code: def test_evaluate_kg(self):
      # a thorough test using real model and dtype double
      d = 2
      dtype = torch.double
      bounds = torch.tensor(
          [[0], [1]], device=self.device, dtype=dtype
      ).repeat(1, d)
      train_X = torch.rand(3, d, device=self.device, dtype=dtype)
      train_Y = torch.rand(3, 1, device=self.device, dtype=dtype)
      model = SingleTaskGP(train_X, train_Y)
      qKG = qKnowledgeGradient(
          model=model,
          num_fantasies=2,
          objective=None,
          X_pending=torch.rand(2, d, device=self.device, dtype=dtype),
          current_value=torch.rand(1, device=self.device, dtype=dtype),
      )
      X = torch.rand(4, 3, d, device=self.device, dtype=dtype)
      options = {"num_inner_restarts": 2, "raw_inner_samples": 3}
      val = qKG.evaluate(
          X,
          bounds=bounds,
          num_restarts=2,
          raw_samples=3,
          options=options,
      )
      # verify output shape
      self.assertEqual(val.size(), torch.Size([4]))
      # verify dtype
      self.assertEqual(val.dtype, dtype)
  
      # test i) no dimension is squeezed out, ii) dtype float, iii) MC objective,
      # and iv) t_batch_mode_transform
      dtype = torch.float
      bounds = torch.tensor([[0], [1]], device=self.device, dtype=dtype)
      train_X = torch.rand(1, 1, device=self.device, dtype=dtype)
      train_Y = torch.rand(1, 1, device=self.device, dtype=dtype)
      model = SingleTaskGP(train_X, train_Y)
      qKG = qKnowledgeGradient(
          model=model,
          num_fantasies=1,
          objective=GenericMCObjective(
              objective=lambda Y, X: Y.norm(dim=-1)
          ),
      )
      X = torch.rand(1, 1, device=self.device, dtype=dtype)
      options = {"num_inner_restarts": 1, "raw_inner_samples": 1}
      val = qKG.evaluate(
          X,
          bounds=bounds,
          num_restarts=1,
          raw_samples=1,
          options=options,
      )
      # verify output shape
      self.assertEqual(val.size(), torch.Size([1]))
      # verify dtype
      self.assertEqual(val.dtype, dtype)
  
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 TestQKnowledgeGradient obj's underlying member method code with example usage: obj.test_evaluate_kg() without return types