code: def test_spectral_clustering(Xl_blobs_easy):
      X, y = Xl_blobs_easy
      X = (X - X.mean(0)) / X.std(0)
      model = SpectralClustering(
          random_state=0, n_clusters=3, n_components=5, gamma=None
      ).fit(X)
      labels = model.labels_.compute()
      y = y.compute()
  
      idx = [(y == i).argmax() for i in range(3)]
      grouped_idx = [np.where(y == y[idx[i]])[0] for i in range(3)]
  
      for indices in grouped_idx:
          assert len(set(labels[indices])) == 1
  
file_path: dask-ml/tests/test_spectral_clustering.py
imports: from functools import partial
  import numpy
  import pytest
  import sklearn.cluster
  from dask_ml import metrics
  from dask_ml.cluster import SpectralClustering
  from dask_ml.datasets import make_blobs
label_desc: Use image node_id 7 for calling a global function with example usage: test_spectral_clustering(Xl_blobs_easy) without return types