code: def lf_conflicts(L, normalize_by_overlaps=False):
      
      conflicts = (L != 0).T @ _conflicted_data_points(L) / L.shape[0]
      if normalize_by_overlaps:
          conflicts /= lf_overlaps(L)
      return np.nan_to_num(conflicts)
docs: Return the **fraction of items each LF labels that are also given a
      different (non-abstain) label by at least one other LF.**
  
      Note that the maximum possible conflict fraction for an LF is the LF's
          overlaps fraction, unless `normalize_by_overlaps=True`, in which case it
          is 1.
  
      Args:
          L: an n x m scipy.sparse matrix where L_{i,j} is the label given by the
              jth LF to the ith candidate
          normalize_by_overlaps: Normalize by overlaps of the LF, so that it
              returns the percent of LF overlaps that have conflicts.