code: def huber(
      inputs: tf.Tensor, quadratic_linear_boundary: float
  ) -> tf.Tensor:
      """Calculates huber loss of `inputs`.
  
      For each value x in `inputs`, the following is calculated:
  
      ```
        0.5 * x^2                  if |x| <= d
        0.5 * d^2 + d * (|x| - d)  if |x| > d
      ```
  
      where d is `quadratic_linear_boundary`.
  
      Args:
        inputs: Input Tensor to calculate the huber loss on.
        quadratic_linear_boundary: The point where the huber loss function changes
          from a quadratic to linear.
  
      Returns:
        `Tensor` of the same shape as `inputs`, containing values calculated
        in the manner described above.
  
      Raises:
        ValueError: if quadratic_linear_boundary < 0.
      """
      if quadratic_linear_boundary < 0:
          raise ValueError("quadratic_linear_boundary must be >= 0.")
  
      abs_x = tf.abs(inputs)
      delta = tf.constant(quadratic_linear_boundary)
      quad = tf.minimum(abs_x, delta)
      # The following expression is the same in value as
      # tf.maximum(abs_x - delta, 0), but importantly the gradient for the
      # expression when abs_x == delta is 0 (for tf.maximum it would be 1). This
      # is necessary to avoid doubling the gradient, since there is already a
      # nonzero contribution to the gradient from the quadratic term.
      lin = abs_x - quad
      return 0.5 * quad**2 + delta * lin
  
label_desc: Use image node_id 1 for calling a global function with example usage: huber(inputs, quadratic_linear_boundary) and returns: unknown