code: def correct_ksearch(
      sample_path, answer_path, kmin, kmax, algorithm, ccore_flag
  ):
      attempts = 15
      testing_result = False
  
      sample = read_sample(sample_path)
      clusters = answer_reader(answer_path).get_clusters()
  
      for _ in range(attempts):
          ksearch_instance = silhouette_ksearch(
              sample, kmin, kmax, algorithm=algorithm, ccore=ccore_flag
          ).process()
          amount = ksearch_instance.get_amount()
          score = ksearch_instance.get_score()
          scores = ksearch_instance.get_scores()
  
          assertion.le(-1.0, score)
          assertion.ge(1.0, score)
          assertion.eq(kmax - kmin, len(scores))
  
          upper_limit = len(clusters) + 1
          lower_limit = len(clusters) - 1
          if lower_limit < 1:
              lower_limit = 1
  
          if (amount > upper_limit) or (amount < lower_limit):
              continue
  
          testing_result = True
          break
  
      assertion.true(testing_result)
  
file_path: pyclustering/pyclustering/cluster/tests/silhouette_templates.py
imports: import math
  from pyclustering.cluster.silhouette import silhouette, silhouette_ksearch
  from pyclustering.samples import answer_reader
  from pyclustering.samples.definitions import SIMPLE_SAMPLES
  from pyclustering.tests.assertion import assertion
  from pyclustering.utils import read_sample, calculate_distance_matrix, distance_metric, type_metric
label_desc: Use image node_id 3 for calling the silhouette_test_template obj's underlying member method code with example usage: obj.correct_ksearch(sample_path, answer_path, kmin, kmax, algorithm, ccore_flag) without return types