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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 score_pairs(self, pairs):
 """Returns the learned Mahalanobis distance between pairs. This distance is defined as: :math:`d_M(x, x') = \sqrt{(x-x')^T M (x-x')}` where ``M`` is the learned Mahalanobis matrix, for every pair of points ``x`` and ``x'``. This corresponds to the euclidean distance between embeddings of the points in a new space, obtained through a linear transformation. Indeed, we have also: :math:`d_M(x, x') = \sqrt{(x_e - x_e')^T (x_e- x_e')}`, with :math:`x_e = L x` (See :class:`MahalanobisMixin`). Parameters pairs : array-like, shape=(n_pairs, 2, n_features) or (n_pairs, 2) 3D Array of pairs to score, with each row corresponding to two points, for 2D array of indices of pairs if the metric learner uses a preprocessor. Returns ------- scores: `numpy.ndarray` of shape=(n_pairs,) The learned Mahalanobis distance for every pair. See Also -------- get_metric : a method that returns a function to compute the metric between two points. The difference with `score_pairs` is that it works on two 1D arrays and cannot use a preprocessor. Besides, the returned function is independent of the metric learner and hence is not modified if the metric learner is. :ref:`mahalanobis_distances` : The section of the project documentation that describes Mahalanobis Distances. """

    pairs = check_input(pairs, type_of_inputs='tuples',
                        preprocessor=self.preprocessor_,
                        estimator=self, tuple_size=2)
    pairwise_diffs = self.transform(pairs[:, 1, :] - pairs[:, 0, :])
    # (for MahalanobisMixin, the embedding is linear so we can just embed the
    # difference)
    return np.sqrt(np.sum(pairwise_diffs**2, axis=-1))