code: def from_dict(cls, model_dict):
      model = cls(model_dict["feature_num"], model_dict["label_num"])
      model_state_dict = {
          k: t.tensor(v) for k, v in model_dict["state_dict"].items()
      }  # convert list back to tensor
      model.load_state_dict(model_state_dict)
      return model
  
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 4 for calling the HomoLRModel obj's underlying member method code with example usage: obj.from_dict(cls, model_dict) and returns: model