code: def main():
      if args.log_path == "":
          logging.basicConfig(
              format="%(asctime)s %(levelname)-8s %(message)s",
              datefmt="%Y-%m-%dT%H:%M:%SZ",
              level=logging.DEBUG,
          )
      else:
          logging.basicConfig(
              format="%(asctime)s %(levelname)-8s %(message)s",
              datefmt="%Y-%m-%dT%H:%M:%SZ",
              level=logging.DEBUG,
              filename=args.log_path,
          )
  
      torch.manual_seed(args.seed)
      if should_distribute():
          print(
              "Using distributed PyTorch with {} backend".format(
                  "GLOO"
              ),
              flush=True,
          )
          dist.init_process_group(backend=dist.Backend.GLOO)
  
      # Load the data and dataset
      print("[INFO]Before data get loaded", flush=True)
      train_data = Dataset("train", args.dataset)
      print(
          "######train data length:", len(train_data.data), flush=True
      )
      valid_data = Dataset("validation", 1)
  
      if is_distributed():
          train_sampler = DistributedSampler(
              train_data,
              num_replicas=WORLD_SIZE,
              rank=RANK,
              shuffle=True,
              drop_last=False,
              seed=args.seed,
          )
          valid_sampler = DistributedSampler(
              valid_data,
              num_replicas=WORLD_SIZE,
              rank=RANK,
              shuffle=True,
              drop_last=False,
              seed=args.seed,
          )
          train_dataloader = DataLoader(
              train_data,
              batch_size=args.batch_size,
              collate_fn=collate_fn,
              sampler=train_sampler,
          )
          valid_dataloader = DataLoader(
              valid_data,
              batch_size=args.test_batch_size,
              collate_fn=collate_fn,
              sampler=valid_sampler,
          )
      else:
          train_dataloader = DataLoader(
              train_data,
              batch_size=args.batch_size,
              shuffle=True,
              collate_fn=collate_fn,
          )
          valid_dataloader = DataLoader(
              valid_data,
              batch_size=args.test_batch_size,
              shuffle=True,
              collate_fn=collate_fn,
          )
  
      print("[INFO]Data get loaded successfully", flush=True)
  
      model = NeuralNetwork().to(device)
      if args.load_model:
          model.load_state_dict("./pert.bin")
  
      if is_distributed():
          Distributor = nn.parallel.DistributedDataParallel
          model = Distributor(model, find_unused_parameters=True)
      loss_fn = nn.CrossEntropyLoss()
      optimizer = AdamW(model.parameters(), lr=args.lr)
  
      model.train()
  
      model, optimizer = ipex.optimize(model, optimizer=optimizer)
  
      total_loss = 0.0
      best_acc = 0.0
  
      for t in range(args.epochs):
          print(
              f"Epoch {t+1}/{args.epochs + 1}\n-------------------------------"
          )
          if is_distributed():
              train_dataloader.sampler.set_epoch(t)
              valid_dataloader.sampler.set_epoch(t)
          start = time.perf_counter()
          total_loss, total_dataset = train_loop(
              args,
              train_dataloader,
              model,
              loss_fn,
              optimizer,
              t + 1,
              total_loss,
          )
          end = time.perf_counter()
          print(
              f"Epoch {t+1}/{args.epochs + 1} Elapsed time:",
              end - start,
              flush=True,
          )
          print(
              f"Epoch {t+1}/{args.epochs + 1} Processed dataset length:",
              total_dataset,
              flush=True,
          )
          msg = "Epoch {}/{} Throughput: {: .4f}".format(
              t + 1,
              args.epochs + 1,
              1.0 * total_dataset / (end - start),
          )
          print(msg, flush=True)
          # valid_acc = test_loop(valid_dataloader, model, mode='Valid')
  
      print("[INFO]Finish all test", flush=True)
  
      if args.save_model:
          torch.save(model.state_dict(), "pert.bin")
      if is_distributed():
          dist.destroy_process_group()
  
imports: import torch
  import argparse
  import os
  import logging
  import time
  from torch import nn
  from datasets import load_dataset
  from torch.utils.data import DataLoader
  from torch.utils.data.distributed import DistributedSampler
  import torch.distributed
  from transformers import BertTokenizer, BertModel, AdamW
  import intel_extension_for_pytorch
label_desc: Use image node_id 6 for calling a global function with example usage: main() without return types