code: def main():
      parser = argparse.ArgumentParser(
          description="Opacus IMDB Example",
          formatter_class=argparse.ArgumentDefaultsHelpFormatter,
      )
      parser.add_argument(
          "-b",
          "--batch-size",
          type=int,
          default=64,
          metavar="B",
          help="input batch size for test",
      )
      parser.add_argument(
          "-n",
          "--epochs",
          type=int,
          default=10,
          metavar="N",
          help="number of epochs to train",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=0.02,
          metavar="LR",
          help="learning rate",
      )
      parser.add_argument(
          "--sigma",
          type=float,
          default=0.56,
          metavar="S",
          help="Noise multiplier",
      )
      parser.add_argument(
          "-c",
          "--max-per-sample-grad_norm",
          type=float,
          default=1.0,
          metavar="C",
          help="Clip per-sample gradients to this norm",
      )
      parser.add_argument(
          "--delta",
          type=float,
          default=1e-5,
          metavar="D",
          help="Target delta (default: 1e-5)",
      )
      parser.add_argument(
          "--max-sequence-length",
          type=int,
          default=256,
          metavar="SL",
          help="Longer sequences will be cut to this length",
      )
      parser.add_argument(
          "--device",
          type=str,
          default="cuda",
          help="GPU ID for this process",
      )
      parser.add_argument(
          "--save-model",
          action="store_true",
          default=False,
          help="Save the trained model",
      )
      parser.add_argument(
          "--disable-dp",
          action="store_true",
          default=False,
          help="Disable privacy training and just train with vanilla optimizer",
      )
      parser.add_argument(
          "--secure-rng",
          action="store_true",
          default=False,
          help="Enable Secure RNG to have trustworthy privacy guarantees. Comes at a performance cost",
      )
      parser.add_argument(
          "--data-root",
          type=str,
          default="../imdb",
          help="Where IMDB is/will be stored",
      )
      parser.add_argument(
          "-j",
          "--workers",
          default=2,
          type=int,
          metavar="N",
          help="number of data loading workers",
      )
  
      args = parser.parse_args()
      device = torch.device(args.device)
  
      raw_dataset = load_dataset("imdb", cache_dir=args.data_root)
      tokenizer = BertTokenizerFast.from_pretrained("bert-base-cased")
      dataset = raw_dataset.map(
          lambda x: tokenizer(
              x["text"],
              truncation=True,
              max_length=args.max_sequence_length,
          ),
          batched=True,
      )
      dataset.set_format(type="torch", columns=["input_ids", "label"])
  
      train_dataset = dataset["train"]
      test_dataset = dataset["test"]
  
      train_loader = DataLoader(
          train_dataset,
          num_workers=args.workers,
          batch_size=args.batch_size,
          collate_fn=padded_collate,
          pin_memory=True,
          shuffle=True,
      )
  
      test_loader = torch.utils.data.DataLoader(
          test_dataset,
          batch_size=args.batch_size,
          shuffle=False,
          num_workers=args.workers,
          collate_fn=padded_collate,
          pin_memory=True,
      )
  
      model = SampleNet(vocab_size=len(tokenizer)).to(device)
      optimizer = optim.Adam(model.parameters(), lr=args.lr)
      privacy_engine = None
      if not args.disable_dp:
          privacy_engine = PrivacyEngine(secure_mode=args.secure_rng)
  
          # TODO: we need to switch poisson sampling back on, but the
          # model exhibits strange behaviour with batch_size=1
          model, optimizer, train_loader = privacy_engine.make_private(
              module=model,
              optimizer=optimizer,
              data_loader=train_loader,
              noise_multiplier=args.sigma,
              max_grad_norm=args.max_per_sample_grad_norm,
              poisson_sampling=False,
          )
  
      mean_accuracy = 0
      for epoch in range(1, args.epochs + 1):
          train(
              args,
              model,
              train_loader,
              optimizer,
              privacy_engine,
              epoch,
          )
          mean_accuracy = evaluate(args, model, test_loader)
  
      torch.save(mean_accuracy, "run_results_imdb_classification.pt")
  
imports: import argparse
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from datasets import load_dataset
  from opacus import PrivacyEngine
  from torch.nn.utils.rnn import pad_sequence
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from transformers import BertTokenizerFast
label_desc: Use image node_id 5 for calling a global function with example usage: main() without return types