code: def pad_batch(features, batch_multiple):
      """Pad batch dim of features to nearest multiple of batch_multiple."""
      feature = list(features.items())[0][1]
      batch_size = tf.shape(feature)[0]
      mod = batch_size % batch_multiple
      has_mod = tf.cast(tf.cast(mod, tf.bool), tf.int32)
      batch_padding = batch_multiple * has_mod - mod
  
      padded_features = {}
      for k, feature in features.items():
          rank = len(feature.shape)
          paddings = [[0, 0] for _ in range(rank)]
          paddings[0][1] = batch_padding
          padded_feature = tf.pad(feature, paddings)
          padded_features[k] = padded_feature
      return padded_features
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import multiprocessing
  import random
  import six
  from six.moves import range
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import mlperf_log
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 15 for calling a global function with example usage: pad_batch(features, batch_multiple) and returns: padded_features