class_docstr: Critic Regularized Regression (CRR) algorithm trainer
  as described in https://arxiv.org/abs/2006.15134
code: def compute_td_loss(self, q_network, state, action, target_q_values):
      all_q_values = q_network(state)
      q_values = (all_q_values * action).sum(dim=1, keepdim=True)
      q_loss = F.mse_loss(q_values, target_q_values)
      return q_loss
  
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 6 for calling the DiscreteCRRTrainer obj's underlying member method code with example usage: obj.compute_td_loss(q_network, state, action, target_q_values) and returns: q_loss