code: def run_mpc_linear_svm(
      epochs=50, examples=50, features=100, lr=0.5, skip_plaintext=False
  ):
      crypten.init()
  
      # Set random seed for reproducibility
      torch.manual_seed(1)
  
      # Initialize x, y, w, b
      x = torch.randn(features, examples)
      w_true = torch.randn(1, features)
      b_true = torch.randn(1)
      y = w_true.matmul(x) + b_true
      y = y.sign()
  
      if not skip_plaintext:
          logging.info("==================")
          logging.info("PyTorch Training")
          logging.info("==================")
          w_torch, b_torch = train_linear_svm(
              x, y, lr=lr, print_time=True
          )
  
      # Encrypt features / labels
      x = crypten.cryptensor(x)
      y = crypten.cryptensor(y)
  
      logging.info("==================")
      logging.info("CrypTen Training")
      logging.info("==================")
      w, b = train_linear_svm(x, y, lr=lr, print_time=True)
  
      if not skip_plaintext:
          logging.info("PyTorch Weights  :")
          logging.info(w_torch)
      logging.info("CrypTen Weights:")
      logging.info(w.get_plain_text())
  
      if not skip_plaintext:
          logging.info("PyTorch Bias  :")
          logging.info(b_torch)
      logging.info("CrypTen Bias:")
      logging.info(b.get_plain_text())
  
file_path: CrypTen/examples/mpc_linear_svm/mpc_linear_svm.py
imports: import logging
  import time
  import crypten
  import torch
  from examples.meters import AverageMeter
label_desc: Use image node_id 3 for calling a global function with example usage: run_mpc_linear_svm(epochs, examples, features, lr, skip_plaintext) without return types