code: def build_slateq_model_and_distribution(
      policy: Policy,
      obs_space: gym.spaces.Space,
      action_space: gym.spaces.Space,
      config: AlgorithmConfigDict,
  ) -> Tuple[ModelV2, Type[TorchDistributionWrapper]]:
      """Build models for SlateQ
  
      Args:
          policy: The policy, which will use the model for optimization.
          obs_space: The policy's observation space.
          action_space: The policy's action space.
          config: The Algorithm's config dict.
  
      Returns:
          Tuple consisting of 1) Q-model and 2) an action distribution class.
      """
      model = SlateQTorchModel(
          obs_space,
          action_space,
          num_outputs=action_space.nvec[0],
          model_config=config["model"],
          name="slateq_model",
          fcnet_hiddens_per_candidate=config[
              "fcnet_hiddens_per_candidate"
          ],
      )
  
      policy.target_model = SlateQTorchModel(
          obs_space,
          action_space,
          num_outputs=action_space.nvec[0],
          model_config=config["model"],
          name="target_slateq_model",
          fcnet_hiddens_per_candidate=config[
              "fcnet_hiddens_per_candidate"
          ],
      )
  
      return model, TorchCategorical
  
imports: import logging
  from typing import Dict, Tuple, Type
  import gymnasium
  import numpy
  from rllib_slate_q.slate_q.slateq_torch_model import SlateQTorchModel
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, 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 TargetNetworkMixin
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, convert_to_torch_tensor, huber_loss
  from ray.rllib.utils.typing import AlgorithmConfigDict, TensorType
label_desc: Use image node_id 1 for calling a global function with example usage: build_slateq_model_and_distribution(policy, obs_space, action_space, config) and returns: model,  TorchCategorical