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="local batch size to use for training",
      )
      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="512,256,128",
          help="Comma separated layer sizes for dense arch.",
      )
      parser.add_argument(
          "--over_arch_layer_sizes",
          type=str,
          default="1024,1024,512,256,1",
          help="Comma separated layer sizes for over arch.",
      )
      parser.add_argument(
          "--embedding_dim",
          type=int,
          default=128,
          help="Size of each embedding.",
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=15.0,
          help="Learning rate.",
      )
      parser.add_argument(
          "--binary_path",
          type=str,
          default="/data/criteo_1tb/criteo_binary/split/",
          help="Location for binary datafiles",
      )
      parser.add_argument(
          "--change_lr",
          dest="change_lr",
          action="store_true",
          help="Flag to determine whether learning rate should be changed part way through training.",
      )
      parser.add_argument(
          "--lr_change_point",
          type=float,
          default=0.80,
          help="The point through training at which learning rate should change to the value set by"
          " lr_after_change_point. The default value is 0.80 which means that 80% through the total iterations (totaled"
          " across all epochs), the learning rate will change.",
      )
      parser.add_argument(
          "--lr_after_change_point",
          type=float,
          default=3.0,
          help="Learning rate after change point in first epoch.",
      )
      parser.add_argument(
          "--throughput_check_freq_within_epoch",
          type=int,
          default=1000,
          help="Frequency at QPS will be output within an epoch.",
      )
      parser.add_argument(
          "--validation_freq_within_epoch",
          type=int,
          default=10000,
          help="Frequency at which validation will be run within an epoch.",
      )
      parser.add_argument(
          "--adagrad",
          dest="adagrad",
          action="store_true",
          help="Flag to determine if adagrad optimizer should be used.",
      )
      return parser.parse_args(argv)
  
imports: import argparse
  import os
  import sys
  import time
  from typing import cast, Iterator, List, Tuple
  import torch
  import torch.distributed
  import torch.nn
  import torchmetrics
  import torchrec
  import torchrec.distributed
  import torchrec.optim
  from nvt_binary_dataloader import NvtBinaryDataloader
  from pyre_extensions import none_throws
  from torchrec import EmbeddingBagCollection
  from torchrec.datasets.criteo import DEFAULT_CAT_NAMES, DEFAULT_INT_NAMES, TOTAL_TRAINING_SAMPLES
  from torchrec.datasets.utils import Batch
  from torchrec.distributed import TrainPipelineSparseDist
  from torchrec.distributed.fused_embeddingbag import FusedEmbeddingBagCollectionSharder
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.types import ModuleSharder
  from torchrec.metrics.throughput import ThroughputMetric
  from torchrec.models.dlrm import DLRM, DLRMTrain
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.modules.fused_embedding_modules import fuse_embedding_optimizer
  from torchrec.optim.keyed import KeyedOptimizerWrapper
  from torchrec.optim.optimizers import in_backward_optimizer_filter
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args(argv) and returns: parser