docstr: Args:
      q_network: states, action -> q-value
      slate_size(int): a fixed slate size
      rl (optional): an instance of the RLParameter class, which
          defines relevant hyperparameters
      optimizer (optional): the optimizer class and
          optimizer hyperparameters for the q network(s) optimizer
      discount_time_scale (optional): use to control the discount factor (gamma)
          relative to the time difference (t2-t1), i.e., gamma^((t2-t1)/time_scale).
          If it is absent, we won't adjust the discount factor by the time difference.
      single_selection (optional): TBD
      next_slate_value_norm_method (optional): how to calculate the next slate value
          when single_selection is False. By default we use NORM_BY_CURRENT_SLATE_SIZE.
      minibatch_size (optional): the size of the minibatch
      evaluation (optional): TBD
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code: def __init__(
      self,
      q_network,
      q_network_target,
      slate_size,
      # Start SlateQTrainerParameters
      rl: rlp.RLParameters = field(  # noqa: B008
          default_factory=lambda: rlp.RLParameters(maxq_learning=False)
      ),
      optimizer: Optimizer__Union = field(  # noqa: B008
          default_factory=Optimizer__Union.default
      ),
      slate_opt_parameters: Optional[rlp.SlateOptParameters] = None,
      discount_time_scale: Optional[float] = None,
      single_selection: bool = True,
      next_slate_value_norm_method: NextSlateValueNormMethod = NextSlateValueNormMethod.NORM_BY_CURRENT_SLATE_SIZE,
      minibatch_size: int = 1024,
      evaluation: rlp.EvaluationParameters = field(  # noqa: B008
          default_factory=lambda: rlp.EvaluationParameters(
              calc_cpe_in_training=False
          )
      ),
  ) -> None:
      """
      Args:
          q_network: states, action -> q-value
          slate_size(int): a fixed slate size
          rl (optional): an instance of the RLParameter class, which
              defines relevant hyperparameters
          optimizer (optional): the optimizer class and
              optimizer hyperparameters for the q network(s) optimizer
          discount_time_scale (optional): use to control the discount factor (gamma)
              relative to the time difference (t2-t1), i.e., gamma^((t2-t1)/time_scale).
              If it is absent, we won't adjust the discount factor by the time difference.
          single_selection (optional): TBD
          next_slate_value_norm_method (optional): how to calculate the next slate value
              when single_selection is False. By default we use NORM_BY_CURRENT_SLATE_SIZE.
          minibatch_size (optional): the size of the minibatch
          evaluation (optional): TBD
      """
      super().__init__()
      self.rl_parameters = rl
  
      self.discount_time_scale = discount_time_scale
      self.single_selection = single_selection
      self.next_slate_value_norm_method = next_slate_value_norm_method
  
      self.q_network = q_network
      self.q_network_target = q_network_target
      self.q_network_optimizer = optimizer
  
      self.slate_size = slate_size
      self.slate_opt_parameters = slate_opt_parameters
  
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 1 to create a new SlateQTrainer object from inherited base classes: RLTrainerMixin, ReAgentLightningModule with example: obj = SlateQTrainer(q_network, q_network_target, slate_size, rl, optimizer, slate_opt_parameters, discount_time_scale, single_selection, next_slate_value_norm_method, minibatch_size, evaluation)