captum/attr/_core/layer/layer_gradient_shap.py [60:105]:
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    def __init__(
        self,
        forward_func: Callable,
        layer: Module,
        device_ids: Union[None, List[int]] = None,
        multiply_by_inputs: bool = True,
    ) -> None:
        r"""
        Args:

            forward_func (callable):  The forward function of the model or any
                        modification of it
            layer (torch.nn.Module): Layer for which attributions are computed.
                        Output size of attribute matches this layer's input or
                        output dimensions, depending on whether we attribute to
                        the inputs or outputs of the layer, corresponding to
                        attribution of each neuron in the input or output of
                        this layer.
            device_ids (list(int)): Device ID list, necessary only if forward_func
                        applies a DataParallel model. This allows reconstruction of
                        intermediate outputs from batched results across devices.
                        If forward_func is given as the DataParallel model itself,
                        then it is not necessary to provide this argument.
            multiply_by_inputs (bool, optional): Indicates whether to factor
                        model inputs' multiplier in the final attribution scores.
                        In the literature this is also known as local vs global
                        attribution. If inputs' multiplier isn't factored in,
                        then this type of attribution method is also called local
                        attribution. If it is, then that type of attribution
                        method is called global.
                        More detailed can be found here:
                        https://arxiv.org/abs/1711.06104

                        In case of layer gradient shap, if `multiply_by_inputs`
                        is set to True, the sensitivity scores for scaled inputs
                        are being multiplied by
                        layer activations for inputs - layer activations for baselines.

        """
        LayerAttribution.__init__(self, forward_func, layer, device_ids)
        GradientAttribution.__init__(self, forward_func)
        self._multiply_by_inputs = multiply_by_inputs

    @typing.overload
    def attribute(
        self,
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captum/attr/_core/layer/layer_gradient_shap.py [328:373]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    def __init__(
        self,
        forward_func: Callable,
        layer: Module,
        device_ids: Union[None, List[int]] = None,
        multiply_by_inputs: bool = True,
    ) -> None:
        r"""
        Args:

            forward_func (callable):  The forward function of the model or any
                        modification of it
            layer (torch.nn.Module): Layer for which attributions are computed.
                        Output size of attribute matches this layer's input or
                        output dimensions, depending on whether we attribute to
                        the inputs or outputs of the layer, corresponding to
                        attribution of each neuron in the input or output of
                        this layer.
            device_ids (list(int)): Device ID list, necessary only if forward_func
                        applies a DataParallel model. This allows reconstruction of
                        intermediate outputs from batched results across devices.
                        If forward_func is given as the DataParallel model itself,
                        then it is not necessary to provide this argument.
            multiply_by_inputs (bool, optional): Indicates whether to factor
                        model inputs' multiplier in the final attribution scores.
                        In the literature this is also known as local vs global
                        attribution. If inputs' multiplier isn't factored in,
                        then this type of attribution method is also called local
                        attribution. If it is, then that type of attribution
                        method is called global.
                        More detailed can be found here:
                        https://arxiv.org/abs/1711.06104

                        In case of layer input minus baseline x gradient,
                        if `multiply_by_inputs` is set to True, the sensitivity scores
                        for scaled inputs are being multiplied by
                        layer activations for inputs - layer activations for baselines.

        """
        LayerAttribution.__init__(self, forward_func, layer, device_ids)
        GradientAttribution.__init__(self, forward_func)
        self._multiply_by_inputs = multiply_by_inputs

    @typing.overload
    def attribute(
        self,
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