code: def predict(self, X, verbose=False):
      if self.model_name[:3] == "XGB":
          ones = [[1] for x in range(X.shape[0])]
          if scipy.sparse.issparse(X):
              # Trying to force XGBoost to play nice with sparse matrices
              X = scipy.sparse.hstack((X, ones))
          else:
              X = np.column_stack([X, ones])
  
      X_predict = X
  
      if (
          self.model_name[:16] == "GradientBoosting"
          or self.model_name[:12] == "DeepLearning"
          or self.model_name
          in [
              "BayesianRidge",
              "LassoLars",
              "OrthogonalMatchingPursuit",
              "ARDRegression",
          ]
      ):
          if scipy.sparse.issparse(X):
              X_predict = X.todense()
          elif isinstance(X, pd.DataFrame):
              X_predict = X.values
      elif self.model_name[:8] == "CatBoost":
          if scipy.sparse.issparse(X):
              X_predict = X.toarray()
          elif isinstance(X, pd.DataFrame):
              X_predict = X.values
      else:
          X_predict = X
  
      if self.model_name[:4] == "LGBM":
          best_iteration = 0
          try:
              best_iteration = self.model.best_iteration_
          except AttributeError:
              best_iteration = self.model.best_iteration
          if best_iteration is None:
              best_iteration = 0
          predictions = self.model.predict(
              X, num_iteration=best_iteration
          )
      else:
          predictions = self.model.predict(X_predict)
      # Handle cases of getting a prediction for a single item.
      # It makes a cleaner interface just to get just the single prediction back, rather than a list with the prediction hidden inside.
  
      if isinstance(predictions, np.ndarray):
          predictions = predictions.tolist()
          if (
              isinstance(predictions, float)
              or isinstance(predictions, int)
              or isinstance(predictions, str)
          ):
              return predictions
  
      if isinstance(predictions[0], list) and len(predictions[0]) == 1:
          predictions = [row[0] for row in predictions]
  
      if len(predictions) == 1:
          return predictions[0]
      else:
          return predictions
  
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 8 for calling the FinalModelATC obj's underlying member method code with example usage: obj.predict(X, verbose) and returns: predictions, predictions, predictions