code: def optimizer_step(
      self,
      epoch: int,
      batch_idx: int,
      optimizer,
      optimizer_idx: int,
      optimizer_closure,
      on_tpu: int = False,
      using_native_amp: int = False,
      using_lbfgs: int = False,
  ) -> None:
      assert epoch == self.trainer.current_epoch
      epoch_trainer_idx = self._get_trainer_idx_from_epoch()
      (
          optimizer_trainer_idx,
          offset,
      ) = self._optimizer_step_to_trainer_idx[optimizer_idx]
      if epoch_trainer_idx == optimizer_trainer_idx:
          # FIXME: epoch argument is not really correct
          # Trainer will see the total epochs, including those epochs they
          # are inactive.
          self._trainers[epoch_trainer_idx].optimizer_step(
              epoch,
              batch_idx,
              optimizer,
              optimizer_idx - offset,
              optimizer_closure,
              on_tpu=on_tpu,
              using_native_amp=using_native_amp,
              using_lbfgs=using_lbfgs,
          )
      # FIXME: this is a hack around https://github.com/PyTorchLightning/pytorch-lightning/pull/9360
      # which assumes that the optimizer closure will be consumed per training step invocation
      # however this is not true in the multi-stage trainer as the training step is called for *all* of the
      # optimizers configured under `trainers` even though only one lightning module is active at a given time
      # A more robust solution would be to use manual optimization, where the lightning trainer does no inspection
      # of the optimization closure for further processing
      elif hasattr(optimizer_closure, "_result"):
          optimizer_closure._result = ClosureResult(closure_loss=None)
  
imports: import bisect
  import functools
  import itertools
  from collections import OrderedDict
  from typing import Dict, List, Tuple
  import torch.nn
  from pytorch_lightning.loops.optimization.optimizer_loop import ClosureResult
  from reagent.core.utils import lazy_property
  from .reagent_lightning_module import ReAgentLightningModule
label_desc: Use image node_id 18 for calling the MultiStageTrainer obj's underlying member method code with example usage: obj.optimizer_step(epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs) without return types