code: def all_pairs_normalized_distances_reference(X):
      
      n_samples, n_cols = X.shape
      # matrix of mean squared difference between between samples
      D = np.ones((n_samples, n_samples), dtype="float32") * np.inf
      for i in range(n_samples):
          diffs = X - X[i, :].reshape((1, n_cols))
          missing_diffs = np.isnan(diffs)
          missing_counts_per_row = missing_diffs.sum(axis=1)
          valid_rows = missing_counts_per_row < n_cols
          D[i, valid_rows] = np.nanmean(
              diffs[valid_rows, :] ** 2,
              axis=1)
      return D
docs: Reference implementation of normalized all-pairs distance, used
      for testing the more efficient implementation above for equivalence.