str, Any]:
                 model,
                 datamodule=datamodule,
             )
+
         """
         return self._data_parameters
 
diff --git a/src/flash/template/classification/data.py b/src/flash/template/classification/data.py
index 9b6ebdeba9..f040164119 100644
--- a/src/flash/template/classification/data.py
+++ b/src/flash/template/classification/data.py
@@ -54,6 +54,7 @@ def load_data(
 
         Returns:
             A sequence of samples / sample metadata.
+
         """
         if not self.predicting and isinstance(examples, np.ndarray):
             self.num_features = examples.shape[1]
@@ -79,6 +80,7 @@ def load_data(self, data: Bunch, target_formatter: Optional[TargetFormatter] = N
 
         Returns:
             A sequence of samples / sample metadata.
+
         """
         return super().load_data(data.data, data.target, target_formatter=target_formatter)
 
@@ -118,6 +120,7 @@ class TemplateData(DataModule):
 
     Next, we add a ``from_numpy`` method and a ``from_sklearn`` method. Finally, we define the ``num_features`` property
     for convenience.
+
     """
 
     input_transform_cls = TemplateInputTransform
@@ -156,6 +159,7 @@ def from_numpy(
 
         Returns:
             The constructed data module.
+
         """
 
         ds_kw = {}
@@ -201,6 +205,7 @@ def from_sklearn(
 
         Returns:
             The constructed data module.
+
         """
         ds_kw = {}
 
@@ -238,6 +243,7 @@ class TemplateVisualization(BaseVisualization):
     the data.
 
     If you want to provide a visualization with your task, you can override these hooks.
+
     """
 
     def show_load_sample(
diff --git a/src/flash/template/classification/model.py b/src/flash/template/classification/model.py
index d8de0b2a15..eb19539fcc 100644
--- a/src/flash/template/classification/model.py
+++ b/src/flash/template/classification/model.py
@@ -39,6 +39,7 @@ class TemplateSKLearnClassifier(ClassificationTask):
             by the :class:`~flash.core.classification.ClassificationTask` depending on the ``multi_label`` argument.
         learning_rate: The learning rate for the optimizer.
         multi_label: If ``True``, this will be treated as a multi-label classification problem.
+
     """
 
     backbones: FlashRegistry = TEMPLATE_BACKBONES
diff --git a/src/flash/text/classification/data.py b/src/flash/text/classification/data.py
index aaafaf4f49..027df62632 100644
--- a/src/flash/text/classification/data.py
+++ b/src/flash/text/classification/data.py
@@ -210,6 +210,7 @@ def from_csv(
 
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -333,6 +334,7 @@ def from_json(
 
             >>> os.remove("train_data.json")
             >>> os.remove("predict_data.json")
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -457,6 +459,7 @@ def from_parquet(
 
             >>> os.remove("train_data.parquet")
             >>> os.remove("predict_data.parquet")
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -560,6 +563,7 @@ def from_hf_datasets(
 
             >>> del train_data
             >>> del predict_data
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -664,6 +668,7 @@ def from_data_frame(
 
             >>> del train_data
             >>> del predict_data
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -750,6 +755,7 @@ def from_lists(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
         ds_kw = {
             "target_formatter": target_formatter,
@@ -813,6 +819,7 @@ def from_labelstudio(
 
         Returns:
             The constructed data module.
+
         """
 
         train_data, val_data, test_data, predict_data = _parse_labelstudio_arguments(
diff --git a/src/flash/text/classification/model.py b/src/flash/text/classification/model.py
index 89a1273836..7725f5e61a 100644
--- a/src/flash/text/classification/model.py
+++ b/src/flash/text/classification/model.py
@@ -52,6 +52,7 @@ class TextClassifier(ClassificationAdapterTask):
         learning_rate: Learning rate to use for training, defaults to `1e-3`
         multi_label: Whether the targets are multi-label or not.
         enable_ort: Enable Torch ONNX Runtime Optimization: https://onnxruntime.ai/docs/#onnx-runtime-for-training
+
     """
 
