code: def evaluate_linear_svm(features, labels, w, b):
      """Compute accuracy on a test set"""
      predictions = w.matmul(features).add(b).sign()
      correct = predictions.mul(labels)
      accuracy = correct.add(1).div(2).mean().get_plain_text()
      if crypten.communicator.get().get_rank() == 0:
          print("Test accuracy %.2f%%" % (accuracy.item() * 100))
  
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 2 for calling a global function with example usage: evaluate_linear_svm(features, labels, w, b) without return types