code: def train_encrypted(
      x_encrypted,
      y_encrypted,
      encrypted_model,
      num_epochs,
      learning_rate,
      batch_size,
      print_freq,
  ):
      rank = comm.get().get_rank()
      loss = crypten.nn.MSELoss()
  
      num_samples = x_encrypted.size(0)
      label_eye = torch.eye(2)
  
      for epoch in range(num_epochs):
          last_progress_logged = 0
          # only print from rank 0 to avoid duplicates for readability
          if rank == 0:
              print(f"Epoch {epoch} in progress:")
  
          for j in range(0, num_samples, batch_size):
              # define the start and end of the training mini-batch
              start, end = j, min(j + batch_size, num_samples)
  
              # switch on autograd for training examples
              x_train = x_encrypted[start:end]
              x_train.requires_grad = True
              y_one_hot = label_eye[y_encrypted[start:end]]
              y_train = crypten.cryptensor(
                  y_one_hot, requires_grad=True
              )
  
              # perform forward pass:
              output = encrypted_model(x_train)
              loss_value = loss(output, y_train)
  
              # backprop
              encrypted_model.zero_grad()
              loss_value.backward()
              encrypted_model.update_parameters(learning_rate)
  
              # log progress
              if j + batch_size - last_progress_logged >= print_freq:
                  last_progress_logged += print_freq
                  print(
                      f"Loss {loss_value.get_plain_text().item():.4f}"
                  )
  
          # compute accuracy every epoch
          pred = output.get_plain_text().argmax(1)
          correct = pred.eq(y_encrypted[start:end])
          correct_count = correct.sum(0, keepdim=True).float()
          accuracy = correct_count.mul_(100.0 / output.size(0))
  
          loss_plaintext = loss_value.get_plain_text().item()
          print(
              f"Epoch {epoch} completed: "
              f"Loss {loss_plaintext:.4f} Accuracy {accuracy.item():.2f}"
          )
  
file_path: CrypTen/examples/mpc_autograd_cnn/mpc_autograd_cnn.py
imports: import tempfile
  import crypten
  import crypten.communicator
  import torch
  import torch.nn
  import torch.nn.functional
  from examples.util import NoopContextManager
  from torchvision import datasets, transforms
label_desc: Use image node_id 2 for calling a global function with example usage: train_encrypted(x_encrypted, y_encrypted, encrypted_model, num_epochs, learning_rate, batch_size, print_freq) without return types