     required_extras: str = "text"
diff --git a/src/flash/text/embedding/model.py b/src/flash/text/embedding/model.py
index 9c73b975b5..da73c6c18d 100644
--- a/src/flash/text/embedding/model.py
+++ b/src/flash/text/embedding/model.py
@@ -47,6 +47,7 @@ class TextEmbedder(Task):
     Args:
         backbone: backbone model to use for the task.
         enable_ort: Enable Torch ONNX Runtime Optimization: https://onnxruntime.ai/docs/#onnx-runtime-for-training
+
     """
 
     required_extras: str = "text"
diff --git a/src/flash/text/question_answering/data.py b/src/flash/text/question_answering/data.py
index d74b4047e0..fdf29f7bb9 100644
--- a/src/flash/text/question_answering/data.py
+++ b/src/flash/text/question_answering/data.py
@@ -207,6 +207,7 @@ def from_csv(
 
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
 
         ds_kw = {
@@ -352,6 +353,7 @@ def from_json(
 
             >>> os.remove("train_data.json")
             >>> os.remove("predict_data.json")
+
         """
 
         ds_kw = {
@@ -634,6 +636,7 @@ def from_squad_v2(
             >>> import os
             >>> os.remove("train_data.json")
             >>> os.remove("predict_data.json")
+
         """
 
         ds_kw = {
@@ -747,6 +750,7 @@ def from_dicts(
 
             >>> del train_data
             >>> del predict_data
+
         """
 
         ds_kw = {
diff --git a/src/flash/text/question_answering/model.py b/src/flash/text/question_answering/model.py
index e999825777..baa04f407b 100644
--- a/src/flash/text/question_answering/model.py
+++ b/src/flash/text/question_answering/model.py
@@ -86,6 +86,7 @@ class QuestionAnsweringTask(Task):
             less than the score of the null answer minus this threshold, the null answer is selected for this example.
             Only useful when `version_2_with_negative=True`.
         use_stemmer: Whether Porter stemmer should be used to strip word suffixes to improve matching.
+
     """
 
     required_extras: str = "text"
diff --git a/src/flash/text/seq2seq/summarization/data.py b/src/flash/text/seq2seq/summarization/data.py
index 04c00547a8..6a7d755781 100644
--- a/src/flash/text/seq2seq/summarization/data.py
+++ b/src/flash/text/seq2seq/summarization/data.py
@@ -190,6 +190,7 @@ def from_csv(
 
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
 
         ds_kw = {
@@ -300,6 +301,7 @@ def from_json(
 
             >>> os.remove("train_data.json")
             >>> os.remove("predict_data.json")
+
         """
 
         ds_kw = {
@@ -393,6 +395,7 @@ def from_hf_datasets(
 
             >>> del train_data
             >>> del predict_data
+
         """
 
         ds_kw = {
@@ -467,6 +470,7 @@ def from_lists(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {}
diff --git a/src/flash/text/seq2seq/summarization/model.py b/src/flash/text/seq2seq/summarization/model.py
index 926a9823b6..fd74ba9d8a 100644
--- a/src/flash/text/seq2seq/summarization/model.py
+++ b/src/flash/text/seq2seq/summarization/model.py
@@ -42,6 +42,7 @@ class SummarizationTask(Seq2SeqTask):
         num_beams: Number of beams to use in validation when generating predictions. Defaults to `4`
         use_stemmer: Whether Porter stemmer should be used to strip word suffixes to improve matching.
         enable_ort: Enable Torch ONNX Runtime Optimization: https://onnxruntime.ai/docs/#onnx-runtime-for-training
+
     """
 
     def __init__(
diff --git a/src/flash/text/seq2seq/translation/data.py b/src/flash/text/seq2seq/translation/data.py
index aaf5c54aee..35865f929b 100644
--- a/src/flash/text/seq2seq/translation/data.py
+++ b/src/flash/text/seq2seq/translation/data.py
@@ -184,6 +184,7 @@ def from_csv(
 
