code: def main():
      # Logging setting.
      logging.basicConfig(
          format="%(asctime)s %(levelname)-8s %(message)s",
          datefmt="%Y-%m-%dT%H:%M:%SZ",
          level=logging.DEBUG,
      )
  
      prepare_env()
      # WARNING: This is only safe in sgx environment, where the memory can not be read
      secret_key = get_key()
  
      encrypted_dataset_path = "/ppml/encryption_dataset.pt"
  
      # Assume we are in customer environment when executing this(which is safe and trusted)
      save_encrypted_dataset(
          args.dataset_path, encrypted_dataset_path, secret_key
      )
  
      # Now we have the encrypted dataset, we can safely distribute it into
      # untrusted environments.
  
      # load the encrypted dataset back and ready for training
      train_dataset = EncryptedDataset(
          encrypted_dataset_path, secret_key
      )
      train_dataloader = DataLoader(
          train_dataset,
          batch_size=16,
          shuffle=True,
          collate_fn=collate_fn,
      )
      print("[INFO]Data get loaded successfully", flush=True)
  
      model = NeuralNetwork().to(device)
      loss_fn = nn.CrossEntropyLoss()
      optimizer = AdamW(model.parameters(), 0.01)
      total_loss = 0.0
  
      print(f"training begin\n-------------------------------")
      start = time.perf_counter()
      total_loss, total_dataset = train_loop(
          train_dataloader, model, loss_fn, optimizer, 1, total_loss
      )
      end = time.perf_counter()
      print(f"Elapsed time:", end - start, flush=True)
      print(f"Processed dataset length:", total_dataset, flush=True)
      msg = "Throughput: {: .4f}".format(
          1.0 * total_dataset / (end - start)
      )
      print(msg, flush=True)
  
imports: from torch.utils.data import DataLoader
  import torch
  import torch.optim
  from contextlib import nullcontext
  import torch.nn.functional
  import os
  import torch.nn
  import time
  from bigdl.nano.pytorch.patching import patch_encryption
  from bigdl.nano.pytorch.encryption import EncryptedDataset
  import logging
  from bigdl.ppml.kms.ehsm.client import generate_primary_key, generate_data_key, get_data_key_plaintext
  from datasets.load import load_from_disk
  import argparse
  from transformers import BertTokenizer, BertModel, AdamW
label_desc: Use image node_id 6 for calling a global function with example usage: main() without return types