code: def main(save_dir: Optional[str]) -> None:
      train(save_dir=save_dir)
  
imports: import os
  from typing import List, Optional
  import click
  import faiss
  import faiss.contrib.torch_utils
  import torch
  from torch import distributed
  from torch.distributed.optim import _apply_optimizer_in_backward
  from torchrec import inference
  from torchrec.datasets.movielens import DEFAULT_RATINGS_COLUMN_NAMES
  from torchrec.distributed import TrainPipelineSparseDist
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.inference.state_dict_transform import state_dict_gather, state_dict_to_device
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.optim.keyed import KeyedOptimizerWrapper
  from torchrec.optim.rowwise_adagrad import RowWiseAdagrad
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 1 for calling a global function with example usage: main(save_dir) without return types