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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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)