code: def _validate_iterate_length(
      iterate_length: Optional[int], times_iterate: int
  ) -> int:
      if iterate_length is None:
          return times_iterate
  
      if iterate_length <= 0:
          warnings.warn(
              f"iterate_length {iterate_length} is not valid. "
              f"Ignoring this argument and iterate over entire dataset once",
              stacklevel=2,
          )
          return times_iterate
  
      if iterate_length > times_iterate:
          warnings.warn(
              f"iterate_length {iterate_length} exceeds sharded dataset length. "
              f"Ignoring this argument and iterate over entire dataset once",
              stacklevel=2,
          )
          return times_iterate
      return iterate_length
  
imports: import abc
  import json
  import logging
  import math
  import os
  import pathlib
  import uuid
  import warnings
  from typing import TYPE_CHECKING, Any, ContextManager, Dict, Optional, Set, Sized, Type
  import torch
  import torch.distributed
  from torch import nn
  from torch.utils import data
  import determined
  from determined import common, core, pytorch
label_desc: Use image node_id 8 for calling a global function with example usage: _validate_iterate_length(iterate_length, times_iterate) and returns: iterate_length, times_iterate, times_iterate, times_iterate