code: def accuracy(model, X, y):
      """Returns the accuracy of model on the given data"""
      correct = sum(
          1
          for label, probs in zip(y, model.classify(X))
          if label == np.argmax(probs)
      )
      return float(correct) / len(y)
  
imports: import neoml
  import numpy
  import itertools
  from sklearn.datasets import fetch_20newsgroups_vectorized
label_desc: Use image node_id 1 for calling a global function with example usage: accuracy(model, X, y) and returns: unknown