code: def prepare_trainer(trainer: "Trainer") -> "Trainer":
      """Prepare your HuggingFace Transformer Trainer for Ray Train.
  
      This utility function enable the trainer integrates with Ray Data Integration.
      Internally, it overrides the `get_train_dataloader` and `get_eval_dataloader`
      methods and inject the data integration logics if the `train_dataset` and
      `eval_dataset` are Ray Data Iterables.
      """
  
      if TRANSFORMERS_IMPORT_ERROR is not None:
          raise TRANSFORMERS_IMPORT_ERROR
  
      base_trainer_class: Type[
          transformers.trainer.Trainer
      ] = trainer.__class__
  
      class RayTransformersTrainer(base_trainer_class):
          """A Wrapper of `transformers.Trainer` for Ray Data Integration."""
  
          def get_train_dataloader(self) -> DataLoader:
              if isinstance(self.train_dataset, _IterableFromIterator):
                  dataset = RayTorchIterableDataset(self.train_dataset)
                  return DataLoader(
                      dataset, batch_size=1, collate_fn=lambda x: x[0]
                  )
              else:
                  return super().get_train_dataloader()
  
          def get_eval_dataloader(
              self, eval_dataset: Optional[Dataset] = None
          ) -> DataLoader:
              if eval_dataset is None:
                  eval_dataset = self.eval_dataset
  
              if isinstance(eval_dataset, _IterableFromIterator):
                  dataset = RayTorchIterableDataset(eval_dataset)
                  return DataLoader(
                      dataset, batch_size=1, collate_fn=lambda x: x[0]
                  )
              else:
                  return super().get_eval_dataloader(eval_dataset)
  
      trainer.__class__ = RayTransformersTrainer
  
      record_extra_usage_tag(
          TagKey.TRAIN_TRANSFORMERS_PREPARE_TRAINER, "1"
      )
      return trainer
  
file_path: ray/python/ray/train/huggingface/transformers/_transformers_utils.py
imports: import logging
  import os
  import shutil
  from tempfile import TemporaryDirectory
  from typing import Iterator, Optional, Type
  from torch.utils.data import DataLoader, Dataset, IterableDataset
  import ray
  from ray._private.usage.usage_lib import TagKey, record_extra_usage_tag
  from ray.data.iterator import _IterableFromIterator
  from ray.train import Checkpoint
  from ray.util import PublicAPI
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_trainer(trainer) and returns: trainer, DataLoader, super, DataLoader, super