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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 _get_abs_corr_mat(self, X_filled, tolerance=1e-6):
 """Get absolute correlation matrix between features. Parameters X_filled : ndarray, shape (n_samples, n_features) Input data with the most recent imputations. tolerance : float, optional (default=1e-6) ``abs_corr_mat`` can have nans, which will be replaced with ``tolerance``. Returns ------- abs_corr_mat : ndarray, shape (n_features, n_features) Absolute correlation matrix of ``X`` at the beginning of the current round. The diagonal has been zeroed out and each feature's absolute correlations with all others have been normalized to sum to 1. """

        n_features = X_filled.shape[1]
        if (self.n_nearest_features is None or
                self.n_nearest_features >= n_features):
            return None
        abs_corr_mat = np.abs(np.corrcoef(X_filled.T))
        # np.corrcoef is not defined for features with zero std
        abs_corr_mat[np.isnan(abs_corr_mat)] = tolerance
        # ensures exploration, i.e. at least some probability of sampling
        np.clip(abs_corr_mat, tolerance, None, out=abs_corr_mat)
        # features are not their own neighbors
        np.fill_diagonal(abs_corr_mat, 0)
        # needs to sum to 1 for np.random.choice sampling
        abs_corr_mat = normalize(abs_corr_mat, norm='l1', axis=0, copy=False)
        return abs_corr_mat