docstr: Get the next action list based on the slate optimization strategy.
docstr_tok: ["Get","the","next","action","list","based","on","the","slate","optimization","strategy","."]
code: def _get_maxq_next_action(
      self, next_state: rlt.FeatureData
  ) -> torch.Tensor:
      """Get the next action list based on the slate optimization strategy."""
      slate_opt_parameters = self.slate_opt_parameters
      assert slate_opt_parameters is not None
  
      if slate_opt_parameters.method == rlp.SlateOptMethod.TOP_K:
          return self._get_maxq_topk(next_state)
      else:
          raise NotImplementedError(
              "SlateQ with optimization method other than TOP_K is not implemented."
          )
  
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 5 for calling the SlateQTrainer obj's underlying member method code with example usage: obj._get_maxq_next_action(next_state) and returns: self