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,
              "batch_size": self.batch_size,
              "learning_rate_param": self.learning_rate_param,
              "init_param": self.init_param,
              "optimizer_param": self.optimizer_param,
              "labels": self.labels,
              "ovr": self.ovr,
              "threshold": self.threshold,
              "floating_point_precision": self.floating_point_precision,
          },
      }
  
imports: import logging
  import numpy
  import torch
  from fate.arch import Context, dataframe
  from fate.ml.abc.module import HeteroModule
  from fate.ml.utils import predict_tools
  from fate.ml.utils._model_param import check_overflow, deserialize_param, initialize_param, serialize_param
  from fate.ml.utils._optimizer import LRScheduler, Optimizer
label_desc: Use image node_id 6 for calling the CoordinatedLRModuleGuest obj's underlying member method code with example usage: obj.get_model() and returns: dict