code: def attribute(
      self,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      baselines: Union[Tensor, Tuple[Tensor, ...]],
      target: TargetType = None,
      additional_forward_args: Any = None,
      return_convergence_delta: Literal[False] = False,
      attribute_to_layer_input: bool = False,
  ) -> Union[Tensor, Tuple[Tensor, ...]]:
      ...
  
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 2 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) without return types