code: def __init__(self, feature_num, label_num=2, l1=0, bias=True) -> None:
      super().__init__()
      assert feature_num >= 2 and isinstance(
          feature_num, int
      ), "feature_num must be int greater than 2"
      assert label_num >= 1 and isinstance(
          label_num, int
      ), "label_num must be int greater than 1"
      self.models = t.nn.ModuleList()
  
      if 2 >= label_num > 0:
          self.models.append(t.nn.Linear(feature_num, 1, bias=bias))
      else:
          # OVR Setting
          for i in range(label_num):
              self.models.append(t.nn.Linear(feature_num, 1, bias=bias))
      self.sigmoid = t.nn.Sigmoid()
      self.softmax = t.nn.Softmax(dim=1)
      self.l1 = l1
  
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 1 to create a new HomoLRModel object from inherited base classes: t.nn with example: obj = HomoLRModel(feature_num, label_num, l1, bias)