code: def __init__(
      self,
      optimize_metric="logloss",
      ml_task=BINARY_CLASSIFICATION,
      is_stacked=False,
      max_single_prediction_time=None,
      fairness_metric=None,
      fairness_threshold=None,
      privileged_groups=None,
      underprivileged_groups=None,
  ):
      self.library_version = "0.1"
      self.uid = str(uuid.uuid4())
  
      self.metric = Metric({"name": optimize_metric})
      self.best_loss = (
          self.metric.get_maximum()
      )  # the best loss obtained by ensemble
      self.models_map = None
      self.selected_models = []
      self.train_time = None
      self.total_best_sum = (
          None  # total sum of predictions, the oof of ensemble
      )
      self.target = None
      self.target_columns = None
      self.sample_weight = None
      self._ml_task = ml_task
      self._optimize_metric = optimize_metric
      self._is_stacked = is_stacked
  
      self._additional_metrics = None
      self._threshold = None
      self._name = "Ensemble_Stacked" if is_stacked else "Ensemble"
      self._scores = []
      self.oof_predictions = None
      self._oof_predictions_fname = None
      self._single_prediction_time = (
          None  # prediction time on single sample
      )
      self._max_single_prediction_time = max_single_prediction_time
      self.model_prediction_time = {}
  
      self._fairness_metric = fairness_metric
      self._fairness_threshold = fairness_threshold
      self._privileged_groups = privileged_groups
      self._underprivileged_groups = underprivileged_groups
      self._is_fair = None
      self.sensitive_features = None
  
imports: import copy
  import json
  import logging
  import operator
  import os
  import time
  import uuid
  import numpy
  import pandas
  from supervised.algorithms.algorithm import BaseAlgorithm
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
  from supervised.exceptions import NotTrainedException
  from supervised.model_framework import ModelFramework
  from supervised.utils.additional_metrics import AdditionalMetrics
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import Metric
  from supervised.utils.jsonencoder import MLJSONEncoder
  import matplotlib.pyplot
  from tabulate import tabulate
  from supervised.utils.learning_curves import LearningCurves
label_desc: Use image node_id 1 to create a new Ensemble object  with example: obj = Ensemble(optimize_metric, ml_task, is_stacked, max_single_prediction_time, fairness_metric, fairness_threshold, privileged_groups, underprivileged_groups)