code: def main(argv: List[str]) -> None:
      args = get_args(argv)
      seed(args.seed)
  
      # device and process group initialization
      device = init_from_env()
  
      path = tempfile.mkdtemp()
      tb_logger = TensorBoardLogger(path)
  
      model = prepare_model(args.input_dim, device)
      optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
      lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
          optimizer, gamma=0.9
      )
      train_accuracy = BinaryAccuracy(device=device)
  
      dataloader = prepare_dataloader(
          args.num_batches_per_epoch,
          args.input_dim,
          args.num_reading_service_workers,
          device,
      )
  
      train_unit = MyTrainUnit(
          module=model,
          optimizer=optimizer,
          lr_scheduler=lr_scheduler,
          device=device,
          train_accuracy=train_accuracy,
          tb_logger=tb_logger,
          log_every_n_steps=args.log_every_n_steps,
      )
  
      train(
          train_unit,
          train_dataloader=dataloader,
          max_epochs=args.max_epochs,
      )
  
imports: import argparse
  import logging
  import sys
  import tempfile
  from argparse import Namespace
  from typing import List, Tuple
  import torch
  import torch.nn
  from torch.utils.data.datapipes.map.combinatorics import ShufflerIterDataPipe
  from torchdata.dataloader2 import DataLoader2, MultiProcessingReadingService
  from torchdata.datapipes.iter import IterableWrapper
  from torcheval.metrics import BinaryAccuracy
  from torchtnt.framework.state import State
  from torchtnt.framework.train import train
  from torchtnt.framework.unit import TrainUnit
  from torchtnt.utils import copy_data_to_device, init_from_env, seed, TLRScheduler
  from torchtnt.utils.loggers import TensorBoardLogger
label_desc: Use image node_id 4 for calling a global function with example usage: main(argv) without return types