code: def padded_sequence_accuracy(
      predictions, labels, weights_fn=common_layers.weights_nonzero
  ):
      """Percentage of times that predictions matches labels everywhere (non-0)."""
      # If the last dimension is 1 then we're using L1/L2 loss.
      if common_layers.shape_list(predictions)[-1] == 1:
          return rounding_sequence_accuracy(
              predictions, labels, weights_fn=weights_fn
          )
      with tf.variable_scope(
          "padded_sequence_accuracy", values=[predictions, labels]
      ):
          (
              padded_predictions,
              padded_labels,
          ) = common_layers.pad_with_zeros(predictions, labels)
          weights = weights_fn(padded_labels)
  
          # Flatten, keeping batch dim (and num_classes dim for predictions)
          # TPU argmax can only deal with a limited number of dimensions
          predictions_shape = common_layers.shape_list(
              padded_predictions
          )
          batch_size = predictions_shape[0]
          num_classes = predictions_shape[-1]
          flat_size = common_layers.list_product(
              common_layers.shape_list(padded_labels)[1:]
          )
          padded_predictions = tf.reshape(
              padded_predictions,
              [
                  batch_size,
                  common_layers.list_product(predictions_shape[1:-1]),
                  num_classes,
              ],
          )
          padded_labels = tf.reshape(
              padded_labels, [batch_size, flat_size]
          )
          weights = tf.reshape(weights, [batch_size, flat_size])
  
          outputs = tf.to_int32(tf.argmax(padded_predictions, axis=-1))
          padded_labels = tf.to_int32(padded_labels)
          not_correct = (
              tf.to_float(tf.not_equal(outputs, padded_labels))
              * weights
          )
          axis = list(range(1, len(outputs.get_shape())))
          correct_seq = 1.0 - tf.minimum(
              1.0, tf.reduce_sum(not_correct, axis=axis)
          )
          return correct_seq, tf.constant(1.0)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import six
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import bleu_hook
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import rouge
  from tensor2tensor.utils import sari_hook
  import tensorflow.compat.v1
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 12 for calling a global function with example usage: padded_sequence_accuracy(predictions, labels, weights_fn) and returns: rounding_sequence_accuracy, correct_seq,  tf