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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 alignment(layer, decay_ratio=2):
 """Encourage neighboring images to be similar. When visualizing the interpolation between two objectives, it's often desireable to encourage analagous boejcts to be drawn in the same position, to make them more comparable. This term penalizes L2 distance between neighboring images, as evaluated at layer. In general, we find this most effective if used with a paramaterization that shares across the batch. (In fact, that works quite well by iteself, so this function may just be obselete.) Args: layer: layer to penalize at. decay_ratio: how much to decay penalty as images move apart in batch. Returns: Objective. """

  def inner(T):
    batch_n = T(layer).get_shape().as_list()[0]
    arr = T(layer)
    accum = 0
    for d in [1, 2, 3, 4]:
      for i in range(batch_n - d):
        a, b = i, i+d
        arr1, arr2 = arr[a], arr[b]
        accum += tf.reduce_mean((arr1-arr2)**2) / decay_ratio**float(d)
    return -accum
  return inner