class_docstr: Critic Regularized Regression (CRR) algorithm trainer
  as described in https://arxiv.org/abs/2006.15134
code: def get_detached_model_outputs(
      self, state
  ) -> Tuple[torch.Tensor, None]:
      # This function is only used in evaluation_data_page.py, in create_from_tensors_dqn(),
      # in order to compute model propensities. The definition of this function in
      # dqn_trainer.py returns two values, and so we also return two values here, for
      # consistency.
      action_scores = self.actor_network(state).action
      return action_scores, None
  
imports: import logging
  from typing import List, Tuple
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.configuration import resolve_defaults
  from reagent.core.dataclasses import field
  from reagent.core.parameters import EvaluationParameters, RLParameters
  from reagent.optimizer import Optimizer__Union, SoftUpdate
  from reagent.training.dqn_trainer_base import DQNTrainerBaseLightning
  from torch import distributions
label_desc: Use image node_id 3 for calling the DiscreteCRRTrainer obj's underlying member method code with example usage: obj.get_detached_model_outputs(state) and returns: action_scores,  None