code: def __init__(self, model, preprocessor, sub_task, params):
      self.model = model
      self.preprocessor = preprocessor
      self.sub_task = sub_task
      self.params = params
      self.use_predict_proba = True
  
      _model = self._get_model()
      if self.preprocessor is not None:
          self.pipeline = pipeline.Pipeline(
              [("preprocessor", self.preprocessor), ("model", _model)]
          )
      else:
          self.pipeline = pipeline.Pipeline([("model", _model)])
  
imports: import copy
  from collections import defaultdict
  from dataclasses import dataclass
  from functools import partial
  from typing import List, Optional
  import numpy
  from sklearn import ensemble, impute, linear_model
  from sklearn import metrics
  from sklearn import naive_bayes, neighbors, pipeline, preprocessing, svm, tree
  from xgboost import XGBClassifier, XGBRegressor
label_desc: Use image node_id 1 to create a new TabularModel object  with example: obj = TabularModel(model, preprocessor, sub_task, params)