class_docstr: A trainer for the DQN algorithm and its variants.
  
  Configures optimizers, builds the losses and train functions for the
  Q-learning based algorithm variants. Supports MaxQ and SARSA style
  TD-learning under both, standard and batch-constrained q-learning.
  During training, updates CPE metrics estimators.
  
  Attributes:
      double_q_learning: a boolean flag whether to use double-q learning.
      minibatch_size: an int number of samples per minibatch.
      minibatches_per_step: an int number of minibatch updates per step.
      q_network: a network object mapping states to q-values of all actions.
      q_network_target: a copy of q-network for training stability used in
          estimating q-values targets.
      q_network_optimizer: an optimizer object for training q-network.
      bcq: a config file for batch-constrained q-learning (BCQ).
      bcq_imitator: if using batch-constrained q-learning, the behavior
          policy used for BCQ training.
      all_action_scores: a torch tensor containing q-network
              predictions from the current states.
docstr: Gets the q values from the model and target networks
docstr_tok: ["Gets","the","q","values","from","the","model","and","target","networks"]
code: def get_detached_model_outputs(
      self, state
  ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
      """Gets the q values from the model and target networks"""
      q_values = self.q_network(state)
      q_values_target = self.q_network_target(state)
      return q_values, q_values_target
  
imports: import logging
  from typing import List, Optional, Tuple
  import reagent.core.types
  import torch
  from reagent.core.configuration import resolve_defaults
  from reagent.core.dataclasses import dataclass, field
  from reagent.core.parameters import EvaluationParameters, RLParameters
  from reagent.optimizer import Optimizer__Union, SoftUpdate
  from reagent.training.dqn_trainer_base import DQNTrainerBaseLightning
  from reagent.training.imitator_training import get_valid_actions_from_imitator
label_desc: Use image node_id 3 for calling the DQNTrainer obj's underlying member method code with example usage: obj.get_detached_model_outputs(state) and returns: q_values,  q_values_target