code: def action_distribution_fn(
      policy: Policy,
      model: SlateQTFModel,
      input_dict,
      *,
      explore,
      is_training,
      **kwargs,
  ):
      """Determine which action to take."""
  
      # First, we transform the observation into its unflattened form.
      observation = input_dict[SampleBatch.OBS]
      # user.shape: [B, E]
      user_obs = observation["user"]
      doc_obs = list(observation["doc"].values())
  
      # Compute scores per candidate.
      scores, score_no_click = score_documents(user_obs, doc_obs)
      # Compute Q-values per candidate.
      q_values = model.get_q_values(user_obs, doc_obs)
  
      with tf.name_scope("select_slate"):
          per_slate_q_values = get_per_slate_q_values(
              policy.slates, score_no_click, scores, q_values
          )
      return (
          per_slate_q_values,
          functools.partial(
              SlateMultiCategorical,
              action_space=policy.action_space,
              all_slates=policy.slates,
          ),
          [],
      )
  
imports: import functools
  import logging
  from typing import Dict
  import gymnasium
  import numpy
  from rllib_slate_q.slate_q.slateq_tf_model import SlateQTFModel
  from ray.rllib.algorithms.dqn.dqn_tf_policy import clip_gradients
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import SlateMultiCategorical
  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, TensorType
label_desc: Use image node_id 4 for calling a global function with example usage: action_distribution_fn(policy, model, input_dict) and returns: per_slate_q_values,  functools,  list