def from_fiftyone(
        cls,
        train_dataset: Optional[SampleCollection] = None,
        val_dataset: Optional[SampleCollection] = None,
        test_dataset: Optional[SampleCollection] = None,
        predict_dataset: Optional[SampleCollection] = None,
        label_field: str = "ground_truth",
        target_formatter: Optional[TargetFormatter] = None,
        input_cls: Type[Input] = ImageClassificationFiftyOneInput,
        transform: INPUT_TRANSFORM_TYPE = ImageClassificationInputTransform,
        transform_kwargs: Optional[Dict] = None,
        **data_module_kwargs,
    ) -> "ImageClassificationData":
        """Load the :class:`~flash.image.classification.data.ImageClassificationData` from FiftyOne ``SampleCollection``
        objects.

        The supported file extensions are: ``.jpg``, ``.jpeg``, ``.png``, ``.ppm``, ``.bmp``, ``.pgm``, ``.tif``,
        ``.tiff``, ``.webp``, and ``.npy``.
        The targets will be extracted from the ``label_field`` in the ``SampleCollection`` objects and can be in any
        of our :ref:`supported classification target formats <formatting_classification_targets>`.
        To learn how to customize the transforms applied for each stage, read our
        :ref:`customizing transforms guide <customizing_transforms>`.

        Args:
            train_dataset: The ``SampleCollection`` to use when training.
            val_dataset: The ``SampleCollection`` to use when validating.
            test_dataset: The ``SampleCollection`` to use when testing.
            predict_dataset: The ``SampleCollection`` to use when predicting.
            label_field: The field in the ``SampleCollection`` objects containing the targets.
            target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
                control how targets are handled. See :ref:`formatting_classification_targets` for more details.
            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.classification.data.ImageClassificationData`.

        Examples
        ________

        .. testsetup::

            >>> from PIL import Image
            >>> rand_image = Image.fromarray(np.random.randint(0, 255, (64, 64, 3), dtype="uint8"))
            >>> _ = [rand_image.save(f"image_{i}.png") for i in range(1, 4)]
            >>> _ = [rand_image.save(f"predict_image_{i}.png") for i in range(1, 4)]

        .. doctest::

            >>> import fiftyone as fo
            >>> from flash import Trainer
            >>> from flash.image import ImageClassifier, ImageClassificationData
            >>> train_dataset = fo.Dataset.from_images(
            ...     ["image_1.png", "image_2.png", "image_3.png"]
            ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            <BLANKLINE>
            ...
            >>> samples = [train_dataset[filepath] for filepath in train_dataset.values("filepath")]
            >>> for sample, label in zip(samples, ["cat", "dog", "cat"]):
            ...     sample["ground_truth"] = fo.Classification(label=label)
            ...     sample.save()
            ...
            >>> predict_dataset = fo.Dataset.from_images(
            ...     ["predict_image_1.png", "predict_image_2.png", "predict_image_3.png"]
            ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            <BLANKLINE>
            ...
            >>> datamodule = ImageClassificationData.from_fiftyone(
            ...     train_dataset=train_dataset,
            ...     predict_dataset=predict_dataset,
            ...     transform_kwargs=dict(image_size=(128, 128)),
            ...     batch_size=2,
            ... )
            >>> datamodule.num_classes
            2
            >>> datamodule.labels
            ['cat', 'dog']
            >>> model = ImageClassifier(backbone="resnet18", num_classes=datamodule.num_classes)
            >>> 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...

        .. testcleanup::

            >>> 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)]
            >>> del train_dataset
            >>> del predict_dataset

        """
        ds_kw = {
            "target_formatter": target_formatter,
        }

        train_input = input_cls(RunningStage.TRAINING, train_dataset, label_field=label_field, **ds_kw)
        ds_kw["target_formatter"] = getattr(train_input, "target_formatter", None)

        return cls(
            train_input,
            input_cls(RunningStage.VALIDATING, val_dataset, label_field=label_field, **ds_kw),
            input_cls(RunningStage.TESTING, test_dataset, label_field=label_field, **ds_kw),
            input_cls(RunningStage.PREDICTING, predict_dataset, **ds_kw),
            transform=transform,
            transform_kwargs=transform_kwargs,
            **data_module_kwargs,
        )
Load the :class:`~flash.image.classification.data.ImageClassificationData` from FiftyOne ``SampleCollection``
        objects.

        The supported file extensions are: ``.jpg``, ``.jpeg``, ``.png``, ``.ppm``, ``.bmp``, ``.pgm``, ``.tif``,
        ``.tiff``, ``.webp``, and ``.npy``.
        The targets will be extracted from the ``label_field`` in the ``SampleCollection`` objects and can be in any
        of our :ref:`supported classification target formats <formatting_classification_targets>`.
        To learn how to customize the transforms applied for each stage, read our
        :ref:`customizing transforms guide <customizing_transforms>`.

        Args:
            train_dataset: The ``SampleCollection`` to use when training.
            val_dataset: The ``SampleCollection`` to use when validating.
            test_dataset: The ``SampleCollection`` to use when testing.
            predict_dataset: The ``SampleCollection`` to use when predicting.
            label_field: The field in the ``SampleCollection`` objects containing the targets.
            target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
                control how targets are handled. See :ref:`formatting_classification_targets` for more details.
            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.classification.data.ImageClassificationData`.

        Examples
        ________

        .. testsetup::

            >>> from PIL import Image
            >>> rand_image = Image.fromarray(np.random.randint(0, 255, (64, 64, 3), dtype="uint8"))
            >>> _ = [rand_image.save(f"image_{i}.png") for i in range(1, 4)]
            >>> _ = [rand_image.save(f"predict_image_{i}.png") for i in range(1, 4)]

        .. doctest::

            >>> import fiftyone as fo
            >>> from flash import Trainer
            >>> from flash.image import ImageClassifier, ImageClassificationData
            >>> train_dataset = fo.Dataset.from_images(
            ...     ["image_1.png", "image_2.png", "image_3.png"]
            ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            <BLANKLINE>
            ...
            >>> samples = [train_dataset[filepath] for filepath in train_dataset.values("filepath")]
            >>> for sample, label in zip(samples, ["cat", "dog", "cat"]):
            ...     sample["ground_truth"] = fo.Classification(label=label)
            ...     sample.save()
            ...
            >>> predict_dataset = fo.Dataset.from_images(
            ...     ["predict_image_1.png", "predict_image_2.png", "predict_image_3.png"]
            ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            <BLANKLINE>
            ...
            >>> datamodule = ImageClassificationData.from_fiftyone(
            ...     train_dataset=train_dataset,
            ...     predict_dataset=predict_dataset,
            ...     transform_kwargs=dict(image_size=(128, 128)),
            ...     batch_size=2,
            ... )
            >>> datamodule.num_classes
            2
            >>> datamodule.labels
            ['cat', 'dog']
            >>> model = ImageClassifier(backbone="resnet18", num_classes=datamodule.num_classes)
            >>> 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...

        .. testcleanup::

            >>> 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)]
            >>> del train_dataset
            >>> del predict_dataset

        
src/flash/image/classification/data.py