code: def experiment_execution_one_cluster_dependence(
      layer_first_size, radius, order
  ):
      print(
          "Experiment: map size =",
          layer_first_size[0] * layer_first_size[1],
          "radius =",
          radius,
          "order =",
          order,
      )
      cluster_sizes = [
          10,
          20,
          30,
          40,
          50,
          60,
          70,
          80,
          90,
          100,
          110,
          120,
          130,
          140,
          150,
      ]
  
      for cluster_size in cluster_sizes:
          # generate data sets
          dataset = []
          dataset += [[random(), random()] for _ in range(cluster_size)]
  
          general_value = 0.0
          amount_attempt = 5
          for _ in range(amount_attempt):
              network = syncsom(
                  dataset,
                  layer_first_size[0],
                  layer_first_size[1],
                  radius,
              )
              (ticks, (dyn_time, dyn_phase)) = timedcall(
                  network.process, False, order
              )
              general_value += ticks
  
          print(
              "Sample: ",
              cluster_size,
              "\t\tExecution time: ",
              general_value / float(amount_attempt),
          )
  
      print("\n")
  
imports: from random import random
  from pyclustering.cluster import cluster_visualizer
  from pyclustering.cluster.syncsom import syncsom
  from pyclustering.samples.definitions import SIMPLE_SAMPLES
  from pyclustering.samples.definitions import FCPS_SAMPLES
  from pyclustering.utils import read_sample, draw_dynamics
  from pyclustering.utils import timedcall
label_desc: Use image node_id 17 for calling a global function with example usage: experiment_execution_one_cluster_dependence(layer_first_size, radius, order) without return types