code: def predict(self, X):
      logger.debug("ModelFramework.predict")
  
      if self.learners is None or len(self.learners) == 0:
          raise Exception("Learnes are not initialized")
      # run predict on all learners and return the average
      y_predicted = None  # np.zeros((X.shape[0],))
      for ind, learner in enumerate(self.learners):
          # preprocessing goes here
          X_data, _, _ = self.preprocessings[ind].transform(
              X.copy(), None
          )
          y_p = learner.predict(X_data)
          y_p = self.preprocessings[ind].inverse_scale_target(y_p)
  
          y_predicted = (
              y_p if y_predicted is None else y_predicted + y_p
          )
  
      y_predicted_average = y_predicted / float(len(self.learners))
  
      y_predicted_final = self.preprocessings[0].prepare_target_labels(
          y_predicted_average
      )
  
      return y_predicted_final
  
imports: import copy
  import gc
  import json
  import logging
  import os
  import sys
  import time
  import uuid
  import zipfile
  import numpy
  import pandas
  from supervised.algorithms.factory import AlgorithmFactory
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.callbacks.callback_list import CallbackList
  from supervised.exceptions import AutoMLException
  from supervised.preprocessing.exclude_missing_target import ExcludeRowsMissingTarget
  from supervised.preprocessing.preprocessing import Preprocessing
  from supervised.utils.additional_metrics import AdditionalMetrics
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import Metric
  from supervised.validation.validation_step import ValidationStep
  from supervised.utils.jsonencoder import MLJSONEncoder
  import joblib
  import optuna
  from supervised.tuner.optuna.tuner import OptunaTuner
  from supervised.utils.config import mem
  from supervised.utils.learning_curves import LearningCurves
label_desc: Use image node_id 15 for calling the ModelFramework obj's underlying member method code with example usage: obj.predict(X) and returns: y_predicted_final