code: def _get_maxq_topk(self, next_state: rlt.FeatureData) -> torch.Tensor:
      candidate_docs = next_state.candidate_docs
      assert candidate_docs is not None
  
      (
          batch_size,
          num_candidates,
          _,
      ) = candidate_docs.float_features.shape
      assert 0 < self.slate_size <= num_candidates
  
      docs = candidate_docs.select_slate(
          torch.arange(num_candidates).repeat(batch_size, 1)
      )
      next_q_values = self._get_unmasked_q_values(
          self.q_network_target, next_state, docs
      ) * self._get_docs_value(docs)
      _, next_actions = torch.topk(
          next_q_values, self.slate_size, dim=1
      )
  
      return next_actions
  
imports: import enum
  import logging
  from typing import Optional
  import reagent.core.parameters
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.dataclasses import field
  from reagent.optimizer import Optimizer__Union, SoftUpdate
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.rl_trainer_pytorch import RLTrainerMixin
label_desc: Use image node_id 6 for calling the SlateQTrainer obj's underlying member method code with example usage: obj._get_maxq_topk(next_state) and returns: next_actions