code: def from_model(self, model: dict):
      if "param" not in model:
          raise ('key "data" is not found in the input model dict')
  
      model_param = model["param"]
      if "model" not in model_param:
          raise ValueError(
              "param dict must have key 'model' that contains the model parameter and structure info"
          )
      self.model = HomoLRModel.from_dict(model_param["model"])
      if self.ovr:
          assert len(self.model.models) == self.label_num, ""
      self.model.l1 = self.l1
      if hasattr(model_param, "optimizer"):
          self.optimizer_state_dict = recover_torch_bytes(
              bytes(model_param["optimizer"], "utf-8")
          )
      self.loaded_meta = model["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 8 for calling the HomoLRClient obj's underlying member method code with example usage: obj.from_model(model) without return types