code: def __init__(
      self,
      observation_space: gym.spaces.Space,
      action_space: gym.spaces.Space,
      config: TrainerConfigDict,
  ):
      self.target_model = None  # assign it in self.make_model
      self._is_action_discrete = isinstance(
          action_space, gym.spaces.Discrete
      )
  
      TorchPolicyV2.__init__(
          self,
          observation_space,
          action_space,
          config,
          max_seq_len=config["model"]["max_seq_len"],
      )
  
      # For discreet action space, use a custom TorchCategorical distribution
      # that supports temperature.
      if self._is_action_discrete:
          assert self.dist_class == TorchCategorical
          self.dist_class = (
              get_torch_categorical_class_with_temperature(
                  config["categorical_distribution_temperature"]
              )
          )
  
      """
          by here your model should include the following
          (We assume state s is already encoded and there is no need to use RNNs/other
          models to encode observations into states):
          1. a nn representing the actor pi(a|s)
            1.1* in case of continuous actions it should be normal / squashed normal
            dist if the action space is bounded?
            1.2 in case of of discrete set of actions the output of the model should be
            a discrete distribution over action classes
          2. a nn representing the critic Q(s, a)
            2.1* in case of continuous actions it should take in concat([s,a]) and output
             a single scalar
            2.2 in case of discrete actions it should take in s and output a logit for
            each action class as well as a scale for matching the reward scale.
          3. for critic it should have n_critic copies of the Q function nn
          4. for each critic it should have a target model copy
          """
  
imports: from typing import Dict, List, Tuple, Type, Union, cast
  import gymnasium
  import numpy
  from rllib_crr.crr.crr_torch_model import CRRModel
  from ray.rllib.algorithms import AlgorithmConfig
  from ray.rllib.algorithms.ddpg.noop_model import TorchNoopModel
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, TorchDistributionWrapper, get_torch_categorical_class_with_temperature
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import TargetNetworkMixin
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.torch_utils import huber_loss, l2_loss
  from ray.rllib.utils.typing import TensorType, TrainerConfigDict
label_desc: Use image node_id 1 to create a new CRRTorchPolicy object from inherited base classes: TorchPolicyV2, TargetNetworkMixin with example: obj = CRRTorchPolicy(observation_space, action_space, config)