code: def kernel_test_data(self):
      data = [
          dict(
              kernel=uniform_radial_kernel,
              distance=gs.array([[1 / 2], [2.0]]),
              bandwidth=1.0,
              expected=gs.array([[1], [0]]),
          ),
          dict(
              kernel=uniform_radial_kernel,
              distance=gs.array([[1 / 2], [2.0]]),
              bandwidth=1 / 4,
              expected=gs.array([[0], [0]]),
          ),
          dict(
              kernel=triangular_radial_kernel,
              distance=gs.array([[1.0], [2]]),
              bandwidth=2.0,
              expected=gs.array([[1 / 2], [0]]),
          ),
          dict(
              kernel=parabolic_radial_kernel,
              distance=gs.array([[1.0], [2]]),
              bandwidth=2.0,
              expected=gs.array(
                  [[3 / 4], [0]],
              ),
          ),
          dict(
              kernel=biweight_radial_kernel,
              distance=gs.array([[1.0], [2]]),
              bandwidth=2.0,
              expected=gs.array([[9 / 16], [0]]),
          ),
          dict(
              kernel=triweight_radial_kernel,
              distance=gs.array([[1.0], [2]]),
              bandwidth=2.0,
              expected=gs.array([[(3 / 4) ** 3], [0]]),
          ),
          dict(
              kernel=tricube_radial_kernel,
              distance=gs.array([[1.0], [2]]),
              bandwidth=2.0,
              expected=gs.array([[(7 / 8) ** 3], [0]]),
          ),
          dict(
              kernel=gaussian_radial_kernel,
              distance=gs.array([[1.0], [2]]),
              bandwidth=2.0,
              expected=gs.array([[gs.exp(-1 / 8)], [gs.exp(-1 / 2)]]),
          ),
          dict(
              kernel=cosine_radial_kernel,
              distance=gs.array([[1.0], [2]]),
              bandwidth=2.0,
              expected=gs.array([[2 ** (1 / 2) / 2], [0]]),
          ),
          dict(
              kernel=logistic_radial_kernel,
              distance=gs.array([[1.0], [2]]),
              bandwidth=2.0,
              expected=gs.array(
                  [
                      [1 / (gs.exp(1 / 2) + 2 + gs.exp(-1 / 2))],
                      [1 / (gs.exp(1.0) + 2 + gs.exp(-1.0))],
                  ]
              ),
          ),
          dict(
              kernel=sigmoid_radial_kernel,
              distance=gs.array([[1.0], [2]]),
              bandwidth=2.0,
              expected=gs.array(
                  [
                      [1 / (gs.exp(1 / 2) + gs.exp(-1 / 2))],
                      [1 / (gs.exp(1.0) + gs.exp(-1.0))],
                  ],
              ),
          ),
          dict(
              kernel=bump_radial_kernel,
              distance=gs.array([[1 / 2], [2.0]]),
              bandwidth=1.0,
              expected=gs.array([[gs.exp(-1 / (3 / 4))], [0]]),
          ),
          dict(
              kernel=inverse_quadratic_radial_kernel,
              distance=gs.array([[1.0], [2.0]]),
              bandwidth=2.0,
              expected=gs.array([[4 / 5], [1 / 2]]),
          ),
          dict(
              kernel=inverse_multiquadric_radial_kernel,
              distance=gs.array([[1.0], [2]]),
              bandwidth=2,
              expected=gs.array(
                  [[2 / 5 ** (1 / 2)], [1 / 2 ** (1 / 2)]]
              ),
          ),
          dict(
              kernel=laplacian_radial_kernel,
              distance=gs.array([[1.0], [2]]),
              bandwidth=2,
              expected=gs.array([[gs.exp(-1 / 2)], [gs.exp(-1.0)]]),
          ),
      ]
      return self.generate_tests(data)
  
imports: import geomstats.backend
  from geomstats.learning.radial_kernel_functions import biweight_radial_kernel, bump_radial_kernel, cosine_radial_kernel, gaussian_radial_kernel, inverse_multiquadric_radial_kernel, inverse_quadratic_radial_kernel, laplacian_radial_kernel, logistic_radial_kernel, parabolic_radial_kernel, sigmoid_radial_kernel, triangular_radial_kernel, tricube_radial_kernel, triweight_radial_kernel, uniform_radial_kernel
  from geomstats.test.data import TestData
label_desc: Use image node_id 1 for calling the RadialKernelFunctionsTestData obj's underlying member method code with example usage: obj.kernel_test_data() and returns: self