code: def predict_intervals(self, X, return_type=None):
      if self.interval_predictors is None:
          print(
              "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
          )
          print("This model was not trained to predict intervals")
          print(
              "Please follow the documentation to tell this model at training time to learn how to predict intervals"
          )
          print(
              "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
          )
          raise ValueError(
              "This model was not trained to predict intervals"
          )
  
      base_prediction = self.predict(X)
  
      result = {"prediction": base_prediction}
      for tup in self.interval_predictors:
          predictor_name = tup[0]
          predictor = tup[1]
          result[predictor_name] = predictor.predict(X)
  
      if scipy.sparse.issparse(X):
          len_input = X.shape[0]
      else:
          len_input = len(X)
  
      if (
          len_input == 1 and return_type is None
      ) or return_type == "dict":
          return result
  
      elif (
          (len_input > 1 and return_type is None)
          or return_type == "df"
          or return_type == "dataframe"
      ):
          return pd.DataFrame(result)
  
      elif return_type == "list":
          if len_input == 1:
              list_result = [base_prediction]
              for tup in self.interval_predictors:
                  list_result.append(result[tup[0]])
          else:
              list_result = []
              for idx in range(len_input):
                  row_result = [base_prediction[idx]]
                  for tup in self.interval_predictors:
                      row_result.append(result[tup[0]][idx])
                  list_result.append(row_result)
  
          return list_result
  
      else:
          print(
              'Please pass in a return_type value of one of the following: ["dict", "dataframe", "df", "list"]'
          )
          raise (
              ValueError(
                  'Please pass in a return_type value of one of the following: ["dict", "dataframe", "df", "list"]'
              )
          )
  
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 9 for calling the FinalModelATC obj's underlying member method code with example usage: obj.predict_intervals(X, return_type) and returns: result, pd, list_result