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
 
         ds_kw = {
@@ -293,6 +294,7 @@ def from_json(
 
             >>> os.remove("train_data.json")
             >>> os.remove("predict_data.json")
+
         """
 
         ds_kw = {
@@ -386,6 +388,7 @@ def from_hf_datasets(
 
             >>> del train_data
             >>> del predict_data
+
         """
 
         ds_kw = {
@@ -460,6 +463,7 @@ def from_lists(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {}
diff --git a/src/flash/text/seq2seq/translation/model.py b/src/flash/text/seq2seq/translation/model.py
index 71fe3834aa..e6efc36c6f 100644
--- a/src/flash/text/seq2seq/translation/model.py
+++ b/src/flash/text/seq2seq/translation/model.py
@@ -42,6 +42,7 @@ class TranslationTask(Seq2SeqTask):
         n_gram: Maximum n_grams to use in metric calculation. Defaults to `4`
         smooth: Apply smoothing in BLEU calculation. Defaults to `True`
         enable_ort: Enable Torch ONNX Runtime Optimization: https://onnxruntime.ai/docs/#onnx-runtime-for-training
+
     """
 
     def __init__(
diff --git a/src/flash/video/classification/data.py b/src/flash/video/classification/data.py
index e55392b482..d47d5d714b 100644
--- a/src/flash/video/classification/data.py
+++ b/src/flash/video/classification/data.py
@@ -159,6 +159,7 @@ def from_files(
             >>> import os
             >>> _ = [os.remove(f"video_{i}.mp4") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_video_{i}.mp4") for i in range(1, 4)]
+
         """
         ds_kw = {
             "clip_sampler": clip_sampler,
@@ -324,6 +325,7 @@ def from_folders(
             >>> import shutil
             >>> shutil.rmtree("train_folder")
             >>> shutil.rmtree("predict_folder")
+
         """
         ds_kw = {
             "clip_sampler": clip_sampler,
@@ -507,6 +509,7 @@ def from_data_frame(
             >>> shutil.rmtree("predict_folder")
             >>> del train_data_frame
             >>> del predict_data_frame
+
         """
         ds_kw = {
             "clip_sampler": clip_sampler,
@@ -635,6 +638,7 @@ def from_tensors(
         .. testcleanup::
 
             >>> del frame
+
         """
 
         train_input = input_cls(
@@ -905,6 +909,7 @@ def from_csv(
             >>> shutil.rmtree("predict_folder")
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
         ds_kw = {
             "clip_sampler": clip_sampler,
@@ -1062,6 +1067,7 @@ def from_fiftyone(
             >>> _ = [os.remove(f"predict_video_{i}.mp4") for i in range(1, 4)]
             >>> del train_dataset
             >>> del predict_dataset
+
         """
         ds_kw = {
             "clip_sampler": clip_sampler,
@@ -1171,6 +1177,7 @@ def from_labelstudio(
                 data_folder='label-studio/media/upload',
                 val_split=0.8,
             )
+
         """
 
         train_data, val_data, test_data, predict_data = _parse_labelstudio_arguments(
diff --git a/tests/helpers/task_tester.py b/tests/helpers/task_tester.py
index c480c1641f..d54a5bad1e 100644
--- a/tests/helpers/task_tester.py
+++ b/tests/helpers/task_tester.py
@@ -154,6 +154,7 @@ class TaskTesterMeta(ABCMeta):
 
     These tests will also be wrapped with the appropriate marks to skip them if the required dependencies are not
     available.
+
     """
 
     @staticmethod
@@ -240,6 +241,7 @@ class TaskTester(metaclass=TaskTesterMeta):
 
     Use the class attributes to control which tests will be run. For example, if ``traceable`` is ``False`` then no JIT
     tracing test will be performed.
+
     """
 
     task: Task