code: def clustering(self, sample_path, answer, amount, ccore, **kwargs):
      attempts = 10
  
      failures = ""
  
      k_max = kwargs.get("k_max", -1)
      random_state = kwargs.get("random_state", None)
      data = read_sample(sample_path)
  
      if isinstance(answer, str):
          reader = answer_reader(answer)
          expected_length_clusters = sorted(
              reader.get_cluster_lengths()
          )
          amount_clusters = len(expected_length_clusters)
  
      elif isinstance(answer, int):
          expected_length_clusters = None
          amount_clusters = answer
  
      else:
          expected_length_clusters = answer
          amount_clusters = len(answer)
  
      for _ in range(attempts):
          gmeans_instance = gmeans(
              data,
              amount,
              ccore,
              k_max=k_max,
              random_state=random_state,
          ).process()
  
          clusters = gmeans_instance.get_clusters()
          centers = gmeans_instance.get_centers()
          wce = gmeans_instance.get_total_wce()
  
          self.assertEqual(amount_clusters, len(centers))
  
          if len(clusters) > 1:
              self.assertGreater(wce, 0.0)
          else:
              self.assertGreaterEqual(wce, 0.0)
  
          if len(clusters) != amount_clusters:
              failures += "1. %d != %d\n" % (
                  len(clusters),
                  amount_clusters,
              )
              continue
  
          unique_indexes = set()
          for cluster in clusters:
              for index_point in cluster:
                  unique_indexes.add(index_point)
  
          if len(data) != len(unique_indexes):
              failures += "2. %d != %d\n" % (
                  len(data),
                  len(unique_indexes),
              )
              continue
  
          if expected_length_clusters is None:
              return
  
          expected_total_length = sum(expected_length_clusters)
          actual_total_length = sum(
              [len(cluster) for cluster in clusters]
          )
          if expected_total_length != actual_total_length:
              failures += "3. %d != %d\n" % (
                  expected_total_length,
                  actual_total_length,
              )
              continue
  
          actual_length_clusters = sorted(
              [len(cluster) for cluster in clusters]
          )
          if expected_length_clusters != actual_length_clusters:
              failures += "4. %s != %s\n" % (
                  str(expected_length_clusters),
                  str(actual_length_clusters),
              )
              continue
  
          return
  
      self.fail(
          "Expected result is not obtained during %d attempts: %s\n"
          % (attempts, failures)
      )
  
imports: import unittest
  import matplotlib
  from pyclustering.samples import answer_reader
  from pyclustering.cluster.gmeans import gmeans
  from pyclustering.utils import read_sample
label_desc: Use image node_id 1 for calling the gmeans_test_template obj's underlying member method code with example usage: obj.clustering(sample_path, answer, amount, ccore) without return types