code: def main():
      args = parser.parse_args()
      device = torch.device(args.device)
      ds = NamesDataset(args.data_root)
      train_len = int(args.train_split * len(ds))
      test_len = len(ds) - train_len
  
      print(f"{train_len} samples for training, {test_len} for testing")
  
      if args.secure_rng:
          try:
              import torchcsprng as prng
          except ImportError as e:
              msg = (
                  "To use secure RNG, you must install the torchcsprng package! "
                  "Check out the instructions here: https://github.com/pytorch/csprng#installation"
              )
              raise ImportError(msg) from e
  
          generator = prng.create_random_device_generator(
              "/dev/urandom"
          )
  
      else:
          generator = None
  
      train_ds, test_ds = torch.utils.data.random_split(
          ds, [train_len, test_len], generator=generator
      )
  
      if args.mode == "rnn":
          rnn_type = DPRNN
      elif args.mode == "gru":
          rnn_type = DPGRU
      elif args.mode == "lstm":
          rnn_type = DPLSTM
      else:
          raise ValueError(f"Invalid network type: {args.mode}")
  
      model = CharNNClassifier(
          rnn_type,
          args.embedding_size,
          args.hidden_size,
          len(ds.labels),
          args.n_layers,
          args.bidirectional,
      )
      model = model.to(device)
  
      train_ds, test_ds = torch.utils.data.random_split(
          ds, [train_len, test_len], generator=generator
      )
  
      train_loader = DataLoader(
          train_ds,
          batch_size=args.batch_size,
          num_workers=1,
          pin_memory=True,
          collate_fn=padded_collate,
      )
  
      test_loader = DataLoader(
          test_ds,
          batch_size=2 * args.batch_size,
          shuffle=False,
          num_workers=1,
          pin_memory=True,
          collate_fn=padded_collate,
      )
  
      criterion = nn.CrossEntropyLoss()
      optimizer = torch.optim.SGD(
          model.parameters(), lr=args.learning_rate
      )
  
      if not args.disable_dp:
          privacy_engine = PrivacyEngine(secure_mode=args.secure_rng)
          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,
          )
      else:
          privacy_engine = None
  
      print(f"Train stats ({args.mode}): \n")
      for epoch in tqdm(range(args.epochs)):
          train(
              model,
              criterion,
              optimizer,
              train_loader,
              epoch,
              privacy_engine,
              args.delta,
              device=device,
          )
          if args.test_every:
              if epoch % args.test_every == 0:
                  test(
                      model,
                      test_loader,
                      privacy_engine,
                      args.delta,
                      device=device,
                  )
  
      mean_acc = test(
          model, test_loader, privacy_engine, args.delta, device=device
      )
      torch.save(
          mean_acc, f"run_results_chr_{args.mode}_classification.pt"
      )
  
imports: import argparse
  from collections import Counter
  from pathlib import Path
  from statistics import mean
  import torch
  import torch.nn
  from opacus import PrivacyEngine
  from opacus.layers import DPGRU, DPLSTM, DPRNN
  from torch.nn.utils.rnn import pad_sequence
  from torch.utils.data import DataLoader, Dataset
  from tqdm import tqdm
label_desc: Use image node_id 4 for calling a global function with example usage: main() without return types