code: def get_model(self):
      all_estimator = {}
      if self.ovr:
          for label, estimator in self.estimator.items():
              all_estimator[label] = estimator.get_model()
      else:
          all_estimator = self.estimator.get_model()
      return {
          "data": {"estimator": all_estimator},
          "meta": {
              "epochs": self.epochs,
              "ovr": self.ovr,
              "early_stop": self.early_stop,
              "tol": self.tol,
              "batch_size": self.batch_size,
              "learning_rate_param": self.learning_rate_param,
              "optimizer_param": self.optimizer_param,
          },
      }
  
imports: import logging
  import torch
  from fate.arch import Context
  from fate.arch.dataframe import DataLoader
  from fate.ml.abc.module import HeteroModule
  from fate.ml.utils._convergence import converge_func_factory
  from fate.ml.utils._optimizer import LRScheduler, Optimizer, separate
label_desc: Use image node_id 5 for calling the CoordinatedLRModuleArbiter obj's underlying member method code with example usage: obj.get_model() and returns: dict