code: def h_function(x, epsilon=1.0):
      """h-function to normalize target Qs, described in the paper [1].
  
      h(x) = sign(x) * [sqrt(abs(x) + 1) - 1] + epsilon * x
  
      Used in [1] in combination with h_inverse:
        targets = h(r + gamma * h_inverse(Q^))
      """
      return tf.sign(x) * (tf.sqrt(tf.abs(x) + 1.0) - 1.0) + epsilon * x
  
imports: from typing import Dict, List, Optional, Tuple
  import gymnasium
  import ray
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS, build_q_model, clip_gradients, compute_q_values, postprocess_nstep_and_prio
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import Categorical
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import LearningRateSchedule, TargetNetworkMixin
  from ray.rllib.policy.tf_policy_template import build_tf_policy
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.tf_utils import huber_loss
  from ray.rllib.utils.typing import AlgorithmConfigDict, ModelInputDict, TensorType
label_desc: Use image node_id 3 for calling a global function with example usage: h_function(x, epsilon) and returns: unknown