class_docstr: Computes element-wise product of gradient and activation for selected
  layer on given inputs.
class_docstr_tok: ["Computes","element-wise","product","of","gradient","and","activation","for","selected","layer","on","given","inputs","."]
code: def multiplies_by_inputs(self):
      return self._multiply_by_inputs
  
file_path: captum/captum/attr/_core/layer/layer_gradient_x_activation.py
imports: from typing import Any, Callable, List, Tuple, Union
  from captum._utils.common import _format_additional_forward_args, _format_output, _format_tensor_into_tuples
  from captum._utils.gradient import compute_layer_gradients_and_eval
  from captum._utils.typing import ModuleOrModuleList, TargetType
  from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the LayerGradientXActivation obj's underlying member method code with example usage: obj.multiplies_by_inputs() and returns: self