code: def __init__(
      self,
      trainers: List[ReAgentLightningModule],
      epochs: List[int],
      assign_reporter_function=None,
      flush_reporter_function=None,
      automatic_optimization: bool = True,
  ) -> None:
      super().__init__(automatic_optimization=automatic_optimization)
      # NB: wrapping in a ModuleList so the state can be saved
      self._trainers = nn.ModuleList(trainers)
      self._assign_reporter_function = assign_reporter_function
      self._flush_reporter_function = (
          functools.partial(flush_reporter_function, self)
          if flush_reporter_function
          else self._flush_reporter
      )
      self._in_testing_loop = False
  
      # Cumulative sum of number of epochs up to the index (of trainers)
      self._trainer_acc_epochs = [0] + epochs
      for i in range(1, len(epochs) + 1):
          self._trainer_acc_epochs[i] += self._trainer_acc_epochs[i - 1]
  
      # Num of epochs for each trainer. Used to check if the sum of them
      # equals to num_epochs used in pytorch-lightning trainer
      self.trainer_epoch_mapping = OrderedDict()
      for t, e in zip(trainers, epochs):
          trainer_name = type(t).__name__
          self.trainer_epoch_mapping[trainer_name] = e
  
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 1 to create a new MultiStageTrainer object from inherited base classes: ReAgentLightningModule with example: obj = MultiStageTrainer(trainers, epochs, assign_reporter_function, flush_reporter_function, automatic_optimization)