code: def get_distribution_inputs_and_class(
      policy: Policy,
      model: ModelV2,
      input_dict: SampleBatch,
      *,
      explore: bool = True,
      is_training: bool = False,
      **kwargs,
  ) -> Tuple[TensorType, type, List[TensorType]]:
      q_vals = compute_q_values(
          policy,
          model,
          input_dict,
          explore=explore,
          is_training=is_training,
      )
      q_vals = q_vals[0] if isinstance(q_vals, tuple) else q_vals
  
      model.tower_stats["q_values"] = q_vals
  
      # Return a Torch TorchCategorical distribution where the temperature
      # parameter is partially binded to the configured value.
      temperature = policy.config[
          "categorical_distribution_temperature"
      ]
  
      return (
          q_vals,
          get_torch_categorical_class_with_temperature(temperature),
          [],  # state-out
      )
  
imports: from typing import Dict, List, Tuple
  import gymnasium
  import ray
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS, Q_SCOPE, Q_TARGET_SCOPE, postprocess_nstep_and_prio
  from ray.rllib.algorithms.dqn.dqn_torch_model import DQNTorchModel
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import get_torch_categorical_class_with_temperature, 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.error import UnsupportedSpaceException
  from ray.rllib.utils.exploration.parameter_noise import ParameterNoise
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, FLOAT_MIN, huber_loss, l2_loss, reduce_mean_ignore_inf, softmax_cross_entropy_with_logits
  from ray.rllib.utils.typing import TensorType, AlgorithmConfigDict
label_desc: Use image node_id 2 for calling a global function with example usage: get_distribution_inputs_and_class(policy, model, input_dict) and returns: q_vals,  get_torch_categorical_class_with_temperature,  list