code: def get_per_slate_q_values(slates, s_no_click, s, q):
      slate_q_values = tf.gather(s * q, slates, axis=1)
      slate_scores = tf.gather(s, slates, axis=1)
      slate_normalizer = tf.reduce_sum(
          input_tensor=slate_scores, axis=2
      ) + tf.expand_dims(s_no_click, 1)
  
      slate_q_values = slate_q_values / tf.expand_dims(
          slate_normalizer, 2
      )
      slate_sum_q_values = tf.reduce_sum(
          input_tensor=slate_q_values, axis=2
      )
      return slate_sum_q_values
  
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 5 for calling a global function with example usage: get_per_slate_q_values(slates, s_no_click, s, q) and returns: slate_sum_q_values