code: def test_sample_all_priors(self):
      for dtype in (torch.float, torch.double):
          train_X = torch.rand(3, 5, device=self.device, dtype=dtype)
          train_Y = torch.rand(3, 1, device=self.device, dtype=dtype)
          model = SingleTaskGP(train_X=train_X, train_Y=train_Y)
          mll = ExactMarginalLogLikelihood(model.likelihood, model)
          mll.to(device=self.device, dtype=dtype)
          original_state_dict = dict(deepcopy(mll.model.state_dict()))
          sample_all_priors(model)
  
          # make sure one of the hyperparameters changed
          self.assertTrue(
              dict(model.state_dict())[
                  "likelihood.noise_covar.raw_noise"
              ]
              != original_state_dict["likelihood.noise_covar.raw_noise"]
          )
          # check that lengthscales are all different
          ls = model.covar_module.base_kernel.raw_lengthscale.view(
              -1
          ).tolist()
          self.assertTrue(
              all(ls[0] != ls[i]) for i in range(1, len(ls))
          )
  
          # change one of the priors to a dummy prior that does not support sampling
          model.covar_module = ScaleKernel(
              MaternKernel(
                  nu=2.5,
                  ard_num_dims=model.train_inputs[0].shape[-1],
                  batch_shape=model._aug_batch_shape,
                  lengthscale_prior=DummyPrior(),
              ),
              batch_shape=model._aug_batch_shape,
              outputscale_prior=GammaPrior(2.0, 0.15),
          )
          original_state_dict = dict(deepcopy(mll.model.state_dict()))
          with warnings.catch_warnings(
              record=True
          ) as ws, settings.debug(True):
              sample_all_priors(model)
              self.assertEqual(len(ws), 1)
              self.assertTrue("rsample" in str(ws[0].message))
  
          # change to dummy prior that raises an unrecognized RuntimeError
          model.covar_module = ScaleKernel(
              MaternKernel(
                  nu=2.5,
                  ard_num_dims=model.train_inputs[0].shape[-1],
                  batch_shape=model._aug_batch_shape,
                  lengthscale_prior=DummyPriorRuntimeError(),
              ),
              batch_shape=model._aug_batch_shape,
              outputscale_prior=GammaPrior(2.0, 0.15),
          )
          with self.assertRaises(RuntimeError):
              sample_all_priors(model)
  
          # the lengthscale should not have changed because sampling is
          # not implemented for DummyPrior
          self.assertTrue(
              torch.equal(
                  dict(model.state_dict())[
                      "covar_module.base_kernel.raw_lengthscale"
                  ],
                  original_state_dict[
                      "covar_module.base_kernel.raw_lengthscale"
                  ],
              )
          )
  
          # set setting_closure to None and make sure RuntimeError is raised
          prior_tuple = model.likelihood.noise_covar._priors[
              "noise_prior"
          ]
          model.likelihood.noise_covar._priors["noise_prior"] = (
              prior_tuple[0],
              prior_tuple[1],
              None,
          )
          with self.assertRaises(RuntimeError):
              sample_all_priors(model)
  
          # test for error when sampling violates constraint
          model = SingleTaskGP(train_X=train_X, train_Y=train_Y)
          mll = ExactMarginalLogLikelihood(model.likelihood, model)
          mll.to(device=self.device, dtype=dtype)
          model.covar_module = ScaleKernel(
              MaternKernel(
                  nu=2.5,
                  ard_num_dims=model.train_inputs[0].shape[-1],
                  batch_shape=model._aug_batch_shape,
                  lengthscale_prior=GammaPrior(3.0, 6.0),
              ),
              batch_shape=model._aug_batch_shape,
              outputscale_prior=UniformPrior(1.0, 2.0),
              outputscale_constraint=GreaterThan(3.0),
          )
          original_state_dict = dict(deepcopy(mll.model.state_dict()))
          with self.assertRaises(RuntimeError):
              sample_all_priors(model)
  
imports: from __future__ import annotations
  import re
  import warnings
  from copy import deepcopy
  from string import ascii_lowercase
  from unittest.mock import MagicMock, patch
  import torch
  from botorch import settings
  from botorch.models import SingleTaskGP
  from botorch.optim.utils import get_data_loader, get_name_filter, get_parameters, get_parameters_and_bounds, model_utils, sample_all_priors
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.constraints import GreaterThan
  from gpytorch.kernels.matern_kernel import MaternKernel
  from gpytorch.kernels.scale_kernel import ScaleKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.priors import UniformPrior
  from gpytorch.priors.prior import Prior
  from gpytorch.priors.torch_priors import GammaPrior
label_desc: Use image node_id 1 for calling the TestSampleAllPriors obj's underlying member method code with example usage: obj.test_sample_all_priors() without return types