code: def dna_transformation(
      prev_image, dna_input, dna_kernel_size, relu_shift
  ):
      """Apply dynamic neural advection to previous image.
  
      Args:
        prev_image: previous image to be transformed.
        dna_input: hidden lyaer to be used for computing DNA transformation.
        dna_kernel_size: dna kernel size.
        relu_shift: shift for ReLU function.
      Returns:
        List of images transformed by the predicted CDNA kernels.
      """
      # Construct translated images.
      prev_image_pad = tf.pad(
          prev_image, [[0, 0], [2, 2], [2, 2], [0, 0]]
      )
      image_height = int(prev_image.get_shape()[1])
      image_width = int(prev_image.get_shape()[2])
  
      inputs = []
      for xkern in range(dna_kernel_size):
          for ykern in range(dna_kernel_size):
              inputs.append(
                  tf.expand_dims(
                      tf.slice(
                          prev_image_pad,
                          [0, xkern, ykern, 0],
                          [-1, image_height, image_width, -1],
                      ),
                      [3],
                  )
              )
      inputs = tf.concat(axis=3, values=inputs)
  
      # Normalize channels to 1.
      kernel = tf.nn.relu(dna_input - relu_shift) + relu_shift
      kernel = tf.expand_dims(
          kernel / tf.reduce_sum(kernel, [3], keep_dims=True), [4]
      )
      return tf.reduce_sum(kernel * inputs, [3], keep_dims=False)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import contrib
  import tensorflow.compat.v1
  from tensorflow.python.ops import summary_op_util
label_desc: Use image node_id 10 for calling a global function with example usage: dna_transformation(prev_image, dna_input, dna_kernel_size, relu_shift) and returns: tf