code: def _eval_metric(input_, topk, correct_predictions, examples, phase):
    
    my_parameters = {}
    if phase in (Phase.test, Phase.infer):
      dtype = tf.float32
      # Create the variables using tf.Variable because we don't want to share.
      count = tf.Variable(tf.constant(0, dtype=dtype),
                          name='count_%d' % topk,
                          collections=[bookkeeper.GraphKeys.TEST_VARIABLES],
                          trainable=False)
      correct = tf.Variable(tf.constant(0, dtype=dtype),
                            name='correct_%d' % topk,
                            collections=[bookkeeper.GraphKeys.TEST_VARIABLES],
                            trainable=False)
      my_parameters['count'] = count
      my_parameters['correct'] = correct
      with input_.g.device(count.device):
        examples = tf.assign_add(count, examples)
      with input_.g.device(correct.device):
        correct_predictions = tf.assign_add(correct, correct_predictions)
    return correct_predictions, examples, my_parameters
docs: Creates the standard tracking varibles if in test and returns accuracy.