code: def parse_args(argv: List[str]) -> argparse.Namespace:
      parser = argparse.ArgumentParser(
          description="torchrec + lightning app"
      )
      parser.add_argument(
          "--min_user_count",
          type=int,
          default=5,
          help="minimum user ratings count",
      )
      parser.add_argument(
          "--min_item_count",
          type=int,
          default=0,
          help="minimum item count for each valid user",
      )
      parser.add_argument(
          "--max_len",
          type=int,
          default=100,
          help="max length of the Bert embedding dimension",
      )
      parser.add_argument(
          "--mask_prob",
          type=float,
          default=0.15,
          help="probability of the mask",
      )
      parser.add_argument(
          "--dataset_name",
          type=str,
          default="ml-1m",
          help="dataset for experiment, current support ml-1m, ml-20m",
      )
      parser.add_argument(
          "--min_rating",
          type=int,
          default=0,
          help="minimum valid rating",
      )
      parser.add_argument(
          "--num_epochs",
          type=int,
          default=100,
          help="the number of epoch to train",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=0.001,
          help="learning rate",
      )
      parser.add_argument(
          "--decay_step",
          type=int,
          default="25",
          help="the step of weight decay",
      )
      parser.add_argument(
          "--weight_decay",
          type=float,
          default=0.0,
          help="weight decay",
      )
      parser.add_argument(
          "--gamma",
          type=float,
          default=1.0,
          help="gamma of the lr scheduler",
      )
      parser.add_argument(
          "--train_batch_size",
          type=int,
          default=128,
          help="train batch size",
      )
      parser.add_argument(
          "--val_batch_size",
          type=int,
          default=128,
          help="val batch size",
      )
      parser.add_argument(
          "--test_batch_size",
          type=int,
          default=128,
          help="test batch size",
      )
      parser.add_argument(
          "--emb_dim",
          type=int,
          default=256,
          help="dimension of the hidden layer embedding",
      )
      parser.add_argument(
          "--nhead",
          type=int,
          default=2,
          help="number of header of attention",
      )
      parser.add_argument(
          "--num_layers",
          type=int,
          default=2,
          help="number of layers of attention",
      )
      parser.add_argument(
          "--dataset_path",
          type=str,
          default=None,
          help="Path to a folder containing the dataset.",
      )
      parser.add_argument(
          "--export_root",
          type=str,
          default="",
          help="Path to save the trained model",
      )
      parser.add_argument(
          "--random_user_count",
          type=int,
          default=10,
          help="number of random users",
      )
      parser.add_argument(
          "--random_item_count",
          type=int,
          default=30,
          help="number of random items",
      )
      parser.add_argument(
          "--random_size",
          type=int,
          default=300,
          help="number of random sample size",
      )
      parser.add_argument(
          "--dupe_factor",
          type=int,
          default=3,
          help="number of duplication while generating the random masked seqs",
      )
      parser.add_argument(
          "--mode",
          type=str,
          default="dmp",
          help="dmp (distirbuted model parallel) or ddp (distributed data parallel)",
      )
      return parser.parse_args(argv)
  
imports: import argparse
  import os
  import sys
  from typing import Any, cast, Dict, List, Union
  import numpy
  import torch
  import torch.nn
  import torch.optim
  import torch.utils.data
  from fbgemm_gpu.split_embedding_configs import EmbOptimType
  from torch import distributed
  from torch.nn.parallel import DistributedDataParallel
  from torchrec.distributed.embedding import EmbeddingCollectionSharder
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.types import ModuleSharder
  from torchrec.optim.keyed import CombinedOptimizer, KeyedOptimizerWrapper
  from torchrec.optim.optimizers import in_backward_optimizer_filter
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args(argv) and returns: parser