code: def _dataset_repro_warning(
      fn: str, data_obj: Any, is_deepspeed_trial: bool = False
  ) -> str:
      disable_repro_check_method = "context.experimental.disable_dataset_reproducibility_checks()"
      if is_deepspeed_trial:
          disable_repro_check_method = (
              "context.disable_dataset_reproducibility_checks()"
          )
  
      return (
          f"{fn}() returned an instance of {type(data_obj).__name__}, which is not a "
          "subclass of det.pytorch.DataLoader.  For most non-Iterable DataSets, "
          "det.pytorch.DataLoader is a drop-in replacement for torch.utils.data.DataLoader "
          "but which offers easy and transparent reproducibility in Determined experiments. "
          "It is highly recommended that you use det.pytorch.DataLoader if possible.  If "
          f"not, you can disable this check by calling {disable_repro_check_method} at some point "
          "in your trial's __init__() method."
      )
  
file_path: determined/harness/determined/pytorch/_data.py
imports: import logging
  from typing import Any, Callable, Dict, Iterator, List, Optional, Sequence, Set, Type, TypeVar, Union, cast
  import numpy
  import torch
  from packaging import version
  from torch.utils.data import BatchSampler, Dataset, RandomSampler, Sampler, SequentialSampler, _utils
  from determined.pytorch import samplers
label_desc: Use image node_id 1 for calling a global function with example usage: _dataset_repro_warning(fn, data_obj, is_deepspeed_trial) and returns: fn, str, type, str, disable_repro_check_method, str