code: def top_segment_proportions(mtx, ns):
      
      # Pretty much just does dispatch
      if not (max(ns) <= mtx.shape[1] and min(ns) > 0):
          raise IndexError("Positions outside range of features.")
      if issparse(mtx):
          if not isspmatrix_csr(mtx):
              mtx = csr_matrix(mtx)
          return top_segment_proportions_sparse_csr(mtx.data, mtx.indptr,
                                                    np.array(ns, dtype=np.int))
      else:
          return top_segment_proportions_dense(mtx, ns)
docs: Calculates total percentage of counts in top ns genes.
  
      Parameters
      ----------
      mtx : `Union[np.array, sparse.spmatrix]`
          Matrix, where each row is a sample, each column a feature.
      ns : `Container[Int]`
          Positions to calculate cumulative proportion at. Values are considered
          1-indexed, e.g. `ns=[50]` will calculate cumulative proportion up to
          the 50th most expressed gene.