code: def transform(self, X):
      
      X_checked = check_input(X, type_of_inputs='classic', estimator=self,
                               preprocessor=self.preprocessor_,
                               accept_sparse=True)
      return X_checked.dot(self.transformer_.T)
docs: Embeds data points in the learned linear embedding space.
  
      Transforms samples in ``X`` into ``X_embedded``, samples inside a new
      embedding space such that: ``X_embedded = X.dot(L.T)``, where ``L`` is
      the learned linear transformation (See :class:`MahalanobisMixin`).
  
      Parameters
      ----------
      X : `numpy.ndarray`, shape=(n_samples, n_features)
        The data points to embed.
  
      Returns
      -------
      X_embedded : `numpy.ndarray`, shape=(n_samples, num_dims)
        The embedded data points.