code: def huber_loss(x: TensorType, delta: float = 1.0) -> TensorType:
      """Computes the huber loss for a given term and delta parameter.
  
      Reference: https://en.wikipedia.org/wiki/Huber_loss
      Note that the factor of 0.5 is implicitly included in the calculation.
  
      Formula:
          L = 0.5 * x^2  for small abs x (delta threshold)
          L = delta * (abs(x) - 0.5*delta)  for larger abs x (delta threshold)
  
      Args:
          x: The input term, e.g. a TD error.
          delta: The delta parmameter in the above formula.
  
      Returns:
          The Huber loss resulting from `x` and `delta`.
      """
      return tf.where(
          tf.abs(x)
          < delta,  # for small x -> apply the Huber correction
          tf.math.square(x) * 0.5,
          delta * (tf.abs(x) - 0.5 * delta),
      )
  
imports: import logging
  from typing import Any, Callable, List, Optional, Type, TYPE_CHECKING, Union
  import gymnasium
  import numpy
  import tree
  from gymnasium.spaces import Discrete, MultiDiscrete
  from ray.rllib.utils.annotations import PublicAPI, DeveloperAPI
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.spaces.space_utils import get_base_struct_from_space
  from ray.rllib.utils.typing import LocalOptimizer, ModelGradients, PartialAlgorithmConfigDict, SpaceStruct, TensorStructType, TensorType
label_desc: Use image node_id 7 for calling a global function with example usage: huber_loss(x, delta) and returns: tf