code: def padded_accuracy(predictions,
                      labels,
                      weights_fn=common_layers.weights_nonzero):
    
    # If the last dimension is 1 then we're using L1/L2 loss.
    if common_layers.shape_list(predictions)[-1] == 1:
      return rounding_accuracy(predictions, labels, weights_fn=weights_fn)
    with tf.variable_scope("padded_accuracy", values=[predictions, labels]):
      padded_predictions, padded_labels = common_layers.pad_with_zeros(
          predictions, labels)
      weights = weights_fn(padded_labels)
      outputs = tf.to_int32(tf.argmax(padded_predictions, axis=-1))
      padded_labels = tf.to_int32(padded_labels)
      return tf.to_float(tf.equal(outputs, padded_labels)), weights
docs: Percentage of times that predictions matches labels on non-0s.