code: def clip_gradient(net, clip_value_min, clip_value_max, name=None):
    
    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
docs: 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.