code: def experiment_execution_time(ccore=False):
      template_clustering(
          [[3.7, 5.5], [6.7, 7.5]], SIMPLE_SAMPLES.SAMPLE_SIMPLE1, ccore
      )
      template_clustering(
          [[3.5, 4.8], [6.9, 7], [7.5, 0.5]],
          SIMPLE_SAMPLES.SAMPLE_SIMPLE2,
          ccore,
      )
      template_clustering(
          [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]],
          SIMPLE_SAMPLES.SAMPLE_SIMPLE3,
          ccore,
      )
      template_clustering(
          [[1.5, 0.0], [1.5, 2.0], [1.5, 4.0], [1.5, 6.0], [1.5, 8.0]],
          SIMPLE_SAMPLES.SAMPLE_SIMPLE4,
          ccore,
      )
      template_clustering(
          [[0.0, 1.0], [0.0, 0.0], [1.0, 1.0], [1.0, 0.0]],
          SIMPLE_SAMPLES.SAMPLE_SIMPLE5,
          ccore,
      )
      template_clustering(
          [[1.0, 4.5], [3.1, 2.7]],
          SIMPLE_SAMPLES.SAMPLE_ELONGATE,
          ccore,
      )
      template_clustering(
          [[1.0, 3.5], [2.0, 0.5], [3.0, 3.0]],
          FCPS_SAMPLES.SAMPLE_LSUN,
          ccore,
      )
      template_clustering(
          [
              [0.2, 0.2],
              [0.0, -2.0],
              [3.0, -3.0],
              [3.0, 3.0],
              [-3.0, 3.0],
              [-3.0, -3.0],
          ],
          FCPS_SAMPLES.SAMPLE_TARGET,
          ccore,
      )
      template_clustering(
          [[0.8, 0.2], [3.0, 0.0]],
          FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS,
          ccore,
      )
      template_clustering(
          [[-1.5, 1.5], [1.5, 1.5]], FCPS_SAMPLES.SAMPLE_WING_NUT, ccore
      )
      template_clustering(
          [[1.1, -1.7, 1.1], [-1.4, 2.5, -1.2]],
          FCPS_SAMPLES.SAMPLE_CHAINLINK,
          ccore,
      )
      template_clustering(
          [
              [0.0, 0.0, 0.0],
              [3.0, 0.0, 0.0],
              [-2.0, 0.0, 0.0],
              [0.0, 3.0, 0.0],
              [0.0, -3.0, 0.0],
              [0.0, 0.0, 2.5],
              [0.0, 0.0, -2.5],
          ],
          FCPS_SAMPLES.SAMPLE_HEPTA,
          ccore,
      )
      template_clustering(
          [[1, 0, 0], [0, 1, 0], [0, -1, 0], [-1, 0, 0]],
          FCPS_SAMPLES.SAMPLE_TETRA,
          ccore,
      )
      template_clustering(
          [[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]],
          FCPS_SAMPLES.SAMPLE_ATOM,
          ccore,
      )
      template_clustering(
          [[0.5, 0.5], [2.3, 2.9]], FCPS_SAMPLES.SAMPLE_ENGY_TIME, ccore
      )
  
imports: import random
  from pyclustering.samples.definitions import SIMPLE_SAMPLES, FCPS_SAMPLES, FAMOUS_SAMPLES
  from pyclustering.cluster import cluster_visualizer_multidim
  from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer
  from pyclustering.cluster.kmeans import kmeans, kmeans_observer, kmeans_visualizer
  from pyclustering.utils import read_sample
  from pyclustering.utils import timedcall
  from pyclustering.utils.metric import distance_metric, type_metric
label_desc: Use image node_id 19 for calling a global function with example usage: experiment_execution_time(ccore) without return types