code: def training_step(
      self, batch, batch_idx: int, optimizer_idx: int = 0
  ):
      assert (optimizer_idx == 0) or (self._num_optimizing_steps > 1)
  
      if self._training_step_generator is None:
          if self._training_batch_type and isinstance(batch, dict):
              batch = self._training_batch_type.from_dict(batch)
          self._training_step_generator = self.train_step_gen(
              batch, batch_idx
          )
  
      ret = next(self._training_step_generator)
  
      if optimizer_idx == self._num_optimizing_steps - 1:
          if not self._verified_steps:
              try:
                  next(self._training_step_generator)
              except StopIteration:
                  self._verified_steps = True
              if not self._verified_steps:
                  raise RuntimeError(
                      "training_step_gen() yields too many times."
                      "The number of yields should match the number of optimizers,"
                      f" in this case {self._num_optimizing_steps}"
                  )
          self._training_step_generator = None
          SummaryWriterContext.increase_global_step()
  
      return ret
  
imports: import inspect
  import logging
  import pytorch_lightning
  import torch
  from reagent.core.tensorboardX import SummaryWriterContext
  from reagent.core.utils import lazy_property
  from typing_extensions import final
label_desc: Use image node_id 10 for calling the ReAgentLightningModule obj's underlying member method code with example usage: obj.training_step(batch, batch_idx, optimizer_idx) and returns: ret