code: def build_r2d2_model_and_distribution(
      policy: Policy,
      obs_space: gym.spaces.Space,
      action_space: gym.spaces.Space,
      config: AlgorithmConfigDict,
  ) -> Tuple[ModelV2, TorchDistributionWrapper]:
      """Build q_model and target_model for DQN
  
      Args:
          policy: The policy, which will use the model for optimization.
          obs_space (gym.spaces.Space): The policy's observation space.
          action_space (gym.spaces.Space): The policy's action space.
          config (AlgorithmConfigDict):
  
      Returns:
          (q_model, TorchCategorical)
              Note: The target q model will not be returned, just assigned to
              `policy.target_model`.
      """
  
      # Create the policy's models and action dist class.
      model, distribution_cls = build_q_model_and_distribution(
          policy, obs_space, action_space, config
      )
  
      # Assert correct model type by checking the init state to be present.
      # For attention nets: These don't necessarily publish their init state via
      # Model.get_initial_state, but may only use the trajectory view API
      # (view_requirements).
      assert (
          model.get_initial_state() != []
          or model.view_requirements.get("state_in_0") is not None
      ), (
          "R2D2 requires its model to be a recurrent one! Try using "
          "`model.use_lstm` or `model.use_attention` in your config "
          "to auto-wrap your model with an LSTM- or attention net."
      )
  
      return model, distribution_cls
  
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 1 for calling a global function with example usage: build_r2d2_model_and_distribution(policy, obs_space, action_space, config) and returns: model,  distribution_cls