code: def padded_accuracy(logits, labels):
    
    with tf.variable_scope("padded_accuracy", values=[logits, labels]):
      logits, labels = _pad_tensors_to_same_length(logits, labels)
      weights = tf.to_float(tf.not_equal(labels, 0))
      outputs = tf.to_int32(tf.argmax(logits, axis=-1))
      padded_labels = tf.to_int32(labels)
      return tf.to_float(tf.equal(outputs, padded_labels)), weights
docs: Percentage of times that predictions matches labels on non-0s.