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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def rock(cls, data, eps, number_clusters, threshold=0.5, ccore=False):
 """ Constructor of the ROCK cluster analysis algorithm :param eps: Connectivity radius (similarity threshold), points are neighbors if distance between them is less than connectivity radius :param number_clusters: Defines number of clusters that should be allocated from the input data set :param threshold: Value that defines degree of normalization that influences on choice of clusters for merging during processing :param ccore: Defines should be CCORE (C++ pyclustering library) used instead of Python code or not. :return: The resulting clustering.rst object """

        data = cls.input_preprocess(data)
        model = rock(data, eps, number_clusters, threshold, ccore)
        return cls(model)