def from_numpy(
        cls,
        train_data: Optional[Collection[np.ndarray]] = None,
        predict_data: Optional[Collection[np.ndarray]] = None,
        input_cls: Type[Input] = ImageNumpyInput,
        transform: INPUT_TRANSFORM_TYPE = StyleTransferInputTransform,
        transform_kwargs: Optional[Dict] = None,
        **data_module_kwargs: Any
    ) -> "StyleTransferData":
        """Load the :class:`~flash.image.style_transfer.data.StyleTransferData` from numpy arrays (or lists of arrays).

        To learn how to customize the transforms applied for each stage, read our
        :ref:`customizing transforms guide <customizing_transforms>`.

        Args:
            train_data: The numpy array or list of arrays to use when training.
            predict_data: The numpy array or list of arrays to use when predicting.
            transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
            input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
            transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
            transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
            data_module_kwargs: Additional keyword arguments to provide to the
              :class:`~flash.core.data.data_module.DataModule` constructor.

        Returns:
            The constructed :class:`~flash.image.style_transfer.data.StyleTransferData`.

        Examples
        ________

        .. doctest::

            >>> import numpy as np
            >>> from flash import Trainer
            >>> from flash.image import StyleTransfer, StyleTransferData
            >>> datamodule = StyleTransferData.from_numpy(
            ...     train_data=[np.random.rand(3, 64, 64), np.random.rand(3, 64, 64), np.random.rand(3, 64, 64)],
            ...     predict_data=[np.random.rand(3, 64, 64)],
            ...     transform_kwargs=dict(image_size=(128, 128)),
            ...     batch_size=2,
            ... )
            >>> model = StyleTransfer()
            >>> trainer = Trainer(fast_dev_run=True)
            >>> trainer.fit(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            Training...
            >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            Predicting...

        """

        return cls(
            input_cls(RunningStage.TRAINING, train_data),
            predict_input=input_cls(RunningStage.PREDICTING, predict_data),
            transform=transform,
            transform_kwargs=transform_kwargs,
            **data_module_kwargs,
        )
Load the :class:`~flash.image.style_transfer.data.StyleTransferData` from numpy arrays (or lists of arrays).

        To learn how to customize the transforms applied for each stage, read our
        :ref:`customizing transforms guide <customizing_transforms>`.

        Args:
            train_data: The numpy array or list of arrays to use when training.
            predict_data: The numpy array or list of arrays to use when predicting.
            transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
            input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
            transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
            transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
            data_module_kwargs: Additional keyword arguments to provide to the
              :class:`~flash.core.data.data_module.DataModule` constructor.

        Returns:
            The constructed :class:`~flash.image.style_transfer.data.StyleTransferData`.

        Examples
        ________

        .. doctest::

            >>> import numpy as np
            >>> from flash import Trainer
            >>> from flash.image import StyleTransfer, StyleTransferData
            >>> datamodule = StyleTransferData.from_numpy(
            ...     train_data=[np.random.rand(3, 64, 64), np.random.rand(3, 64, 64), np.random.rand(3, 64, 64)],
            ...     predict_data=[np.random.rand(3, 64, 64)],
            ...     transform_kwargs=dict(image_size=(128, 128)),
            ...     batch_size=2,
            ... )
            >>> model = StyleTransfer()
            >>> trainer = Trainer(fast_dev_run=True)
            >>> trainer.fit(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            Training...
            >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            Predicting...

        
src/flash/image/style_transfer/data.py