code: def __init__(self, loader: DataLoader) -> None:
      self._dataset = loader.dataset
      self._shared_seed = None
      self._pg = None
      if isinstance(self._dataset, IterDataPipe):
          if dist.is_available() and dist.is_initialized():
              self._pg = dist.new_group(backend="gloo")
          self._shared_seed = _share_dist_seed(
              loader.generator, self._pg
          )
          shared_rng = torch.Generator()
          shared_rng.manual_seed(self._shared_seed)
          self._dataset = (
              torch.utils.data.graph_settings.apply_random_seed(
                  self._dataset, shared_rng
              )
          )
      self._dataset_kind = loader._dataset_kind
      self._IterableDataset_len_called = (
          loader._IterableDataset_len_called
      )
      self._auto_collation = loader._auto_collation
      self._drop_last = loader.drop_last
      self._index_sampler = loader._index_sampler
      self._num_workers = loader.num_workers
      ws, rank = _get_distributed_settings()
      self._world_size = ws
      self._rank = rank
      # for other backends, pin_memory_device need to set. if not set
      # default behaviour is CUDA device. if pin_memory_device is selected
      # and pin_memory is not set, the default behaviour false.
      if len(loader.pin_memory_device) == 0:
          self._pin_memory = (
              loader.pin_memory and torch.cuda.is_available()
          )
          self._pin_memory_device = None
      else:
          if not loader.pin_memory:
              warn_msg = (
                  "pin memory device is set and pin_memory flag is not used then device pinned memory won't be used"
                  "please set pin_memory to true, if you need to use the device pin memory"
              )
              warnings.warn(warn_msg)
  
          self._pin_memory = loader.pin_memory
          self._pin_memory_device = loader.pin_memory_device
      self._timeout = loader.timeout
      self._collate_fn = loader.collate_fn
      self._sampler_iter = iter(self._index_sampler)
      self._base_seed = (
          torch.empty((), dtype=torch.int64)
          .random_(generator=loader.generator)
          .item()
      )
      self._persistent_workers = loader.persistent_workers
      self._num_yielded = 0
      self._profile_name = (
          f"enumerate(DataLoader)#{self.__class__.__name__}.__next__"
      )
  
imports: import functools
  import itertools
  import logging
  import os
  import queue
  import threading
  import warnings
  from typing import Any, Callable, Iterable, TypeVar, Generic, List, Optional, Union
  import multiprocessing
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.utils.data.graph_settings
  from torch._utils import ExceptionWrapper
  from .None import IterDataPipe, MapDataPipe, IterableDataset, Sampler, SequentialSampler, RandomSampler, BatchSampler, Dataset
  from torch.utils.data.datapipes.datapipe import _IterDataPipeSerializationWrapper, _MapDataPipeSerializationWrapper
  from .None import _utils
label_desc: Use image node_id 1 to create a new _BaseDataLoaderIter object  with example: obj = _BaseDataLoaderIter(loader)