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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def transform(self, X):
 """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. """

    X_checked = check_input(X, type_of_inputs='classic', estimator=self,
                             preprocessor=self.preprocessor_,
                             accept_sparse=True)
    return X_checked.dot(self.transformer_.T)