code: def get_distribution_inputs_and_class(
      policy: Policy,
      model: ModelV2,
      *,
      input_dict: ModelInputDict,
      state_batches: Optional[List[TensorType]] = None,
      seq_lens: Optional[TensorType] = None,
      explore: bool = True,
      is_training: bool = False,
      **kwargs,
  ) -> Tuple[TensorType, type, List[TensorType]]:
      if policy.config["framework"] == "torch":
          from ray.rllib.algorithms.r2d2.r2d2_torch_policy import (
              compute_q_values as torch_compute_q_values,
          )
  
          func = torch_compute_q_values
      else:
          func = compute_q_values
  
      q_vals, logits, probs_or_logits, state_out = func(
          policy,
          model,
          input_dict,
          state_batches,
          seq_lens,
          explore,
          is_training,
      )
  
      policy.q_values = q_vals
      if not hasattr(policy, "q_func_vars"):
          policy.q_func_vars = model.variables()
  
      action_dist_class = (
          TorchCategorical
          if policy.config["framework"] == "torch"
          else Categorical
      )
  
      return policy.q_values, action_dist_class, state_out
  
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 5 for calling a global function with example usage: get_distribution_inputs_and_class(policy, model) and returns: policy,  action_dist_class,  state_out