class_docstr: Computes conductance with respect to the given layer. The
  returned output is in the shape of the layer's output, showing the total
  conductance of each hidden layer neuron.
  
  The details of the approach can be found here:
  https://arxiv.org/abs/1805.12233
  https://arxiv.org/abs/1807.09946
  
  Note that this provides the total conductance of each neuron in the
  layer's output. To obtain the breakdown of a neuron's conductance by input
  features, utilize NeuronConductance instead, and provide the target
  neuron index.
code: def attribute(
      self,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      baselines: BaselineType = None,
      target: TargetType = None,
      additional_forward_args: Any = None,
      n_steps: int = 50,
      method: str = "gausslegendre",
      internal_batch_size: Union[None, int] = None,
      *,
      return_convergence_delta: Literal[True],
      attribute_to_layer_input: bool = False,
  ) -> Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]:
      ...
  
imports: import typing
  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
  from captum._utils.gradient import compute_layer_gradients_and_eval
  from captum._utils.typing import BaselineType, Literal, TargetType
  from captum.attr._utils.approximation_methods import approximation_parameters
  from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
  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 3 for calling the LayerConductance obj's underlying member method code with example usage: obj.attribute(inputs, baselines, target, additional_forward_args, n_steps, method, internal_batch_size) without return types