code: def save(module: DistributedModelParallel):
      """
      Save the dynamic embedding part of the model.
      """
      if not hasattr(module, "_id_transformer_group"):
          raise ValueError(
              "No _id_transformer_group property for module, is this a module with dynamic embeding?"
          )
      if not isinstance(
          module._id_transformer_group, IDTransformerGroup
      ):
          raise ValueError(
              "module._id_transformer_group property is not IDTransformerGroup, is this a module with dynamic embeding?"
          )
  
      module._id_transformer_group.save()
  
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 3 for calling a global function with example usage: save(module) without return types