class_docstr: Wrapper class for ray data iterables.
class_docstr_tok: ["Wrapper","class","for","ray","data","iterables","."]
code: def __init__(self, data_iterable) -> None:
      super().__init__()
      self.data_iterable = data_iterable
  
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 to create a new RayTorchIterableDataset object from inherited base classes: IterableDataset with example: obj = RayTorchIterableDataset(data_iterable)