code: def get_distribution_inputs_and_class(
      policy: Policy,
      model: ModelV2,
      obs_batch: TensorType,
      *,
      explore: bool = True,
      **kwargs,
  ) -> Tuple[TensorType, Type[TFActionDistribution], List[TensorType]]:
      """The action distribution function to be used the algorithm.
  
      An action distribution function is used to customize the choice of action
      distribution class and the resulting action distribution inputs (to
      parameterize the distribution object).
      After parameterizing the distribution, a `sample()` call
      will be made on it to generate actions.
  
      Args:
          policy: The Policy being queried for actions and calling this
              function.
          model: The SAC specific Model to use to generate the
              distribution inputs (see sac_tf|torch_model.py). Must support the
              `get_action_model_outputs` method.
          obs_batch: The observations to be used as inputs to the
              model.
          explore: Whether to activate exploration or not.
  
      Returns:
          Tuple[TensorType, Type[TFActionDistribution], List[TensorType]]: The
              dist inputs, dist class, and a list of internal state outputs
              (in the RNN case).
      """
      # Get base-model (forward) output (this should be a noop call).
      forward_out, state_out = model(
          SampleBatch(
              obs=obs_batch,
              _is_training=policy._get_is_training_placeholder(),
          ),
          [],
          None,
      )
      # Use the base output to get the policy outputs from the SAC model's
      # policy components.
      distribution_inputs, _ = model.get_action_model_outputs(
          forward_out
      )
      # Get a distribution class to be used with the just calculated dist-inputs.
      action_dist_class = _get_dist_class(
          policy, policy.config, policy.action_space
      )
  
      return distribution_inputs, action_dist_class, state_out
  
imports: import copy
  import gymnasium
  from gymnasium.spaces import Box, Discrete
  from functools import partial
  import logging
  from typing import Dict, List, Optional, Tuple, Type, Union
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.algorithms.dqn.dqn_tf_policy import postprocess_nstep_and_prio, PRIO_WEIGHTS
  from ray.rllib.algorithms.sac.sac_tf_model import SACTFModel
  from ray.rllib.algorithms.sac.sac_torch_model import SACTorchModel
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.models import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import Beta, Categorical, DiagGaussian, Dirichlet, SquashedGaussian, TFActionDistribution
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import TargetNetworkMixin
  from ray.rllib.policy.tf_policy_template import build_tf_policy
  from ray.rllib.utils.error import UnsupportedSpaceException
  from ray.rllib.utils.framework import get_variable, try_import_tf
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.utils.tf_utils import huber_loss, make_tf_callable
  from ray.rllib.utils.typing import AgentID, LocalOptimizer, ModelGradients, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 4 for calling a global function with example usage: get_distribution_inputs_and_class(policy, model, obs_batch) and returns: distribution_inputs,  action_dist_class,  state_out