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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 cumsum(x, axis=0, exclusive=False):
 """TPU hack for tf.cumsum. This is equivalent to tf.cumsum and is faster on TPU as of 04/2018 unless the axis dimension is very large. Args: x: a Tensor axis: an integer exclusive: a boolean Returns: Tensor of the same shape as x. """

  if not is_xla_compiled():
    return tf.cumsum(x, axis=axis, exclusive=exclusive)
  x_shape = shape_list(x)
  rank = len(x_shape)
  length = x_shape[axis]
  my_range = tf.range(length)
  comparator = tf.less if exclusive else tf.less_equal
  mask = tf.cast(
      comparator(tf.expand_dims(my_range, 1), tf.expand_dims(my_range, 0)),
      x.dtype)
  ret = tf.tensordot(x, mask, axes=[[axis], [0]])
  if axis != rank - 1:
    ret = tf.transpose(
        ret,
        list(range(axis)) + [rank - 1] + list(range(axis, rank - 1)))
  return ret