code: def get_model(self) -> ModelIO:
      param = {}
      if self.model is not None:
          param["model"] = self.model.to_dict()
      if self.optimizer is not None:
          param["optimizer"] = str(
              get_torch_bytes(self.optimizer.state_dict())
          )
  
      meta = {
          "batch_size": self.batch_size,
          "max_iter": self.max_iter,
          "threshold": self.threshold,
          "optimizer_param": self.optimizer_param,
          "learning_rate_param": self.learning_rate_param,
          "init_param": self.init_param,
          "ovr": self.ovr,
          "label_num": self.label_num,
      }
  
      return {"param": param, "meta": meta}
  
imports: import torch.nn
  from fate.arch.dataframe import DataFrame
  from fate.ml.abc.module import HomoModule
  from fate.ml.utils.model_io import ModelIO
  from fate.arch import Context
  import logging
  import torch
  from fate.ml.nn.homo.fedavg import FedAVGClient, TrainingArguments, FedAVGArguments
  from transformers import default_data_collator
  import functools
  import tempfile
  from fate.ml.utils.predict_tools import array_to_predict_df
  from fate.ml.utils.predict_tools import MULTI, BINARY
  from fate.ml.nn.dataset.table import TableDataset
  from fate.ml.utils._optimizer import optimizer_factory, lr_scheduler_factory
label_desc: Use image node_id 7 for calling the HomoLRClient obj's underlying member method code with example usage: obj.get_model() and returns: dict