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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 _clip_gradient_op(dtype):
 """Create an op that clips gradients using a Defun. The tensorflow Defun decorator creates an op and tensorflow caches these op automatically according to `func_name`. Using a Defun decorator twice with the same `func_name` does not create a new op, instead the cached op is used. This method produces a new op the first time it is called with a given `dtype` argument, and then uses the cached op each time it is called after that with the same `dtype`. The min and max clip values are given as arguments for the forward pass method so that they can be used in the backwards pass. Args: dtype: the dtype of the net whose gradient is being clipped. Returns: The op that clips gradients. """


  def clip_gradient_backward(op, grad):
    clip_value_min = op.inputs[1]
    clip_value_max = op.inputs[2]
    clipped_grad = tf.clip_by_value(grad, clip_value_min, clip_value_max)
    return clipped_grad, None, None

  def clip_gradient_forward(x, clip_value_min, clip_value_max):
    del clip_value_min  # Unused.
    del clip_value_max  # Unused.
    return x

  func_name = "ClipGradient_{}".format(dtype.name)
  return function.Defun(
      dtype, dtype, dtype,
      python_grad_func=clip_gradient_backward,
      func_name=func_name)(clip_gradient_forward)