class_docstr: Computes conductance with respect to particular hidden neuron. The
  returned output is in the shape of the input, showing the attribution
  / conductance of each input feature to the selected hidden layer neuron.
  The details of the approach can be found here:
  https://arxiv.org/abs/1805.12233
class_docstr_tok: ["Computes","conductance","with","respect","to","particular","hidden","neuron",".","The","returned","output","is","in","the","shape","of","the","input",",","showing","the","attribution","\/","conductance","of","each","input","feature","to","the","selected","hidden","layer","neuron",".","The","details","of","the","approach","can","be","found","here",":","https",":","\/\/arxiv.org\/abs\/1805.12233"]
code: def multiplies_by_inputs(self):
      return self._multiply_by_inputs
  
file_path: captum/captum/attr/_core/neuron/neuron_conductance.py
imports: import warnings
  from typing import Any, Callable, List, Tuple, Union
  import torch
  from captum._utils.common import _expand_additional_forward_args, _expand_target, _format_additional_forward_args, _format_output, _is_tuple, _verify_select_neuron
  from captum._utils.gradient import compute_layer_gradients_and_eval
  from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.approximation_methods import approximation_parameters
  from captum.attr._utils.attribution import GradientAttribution, NeuronAttribution
  from captum.attr._utils.batching import _batch_attribution
  from captum.attr._utils.common import _format_input_baseline, _reshape_and_sum, _validate_input
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 4 for calling the NeuronConductance obj's underlying member method code with example usage: obj.multiplies_by_inputs() and returns: self