code: def h_inverse(x, epsilon=1.0):
      """Inverse if the above h-function, described in the paper [1].
  
      If x > 0.0:
      h-1(x) = [2eps * x + (2eps + 1) - sqrt(4eps x + (2eps + 1)^2)] /
          (2 * eps^2)
  
      If x < 0.0:
      h-1(x) = [2eps * x + (2eps + 1) + sqrt(-4eps x + (2eps + 1)^2)] /
          (2 * eps^2)
      """
      two_epsilon = epsilon * 2
      if_x_pos = (
          two_epsilon * x
          + (two_epsilon + 1.0)
          - torch.sqrt(4.0 * epsilon * x + (two_epsilon + 1.0) ** 2)
      ) / (2.0 * epsilon**2)
      if_x_neg = (
          two_epsilon * x
          - (two_epsilon + 1.0)
          + torch.sqrt(-4.0 * epsilon * x + (two_epsilon + 1.0) ** 2)
      ) / (2.0 * epsilon**2)
      return torch.where(x < 0.0, if_x_neg, if_x_pos)
  
imports: from typing import Dict, Tuple
  import gymnasium
  import ray
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS, postprocess_nstep_and_prio
  from ray.rllib.algorithms.dqn.dqn_torch_policy import adam_optimizer, build_q_model_and_distribution, compute_q_values
  from ray.rllib.algorithms.r2d2.r2d2_tf_policy import get_distribution_inputs_and_class
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.policy_template import build_policy_class
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import LearningRateSchedule, TargetNetworkMixin
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.torch_utils import FLOAT_MIN, apply_grad_clipping, concat_multi_gpu_td_errors, huber_loss, sequence_mask
  from ray.rllib.utils.typing import AlgorithmConfigDict, TensorType
label_desc: Use image node_id 4 for calling a global function with example usage: h_inverse(x, epsilon) and returns: torch