code: def test_sampling_shape(self):
      X = np.arange(-5, 5, 0.1).reshape((-1, 1))
      X_test = np.arange(-5.05, 5.05, 0.1).reshape((-1, 1))
      for shape in (None, (-1, 1)):
          if shape is None:
              y = np.sin(X).flatten()
          else:
              y = np.sin(X).reshape(shape)
  
          rng = np.random.RandomState(1)
          for gp in (
              get_gp(
                  n_dimensions=1, rs=rng, noise=1e-10, normalize_y=False
              ),
              get_gp(
                  n_dimensions=1, rs=rng, noise=1e-10, normalize_y=True
              ),
          ):
              gp._train(X, y)
              func = gp.sample_functions(X_test=X_test, n_funcs=1)
              self.assertEqual(func.shape, (101, 1), msg=shape)
              func = gp.sample_functions(X_test=X_test, n_funcs=2)
              self.assertEqual(func.shape, (101, 2))
  
imports: import unittest.mock
  import numpy
  import pyro
  import torch
  from botorch.models.kernels.categorical import CategoricalKernel
  from gpytorch.constraints.constraints import Interval
  from gpytorch.kernels import MaternKernel, ProductKernel, ScaleKernel
  from gpytorch.likelihoods.gaussian_likelihood import GaussianLikelihood
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.models.exact_gp import ExactGP
  from gpytorch.priors import HorseshoePrior, LogNormalPrior, UniformPrior
  from gpytorch.utils.errors import NotPSDError
  from smac.configspace import CategoricalHyperparameter, ConfigurationSpace, UniformFloatHyperparameter
  from smac.model.gaussian_process.gpytorch_gaussian_process import GPyTorchGaussianProcess
  from ._test_gp import TestGP, get_cat_data, get_cont_data
label_desc: Use image node_id 10 for calling the TestGPGPyTorch obj's underlying member method code with example usage: obj.test_sampling_shape() without return types