code: def get_distribution_inputs_and_class(
      policy: Policy,
      model: ModelV2,
      input_dict: SampleBatch,
      *,
      explore=True,
      **kwargs,
  ):
      q_vals = compute_q_values(
          policy, model, input_dict, state_batches=None, explore=explore
      )
      q_vals = q_vals[0] if isinstance(q_vals, tuple) else q_vals
  
      policy.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 (
          policy.q_values,
          get_categorical_class_with_temperature(temperature),
          [],
      )
  
imports: from typing import Dict
  import gymnasium
  import numpy
  import ray
  from ray.rllib.algorithms.dqn.distributional_q_tf_model import DistributionalQTFModel
  from ray.rllib.algorithms.simple_q.utils import Q_SCOPE, Q_TARGET_SCOPE
  from ray.rllib.evaluation.postprocessing import adjust_nstep
  from ray.rllib.models import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import get_categorical_class_with_temperature
  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.error import UnsupportedSpaceException
  from ray.rllib.utils.exploration import ParameterNoise
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.tf_utils import huber_loss, l2_loss, make_tf_callable, minimize_and_clip, reduce_mean_ignore_inf
  from ray.rllib.utils.typing import AlgorithmConfigDict, ModelGradients, TensorType
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: policy,  get_categorical_class_with_temperature,  list