code: def test_spectral_clustering(
      eigen_solver, assign_labels, csr_container
  ):
      S = np.array(
          [
              [1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
              [1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
              [1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
              [0.2, 0.2, 0.2, 1.0, 1.0, 1.0, 1.0],
              [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
              [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
              [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
          ]
      )
  
      for mat in (S, csr_container(S)):
          model = SpectralClustering(
              random_state=0,
              n_clusters=2,
              affinity="precomputed",
              eigen_solver=eigen_solver,
              assign_labels=assign_labels,
          ).fit(mat)
          labels = model.labels_
          if labels[0] == 0:
              labels = 1 - labels
  
          assert adjusted_rand_score(labels, [1, 1, 1, 0, 0, 0, 0]) == 1
  
          model_copy = pickle.loads(pickle.dumps(model))
          assert model_copy.n_clusters == model.n_clusters
          assert model_copy.eigen_solver == model.eigen_solver
          assert_array_equal(model_copy.labels_, model.labels_)
  
imports: import pickle
  import re
  import numpy
  import pytest
  from scipy.linalg import LinAlgError
  from sklearn.cluster import SpectralClustering, spectral_clustering
  from sklearn.cluster._spectral import cluster_qr, discretize
  from sklearn.datasets import make_blobs
  from sklearn.feature_extraction import img_to_graph
  from sklearn.metrics import adjusted_rand_score
  from sklearn.metrics.pairwise import kernel_metrics, rbf_kernel
  from sklearn.neighbors import NearestNeighbors
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_array_equal
  from sklearn.utils.fixes import COO_CONTAINERS, CSR_CONTAINERS
label_desc: Use image node_id 1 for calling a global function with example usage: test_spectral_clustering(eigen_solver, assign_labels, csr_container) without return types