code: def _call_custom_attribution_func(
      custom_attribution_func: Callable[..., Tuple[Tensor, ...]],
      multipliers: Tuple[Tensor, ...],
      inputs: Tuple[Tensor, ...],
      baselines: Tuple[Tensor, ...],
  ) -> Tuple[Tensor, ...]:
      assert callable(custom_attribution_func), (
          "`custom_attribution_func`"
          " must be a callable function but {} provided".format(
              type(custom_attribution_func)
          )
      )
      custom_attr_func_params = signature(
          custom_attribution_func
      ).parameters
  
      if len(custom_attr_func_params) == 1:
          return custom_attribution_func(multipliers)
      elif len(custom_attr_func_params) == 2:
          return custom_attribution_func(multipliers, inputs)
      elif len(custom_attr_func_params) == 3:
          return custom_attribution_func(multipliers, inputs, baselines)
      else:
          raise AssertionError(
              "`custom_attribution_func` must take at least one and at most 3 arguments."
          )
  
imports: import typing
  from inspect import signature
  from typing import Any, Callable, List, Tuple, TYPE_CHECKING, Union
  import torch
  from captum._utils.common import _format_baseline, _format_output, _format_tensor_into_tuples, _validate_input
  from captum._utils.typing import BaselineType, Literal, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.approximation_methods import SUPPORTED_METHODS
  from torch import Tensor
label_desc: Use image node_id 18 for calling a global function with example usage: _call_custom_attribution_func(custom_attribution_func, multipliers, inputs, baselines) and returns: custom_attribution_func, custom_attribution_func, custom_attribution_func