code: def __init__(
      self,
      id_transformer_group: IDTransformerGroup,
      dataloader,
      *,
      data_info: Dict[int, str] = None,
      paths: List[str] = None,
      num_prefetch=0,
  ):
      self._id_transformer_group = id_transformer_group
  
      if data_info is not None:
          for _, path in data_info.items():
              if path not in self._id_transformer_group:
                  raise ValueError(
                      f"invalid path `{path}` data_info. No id transformer for this path."
                  )
      else:
          self._paths = paths
  
      self._data_info = data_info
  
      self._data_queue = queue.Queue(maxsize=num_prefetch)
      self._done_event = threading.Event()
  
      self._dataloader = dataloader
      self._num_prefetch = num_prefetch
  
imports: import queue
  import threading
  from typing import Dict, List, Union
  import torch
  from torch.utils.data._utils import MP_STATUS_CHECK_INTERVAL
  from torchrec import EmbeddingBagConfig, EmbeddingConfig
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
  from .id_transformer_group import IDTransformerGroup
label_desc: Use image node_id 1 to create a new DataLoader object  with example: obj = DataLoader(id_transformer_group, dataloader)