code: def __init__(
      self,
      q_network,
      q_network_target,
      reward_network: Optional[nn.Module] = None,
      # Start ParametricDQNTrainerParameters
      rl: rlp.RLParameters = field(
          default_factory=rlp.RLParameters
      ),  # noqa: B008
      double_q_learning: bool = True,
      minibatches_per_step: int = 1,
      optimizer: Optimizer__Union = field(  # noqa: B008
          default_factory=Optimizer__Union.default
      ),
      log_tensorboard: bool = False,
  ) -> None:
      super().__init__()
      self.rl_parameters = rl
  
      self.double_q_learning = double_q_learning
      self.minibatches_per_step = minibatches_per_step or 1
  
      self.q_network = q_network
      self.q_network_target = q_network_target
      self.reward_network = reward_network
      self.optimizer = optimizer
      self.log_tensorboard = log_tensorboard
  
      if rl.q_network_loss == "mse":
          self.q_network_loss = F.mse_loss
      elif rl.q_network_loss == "huber":
          self.q_network_loss = F.smooth_l1_loss
      elif rl.q_network_loss == "bce_with_logits":
          # The loss is only used when gamma = 0, reward is between 0 and 1
          # and we need to calculate NE as metrics.
          assert (
              rl.gamma == 0
          ), "bce_with_logits loss is only supported when gamma is 0."
          self.q_network_loss = F.binary_cross_entropy_with_logits
      else:
          raise Exception(
              "Q-Network loss type {} not valid loss.".format(
                  rl.q_network_loss
              )
          )
  
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 1 to create a new ParametricDQNTrainer object from inherited base classes: DQNTrainerMixin, RLTrainerMixin, ReAgentLightningModule with example: obj = ParametricDQNTrainer(q_network, q_network_target, reward_network, rl, double_q_learning, minibatches_per_step, optimizer, log_tensorboard)