code: def transform(self, X):
          '''
          Transforms X according to the linear transformation corresponding to
          flipping the input eigenvalues.
  
          Parameters
          ----------
          X : array, shape [n_test, n]
              The test similarities to training points.
  
          Returns
          -------
          Xt : array, shape [n_test, n]
              The transformed test similarites to training points.
          '''
          n = self.flip_.shape[0]
          if X.ndim != 2 or X.shape[1] != n:
              msg = "X should have {} columns, the number of samples at fit time"
              raise TypeError(msg.format(self.flip_.shape[0]))
          return np.dot(X, self.flip_f transform(self, X):
          '''
          Transforms X according to the linear transformation corresponding to
          flipping the input eigenvalues.
  
          Parameters
          ----------
          X : array, shape [n_test, n]
              The test similarities to training points.
  
          Returns
          -------
          Xt : array, shape [n_test, n]
              The transformed test similarites to training points.
          '''
          n = self.flip_.shape[0]
          if X.ndim != 2 or X.shape[1] != n:
              msg = "X should have {} columns, the number of samples at fit time"
              raise TypeError(msg.format(self.flip_.shape[0]))
          return np.dot(X, self.flip_)
docs: Transforms X according to the linear transformation corresponding to
          flipping the input eigenvalues.
  
          Parameters
          ----------
          X : array, shape [n_test, n]
              The test similarities to training points.
  
          Returns
          -------
          Xt : array, shape [n_test, n]
              The transformed test similarites to training points.