code: def wrap(
      url: str,
      dataloader,
      module: DistributedModelParallel,
      configs_dict: Dict[
          str, Union[List[EmbeddingBagConfig], List[EmbeddingConfig]]
      ],
      *,
      data_info: Dict[int, str] = None,
      eviction_config=None,
      transform_config=None,
      ps_config=None,
      parallel=True,
      num_prefetch=0,
  ):
      """
      DataLoader to transform data from global id to cache id.
  
      Args:
          url: configuration for PS, e.g. redis://127.0.0.1:6379/?prefix=model.
          dataloader: dataloader to transform.
          module: DMP module that need dynamic embedding.
          configs_dict: a dictionary that maps the module path of the sharded module to its embedding
              configs or embeddingbag configs. The plan of `module` should contain the module path
              in `configs_dict`.
          data_info: dict keyed by int index of module path. For example, if the dataloader produces
              `label, kjt1, kjt2` each iteration and `kjt1` and `kjt2` are inputs to modules of path
              `emb1` and `emb2` respectively, then `data_info` should be `{ 1: "emb1", 2: "emb2" }`.
          eviction_config: configuration for eviction policy. Default is `{"type": "mixed_lru_lfu"}`
          transform_config: configuration for the transformer. Default is `{"type": "naive"}`
          transform_config: configuration for the ps. Default is `{"chunk_size": 8 * 1024 * 1024}
          parallel: Whether the IDTransformerCollections will run paralell. When set to True,
              IDTransformerGroup will start a thread for each IDTransformerCollection.
          num_prefetch: number of samples to prefetch.
  
      Return:
          DataLoader: the dataloader to transform data.
          DistributedModelParallel: model with id_transformer_group attached.
  
      Example:
          class Model(nn.Module):
              def __init__(self, config1, config2):
                  super().__init__()
                  self.emb1 = EmbeddingCollection(tables=config1, device=torch.device("meta"))
                  self.emb2 = EmbeddingCollection(tables=config2, device=torch.device("meta"))
                  ...
  
              def forward(self, kjt1, kjt2):
                  ...
  
          m = Model(config1, config2)
          m = DistributedModelParallel(m)
          dataloader, m = tde.wrap("redis://127.0.0.1:6379/", dataloader, m, { "emb1": config1, "emb2": config2 })
  
          for label, kjt1, kjt2 in dataloader:
              output = m(kjt1, kjt2)
              ...
      """
      id_transformer_group = IDTransformerGroup(
          url,
          module,
          configs_dict,
          eviction_config=eviction_config,
          transform_config=transform_config,
          ps_config=ps_config,
          parallel=parallel,
      )
      paths = list(configs_dict.keys())
      # Attach the id transformer group to module for saving.
      module._id_transformer_group = id_transformer_group
  
      return (
          DataLoader(
              id_transformer_group=id_transformer_group,
              dataloader=dataloader,
              data_info=data_info,
              paths=paths,
              num_prefetch=num_prefetch,
          ),
          module,
      )
  
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 2 for calling a global function with example usage: wrap(url, dataloader, module, configs_dict) and returns: DataLoader,  module