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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 integrity_score(self, X, method='predict', normalize=True, num_format=lambda x: str(x)):
 """ Compute the accuracy of the ported classifier. Parameters :param X : ndarray, shape (n_samples, n_features) Input data. :param method : string, default: 'predict' The method which should be tested. :param normalize : bool, default: True If ``False``, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples. :param num_format : lambda x, default: lambda x: str(x) The representation of the floating-point values. Returns ------- score : float If ``normalize == True``, return the correctly classified samples (float), else it returns the number of correctly classified samples (int). The best performance is 1 with ``normalize == True`` and the number of samples with ``normalize == False``. """

        X = np.array(X)
        if not X.ndim > 1:
            X = np.array([X])

        method = str(method).strip().lower()
        if method not in ['predict', 'predict_proba']:
            error = "The given method '{}' isn't supported.".format(method)
            raise AttributeError(error)

        if method == 'predict':
            y_true = self.estimator.predict(X)
            y_pred = self.predict(X, tnp_dir='tmp_integrity_score',
                                  keep_tmp_dir=True, num_format=num_format)
            return accuracy_score(y_true, y_pred, normalize=normalize)

        return False