code: def unprepare_for_iw(x, k):
      """Unprepare feature for importance sampling."""
      batch_size_times_k = common_layers.shape_list(x)[0]
      remaining_shape = common_layers.shape_list(x)[1:]
      x = tf.reshape(x, [k, batch_size_times_k // k] + remaining_shape)
      return x
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import transformer_glow_layers_ops
  from tensor2tensor.models.transformer import transformer_decoder_layer
  from tensor2tensor.models.transformer import transformer_encoder
  from tensor2tensor.models.transformer import transformer_prepare_encoder
  from tensor2tensor.utils import learning_rate
  from tensor2tensor.utils import mlperf_log
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 21 for calling a global function with example usage: unprepare_for_iw(x, k) and returns: x