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)
  
      mesh_args = [
          torch.Tensor(list(range(num_candidates)))
      ] * slate_size
      slates = torch.stack(torch.meshgrid(*mesh_args), dim=-1)
      slates = torch.reshape(slates, shape=(-1, slate_size))
      # Filter slates that include duplicates to ensure each document is picked
      # at most once.
      unique_mask = []
      for i in range(slates.shape[0]):
          x = slates[i]
          unique_mask.append(len(x) == len(torch.unique(x)))
      unique_mask = torch.Tensor(unique_mask).bool().unsqueeze(1)
      # slates.shape: [A, S]
      slates = torch.masked_select(slates, mask=unique_mask).reshape(
          [-1, slate_size]
      )
  
      # Store all possible slates only once in policy object.
      policy.slates = slates.long()
      # [1, AxS] Useful for torch.take_along_dim()
      policy.slates_indices = policy.slates.reshape(-1).unsqueeze(0)
  
imports: import logging
  from typing import Dict, Tuple, Type
  import gymnasium
  import numpy
  from rllib_slate_q.slate_q.slateq_torch_model import SlateQTorchModel
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, TorchDistributionWrapper
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.policy_template import build_policy_class
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import TargetNetworkMixin
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, convert_to_torch_tensor, 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