class_docstr: ImageFeature is used to visualize image features in Insights. It expects an image in
  NCHW format. If C has a dimension of 1, its assumed to be a greyscale image.
  If it has a dimension of 3, its expected to be in RGB format.
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docstr: Args:
      name (str): The label of the specific feature. For example, an
                  ImageFeature's name can be "Photo".
      baseline_transforms (list, Callable, optional): Optional list of
                  callables (e.g. functions) to be called on the input tensor
                  to construct multiple baselines. Currently only one baseline
                  is supported. See
                  :py:class:`.IntegratedGradients` for more
                  information about baselines.
      input_transforms (list, Callable, optional): A list of transforms
                  or transform to be applied to the input. For images,
                  normalization is often applied here.
      visualization_transform (Callable, optional): Optional callable (e.g.
                  function) applied as a postprocessing step of the original
                  input data (before input_transforms) to convert it to a
                  format to be visualized.
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code: def __init__(
      self,
      name: str,
      baseline_transforms: Union[Callable, List[Callable]],
      input_transforms: Union[Callable, List[Callable]],
      visualization_transform: Optional[Callable] = None,
  ) -> None:
      r"""
      Args:
          name (str): The label of the specific feature. For example, an
                      ImageFeature's name can be "Photo".
          baseline_transforms (list, Callable, optional): Optional list of
                      callables (e.g. functions) to be called on the input tensor
                      to construct multiple baselines. Currently only one baseline
                      is supported. See
                      :py:class:`.IntegratedGradients` for more
                      information about baselines.
          input_transforms (list, Callable, optional): A list of transforms
                      or transform to be applied to the input. For images,
                      normalization is often applied here.
          visualization_transform (Callable, optional): Optional callable (e.g.
                      function) applied as a postprocessing step of the original
                      input data (before input_transforms) to convert it to a
                      format to be visualized.
      """
      super().__init__(
          name,
          baseline_transforms=baseline_transforms,
          input_transforms=input_transforms,
          visualization_transform=visualization_transform,
      )
  
imports: import base64
  import warnings
  from collections import namedtuple
  from io import BytesIO
  from typing import Callable, List, Optional, Union
  from captum._utils.common import safe_div
  from captum.attr._utils import visualization
  from captum.insights.attr_vis._utils.transforms import format_transforms
label_desc: Use image node_id 1 to create a new ImageFeature object from inherited base classes: BaseFeature with example: obj = ImageFeature(name, baseline_transforms, input_transforms, visualization_transform)