code: def run(
      self,
      data_loaders: List["DataLoader"],
      workflow: List[Tuple[str, int]],
      max_epochs: Optional[int] = None,
      **kwargs,
  ) -> List[Dict]:
      invalidInputError(
          isinstance(data_loaders, list),
          "data_loaders should be a list",
      )
      invalidInputError(
          is_list_of(workflow, tuple),
          "workflow shoud be a list of tuple",
      )
      invalidInputError(
          len(data_loaders) == len(workflow),
          "data_loaders and workflow should have the same length",
      )
  
      if max_epochs is not None:
          warnings.warn(
              "setting max_epochs in run is deprecated, "
              "please set max_epochs in runner_config",
              DeprecationWarning,
          )
          self._max_epochs = max_epochs
  
      invalidInputError(
          self._max_epochs is not None,
          "max_epochs must be specified during instantiation",
      )
  
      for i, flow in enumerate(workflow):
          mode, epochs = flow
          if mode == "train":
              self._max_iters = self._max_epochs * len(data_loaders[i])
              break
  
      work_dir = self.work_dir if self.work_dir is not None else "NONE"
      self.logger.info(
          "Start running, host: %s, work_dir: %s",
          get_host_info(),
          work_dir,
      )
      self.logger.info(
          "Hooks will be executed in the following order:\n%s",
          self.get_hook_info(),
      )
      self.logger.info(
          "workflow: %s, max: %d epochs", workflow, self._max_epochs
      )
      stats_list = list()
  
      self.call_hook("before_run")
  
      while self.epoch < self._max_epochs:
          for i, flow in enumerate(workflow):
              mode, epochs = flow
              if isinstance(mode, str):  # self.train()
                  if not hasattr(self, mode):
                      invalidInputError(
                          False,
                          f'runner has no method named "{mode}" to run an epoch',
                      )
                  epoch_runner = getattr(self, mode)
              else:
                  invalidInputError(
                      False,
                      "mode in workflow must be a str, "
                      "but got {}".format(type(mode)),
                  )
  
              for _ in range(epochs):
                  if mode == "train" and self.epoch >= self._max_epochs:
                      break
                  train_stats = epoch_runner(data_loaders[i], **kwargs)
                  stats = dict(epoch=self.epoch, **train_stats)
                  stats_list.append(stats)
  
      time.sleep(1)  # wait for some hooks like loggers to finish
      self.call_hook("after_run")
      return stats_list
  
imports: import os
  import re
  import tempfile
  import time
  import mmcv
  import numpy
  import torch
  import warnings
  from collections import OrderedDict
  from torch.optim import Optimizer
  from mmcv.runner import EpochBasedRunner, DistEvalHook
  from mmcv.runner.utils import get_host_info
  from mmcv.parallel import is_module_wrapper
  from mmcv.fileio.file_client import BaseStorageBackend
  from mmcv.utils.misc import is_list_of
  from mmcv.runner.checkpoint import CheckpointLoader
  from bigdl.orca.learn.pytorch.utils import get_batchsize
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca.learn.pytorch.core.base_runner import BaseRunner
  from .distributed import MMDistributedDataParallel
  from typing import TYPE_CHECKING, Union, Any, Dict, List, Optional, Tuple, Callable
label_desc: Use image node_id 5 for calling the MMCVRayEpochRunner obj's underlying member method code with example usage: obj.run(data_loaders, workflow, max_epochs) and returns: stats_list