code: def train_step_gen(
      self, training_batch: rlt.PolicyGradientInput, batch_idx: int
  ):
      self._check_input(training_batch)
      actions = training_batch.action
      rewards = training_batch.reward.detach()
      scorer_inputs = []
      if (
          inspect.getattr_static(training_batch, "graph", None)
          is not None
      ):
          # GNN
          scorer_inputs.append(training_batch.graph)
      else:
          scorer_inputs.append(training_batch.state)
      if training_batch.possible_actions_mask is not None:
          scorer_inputs.append(training_batch.possible_actions_mask)
      scores = self.scorer(*scorer_inputs)
      characteristic_eligibility = self.sampler.log_prob(
          scores, actions
      ).float()
      offset_reinforcement = discounted_returns(
          torch.clamp(rewards, max=self.reward_clip).clone(), self.gamma
      )
      if self.normalize:
          offset_reinforcement = whiten(
              offset_reinforcement, subtract_mean=self.subtract_mean
          )
      elif self.subtract_mean:
          offset_reinforcement -= offset_reinforcement.mean()
      if self.offset_clamp_min:
          offset_reinforcement = offset_reinforcement.clamp(min=0)
      if self.value_net is not None:
          assert not (self.normalize or self.subtract_mean)
          baselines = self.value_net(training_batch.state).squeeze()
          yield self.value_loss_fn(baselines, offset_reinforcement)
          # subtract learned value function baselines from rewards
          offset_reinforcement = offset_reinforcement - baselines
  
      if self.off_policy:
          characteristic_eligibility = torch.exp(
              torch.clamp(
                  characteristic_eligibility - training_batch.log_prob,
                  max=math.log(float(self.clip_param)),
              )
          ).float()
  
      loss = (
          -(offset_reinforcement.float().detach())
          @ characteristic_eligibility
      )
      if self.do_log_metrics:
          detached_loss = loss.detach().cpu().item() / len(
              offset_reinforcement
          )
          self.losses.append(detached_loss)
          detached_ips_ratio_mean = (
              characteristic_eligibility.detach().mean().cpu().item()
          )
          self.ips_ratio_means.append(detached_ips_ratio_mean)
          assert self.logger is not None
          self.logger.log_metrics(
              {
                  "Training_loss/per_iteration": detached_loss,
                  "IPS_ratio_mean/per_iteration": detached_ips_ratio_mean,
              },
              step=self.all_batches_processed,
          )
      yield loss
  
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 4 for calling the ReinforceTrainer obj's underlying member method code with example usage: obj.train_step_gen(training_batch, batch_idx) without return types