code: def bmus_with_errors(self, data):
          
          bmus, seconds, mdst = self._som._bmus_and_seconds(data)
          quant_error = np.sqrt(mdst, out=mdst).mean()
          topog_error = self._som._topographic_error(bmus, seconds, data.shape[0])
          return self._som._to_bmus(bmus), quant_error, topog_error
docs: \
          Compute common error metrics (Quantization err. and Topographic err.)
          for this data.
  
          :param data: sparse input matrix (ideal dtype is `numpy.float32`)
          :type data: :class:`scipy.sparse.csr_matrix`
          :returns: the BMUs, the QE and the TE
          :rtype: tuple