code: def clustering(path, amount, threshold, expected, ccore, **kwargs):
      metric = kwargs.get(
          "metric", distance_metric(type_metric.EUCLIDEAN)
      )
  
      sample = read_sample(path)
  
      mbsas_instance = mbsas(
          sample, amount, threshold, ccore=ccore, metric=metric
      )
      mbsas_instance.process()
  
      clusters = mbsas_instance.get_clusters()
      representatives = mbsas_instance.get_representatives()
  
      obtained_length = 0
      obtained_cluster_length = []
      for cluster in clusters:
          obtained_length += len(cluster)
          obtained_cluster_length.append(len(cluster))
  
      assertion.eq(len(sample), obtained_length)
      assertion.eq(len(expected), len(clusters))
      assertion.eq(len(expected), len(representatives))
      assertion.ge(amount, len(clusters))
  
      dimension = len(sample[0])
      for rep in representatives:
          assertion.eq(dimension, len(rep))
  
      expected.sort()
      obtained_cluster_length.sort()
  
      assertion.eq(expected, obtained_cluster_length)
  
imports: import matplotlib
  from pyclustering.tests.assertion import assertion
  from pyclustering.cluster.mbsas import mbsas
  from pyclustering.utils import read_sample
  from pyclustering.utils.metric import type_metric, distance_metric
label_desc: Use image node_id 1 for calling the mbsas_test_template obj's underlying member method code with example usage: obj.clustering(path, amount, threshold, expected, ccore) without return types