code: def __init__(
      self,
      iterables: List[Iterable],
      limits: Optional[List[Union[int, float]]] = None,
  ) -> None:
      if limits is not None and len(limits) != len(iterables):
          raise ValueError(
              f"Mismatch in number of limits ({len(limits)}) and number of iterables ({len(iterables)})"
          )
      self.iterables = iterables
      self.iterators: List[Iterator] = []
      self._idx = 0  # what would be batch_idx
      self.limits = limits
  
imports: import contextlib
  from collections.abc import Iterable
  from typing import Any, Callable, Dict, Iterator, List, Literal, Optional, Tuple, Type, Union
  from torch.utils.data.dataloader import _BaseDataLoaderIter, _MultiProcessingDataLoaderIter
  from typing_extensions import Self, TypedDict
  from lightning.fabric.utilities.data import sized_len
  from lightning.pytorch.utilities._pytree import _map_and_unflatten, _tree_flatten, tree_unflatten
label_desc: Use image node_id 1 to create a new _ModeIterator object from inherited base classes: Iterator with example: obj = _ModeIterator(iterables, limits)