code: def test_evaluate_q_knowledge_gradient(self):
      # Stop gap measure to avoid test failures on Ampere devices
      # TODO: Find an elegant way of disallowing tf32 for botorch/gpytorch
      # without blanket-disallowing it for all of torch.
      torch.backends.cuda.matmul.allow_tf32 = False
  
      for dtype in (torch.float, torch.double):
          # basic test
          n_f = 4
          mean = torch.rand(n_f, 1, 1, device=self.device, dtype=dtype)
          variance = torch.rand(
              n_f, 1, 1, device=self.device, dtype=dtype
          )
          mfm = MockModel(MockPosterior(mean=mean, variance=variance))
          with mock.patch.object(
              MockModel, "fantasize", return_value=mfm
          ) as patch_f:
              with mock.patch(
                  NO, new_callable=mock.PropertyMock
              ) as mock_num_outputs:
                  mock_num_outputs.return_value = 1
                  mm = MockModel(None)
                  qKG = qKnowledgeGradient(model=mm, num_fantasies=n_f)
                  X = torch.rand(
                      n_f + 1, 1, device=self.device, dtype=dtype
                  )
                  val = qKG(X)
                  patch_f.assert_called_once()
                  cargs, ckwargs = patch_f.call_args
                  self.assertEqual(
                      ckwargs["X"].shape, torch.Size([1, 1, 1])
                  )
          self.assertAllClose(val, mean.mean(), atol=1e-4)
          self.assertTrue(
              torch.equal(qKG.extract_candidates(X), X[..., :-n_f, :])
          )
          # batched evaluation
          b = 2
          mean = torch.rand(n_f, b, 1, device=self.device, dtype=dtype)
          variance = torch.rand(
              n_f, b, 1, device=self.device, dtype=dtype
          )
          mfm = MockModel(MockPosterior(mean=mean, variance=variance))
          X = torch.rand(b, n_f + 1, 1, device=self.device, dtype=dtype)
          with mock.patch.object(
              MockModel, "fantasize", return_value=mfm
          ) as patch_f:
              with mock.patch(
                  NO, new_callable=mock.PropertyMock
              ) as mock_num_outputs:
                  mock_num_outputs.return_value = 1
                  mm = MockModel(None)
                  qKG = qKnowledgeGradient(model=mm, num_fantasies=n_f)
                  val = qKG(X)
                  patch_f.assert_called_once()
                  cargs, ckwargs = patch_f.call_args
                  self.assertEqual(
                      ckwargs["X"].shape, torch.Size([b, 1, 1])
                  )
          self.assertTrue(
              torch.allclose(
                  val, mean.mean(dim=0).squeeze(-1), atol=1e-4
              )
          )
          self.assertTrue(
              torch.equal(qKG.extract_candidates(X), X[..., :-n_f, :])
          )
          # pending points and current value
          X_pending = torch.rand(2, 1, device=self.device, dtype=dtype)
          mean = torch.rand(n_f, 1, 1, device=self.device, dtype=dtype)
          variance = torch.rand(
              n_f, 1, 1, device=self.device, dtype=dtype
          )
          mfm = MockModel(MockPosterior(mean=mean, variance=variance))
          current_value = torch.rand(1, device=self.device, dtype=dtype)
          X = torch.rand(n_f + 1, 1, device=self.device, dtype=dtype)
          with mock.patch.object(
              MockModel, "fantasize", return_value=mfm
          ) as patch_f:
              with mock.patch(
                  NO, new_callable=mock.PropertyMock
              ) as mock_num_outputs:
                  mock_num_outputs.return_value = 1
                  mm = MockModel(None)
                  qKG = qKnowledgeGradient(
                      model=mm,
                      num_fantasies=n_f,
                      X_pending=X_pending,
                      current_value=current_value,
                  )
                  val = qKG(X)
                  patch_f.assert_called_once()
                  cargs, ckwargs = patch_f.call_args
                  self.assertEqual(
                      ckwargs["X"].shape, torch.Size([1, 3, 1])
                  )
  
          expected = (mean.mean() - current_value).reshape([])
          self.assertAllClose(val, expected, atol=1e-4)
          self.assertTrue(
              torch.equal(qKG.extract_candidates(X), X[..., :-n_f, :])
          )
          # test objective (inner MC sampling)
          objective = GenericMCObjective(
              objective=lambda Y, X: Y.norm(dim=-1)
          )
          samples = torch.randn(
              3, 1, 1, device=self.device, dtype=dtype
          )
          mfm = MockModel(MockPosterior(samples=samples))
          X = torch.rand(n_f + 1, 1, device=self.device, dtype=dtype)
          with mock.patch.object(
              MockModel, "fantasize", return_value=mfm
          ) as patch_f:
              with mock.patch(
                  NO, new_callable=mock.PropertyMock
              ) as mock_num_outputs:
                  mock_num_outputs.return_value = 1
                  mm = MockModel(None)
                  qKG = qKnowledgeGradient(
                      model=mm, num_fantasies=n_f, objective=objective
                  )
                  val = qKG(X)
                  patch_f.assert_called_once()
                  cargs, ckwargs = patch_f.call_args
                  self.assertEqual(
                      ckwargs["X"].shape, torch.Size([1, 1, 1])
                  )
          self.assertAllClose(val, objective(samples).mean(), atol=1e-4)
          self.assertTrue(
              torch.equal(qKG.extract_candidates(X), X[..., :-n_f, :])
          )
          # test scalarized posterior transform
          weights = torch.rand(2, device=self.device, dtype=dtype)
          post_tf = ScalarizedPosteriorTransform(weights=weights)
          mean = torch.tensor(
              [1.0, 0.5], device=self.device, dtype=dtype
          ).expand(n_f, 1, 2)
          cov = torch.tensor(
              [[1.0, 0.1], [0.1, 0.5]], device=self.device, dtype=dtype
          ).expand(n_f, 2, 2)
          posterior = GPyTorchPosterior(
              MultitaskMultivariateNormal(mean, cov)
          )
          mfm = MockModel(posterior)
          with mock.patch.object(
              MockModel, "fantasize", return_value=mfm
          ) as patch_f:
              with mock.patch(
                  NO, new_callable=mock.PropertyMock
              ) as mock_num_outputs:
                  mock_num_outputs.return_value = 2
                  mm = MockModel(None)
                  qKG = qKnowledgeGradient(
                      model=mm,
                      num_fantasies=n_f,
                      posterior_transform=post_tf,
                  )
                  val = qKG(X)
                  patch_f.assert_called_once()
                  cargs, ckwargs = patch_f.call_args
                  self.assertEqual(
                      ckwargs["X"].shape, torch.Size([1, 1, 1])
                  )
                  val_expected = (mean * weights).sum(-1).mean(0)[0]
                  self.assertAllClose(val, val_expected)
  
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 2 for calling the TestQKnowledgeGradient obj's underlying member method code with example usage: obj.test_evaluate_q_knowledge_gradient() without return types