code: def attribute(  # type: ignore
      self,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      baselines: Union[Tensor, Tuple[Tensor, ...]],
      target: TargetType = None,
      additional_forward_args: Any = None,
      return_convergence_delta: bool = False,
      attribute_to_layer_input: bool = False,
  ) -> Union[
      Tensor,
      Tuple[Tensor, ...],
      Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor],
  ]:
      inputs, baselines = _format_input_baseline(inputs, baselines)
      rand_coefficient = torch.tensor(
          np.random.uniform(0.0, 1.0, inputs[0].shape[0]),
          device=inputs[0].device,
          dtype=inputs[0].dtype,
      )
  
      input_baseline_scaled = tuple(
          _scale_input(input, baseline, rand_coefficient)
          for input, baseline in zip(inputs, baselines)
      )
      grads, _ = compute_layer_gradients_and_eval(
          self.forward_func,
          self.layer,
          input_baseline_scaled,
          target,
          additional_forward_args,
          device_ids=self.device_ids,
          attribute_to_layer_input=attribute_to_layer_input,
      )
  
      attr_baselines = _forward_layer_eval(
          self.forward_func,
          baselines,
          self.layer,
          additional_forward_args=additional_forward_args,
          device_ids=self.device_ids,
          attribute_to_layer_input=attribute_to_layer_input,
      )
  
      attr_inputs = _forward_layer_eval(
          self.forward_func,
          inputs,
          self.layer,
          additional_forward_args=additional_forward_args,
          device_ids=self.device_ids,
          attribute_to_layer_input=attribute_to_layer_input,
      )
  
      if self.multiplies_by_inputs:
          input_baseline_diffs = tuple(
              input - baseline
              for input, baseline in zip(attr_inputs, attr_baselines)
          )
          attributions = tuple(
              input_baseline_diff * grad
              for input_baseline_diff, grad in zip(
                  input_baseline_diffs, grads
              )
          )
      else:
          attributions = grads
  
      return _compute_conv_delta_and_format_attrs(
          self,
          return_convergence_delta,
          attributions,
          baselines,
          inputs,
          additional_forward_args,
          target,
          cast(
              Union[Literal[True], Literal[False]],
              len(attributions) > 1,
          ),
      )
  
imports: import typing
  from typing import Any, Callable, cast, List, Tuple, Union
  import numpy
  import torch
  from captum._utils.gradient import _forward_layer_eval, compute_layer_gradients_and_eval
  from captum._utils.typing import Literal, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.gradient_shap import _scale_input
  from captum.attr._core.noise_tunnel import NoiseTunnel
  from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
  from captum.attr._utils.common import _compute_conv_delta_and_format_attrs, _format_callable_baseline, _format_input_baseline
  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 LayerInputBaselineXGradient obj's underlying member method code with example usage: obj.attribute(inputs, baselines, target, additional_forward_args, return_convergence_delta, attribute_to_layer_input) and returns: _compute_conv_delta_and_format_attrs