class_docstr: The ``ImageClassificationData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of
  classmethods for loading data for image classification.
docstr: Load the :class:`~flash.image.classification.data.ImageClassificationData` from numpy arrays (or lists of
  arrays) and corresponding lists of targets.
  
  The targets 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_data: The numpy array or list of arrays to use when training.
      train_targets: The list of targets to use when training.
      val_data: The numpy array or list of arrays to use when validating.
      val_targets: The list of targets to use when validating.
      test_data: The numpy array or list of arrays to use when testing.
      test_targets: The list of targets to use when testing.
      predict_data: The numpy array or list of arrays 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.image.classification.data.ImageClassificationData`.
  
  Examples
  ________
  
  .. doctest::
  
      >>> import numpy as np
      >>> from flash import Trainer
      >>> from flash.image import ImageClassifier, ImageClassificationData
      >>> datamodule = ImageClassificationData.from_numpy(
      ...     train_data=[np.random.rand(3, 64, 64), np.random.rand(3, 64, 64), np.random.rand(3, 64, 64)],
      ...     train_targets=["cat", "dog", "cat"],
      ...     predict_data=[np.random.rand(3, 64, 64)],
      ...     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...
code: def from_numpy(
      cls,
      train_data: Optional[Collection[np.ndarray]] = None,
      train_targets: Optional[Collection[Any]] = None,
      val_data: Optional[Collection[np.ndarray]] = None,
      val_targets: Optional[Sequence[Any]] = None,
      test_data: Optional[Collection[np.ndarray]] = None,
      test_targets: Optional[Sequence[Any]] = None,
      predict_data: Optional[Collection[np.ndarray]] = None,
      target_formatter: Optional[TargetFormatter] = None,
      input_cls: Type[Input] = ImageClassificationNumpyInput,
      transform: INPUT_TRANSFORM_TYPE = ImageClassificationInputTransform,
      transform_kwargs: Optional[Dict] = None,
      **data_module_kwargs: Any,
  ) -> "ImageClassificationData":
      """Load the :class:`~flash.image.classification.data.ImageClassificationData` from numpy arrays (or lists of
      arrays) and corresponding lists of targets.
  
      The targets 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_data: The numpy array or list of arrays to use when training.
          train_targets: The list of targets to use when training.
          val_data: The numpy array or list of arrays to use when validating.
          val_targets: The list of targets to use when validating.
          test_data: The numpy array or list of arrays to use when testing.
          test_targets: The list of targets to use when testing.
          predict_data: The numpy array or list of arrays 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.image.classification.data.ImageClassificationData`.
  
      Examples
      ________
  
      .. doctest::
  
          >>> import numpy as np
          >>> from flash import Trainer
          >>> from flash.image import ImageClassifier, ImageClassificationData
          >>> datamodule = ImageClassificationData.from_numpy(
          ...     train_data=[np.random.rand(3, 64, 64), np.random.rand(3, 64, 64), np.random.rand(3, 64, 64)],
          ...     train_targets=["cat", "dog", "cat"],
          ...     predict_data=[np.random.rand(3, 64, 64)],
          ...     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...
  
      """
      ds_kw = {
          "target_formatter": target_formatter,
      }
  
      train_input = input_cls(
          RunningStage.TRAINING, train_data, train_targets, **ds_kw
      )
      ds_kw["target_formatter"] = getattr(
          train_input, "target_formatter", None
      )
  
      return cls(
          train_input,
          input_cls(
              RunningStage.VALIDATING, val_data, val_targets, **ds_kw
          ),
          input_cls(
              RunningStage.TESTING, test_data, test_targets, **ds_kw
          ),
          input_cls(RunningStage.PREDICTING, predict_data, **ds_kw),
          transform=transform,
          transform_kwargs=transform_kwargs,
          **data_module_kwargs,
      )
  
imports: from typing import Any, Callable, Collection, Dict, List, Optional, Sequence, Tuple, Type, Union
  import numpy
  import pandas
  from torch import Tensor
  from torch.utils.data import Dataset
  from flash.core.data.base_viz import BaseVisualization
  from flash.core.data.callback import BaseDataFetcher
  from flash.core.data.data_module import DataModule, DatasetInput
  from flash.core.data.io.input import DataKeys, Input
  from flash.core.data.io.input_transform import INPUT_TRANSFORM_TYPE
  from flash.core.data.utilities.classification import TargetFormatter
  from flash.core.data.utilities.paths import PATH_TYPE
  from flash.core.integrations.labelstudio.input import LabelStudioImageClassificationInput, _parse_labelstudio_arguments
  from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _MATPLOTLIB_AVAILABLE, _TOPIC_IMAGE_AVAILABLE, Image, requires
  from flash.core.utilities.stages import RunningStage
  from flash.image.classification.input import ImageClassificationCSVInput, ImageClassificationDataFrameInput, ImageClassificationFiftyOneInput, ImageClassificationFilesInput, ImageClassificationFolderInput, ImageClassificationImageInput, ImageClassificationNumpyInput, ImageClassificationTensorInput
  from flash.image.classification.input_transform import ImageClassificationInputTransform
label_desc: Use image node_id 3 for calling the ImageClassificationData obj's underlying member method code with example usage: obj.from_numpy(cls, train_data, train_targets, val_data, val_targets, test_data, test_targets, predict_data, target_formatter, input_cls, transform, transform_kwargs) and returns: cls