code: def generate_multilabel_dataset_with_correlations():
      # Generate a multilabel data set from a multiclass dataset as a way of
      # by representing the integer number of the original class using a binary
      # encoding.
      X, y = make_classification(
          n_samples=1000,
          n_features=100,
          n_classes=16,
          n_informative=10,
          random_state=0,
      )
  
      Y_multi = np.array(
          [[int(yyy) for yyy in format(yy, "#06b")[2:]] for yy in y]
      )
      return X, Y_multi
  
imports: import re
  import numpy
  import pytest
  from joblib import cpu_count
  from sklearn import datasets
  from sklearn.base import ClassifierMixin, clone
  from sklearn.datasets import load_linnerud, make_classification, make_multilabel_classification, make_regression
  from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.ensemble import GradientBoostingRegressor, RandomForestClassifier, StackingRegressor
  from sklearn.exceptions import NotFittedError
  from sklearn.impute import SimpleImputer
  from sklearn.linear_model import Lasso, LinearRegression, LogisticRegression, OrthogonalMatchingPursuit, PassiveAggressiveClassifier, Ridge, SGDClassifier, SGDRegressor
  from sklearn.metrics import jaccard_score, mean_squared_error
  from sklearn.model_selection import GridSearchCV, train_test_split
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.multioutput import ClassifierChain, MultiOutputClassifier, MultiOutputRegressor, RegressorChain
  from sklearn.pipeline import make_pipeline
  from sklearn.svm import LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils import shuffle
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import BSR_CONTAINERS, COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS, DOK_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 21 for calling a global function with example usage: generate_multilabel_dataset_with_correlations() and returns: X,  Y_multi