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, action
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """Gets the q values from the model and target networks"""
      q_values = self.q_network(state, action)
      q_values_target = self.q_network_target(state, action)
      return q_values, q_values_target
  
imports: import logging
  from typing import Optional, Tuple
  import reagent.core.parameters
  import reagent.core.types
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core.configuration import resolve_defaults
  from reagent.core.dataclasses import field
  from reagent.optimizer import Optimizer__Union, SoftUpdate
  from reagent.training.dqn_trainer_base import DQNTrainerMixin
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.rl_trainer_pytorch import RLTrainerMixin
label_desc: Use image node_id 4 for calling the ParametricDQNTrainer obj's underlying member method code with example usage: obj.get_detached_model_outputs(state, action) and returns: q_values,  q_values_target