code: def predict_proba_test_data(self):
      X_train_1d, y_train_1d = self._get_1d_dataset()
      X_train_2d, y_train_2d = self._get_2d_dataset()
      data = [
          dict(
              estimator=KernelDensityEstimationClassifier(
                  Euclidean(dim=1),
                  kernel="uniform",
              ),
              X_train=X_train_1d,
              y_train=y_train_1d,
              X_test=gs.array([[0.9]]),
              expected=gs.array([[1 / 2, 1 / 2]]),
          ),
          dict(
              estimator=KernelDensityEstimationClassifier(
                  Euclidean(dim=2),
                  kernel="uniform",
              ),
              X_train=X_train_2d,
              y_train=y_train_2d,
              X_test=gs.array([[0.9, 0.0]]),
              expected=gs.array([[1 / 2, 1 / 2]]),
          ),
          dict(
              estimator=KernelDensityEstimationClassifier(
                  Euclidean(dim=2),
                  kernel="distance",
              ),
              X_train=X_train_2d,
              y_train=y_train_2d,
              X_test=gs.array([[1.0, 0.0]]),
              expected=gs.array([[1, 0]]),
          ),
          dict(
              estimator=KernelDensityEstimationClassifier(
                  Euclidean(dim=2),
                  kernel=triangular_radial_kernel,
                  bandwidth=2.0,
              ),
              X_train=X_train_2d,
              y_train=y_train_2d,
              X_test=gs.array([[1.0, 0.0]]),
              expected=gs.array([[3 / 4, 1 / 4]]),
          ),
      ]
      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 5 for calling the KernelDensityEstimationClassifierTestData obj's underlying member method code with example usage: obj.predict_proba_test_data() and returns: self