code: def _validate_input(
      inputs: Tuple[Tensor, ...],
      baselines: Tuple[Union[Tensor, int, float], ...],
      n_steps: int = 50,
      method: str = "riemann_trapezoid",
      draw_baseline_from_distrib: bool = False,
  ) -> None:
      _validate_input_basic(
          inputs, baselines, draw_baseline_from_distrib
      )
      assert n_steps >= 0, (
          "The number of steps must be a positive integer. "
          "Given: {}".format(n_steps)
      )
  
      assert method in SUPPORTED_METHODS, (
          "Approximation method must be one for the following {}. "
          "Given {}".format(SUPPORTED_METHODS, method)
      )
  
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 3 for calling a global function with example usage: _validate_input(inputs, baselines, n_steps, method, draw_baseline_from_distrib) without return types