code: def rock(sample, eps, number_clusters, threshold):
      """
      @brief Clustering algorithm ROCK returns allocated clusters and noise that are consisted from input data.
      @details Calculation is performed via CCORE (C/C++ part of the pyclustering)."
  
      @param[in] sample: input data - list of points where each point is represented by list of coordinates.
      @param[in] eps: connectivity radius (similarity threshold), points are neighbors if distance between them is less than connectivity radius.
      @param[in] number_clusters: defines number of clusters that should be allocated from the input data set.
      @param[in] threshold: value that defines degree of normalization that influences on choice of clusters for merging during processing.
  
      @return List of allocated clusters, each cluster contains indexes of objects in list of data.
  
      """
  
      pointer_data = package_builder(sample, c_double).create()
  
      ccore = ccore_library.get()
  
      ccore.rock_algorithm.restype = POINTER(pyclustering_package)
      package = ccore.rock_algorithm(
          pointer_data,
          c_double(eps),
          c_size_t(number_clusters),
          c_double(threshold),
      )
  
      list_of_clusters = package_extractor(package).extract()
      ccore.free_pyclustering_package(package)
  
      return list_of_clusters
  
label_desc: Use image node_id 1 for calling a global function with example usage: rock(sample, eps, number_clusters, threshold) and returns: list_of_clusters