code: def clustering(
      path,
      intervals,
      density_threshold,
      expected_clusters,
      expected_noise,
      ccore_enabled,
      **kwargs,
  ):
      sample = read_sample(path)
      dimension = len(sample[0])
  
      clique_instance = clique(
          sample, intervals, density_threshold, ccore=ccore_enabled
      )
      clique_instance.process()
  
      clusters = clique_instance.get_clusters()
      noise = clique_instance.get_noise()
      cells = clique_instance.get_cells()
  
      assertion.eq(len(cells), pow(intervals, dimension))
  
      obtained_length = len(noise)
      obtained_cluster_length = []
      for cluster in clusters:
          obtained_length += len(cluster)
          obtained_cluster_length.append(len(cluster))
  
      obtained_cluster_length.sort()
  
      assertion.eq(len(sample), obtained_length)
      assertion.eq(expected_noise, len(noise))
  
      if expected_clusters is not None:
          assertion.eq(len(expected_clusters), len(clusters))
          assertion.eq(expected_clusters, obtained_cluster_length)
  
      covered_points = set()
      for cell in cells:
          points = cell.points
          for index_point in points:
              covered_points.add(index_point)
  
      assertion.eq(len(sample), len(covered_points))
      return clique_instance
  
imports: import matplotlib
  from pyclustering.tests.assertion import assertion
  from pyclustering.cluster.clique import clique, clique_visualizer
  from pyclustering.utils import read_sample
label_desc: Use image node_id 1 for calling the clique_test_template obj's underlying member method code with example usage: obj.clustering(path, intervals, density_threshold, expected_clusters, expected_noise, ccore_enabled) and returns: clique_instance