How to use the pyclustering.samples.definitions.SIMPLE_SAMPLES.SAMPLE_SIMPLE5 function in pyclustering

def testLengthProcessedByCore(self):
        self.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 0.7, 0, 10, True);  
        self.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 0.5, 0, 10, True); 
        self.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, 0.3, 0, 15, True);
        self.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, 1, 0, 15, True);
        self.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, 0.1, 0, 20, True);
        self.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, 5, 0, 20, True);
        self.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE4, 0.1, 0, 10, True);
        self.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE4, 10, 65, 75, True);
        self.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 0.1, 0, 10, True);
        self.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 0.3, 0, 10, True);
        self.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 0.6, 0, 10, True);

def testAutostopFourNeuronsSquareClusters(self):
        self.templateTestAwardNeurons(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 2, 2, 100, [15, 15, 15, 15], True);
        self.templateTestAwardNeurons(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 1, 4, 100, [15, 15, 15, 15], True);

def cluster_sample5():
    start_centers = [[0.0, 1.0], [0.0, 0.0], [1.0, 1.0], [1.0, 0.0]]
    template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE5)

def cluster_sample5():
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 1.0, 2.0);

def sample_simple_05():
    elbow_analysis(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 1, 10)

def cluster_sample5():
    template_clustering(4, SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 10, 5);

def cluster_sample5_without_initial_centers():
    template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE5, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
    template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE5, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)

def cluster_simple5():
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 1, 4, 0.5);

def cluster_sample5():
    start_medians = [[0.0, 1.0], [0.0, 0.0], [1.0, 1.0], [1.0, 0.0]]
    template_clustering(start_medians, SIMPLE_SAMPLES.SAMPLE_SIMPLE5)

def cluster_simple_sample():
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 8, 0)
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, 5, 0)
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, 5, 0)
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE4, 10, 0)
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 5, 0)
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE6, 5, 0)
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, 5, 0)
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE8, 15, 0)
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE9, 7, 0)
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE10, 7, 0)
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE11, 5, 0)
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE12, 7, 0)
    template_clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE13, 2, 0)
    template_clustering(SIMPLE_SAMPLES.SAMPLE_ELONGATE, 7, 0)