captum/attr/_core/gradient_shap.py [294:323]:
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        r"""
        Args:

            forward_func (function): The forward function of the model or
                        any modification of it
            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 gradient shap, if `multiply_by_inputs`
                        is set to True, the sensitivity scores of scaled inputs
                        are being multiplied by (inputs - baselines).

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

    @typing.overload
    def attribute(
        self,
        inputs: TensorOrTupleOfTensorsGeneric,
        baselines: BaselineType = None,
        target: TargetType = None,
        additional_forward_args: Any = None,
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captum/attr/_core/integrated_gradients.py [53:85]:
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        r"""
        Args:

            forward_func (callable):  The forward function of the model or any
                    modification of it
            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 that 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 integrated gradients, if `multiply_by_inputs`
                    is set to True, final sensitivity scores are being multiplied by
                    (inputs - baselines).
        """
        GradientAttribution.__init__(self, forward_func)
        self._multiply_by_inputs = multiply_by_inputs

    # The following overloaded method signatures correspond to the case where
    # return_convergence_delta is False, then only attributions are returned,
    # and when return_convergence_delta is True, the return type is
    # a tuple with both attributions and deltas.
    @typing.overload
    def attribute(
        self,
        inputs: TensorOrTupleOfTensorsGeneric,
        baselines: BaselineType = None,
        target: TargetType = None,
        additional_forward_args: Any = None,
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