code: def __init__(
      self,
      bottom_model_conf: Optional[Dict] = None,
      top_model_conf: Optional[Dict] = None,
      agglayer_arg_conf: Optional[Dict] = None,
      top_model_strategy_arg_conf: Optional[Dict] = None,
      dataset_conf: Optional[Dict] = None,
      optimizer_conf: Optional[Dict] = None,
      training_args_conf: Optional[Dict] = None,
      loss_conf: Optional[Dict] = None,
      data_collator_conf: Optional[Dict] = None,
      tokenizer_conf: Optional[Dict] = None,
      task_type: Literal[
          "binary", "multi", "regression", "others"
      ] = "binary",
      threshold: float = 0.5,
  ):
      super().__init__()
      self.bottom_model_conf = bottom_model_conf
      self.top_model_conf = top_model_conf
      self.agglayer_arg_conf = agglayer_arg_conf
      self.top_model_strategy_arg_conf = top_model_strategy_arg_conf
      self.dataset_conf = dataset_conf
      self.optimizer_conf = optimizer_conf
      self.training_args_conf = training_args_conf
      self.loss_conf = loss_conf
      self.data_collator_conf = data_collator_conf
      self.tokenizer_conf = tokenizer_conf
      self.task_type = task_type
      self.threshold = threshold
  
      # setup var
      self.trainer = None
      self.model = None
  
imports: import logging
  from fate.components.components.nn.loader import Loader
  from typing import Optional, Union, Dict, Literal
  from fate.arch.dataframe import DataFrame
  from fate.components.components.nn.nn_runner import NNRunner, loader_load_from_conf, load_model_dict_from_path, dir_warning, run_dataset_func
  from transformers.trainer_utils import get_last_checkpoint
  from fate.ml.nn.hetero.hetero_nn import HeteroNNTrainerGuest, HeteroNNTrainerHost, TrainingArguments
  from fate.ml.nn.model_zoo.hetero_nn_model import HeteroNNModelGuest, HeteroNNModelHost
  from fate.components.components.utils import consts
  from fate.ml.nn.dataset.table import TableDataset, Dataset
  from fate.ml.nn.model_zoo.hetero_nn_model import FedPassArgument, StdAggLayerArgument, parse_agglayer_conf, TopModelStrategyArguments
label_desc: Use image node_id 1 to create a new DefaultRunner object from inherited base classes: NNRunner with example: obj = DefaultRunner(bottom_model_conf, top_model_conf, agglayer_arg_conf, top_model_strategy_arg_conf, dataset_conf, optimizer_conf, training_args_conf, loss_conf, data_collator_conf, tokenizer_conf, task_type, threshold)