code: def _multiclass_precision_recall_curve_arg_validation(
      num_classes: int,
      thresholds: Optional[Union[int, List[float], Tensor]] = None,
      ignore_index: Optional[int] = None,
      average: Optional[Literal["micro", "macro"]] = None,
  ) -> None:
      """Validate non tensor input.
  
      - ``num_classes`` has to be an int larger than 1
      - ``threshold`` has to be None | a 1d tensor | a list of floats in the [0,1] range | an int
      - ``ignore_index`` has to be None or int
  
      """
      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 average not in (None, "micro", "macro"):
          raise ValueError(
              f"Expected argument `average` to be one of None, 'micro' or 'macro', but got {average}"
          )
      _binary_precision_recall_curve_arg_validation(
          thresholds, ignore_index
      )
  
imports: from typing import List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor, tensor
  from torch.nn import functional
  from typing_extensions import Literal
  from torchmetrics.utilities.checks import _check_same_shape
  from torchmetrics.utilities.compute import _safe_divide, interp
  from torchmetrics.utilities.data import _bincount, _cumsum
  from torchmetrics.utilities.enums import ClassificationTask
label_desc: Use image node_id 11 for calling a global function with example usage: _multiclass_precision_recall_curve_arg_validation(num_classes, thresholds, ignore_index, average) without return types