code: def tnr(y_true, y_pred, round=True):
      
      y_true, y_pred = _mask_value_nan(y_true, y_pred)
      if round:
          y_true = np.round(y_true)
          y_pred = np.round(y_pred)
      c = skm.confusion_matrix(y_true, y_pred)
      return c[0, 0] / c[0].sum()
docs: True negative rate `tn / (tn + fp)`