code: def NME_SpectralClustering_sklearn(A, num_clusters, pbest):
      Ap = Threshold(A, pbest)
      Ap = (Ap + np.transpose(Ap)) / 2
      model = SpectralClustering(
          n_clusters=num_clusters,
          affinity="precomputed",
          random_state=0,
      )
      labels = model.fit_predict(Ap)
      return labels
  
file_path: espnet/egs/libri_css/asr1/diarization/spec_clust.py
imports: import argparse
  import numpy
  import scipy
  from kaldiio import ReadHelper
  from sklearn.cluster import SpectralClustering
label_desc: Use image node_id 11 for calling a global function with example usage: NME_SpectralClustering_sklearn(A, num_clusters, pbest) and returns: labels