docstr: return {
              "w": self.w.tolist(),
              "optimizer": self.optimizer.state_dict(),
              "lr_scheduler": self.lr_scheduler.state_dict(),
              "end_epoch": self.end_epoch,
              "is_converged": self.is_converged
  }
docstr_tok: ["return","{","``","w","''",":","self.w.tolist","(",")",",","``","optimizer","''",":","self.optimizer.state_dict","(",")",",","``","lr_scheduler","''",":","self.lr_scheduler.state_dict","(",")",",","``","end_epoch","''",":","self.end_epoch",",","``","is_converged","''",":","self.is_converged","}"]
code: def get_model(self):
      """return {
                  "w": self.w.tolist(),
                  "optimizer": self.optimizer.state_dict(),
                  "lr_scheduler": self.lr_scheduler.state_dict(),
                  "end_epoch": self.end_epoch,
                  "is_converged": self.is_converged
      }"""
      param = serialize_param(self.w, False)
      return {
          "param": param,
          "optimizer": self.optimizer.state_dict(),
          "lr_scheduler": self.lr_scheduler.state_dict(),
          "end_epoch": self.end_epoch,
          "is_converged": self.is_converged,
          "header": self.header,
      }
  
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._model_param import deserialize_param, initialize_param, serialize_param
  from fate.ml.utils._optimizer import LRScheduler, Optimizer
label_desc: Use image node_id 6 for calling the CoordinatedLinREstimatorHost obj's underlying member method code with example usage: obj.get_model() and returns: dict