def from_data_frame(
        cls,
        categorical_fields: Optional[Union[str, List[str]]] = None,
        numerical_fields: Optional[Union[str, List[str]]] = None,
        target_fields: Optional[Union[str, List[str]]] = None,
        parameters: Optional[Dict[str, Any]] = None,
        train_data_frame: Optional[DataFrame] = None,
        val_data_frame: Optional[DataFrame] = None,
        test_data_frame: Optional[DataFrame] = None,
        predict_data_frame: Optional[DataFrame] = None,
        target_formatter: Optional[TargetFormatter] = None,
        input_cls: Type[Input] = TabularClassificationDataFrameInput,
        transform: INPUT_TRANSFORM_TYPE = InputTransform,
        transform_kwargs: Optional[Dict] = None,
        **data_module_kwargs: Any,
    ) -> "TabularClassificationData":
        """Creates a :class:`~flash.tabular.classification.data.TabularClassificationData` object from the given data
        frames.

        .. note::

            The ``categorical_fields``, ``numerical_fields``, and ``target_fields`` do not need to be provided if
            ``parameters`` are passed instead. These can be obtained from the
            :attr:`~flash.tabular.data.TabularData.parameters` attribute of the
            :class:`~flash.tabular.data.TabularData` object that contains your training data.

        The targets will be extracted from the ``target_fields`` in the data frames 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:
            categorical_fields: The fields (column names) in the data frames containing categorical data.
            numerical_fields: The fields (column names) in the data frames containing numerical data.
            target_fields: The field (column name) or list of fields in the data frames containing the targets.
            parameters: Parameters to use if ``categorical_fields``, ``numerical_fields``, and ``target_fields`` are not
                provided (e.g. when loading data for inference or validation).
            train_data_frame: The DataFrame to use when training.
            val_data_frame: The DataFrame to use when validating.
            test_data_frame: The DataFrame to use when testing.
            predict_data_frame: The DataFrame to use when predicting.
            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.tabular.classification.data.TabularClassificationData`.

        Examples
        ________

        .. testsetup::

            >>> from pandas import DataFrame
            >>> train_data = DataFrame.from_dict({
            ...     "animal": ["cat", "dog", "cat"],
            ...     "friendly": ["yes", "yes", "no"],
            ...     "weight": [6, 10, 5],
            ... })
            >>> predict_data = DataFrame.from_dict({
            ...     "friendly": ["yes", "no", "yes"],
            ...     "weight": [7, 12, 5],
            ... })

        We have a DataFrame ``train_data`` with the following contents:

        .. doctest::

            >>> train_data.head(3)
              animal friendly  weight
            0    cat      yes       6
            1    dog      yes      10
            2    cat       no       5

        and a DataFrame ``predict_data`` with the following contents:

        .. doctest::

            >>> predict_data.head(3)
              friendly  weight
            0      yes       7
            1       no      12
            2      yes       5

        .. doctest::

            >>> from flash import Trainer
            >>> from flash.tabular import TabularClassifier, TabularClassificationData
            >>> datamodule = TabularClassificationData.from_data_frame(
            ...     "friendly",
            ...     "weight",
            ...     "animal",
            ...     train_data_frame=train_data,
            ...     predict_data_frame=predict_data,
            ...     batch_size=4,
            ... )
            >>> datamodule.num_classes
            2
            >>> datamodule.labels
            ['cat', 'dog']
            >>> model = TabularClassifier.from_data(datamodule, backbone="tabnet")
            >>> 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::

            >>> del train_data
            >>> del predict_data

        """
        ds_kw = {
            "target_formatter": target_formatter,
            "categorical_fields": categorical_fields,
            "numerical_fields": numerical_fields,
            "target_fields": target_fields,
            "parameters": parameters,
        }

        train_input = input_cls(RunningStage.TRAINING, train_data_frame, **ds_kw)
        ds_kw["parameters"] = train_input.parameters if train_input else parameters
        ds_kw["target_formatter"] = getattr(train_input, "target_formatter", None)

        return cls(
            train_input,
            input_cls(RunningStage.VALIDATING, val_data_frame, **ds_kw),
            input_cls(RunningStage.TESTING, test_data_frame, **ds_kw),
            input_cls(RunningStage.PREDICTING, predict_data_frame, **ds_kw),
            transform=transform,
            transform_kwargs=transform_kwargs,
            **data_module_kwargs,
        )
Creates a :class:`~flash.tabular.classification.data.TabularClassificationData` object from the given data
        frames.

        .. note::

            The ``categorical_fields``, ``numerical_fields``, and ``target_fields`` do not need to be provided if
            ``parameters`` are passed instead. These can be obtained from the
            :attr:`~flash.tabular.data.TabularData.parameters` attribute of the
            :class:`~flash.tabular.data.TabularData` object that contains your training data.

        The targets will be extracted from the ``target_fields`` in the data frames 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:
            categorical_fields: The fields (column names) in the data frames containing categorical data.
            numerical_fields: The fields (column names) in the data frames containing numerical data.
            target_fields: The field (column name) or list of fields in the data frames containing the targets.
            parameters: Parameters to use if ``categorical_fields``, ``numerical_fields``, and ``target_fields`` are not
                provided (e.g. when loading data for inference or validation).
            train_data_frame: The DataFrame to use when training.
            val_data_frame: The DataFrame to use when validating.
            test_data_frame: The DataFrame to use when testing.
            predict_data_frame: The DataFrame to use when predicting.
            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.tabular.classification.data.TabularClassificationData`.

        Examples
        ________

        .. testsetup::

            >>> from pandas import DataFrame
            >>> train_data = DataFrame.from_dict({
            ...     "animal": ["cat", "dog", "cat"],
            ...     "friendly": ["yes", "yes", "no"],
            ...     "weight": [6, 10, 5],
            ... })
            >>> predict_data = DataFrame.from_dict({
            ...     "friendly": ["yes", "no", "yes"],
            ...     "weight": [7, 12, 5],
            ... })

        We have a DataFrame ``train_data`` with the following contents:

        .. doctest::

            >>> train_data.head(3)
              animal friendly  weight
            0    cat      yes       6
            1    dog      yes      10
            2    cat       no       5

        and a DataFrame ``predict_data`` with the following contents:

        .. doctest::

            >>> predict_data.head(3)
              friendly  weight
            0      yes       7
            1       no      12
            2      yes       5

        .. doctest::

            >>> from flash import Trainer
            >>> from flash.tabular import TabularClassifier, TabularClassificationData
            >>> datamodule = TabularClassificationData.from_data_frame(
            ...     "friendly",
            ...     "weight",
            ...     "animal",
            ...     train_data_frame=train_data,
            ...     predict_data_frame=predict_data,
            ...     batch_size=4,
            ... )
            >>> datamodule.num_classes
            2
            >>> datamodule.labels
            ['cat', 'dog']
            >>> model = TabularClassifier.from_data(datamodule, backbone="tabnet")
            >>> 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::

            >>> del train_data
            >>> del predict_data

        
src/flash/tabular/classification/data.py