captum/attr/_core/neuron/neuron_deep_lift.py [238:262]:
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                " 0.6.0 due to changes in PyTorch's full backward hook"
                " behavior. To obtain attributions for a neuron's"
                " output, please attribute with respect to the next layer's input"
            )
            dl.skip_new_hook_layer = self.layer  # type: ignore
        else:
            dl.skip_new_hook_layer = None  # type: ignore
        dl.gradient_func = construct_neuron_grad_fn(
            self.layer,
            neuron_selector,
            attribute_to_neuron_input=attribute_to_neuron_input,
        )

        # NOTE: using __wrapped__ to not log
        return dl.attribute.__wrapped__(  # type: ignore
            dl,  # self
            inputs,
            baselines,
            additional_forward_args=additional_forward_args,
            custom_attribution_func=custom_attribution_func,
        )

    @property
    def multiplies_by_inputs(self):
        return self._multiply_by_inputs
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captum/attr/_core/neuron/neuron_deep_lift.py [475:499]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                " 0.6.0 due to changes in PyTorch's full backward hook"
                " behavior. To obtain attributions for a neuron's"
                " output, please attribute with respect to the next layer's input"
            )
            dl.skip_new_hook_layer = self.layer  # type: ignore
        else:
            dl.skip_new_hook_layer = None  # type: ignore
        dl.gradient_func = construct_neuron_grad_fn(
            self.layer,
            neuron_selector,
            attribute_to_neuron_input=attribute_to_neuron_input,
        )

        # NOTE: using __wrapped__ to not log
        return dl.attribute.__wrapped__(  # type: ignore
            dl,  # self
            inputs,
            baselines,
            additional_forward_args=additional_forward_args,
            custom_attribution_func=custom_attribution_func,
        )

    @property
    def multiplies_by_inputs(self):
        return self._multiply_by_inputs
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