code: def convert_TwoTower_to_TwoTowerRetrieval(
      sd: OrderedDict[str, torch.Tensor],
      query_tables: List[str],
      candidate_tables: List[str],
  ) -> OrderedDict[str, torch.Tensor]:
      for query_table in query_tables:
          sd[f"query_ebc.embedding_bags.{query_table}.weight"] = sd.pop(
              f"two_tower.ebc.embedding_bags.{query_table}.weight"
          )
      for candidate_table in candidate_tables:
          sd[
              f"candidate_ebc.embedding_bags.{candidate_table}.weight"
          ] = sd.pop(
              f"two_tower.ebc.embedding_bags.{candidate_table}.weight"
          )
      return sd
  
imports: from typing import Any, List, Mapping, Optional, OrderedDict, Tuple, Union
  import faiss
  import faiss.contrib.torch_utils
  import torch
  from torch import nn
  from torchrec.datasets.utils import Batch
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.modules.mlp import MLP
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 1 for calling a global function with example usage: convert_TwoTower_to_TwoTowerRetrieval(sd, query_tables, candidate_tables) and returns: sd