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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 silhouette_score(X, labels, metric='euclidean', sample_size=None, random_state=None, **kwds):
 """Compute the mean Silhouette Coefficient of all samples. The Silhouette Coefficient is calculated using the mean intra-cluster distance (``a``) and the mean nearest-cluster distance (``b``) for each sample. The Silhouette Coefficient for a sample is ``(b - a) / max(a, b)``. To clarify, ``b`` is the distance between a sample and the nearest cluster that the sample is not a part of. Note that Silhouette Coefficient is only defined if number of labels is 2 <= n_labels <= n_samples - 1. This function returns the mean Silhouette Coefficient over all samples. To obtain the values for each sample, use :func:`silhouette_samples`. The best value is 1 and the worst value is -1. Values near 0 indicate overlapping clusters. Negative values generally indicate that a sample has been assigned to the wrong cluster, as a different cluster is more similar. Read more in the :ref:`User Guide <silhouette_coefficient>`. Parameters X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \ [n_samples_a, n_features] otherwise Array of pairwise distances between samples, or a feature array. labels : array, shape = [n_samples] Predicted labels for each sample. metric : string, or callable The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by :func:`metrics.pairwise.pairwise_distances <sklearn.metrics.pairwise.pairwise_distances>`. If X is the distance array itself, use ``metric="precomputed"``. sample_size : int or None The size of the sample to use when computing the Silhouette Coefficient on a random subset of the data. If ``sample_size is None``, no sampling is used. random_state : int, RandomState instance or None, optional (default=None) The generator used to randomly select a subset of samples. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used when ``sample_size is not None``. **kwds : optional keyword parameters Any further parameters are passed directly to the distance function. If using a scipy.spatial.distance metric, the parameters are still metric dependent. See the scipy docs for usage examples. Returns ------- silhouette : float Mean Silhouette Coefficient for all samples. References .. [1] `Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis". Computational and Applied Mathematics 20: 53-65. <http://www.sciencedirect.com/science/article/pii/0377042787901257>`_ .. [2] `Wikipedia entry on the Silhouette Coefficient <https://en.wikipedia.org/wiki/Silhouette_(clustering)>`_ """

    if sample_size is not None:
        X, labels = check_X_y(X, labels, accept_sparse=['csc', 'csr'])
        random_state = check_random_state(random_state)
        indices = random_state.permutation(X.shape[0])[:sample_size]
        if metric == "precomputed":
            X, labels = X[indices].T[indices].T, labels[indices]
        else:
            X, labels = X[indices], labels[indices]
    return np.mean(silhouette_samples(X, labels, metric=metric, **kwds))