code: def parse_args(argv: List[str]) -> argparse.Namespace:
      parser = argparse.ArgumentParser(
          description="torchrec dlrm example trainer"
      )
      parser.add_argument(
          "--epochs",
          type=int,
          default=1,
          help="number of epochs to train",
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=32,
          help="batch size to use for training",
      )
      parser.add_argument(
          "--num_batches",
          type=int,
          default=10,
          help="batch size to use for training",
      )
      parser.add_argument(
          "--test_batch_size",
          type=int,
          default=10,
          help="batch size to use for validation and testing",
      )
      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=None,
          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(
          "--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(
          "--pin_memory",
          dest="pin_memory",
          action="store_true",
          help="Use pinned memory when loading data.",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=None,
          help="seed",
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=0.1,
          help="Learning rate.",
      )
      parser.add_argument(
          "--log_every_n_steps",
          type=int,
          default=10,
          help="log every n steps",
      )
      parser.add_argument("--lr_warmup_steps", type=int, default=0)
      parser.add_argument("--lr_decay_start", type=int, default=0)
      parser.add_argument("--lr_decay_steps", type=int, default=0)
  
      parser.set_defaults(
          pin_memory=None,
      )
      return parser.parse_args(argv)
  
imports: import argparse
  import sys
  import tempfile
  import uuid
  from typing import Iterator, List, Optional, Tuple
  import torch
  from pyre_extensions import none_throws
  from torch import distributed
  from torch.distributed import launcher
  from torch.distributed.optim.apply_optimizer_in_backward import _apply_optimizer_in_backward
  from torch.utils.data import DataLoader
  from torcheval.metrics.classification.auroc import BinaryAUROC
  from torcheval.metrics.toolkit import sync_and_compute
  from torchrec import EmbeddingBagCollection
  from torchrec.datasets.criteo import DEFAULT_CAT_NAMES, DEFAULT_INT_NAMES
  from torchrec.datasets.random import RandomRecDataset
  from torchrec.distributed import TrainPipelineSparseDist
  from torchrec.distributed.comm import get_local_size
  from torchrec.distributed.model_parallel import DistributedModelParallel, get_default_sharders
  from torchrec.distributed.planner import EmbeddingShardingPlanner, Topology
  from torchrec.distributed.planner.storage_reservations import HeuristicalStorageReservation
  from torchrec.models.dlrm import DLRM, DLRMTrain
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.optim.keyed import KeyedOptimizerWrapper
  from torchrec.optim.optimizers import in_backward_optimizer_filter
  from torchtnt.framework.callbacks import TQDMProgressBar
  from torchtnt.framework.fit import fit
  from torchtnt.framework.state import State
  from torchtnt.framework.unit import EvalUnit, TrainUnit
  from torchtnt.utils import get_process_group_backend_from_device, init_from_env, rank_zero_print
  from torchtnt.utils.loggers.tensorboard import TensorBoardLogger
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args(argv) and returns: parser