code: def parse_args(argv: List[str]) -> argparse.Namespace:
      parser = argparse.ArgumentParser(
          description="torchrec dlrm model packager"
      )
      parser.add_argument(
          "--num_embeddings",
          type=int,
          default=100_000,
          help="max_ind_size. The number of embeddings in each embedding table. Defaults"
          " to 100_000 if num_embeddings_per_feature is not supplied.",
      )
      parser.add_argument(
          "--num_embeddings_per_feature",
          type=str,
          default="45833188,36746,17245,7413,20243,3,7114,1441,62,29275261,1572176,345138,"
          "10,2209,11267,128,4,974,14,48937457,11316796,40094537,452104,12606,104,35",
          help="Comma separated max_ind_size per sparse feature. The number of embeddings"
          " in each embedding table. 26 values are expected for the Criteo dataset.",
      )
      parser.add_argument(
          "--sparse_feature_names",
          type=str,
          default=",".join(DEFAULT_CAT_NAMES),
          help="Comma separated names of the sparse features.",
      )
      parser.add_argument(
          "--dense_arch_layer_sizes",
          type=str,
          default="512,256,64",
          help="Comma separated layer sizes for dense arch.",
      )
      parser.add_argument(
          "--over_arch_layer_sizes",
          type=str,
          default="512,512,256,1",
          help="Comma separated layer sizes for over arch.",
      )
      parser.add_argument(
          "--embedding_dim",
          type=int,
          default=64,
          help="Size of each embedding.",
      )
      parser.add_argument(
          "--num_dense_features",
          type=int,
          default=len(DEFAULT_INT_NAMES),
          help="Number of dense features.",
      )
      parser.add_argument(
          "--output_path",
          type=str,
          help="Output path of model package.",
      )
      return parser.parse_args(argv)
  
file_path: torchrec/examples/inference/dlrm_packager.py
imports: import argparse
  import sys
  from typing import List
  from dlrm_predict import DLRMModelConfig, DLRMPredictFactory
  from torch.package import PackageExporter
  from torchrec.datasets.criteo import DEFAULT_CAT_NAMES, DEFAULT_INT_NAMES
  from torchrec.inference.model_packager import PredictFactoryPackager
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args(argv) and returns: parser