captum/insights/attr_vis/features.py [87:114]:
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    ) -> 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,
        )

    @staticmethod
    def visualization_type() -> str:
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captum/insights/attr_vis/features.py [162:200]:
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    ) -> 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.
                        For text features, a common baseline is a tensor of indices
                        corresponding to PAD with the same size as the input
                        tensor. See :py:class:`.TokenReferenceBase` for more
                        information.
            input_transforms (list, callable, optional): A list of transforms
                        or transform to be applied to the input. For text, a common
                        transform is to convert the tokenized input tensor into an
                        interpretable embedding. See
                        :py:class:`.InterpretableEmbeddingBase`
                        and
                        :py:func:`~.configure_interpretable_embedding_layer`
                        for more information.
            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
                        suitable format for visualization. For text features,
                        a common function is to convert the token indices to their
                        corresponding (sub)words.
        """
        super().__init__(
            name,
            baseline_transforms=baseline_transforms,
            input_transforms=input_transforms,
            visualization_transform=visualization_transform,
        )

    @staticmethod
    def visualization_type() -> str:
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