code: def _multiclass_confusion_matrix_arg_validation(
      num_classes: int,
      ignore_index: Optional[int] = None,
      normalize: Optional[
          Literal["true", "pred", "all", "none"]
      ] = None,
  ) -> None:
      """Validate non tensor input.
  
      - ``num_classes`` has to be a int larger than 1
      - ``ignore_index`` has to be None or int
      - ``normalize`` has to be "true" | "pred" | "all" | "none" | None
  
      """
      if not isinstance(num_classes, int) or num_classes < 2:
          raise ValueError(
              f"Expected argument `num_classes` to be an integer larger than 1, but got {num_classes}"
          )
      if ignore_index is not None and not isinstance(ignore_index, int):
          raise ValueError(
              f"Expected argument `ignore_index` to either be `None` or an integer, but got {ignore_index}"
          )
      allowed_normalize = ("true", "pred", "all", "none", None)
      if normalize not in allowed_normalize:
          raise ValueError(
              f"Expected argument `normalize` to be one of {allowed_normalize}, but got {normalize}."
          )
  
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.utilities.checks import _check_same_shape
  from torchmetrics.utilities.data import _bincount
  from torchmetrics.utilities.enums import ClassificationTask
  from torchmetrics.utilities.prints import rank_zero_warn
label_desc: Use image node_id 8 for calling a global function with example usage: _multiclass_confusion_matrix_arg_validation(num_classes, ignore_index, normalize) without return types