code: def build_ensemble(cls, **kwargs):
      """Build ML-Ensemble"""
      ens = cls(**kwargs)
  
      use = [
          "ExtraTrees",
          "RandomForest",
          "LogisticRegression-SAG",
          "MLP-adam",
      ]
  
      meta = RandomForestClassifier(
          n_estimators=100, random_state=0, n_jobs=-1
      )
      base_learners = list()
      for est_name, est in ESTIMATORS.items():
          e = clone(est)
          if est_name not in use:
              continue
          elif est_name == "MLP-adam":
              e.verbose = False
          try:
              e.set_params(**{"n_jobs": 1})
          except ValueError:
              pass
  
          base_learners.append((est_name, e))
      ens.add(base_learners, proba=True, shuffle=True, random_state=1)
      ens.add_meta(meta, shuffle=True, random_state=2)
      return ens
  
imports: from __future__ import division, print_function
  import os
  from time import time
  import argparse
  import numpy
  from mlens.utils import safe_print
  from mlens.ensemble import Subsemble
  from sklearn.cluster import MiniBatchKMeans
  from sklearn.datasets import fetch_mldata
  from sklearn.datasets import get_data_home
  from sklearn.ensemble import ExtraTreesClassifier
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.dummy import DummyClassifier
  from sklearn.externals.joblib import Memory
  from sklearn.kernel_approximation import Nystroem
  from sklearn.kernel_approximation import RBFSampler
  from sklearn.metrics import zero_one_loss
  from sklearn.pipeline import make_pipeline
  from sklearn.svm import LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils import check_array
  from sklearn.base import clone
  from sklearn.linear_model import LogisticRegression
  from sklearn.neural_network import MLPClassifier
label_desc: Use image node_id 2 for calling a global function with example usage: build_ensemble(cls) and returns: ens