code: def _compute_ranks(self, sigma, v_matrix):
      assert (
          len(sigma) == v_matrix.shape[0]
      ), "Matrices should be multiplicable"
  
      dimensions = max(
          LsaSummarizer.MIN_DIMENSIONS,
          int(len(sigma) * LsaSummarizer.REDUCTION_RATIO),
      )
      powered_sigma = tuple(
          s**2 if i < dimensions else 0.0 for i, s in enumerate(sigma)
      )
  
      ranks = []
      # iterate over columns of matrix (rows of transposed matrix)
      for column_vector in v_matrix.T:
          rank = sum(
              s * v**2 for s, v in zip(powered_sigma, column_vector)
          )
          ranks.append(math.sqrt(rank))
  
      return ranks
  
file_path: sumy/sumy/summarizers/lsa.py
imports: from __future__ import absolute_import
  from __future__ import division, print_function, unicode_literals
  import math
  from warnings import warn
  from ._summarizer import AbstractSummarizer
label_desc: Use image node_id 8 for calling the LsaSummarizer obj's underlying member method code with example usage: obj._compute_ranks(sigma, v_matrix) and returns: ranks