class_docstr: !
  @brief Class represents MBSAS (Modified Basic Sequential Algorithmic Scheme).
  @details Interface of MBSAS algorithm is the same as for BSAS. This algorithm performs clustering in two steps.
            The first - is determination of amount of clusters. The second - is assignment of points that were not
            marked as a cluster representatives to clusters.
  
  Code example of MBSAS usage:
  @code
      from pyclustering.cluster.bsas import bsas_visualizer
      from pyclustering.cluster.mbsas import mbsas
      from pyclustering.utils import read_sample
      from pyclustering.samples.definitions import SIMPLE_SAMPLES
  
      # Read data sample from 'Simple02.data'.
      sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE2)
  
      # Prepare algorithm's parameters.
      max_clusters = 3
      threshold = 1.0
  
      # Create instance of MBSAS algorithm.
      mbsas_instance = mbsas(sample, max_clusters, threshold)
      mbsas_instance.process()
  
      # Get clustering results.
      clusters = mbsas_instance.get_clusters()
      representatives = mbsas_instance.get_representatives()
  
      # Display results.
      bsas_visualizer.show_clusters(sample, clusters, representatives)
  @endcode
  
  @see pyclustering.cluster.bsas, pyclustering.cluster.ttsas
code: def __process_by_ccore(self):
      ccore_metric = metric_wrapper.create_instance(self._metric)
      self._clusters, self._representatives = mbsas_wrapper(
          self._data,
          self._amount,
          self._threshold,
          ccore_metric.get_pointer(),
      )
  
file_path: pyclustering/pyclustering/cluster/mbsas.py
imports: from pyclustering.core.mbsas_wrapper import mbsas
  from pyclustering.core.metric_wrapper import metric_wrapper
  from pyclustering.cluster.bsas import bsas
label_desc: Use image node_id 3 for calling the mbsas obj's underlying member method code with example usage: obj.__process_by_ccore() without return types