code: def get_model(self):
      param = serialize_param(
          self.w, self.init_param.get("fit_intercept")
      )
      return {
          # "w": w,
          # "intercept": intercept,
          "param": param,
          "optimizer": self.optimizer.state_dict(),
          "lr_scheduler": self.lr_scheduler.state_dict(),
          "end_epoch": self.end_epoch,
          "is_converged": self.is_converged,
          "fit_intercept": self.init_param.get("fit_intercept"),
          "header": self.header,
      }
  
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 CoordinatedLREstimatorGuest obj's underlying member method code with example usage: obj.get_model() and returns: dict