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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(net, clip_value_min, clip_value_max, name=None):
 """Clips respective gradients of a given tensor. Acts as identity for the forward pass, but clips gradient tensor element-wise by value during the backward pass. Any gradient values less than `clip_value_min` or greater than `clip_values_max` are set to the respective limit values. Args: net: A `tf.Tensor`. clip_value_min: A 0-D Tensor or scalar. The minimum value to clip by. clip_value_max: A 0-D Tensor or scalar. The maximum value to clip by. name: A name for the operation (optional, default 'clip_gradient'). Returns: A `tf.Tensor` with the same type as the input tensor. Raises: ValueError: If `net` dtype is non-float. """

  if not net.dtype.is_floating:
    raise ValueError("clip_gradient does not support non-float `net` inputs.")

  with tf.name_scope(name, "clip_gradient", values=[net]):
    dtype = net.dtype.base_dtype  # Convert ref dtypes to regular dtypes.
    min_tensor = tf.convert_to_tensor(clip_value_min, dtype=dtype)
    max_tensor = tf.convert_to_tensor(clip_value_max, dtype=dtype)

    clip_gradient_op = _clip_gradient_op(dtype)
    output = clip_gradient_op(net, min_tensor, max_tensor)
    output.set_shape(net.get_shape())

  return output