code: def postprocess_image(x, rows, cols, hparams):
      """Postprocessing after decoding.
  
      Args:
        x: Tensor of shape [batch, ...], where ... can be any rank such that the
          number of elements in x is batch * rows * cols * hparams.hidden_size.
        rows: Integer representing number of rows in a 2-D data point.
        cols: Integer representing number of columns in a 2-D data point.
        hparams: HParams set.
  
      Returns:
        Tensor of shape [batch, rows, cols, depth], where depth is
        hparams.num_mixtures * 10 if hparams.likelihood is DMOL, otherwise 256. In
        the special case of inference and block raster scan order, it is a Tensor
        of shape [batch, num_blocks_rows, num_block_cols, block_length, block_width,
        depth].
      """
      batch = common_layers.shape_list(x)[0]
      x = tf.reshape(x, [batch, rows, cols, hparams.hidden_size])
      likelihood = getattr(hparams, "likelihood", DistributionType.CAT)
      if likelihood == DistributionType.DMOL:
          depth = hparams.num_mixtures * 10
          targets = tf.layers.dense(
              x,
              depth,
              use_bias=False,
              activation=None,
              name="output_conv",
          )
      else:
          depth = 256
          targets = tf.layers.dense(
              x,
              depth,
              use_bias=True,
              activation=None,
              name="output_conv",
          )
      if (
          hparams.mode == tf_estimator.ModeKeys.PREDICT
          and hparams.block_raster_scan
      ):
          y = targets
          yshape = common_layers.shape_list(y)
          block_length = hparams.query_shape[0]
          block_width = hparams.query_shape[1]
  
          # Break into block row wise.
          y = tf.reshape(
              y,
              [
                  batch,
                  yshape[1] // block_length,
                  block_length,
                  yshape[2],
                  depth,
              ],
          )
          yshape = common_layers.shape_list(y)
          # Break into blocks width wise.
          y_blocks = tf.reshape(
              y,
              [
                  batch,
                  yshape[1],
                  yshape[2],
                  yshape[3] // block_width,
                  block_width,
                  depth,
              ],
          )
  
          # Reshape targets as [batch, num_blocks_rows, num_block_cols, block_length,
          # block_width, depth].
          targets = tf.transpose(y_blocks, [0, 1, 3, 2, 4, 5])
  
      return targets
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import expert_utils
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 14 for calling a global function with example usage: postprocess_image(x, rows, cols, hparams) and returns: targets