class_docstr: Approximates the integral of gradients for a particular neuron
  along the path from a baseline input to the given input.
  If no baseline is provided, the default baseline is the zero tensor.
  More details regarding the integrated gradient method can be found in the
  original paper here:
  https://arxiv.org/abs/1703.01365
  
  Note that this method is equivalent to applying integrated gradients
  where the output is the output of the identified neuron.
code: def multiplies_by_inputs(self):
      return self._multiply_by_inputs
  
file_path: captum/captum/attr/_core/neuron/neuron_integrated_gradients.py
imports: from typing import Any, Callable, List, Tuple, Union
  from captum._utils.gradient import construct_neuron_grad_fn
  from captum._utils.typing import TensorOrTupleOfTensorsGeneric
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from captum.attr._utils.attribution import GradientAttribution, NeuronAttribution
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 3 for calling the NeuronIntegratedGradients obj's underlying member method code with example usage: obj.multiplies_by_inputs() and returns: self