code: def __init__(
      self,
      model,
      model_name=None,
      ml_for_analytics=False,
      type_of_estimator="classifier",
      output_column=None,
      name=None,
      _scorer=None,
      training_features=None,
      column_descriptions=None,
      feature_learning=False,
      uncertainty_model=None,
      uc_results=None,
      training_prediction_intervals=False,
      min_step_improvement=0.0001,
      interval_predictors=None,
      keep_cat_features=False,
      is_hp_search=None,
      X_test=None,
      y_test=None,
  ):
      self.model = model
      self.model_name = model_name
      self.ml_for_analytics = ml_for_analytics
      self.type_of_estimator = type_of_estimator
      self.name = name
      self.training_features = training_features
      self.column_descriptions = column_descriptions
      self.feature_learning = feature_learning
      self.uncertainty_model = uncertainty_model
      self.uc_results = uc_results
      self.training_prediction_intervals = training_prediction_intervals
      self.min_step_improvement = min_step_improvement
      self.interval_predictors = interval_predictors
      self.is_hp_search = is_hp_search
      self.keep_cat_features = keep_cat_features
      self.X_test = X_test
      self.y_test = y_test
      self.memory_optimized = False
  
      if self.type_of_estimator == "classifier":
          self._scorer = _scorer
      else:
          self._scorer = _scorer
  
imports: from collections import Iterable
  from copy import deepcopy
  import datetime
  import gc
  import os
  import random
  import warnings
  import numpy
  import pandas
  import scipy
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.model_selection import train_test_split
  from sklearn import __version__
  from auto_ml import utils_models
  from auto_ml.utils_models import get_name_from_model
label_desc: Use image node_id 1 to create a new FinalModelATC object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = FinalModelATC(model, model_name, ml_for_analytics, type_of_estimator, output_column, name, _scorer, training_features, column_descriptions, feature_learning, uncertainty_model, uc_results, training_prediction_intervals, min_step_improvement, interval_predictors, keep_cat_features, is_hp_search, X_test, y_test)