code: def _create_L_ind(self, L):
          
          # TODO: Update LabelModel to keep L variants as sparse matrices
          # throughout and remove this line.
          if issparse(L):
              L = L.todense()
  
          L_ind = np.zeros((self.n, self.m * self.k))
          for y in range(1, self.k + 1):
              # A[x::y] slices A starting at x at intervals of y
              # e.g., np.arange(9)[0::3] == np.array([0,3,6])
              L_ind[:, (y - 1) :: self.k] = np.where(L == y, 1, 0)
          return L_ind
docs: Convert a label matrix with labels in 0...k to a one-hot format
  
          Args:
              L: An [n,m] scipy.sparse label matrix with values in {0,1,...,k}
  
          Returns:
              L_ind: An [n,m*k] dense np.ndarray with values in {0,1}
  
          Note that no column is required for 0 (abstain) labels.