def transform(self, X):
        X = check_array(X)
        distances, indices = self.nbrs_.kneighbors(X, return_distance=True)

        # Create the graph of shortest distances from X to self.training_data_
        # via the nearest neighbors of X.
        # This can be done as a single array operation, but it potentially
        # takes a lot of memory.  To avoid that, use a loop:
        G_X = np.zeros((X.shape[0], self.training_data_.shape[0]))
        for i in range(X.shape[0]):
            G_X[i] = np.min(self.dist_matrix_[indices[i]] +
                            distances[i][:, None], 0)

        G_X **= 2
        G_X *= -0.5

        return self.kernel_pca_.transform(G_X)
Test report with empty results
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/manifold/isomap.py/Isomap.transform--crossover with--dataset/ETHPy150Open TheGhouls/oct/tests/test_report.py/ReportTest.test_empty_results