class_docstr: Implementation of QR-DQN (Quantile Regression Deep Q-Network)
  
  See https://arxiv.org/abs/1710.10044 for details
class_docstr_tok: ["Implementation","of","QR-DQN","(","Quantile","Regression","Deep","Q-Network",")","See","https",":","\/\/arxiv.org\/abs\/1710.10044","for","details"]
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: rlt.FeatureData
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """Gets the q values from the model and target networks"""
      q_values = self.q_network(state).mean(dim=2)
      q_values_target = self.q_network_target(state).mean(dim=2)
      return q_values, q_values_target
  
file_path: ReAgent/reagent/training/qrdqn_trainer.py
imports: import logging
  from typing import List, Tuple
  import reagent.core.types
  import torch
  from reagent.core.configuration import resolve_defaults
  from reagent.core.dataclasses import field
  from reagent.core.parameters import EvaluationParameters, RLParameters
  from reagent.optimizer import SoftUpdate
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.dqn_trainer_base import DQNTrainerBaseLightning
label_desc: Use image node_id 7 for calling the QRDQNTrainer obj's underlying member method code with example usage: obj.get_detached_model_outputs(state) and returns: q_values,  q_values_target