code: def test_fixed_evaluation_qMFKG(self):
      # mock test qMFKG.evaluate() with expand, project & cost aware utility
      for dtype in (torch.float, torch.double):
          mean = torch.zeros(1, 1, 1, device=self.device, dtype=dtype)
          mm = MockModel(MockPosterior(mean=mean))
          cau = GenericCostAwareUtility(mock_util)
          n_f = 4
          mean = torch.rand(
              n_f, 2, 1, 1, device=self.device, dtype=dtype
          )
          variance = torch.rand(
              n_f, 2, 1, 1, device=self.device, dtype=dtype
          )
          mfm = MockModel(MockPosterior(mean=mean, variance=variance))
          with ExitStack() as es:
              patch_f = es.enter_context(
                  mock.patch.object(
                      MockModel, "fantasize", return_value=mfm
                  )
              )
              mock_num_outputs = es.enter_context(
                  mock.patch(NO, new_callable=mock.PropertyMock)
              )
              es.enter_context(
                  mock.patch(
                      "botorch.optim.optimize.optimize_acqf",
                      return_value=(
                          torch.ones(
                              1, 1, 1, device=self.device, dtype=dtype
                          ),
                          torch.ones(
                              1, device=self.device, dtype=dtype
                          ),
                      ),
                  ),
              )
              es.enter_context(
                  mock.patch(
                      "botorch.generation.gen.gen_candidates_scipy",
                      return_value=(
                          torch.ones(
                              1, 1, 1, device=self.device, dtype=dtype
                          ),
                          torch.ones(
                              1, device=self.device, dtype=dtype
                          ),
                      ),
                  ),
              )
  
              mock_num_outputs.return_value = 1
              qMFKG = qMultiFidelityKnowledgeGradient(
                  model=mm,
                  num_fantasies=n_f,
                  X_pending=torch.rand(
                      1, 1, 1, device=self.device, dtype=dtype
                  ),
                  current_value=torch.zeros(
                      1, device=self.device, dtype=dtype
                  ),
                  cost_aware_utility=cau,
                  project=lambda X: torch.zeros_like(X),
                  expand=lambda X: torch.ones_like(X),
              )
              val = qMFKG.evaluate(
                  X=torch.zeros(
                      1, 1, 1, device=self.device, dtype=dtype
                  ),
                  bounds=torch.tensor(
                      [[0.0], [1.0]], device=self.device, dtype=dtype
                  ),
                  num_restarts=1,
                  raw_samples=1,
              )
              patch_f.asset_called_once()
              cargs, ckwargs = patch_f.call_args
              self.assertTrue(
                  torch.equal(
                      ckwargs["X"],
                      torch.ones(
                          1, 2, 1, device=self.device, dtype=dtype
                      ),
                  )
              )
              self.assertEqual(
                  val,
                  cau(
                      None,
                      torch.ones(1, device=self.device, dtype=dtype),
                  ),
              )
              # test with defaults - should see no errors
              qMFKG = qMultiFidelityKnowledgeGradient(
                  model=mm,
                  num_fantasies=n_f,
              )
              qMFKG.evaluate(
                  X=torch.zeros(
                      1, 1, 1, device=self.device, dtype=dtype
                  ),
                  bounds=torch.tensor(
                      [[0.0], [1.0]], device=self.device, dtype=dtype
                  ),
                  num_restarts=1,
                  raw_samples=1,
              )
  
imports: from contextlib import ExitStack
  from unittest import mock
  import torch
  from botorch.acquisition.analytic import PosteriorMean, ScalarizedPosteriorMean
  from botorch.acquisition.cost_aware import GenericCostAwareUtility
  from botorch.acquisition.knowledge_gradient import _get_value_function, _split_fantasy_points, ProjectedAcquisitionFunction, qKnowledgeGradient, qMultiFidelityKnowledgeGradient
  from botorch.acquisition.monte_carlo import qExpectedImprovement, qSimpleRegret
  from botorch.acquisition.objective import GenericMCObjective, ScalarizedPosteriorTransform
  from botorch.acquisition.utils import project_to_sample_points
  from botorch.exceptions.errors import UnsupportedError
  from botorch.generation.gen import gen_candidates_scipy
  from botorch.models import SingleTaskGP
  from botorch.optim.optimize import optimize_acqf
  from botorch.optim.utils import _filter_kwargs
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from botorch.sampling.normal import IIDNormalSampler, SobolQMCNormalSampler
  from botorch.utils.test_helpers import DummyNonScalarizingPosteriorTransform
  from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior
  from gpytorch.distributions import MultitaskMultivariateNormal
label_desc: Use image node_id 3 for calling the TestQMultiFidelityKnowledgeGradient obj's underlying member method code with example usage: obj.test_fixed_evaluation_qMFKG() without return types