code: def _validate_target(num_samples: int, target: TargetType) -> None:
      if isinstance(target, list) or (
          isinstance(target, torch.Tensor) and torch.numel(target) > 1
      ):
          assert num_samples == len(target), (
              "The number of samples provied in the"
              "input {} does not match with the number of targets. {}".format(
                  num_samples, len(target)
              )
          )
  
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 2 for calling a global function with example usage: _validate_target(num_samples, target) without return types