code: def _get_params():
      space = Hypersphere(dim=2)
  
      intercept_sphere_true = gs.array([0.0, -1.0, 0.0])
      coef_sphere_true = gs.array([1.0, 0.0, 0.5])
  
      prior = lambda x: space.metric.exp(
          x * coef_sphere_true,
          base_point=intercept_sphere_true,
      )
  
      kernel = ConstantKernel(1.0, (1e-3, 1e3)) * RBF(10.0, (1e-2, 1e2))
  
      return space, prior, kernel
  
file_path: geomstats/tests/tests_geomstats/test_learning/test_wrapped_gaussian_process.py
imports: import pytest
  from sklearn.gaussian_process.kernels import RBF, ConstantKernel
  import geomstats.backend
  from geomstats.geometry.hypersphere import Hypersphere
  from geomstats.learning.wrapped_gaussian_process import WrappedGaussianProcess
  from geomstats.test.parametrizers import DataBasedParametrizer
  from geomstats.test_cases.learning.wrapped_gaussian_process import WrappedGaussianProcessTestCase
  from .data.wrapped_gaussian_process import WrappedGaussianProcessTestData
label_desc: Use image node_id 1 for calling a global function with example usage: _get_params() and returns: space,  prior,  kernel