code: def setUp(self):
      self.X, self.y = load_breast_cancer(return_X_y=True)
  
      self.n_clusters = 5
      self.n_estimators = 3
  
      # Initialize a set of estimators
      estimators = [
          KMeans(n_clusters=self.n_clusters),
          MiniBatchKMeans(n_clusters=self.n_clusters),
          AgglomerativeClustering(n_clusters=self.n_clusters),
      ]
  
      # Clusterer Ensemble without initializing a new Class
      self.original_labels = np.zeros(
          [self.X.shape[0], self.n_estimators]
      )
  
      for i, estimator in enumerate(estimators):
          estimator.fit(self.X)
          self.original_labels[:, i] = estimator.labels_
  
imports: import os
  import sys
  import unittest
  import numpy
  from sklearn.cluster import KMeans
  from sklearn.cluster import MiniBatchKMeans
  from sklearn.cluster import AgglomerativeClustering
  from sklearn.datasets import load_breast_cancer
  from numpy.testing import assert_equal
  from combo.models.cluster_comb import ClustererEnsemble
  from combo.models.cluster_comb import clusterer_ensemble_scores
label_desc: Use image node_id 1 for calling the TestClustererEnsembleScores obj's underlying member method code with example usage: obj.setUp() without return types