code: def huber_loss(input_tensor, quadratic_linear_boundary, name=None):
      """Calculates huber loss of `input_tensor`.
  
      For each value x in `input_tensor`, 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`.
  
      When `input_tensor` is a loss this results in a form of gradient clipping.
      This is, for instance, how gradients are clipped in DQN and its variants.
  
      Args:
        input_tensor: `Tensor`, input values to calculate the huber loss on.
        quadratic_linear_boundary: `float`, the point where the huber loss function
          changes from a quadratic to linear.
        name: `string`, name for the operation (optional).
  
      Returns:
        `Tensor` of the same shape as `input_tensor`, 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.")
  
      with tf.name_scope(
          name,
          default_name="huber_loss",
          values=[input_tensor, quadratic_linear_boundary],
      ):
          abs_x = tf.abs(input_tensor)
          delta = 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
          # non-zero contribution to the gradient from the quadratic term.
          lin = abs_x - quad
          return 0.5 * quad**2 + delta * lin
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: huber_loss(input_tensor, quadratic_linear_boundary, name) and returns: unknown