code: def clustering_random_points(amount_points, amount_centers, ccore):
      sample = [
          [random.random(), random.random()]
          for _ in range(amount_points)
      ]
      centers = [
          [random.random(), random.random()]
          for _ in range(amount_centers)
      ]
  
      kmeans_instance = kmeans(sample, centers, 0.0001, ccore)
      (ticks, _) = timedcall(kmeans_instance.process)
  
      print(
          "Execution time (" + str(amount_points) + " 2D-points):",
          ticks,
      )
  
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 20 for calling a global function with example usage: clustering_random_points(amount_points, amount_centers, ccore) without return types