code: def predict_test_data(self):
      X_train_1d, y_train_1d = self._get_1d_dataset()
      X_train_2d, y_train_2d = self._get_2d_dataset()
      (
          X_train_poincare,
          y_train_poincare,
      ) = self._get_hyperbolic_dataset()
      (
          X_train_hyperboloid,
          y_train_hyperboloid,
      ) = self._get_hyperbolic_dataset(intrinsic=False)
      X_test_poincare = gs.array(
          [
              [1 / 2, 1 / 5],
              [1 / 2, 0],
              [1 / 2, -1 / 5],
              [-1 / 2, 1 / 5],
              [-1 / 2, 0],
              [-1 / 2, -1 / 5],
          ]
      )
  
      data = [
          dict(
              estimator=KernelDensityEstimationClassifier(
                  Euclidean(dim=1)
              ),
              X_train=X_train_1d,
              y_train=y_train_1d,
              X_test=gs.array([[1.1]]),
              y_test=gs.array([0.0]),
          ),
          dict(
              estimator=KernelDensityEstimationClassifier(
                  Euclidean(dim=2)
              ),
              X_train=X_train_2d,
              y_train=y_train_2d,
              X_test=gs.array([[1.1, 0.0]]),
              y_test=gs.array([0]),
          ),
          dict(
              estimator=KernelDensityEstimationClassifier(
                  Hypersphere(dim=2)
              ),
              X_train=gs.array(
                  [
                      [1, 0, 0],
                      [3 ** (1 / 2) / 2, 1 / 2, 0],
                      [3 ** (1 / 2) / 2, -1 / 2, 0],
                      [0, 0, 1],
                      [0, 1 / 2, 3 ** (1 / 2) / 2],
                      [0, -1 / 2, 3 ** (1 / 2) / 2],
                  ]
              ),
              y_train=gs.array([0, 0, 0, 1, 1, 1]),
              X_test=gs.array(
                  [
                      [2 ** (1 / 2) / 2, 2 ** (1 / 2) / 2, 0],
                      [0, 1 / 2, -(3 ** (1 / 2)) / 2],
                      [0, -1 / 2, -(3 ** (1 / 2)) / 2],
                      [-(3 ** (1 / 2)) / 2, 1 / 2, 0],
                      [-(3 ** (1 / 2)) / 2, -1 / 2, 0],
                      [0, 2 ** (1 / 2) / 2, 2 ** (1 / 2) / 2],
                  ]
              ),
              y_test=gs.array([0, 0, 0, 1, 1, 1]),
          ),
          dict(
              estimator=KernelDensityEstimationClassifier(
                  PoincareBall(dim=2), kernel="distance"
              ),
              X_train=X_train_poincare,
              y_train=y_train_poincare,
              X_test=X_test_poincare,
              y_test=gs.array([0, 0, 0, 1, 1, 1]),
          ),
          dict(
              estimator=KernelDensityEstimationClassifier(
                  Hyperboloid(dim=2), kernel="distance"
              ),
              X_train=X_train_hyperboloid,
              y_train=y_train_hyperboloid,
              X_test=_Hyperbolic.change_coordinates_system(
                  X_test_poincare,
                  from_coordinates_system="intrinsic",
                  to_coordinates_system="extrinsic",
              ),
              y_test=gs.array([0, 0, 0, 1, 1, 1]),
          ),
      ]
      return self.generate_tests(data)
  
file_path: geomstats/tests/tests_geomstats/test_learning/data/kernel_density_estimation_classifier.py
imports: import geomstats.backend
  from geomstats.geometry._hyperbolic import _Hyperbolic
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.hyperboloid import Hyperboloid
  from geomstats.geometry.hypersphere import Hypersphere
  from geomstats.geometry.poincare_ball import PoincareBall
  from geomstats.learning.kernel_density_estimation_classifier import KernelDensityEstimationClassifier
  from geomstats.learning.radial_kernel_functions import triangular_radial_kernel
  from geomstats.test.data import TestData
label_desc: Use image node_id 4 for calling the KernelDensityEstimationClassifierTestData obj's underlying member method code with example usage: obj.predict_test_data() and returns: self