code: def __init__(
      self,
      epochs: int = 5,
      batch_size: int = None,
      optimizer_param={
          "method": "sgd",
          "optimizer_params": {"lr": 0.01, "weight_decay": 0},
      },
      learning_rate_scheduler={
          "method": "constant",
          "scheduler_params": {"factor": 1.0},
      },
      init_param={
          "method": "random",
          "fill_val": 1.0,
          "fit_intercept": True,
      },
      threshold: float = 0.5,
      ovr=False,
      label_num=None,
  ) -> None:
      super().__init__()
      self.df_schema = None
      self.train_set = None
      self.validate_set = None
      self.predict_set = None
  
      # set vars
      self.max_iter = epochs
      self.batch_size = batch_size
      self.optimizer_param = update_params(
          optimizer_param, DEFAULT_OPT_PARAM, name="optimizer"
      )
      self.learning_rate_param = update_params(
          learning_rate_scheduler,
          DEFAULT_LR_SCHEDULER_PARAM,
          name="learning_rate_scheduler",
      )
      self.init_param = update_params(
          init_param, DEFAULT_INIT_PARAM, name="init_param"
      )
      self.threshold = threshold
      self.run_ovr = False
      self.train_feature_num = None
      self.validate_feature_num = None
      self.ovr = ovr
      self.label_num = label_num
  
      if self.ovr:
          if self.label_num is None or self.label_num < 2:
              raise ValueError(
                  "label_num must be greater than 2 when ovr is True, but got {}".format(
                      self.label_num
                  )
              )
  
      # models & optimizer & schduler
      self.model = None
      self.optimizer = None
      self.scheduler = None
      self.optimizer_state_dict = None
      self.trainer = None
  
      # loaded meta
      self.loaded_meta = None
  
      # reg
      self.l1 = 0
      self.l2 = 0
  
      # for testing
      self.local_mode = False
  
      # checkping param
      assert self.max_iter > 0 and isinstance(
          self.max_iter, int
      ), "max_iter must be int greater than 0"
      if self.batch_size is not None:
          assert self.batch_size > 0 and isinstance(
              self.batch_size, int
          ), "batch_size must be int greater than 0 or None"
      assert (
          self.threshold > 0 and self.threshold < 1
      ), "threshold must be float between 0 and 1"
  
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 HomoLRClient object from inherited base classes: HomoModule with example: obj = HomoLRClient(epochs, batch_size, optimizer_param, learning_rate_scheduler, init_param, threshold, ovr, label_num)