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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_samples(self, X):
 """Return the per-sample likelihood of the data under the model. Compute the log probability of X under the model and return the posterior distribution (responsibilities) of each mixture component for each element of X. Parameters X: array_like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. Returns ------- logprob : array_like, shape (n_samples,) Log probabilities of each data point in X. responsibilities : array_like, shape (n_samples, n_components) Posterior probabilities of each mixture component for each observation """

        check_is_fitted(self, 'means_')

        X = check_array(X)
        if X.ndim == 1:
            X = X[:, np.newaxis]
        if X.size == 0:
            return np.array([]), np.empty((0, self.n_components))
        if X.shape[1] != self.means_.shape[1]:
            raise ValueError('The shape of X  is not compatible with self')

        lpr = (log_multivariate_normal_density(X, self.means_, self.covars_,
                                               self.covariance_type)
               + np.log(self.weights_))
        logprob = logsumexp(lpr, axis=1)
        responsibilities = np.exp(lpr - logprob[:, np.newaxis])
        return logprob, responsibilities