code: def train_linear_svm(
      features, labels, epochs=50, lr=0.5, print_time=False
  ):
      # Initialize random weights
      w = features.new(torch.randn(1, features.size(0)))
      b = features.new(torch.randn(1))
  
      if print_time:
          pt_time = AverageMeter()
          end = time.time()
  
      for epoch in range(epochs):
          # Forward
          label_predictions = w.matmul(features).add(b).sign()
  
          # Compute accuracy
          correct = label_predictions.mul(labels)
          accuracy = correct.add(1).div(2).mean()
          if crypten.is_encrypted_tensor(accuracy):
              accuracy = accuracy.get_plain_text()
  
          # Print Accuracy once
          if crypten.communicator.get().get_rank() == 0:
              print(
                  f"Epoch {epoch} --- Training Accuracy %.2f%%"
                  % (accuracy.item() * 100)
              )
  
          # Backward
          loss_grad = -labels * (1 - correct) * 0.5  # Hinge loss
          b_grad = loss_grad.mean()
          w_grad = loss_grad.matmul(features.t()).div(loss_grad.size(1))
  
          # Update
          w -= w_grad * lr
          b -= b_grad * lr
  
          if print_time:
              iter_time = time.time() - end
              pt_time.add(iter_time)
              logging.info(
                  "    Time %.6f (%.6f)" % (iter_time, pt_time.value())
              )
              end = time.time()
  
      return w, b
  
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 1 for calling a global function with example usage: train_linear_svm(features, labels, epochs, lr, print_time) and returns: w,  b