code: def attribute(  # type: ignore
      self,
      inputs: TensorOrTupleOfTensorsGeneric,
      baselines: BaselineType = None,
      target: TargetType = None,
      additional_forward_args: Any = None,
      return_convergence_delta: bool = False,
  ) -> Union[
      TensorOrTupleOfTensorsGeneric,
      Tuple[TensorOrTupleOfTensorsGeneric, Tensor],
  ]:
      # Keeps track whether original input is a tuple or not before
      # converting it into a tuple.
      is_inputs_tuple = _is_tuple(inputs)
      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 = self.gradient_func(
          self.forward_func,
          input_baseline_scaled,
          target,
          additional_forward_args,
      )
  
      if self.multiplies_by_inputs:
          input_baseline_diffs = tuple(
              input - baseline
              for input, baseline in zip(inputs, 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,
          is_inputs_tuple,
      )
  
imports: import typing
  from typing import Any, Callable, Tuple, Union
  import numpy
  import torch
  from captum._utils.common import _is_tuple
  from captum._utils.typing import BaselineType, Literal, TargetType, Tensor, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.noise_tunnel import NoiseTunnel
  from captum.attr._utils.attribution import GradientAttribution
  from captum.attr._utils.common import _compute_conv_delta_and_format_attrs, _format_callable_baseline, _format_input_baseline
  from captum.log import log_usage
label_desc: Use image node_id 4 for calling the InputBaselineXGradient obj's underlying member method code with example usage: obj.attribute(inputs, baselines, target, additional_forward_args, return_convergence_delta) and returns: _compute_conv_delta_and_format_attrs