code: def accuracy(logits, labels):
      """
      Return accuracy of the array of logits (or label predictions) wrt the labels
      :param logits: this can either be logits, probabilities, or a single label
      :param labels: the correct labels to match against
      :return: the accuracy as a float
      """
      assert len(logits) == len(labels)
  
      if len(np.shape(logits)) > 1:
          # Predicted labels are the argmax over axis 1
          predicted_labels = np.argmax(logits, axis=1)
      else:
          # Input was already labels
          assert len(np.shape(logits)) == 1
          predicted_labels = logits
  
      # Check against correct labels to compute correct guesses
      correct = np.sum(predicted_labels == labels.reshape(len(labels)))
  
      # Divide by number of labels to obtain accuracy
      accuracy = float(correct) / len(labels)
  
      # Return float value
      return accuracy
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: accuracy(logits, labels) and returns: accuracy