code: def setup_early(policy, obs_space, action_space, config):
      """Obtain all possible slates given current docs in the candidate set."""
  
      num_candidates = action_space.nvec[0]
      slate_size = len(action_space.nvec)
      num_all_slates = np.prod(
          [(num_candidates - i) for i in range(slate_size)]
      )
  
      mesh_args = [list(range(num_candidates))] * slate_size
      slates = tf.stack(tf.meshgrid(*mesh_args), axis=-1)
      slates = tf.reshape(slates, shape=(-1, slate_size))
      # Filter slates that include duplicates to ensure each document is picked
      # at most once.
      unique_mask = tf.map_fn(
          lambda x: tf.equal(
              tf.size(input=x), tf.size(input=tf.unique(x)[0])
          ),
          slates,
          dtype=tf.bool,
      )
      # slates.shape: [A, S]
      slates = tf.boolean_mask(tensor=slates, mask=unique_mask)
      slates.set_shape([num_all_slates, slate_size])
  
      # Store all possible slates only once in policy object.
      policy.slates = 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 7 for calling a global function with example usage: setup_early(policy, obs_space, action_space, config) without return types