code: def main(argv: List[str]) -> None:
      args = parse_args(argv)
      for name, val in vars(args).items():
          try:
              vars(args)[name] = list(map(int, val.split(",")))
          except (ValueError, AttributeError):
              pass
  
      rank_zero_print(
          f"PARAMS: (lr, batch_size, warmup_steps, decay_start, decay_steps): {(args.learning_rate, args.batch_size, args.lr_warmup_steps, args.lr_decay_start, args.lr_decay_steps)}"
      )
      device = init_from_env()
      backend = get_process_group_backend_from_device(device)
  
      eb_configs = init_embdedding_configs(
          args.embedding_dim,
          args.num_embeddings_per_feature,
          args.num_embeddings,
      )
  
      model = init_model(
          eb_configs,
          args.dense_arch_layer_sizes,
          args.over_arch_layer_sizes,
          args.learning_rate,
          args.batch_size,
          device,
      )
  
      optimizer = init_optimizer(model, args.learning_rate)
      tb_logger = init_logger()
      auroc = BinaryAUROC(device=device)
  
      my_unit = MyUnit(
          module=model,
          optimizer=optimizer,
          device=device,
          tb_logger=tb_logger,
          train_auroc=auroc,
          log_every_n_steps=10,
      )
  
      train_dataloader = init_dataloader(
          args.batch_size,
          args.num_batches,
          args.num_embeddings,
          backend,
          args.num_embeddings_per_feature,
          args.seed,
          args.pin_memory,
      )
      eval_dataloader = init_dataloader(
          args.test_batch_size,
          args.num_batches,
          args.num_embeddings,
          backend,
          args.num_embeddings_per_feature,
          args.seed,
          args.pin_memory,
      )
  
      tqdm_callback = TQDMProgressBar()
      fit(
          my_unit,
          train_dataloader=train_dataloader,
          eval_dataloader=eval_dataloader,
          max_epochs=args.epochs,
          callbacks=[tqdm_callback],
      )
  
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 7 for calling a global function with example usage: main(argv) without return types