code: def __init__(
      self,
      policy: Policy,
      gamma: float = 0.0,
      optimizer: Optimizer__Union = field(  # noqa: B008
          default_factory=Optimizer__Union.default
      ),
      optimizer_value_net: Optimizer__Union = field(  # noqa: B008
          default_factory=Optimizer__Union.default
      ),
      actions: List[str] = field(default_factory=list),  # noqa: B008
      off_policy: bool = False,
      reward_clip: float = 1e6,
      clip_param: float = 1e6,
      normalize: bool = True,
      subtract_mean: bool = True,
      offset_clamp_min: bool = False,
      value_net: Optional[ModelBase] = None,
      do_log_metrics: bool = False,
  ):
      super().__init__()
      self._actions = actions
      self.scorer = policy.scorer
      self.sampler = policy.sampler
      self.gamma = gamma
      self.off_policy = off_policy
      self.reward_clip = reward_clip
      self.clip_param = clip_param
      self.normalize = normalize
      self.subtract_mean = subtract_mean
      self.offset_clamp_min = offset_clamp_min
      self.optimizer = optimizer
      self.optimizer_value_net = optimizer_value_net
      if value_net is not None:
          if self.normalize or self.subtract_mean:
              raise RuntimeError(
                  "Can't apply a baseline and reward normalization \
                      (or mean subtraction) simultaneously."
              )
          self.value_net = value_net
          self.value_loss_fn = torch.nn.MSELoss(reduction="mean")
      else:
          self.value_net = None
      self.do_log_metrics = do_log_metrics
      if self.do_log_metrics:
          self.losses = []
          self.ips_ratio_means = []
  
imports: import inspect
  import logging
  import math
  from dataclasses import field
  from typing import List, Optional
  import reagent.core.types
  import torch
  import torch.optim
  from reagent.gym.policies.policy import Policy
  from reagent.models.base import ModelBase
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.utils import discounted_returns, whiten
label_desc: Use image node_id 1 to create a new ReinforceTrainer object from inherited base classes: ReAgentLightningModule with example: obj = ReinforceTrainer(policy, gamma, optimizer, optimizer_value_net, actions, off_policy, reward_clip, clip_param, normalize, subtract_mean, offset_clamp_min, value_net, do_log_metrics)