code: def test_q_upper_confidence_bound_batch(self):
      # TODO: T41739913 Implement tests for all MCAcquisitionFunctions
      for dtype in (torch.float, torch.double):
          samples = torch.zeros(
              2, 2, 1, device=self.device, dtype=dtype
          )
          samples[0, 0, 0] = 1.0
          mm = MockModel(MockPosterior(samples=samples))
          # X is a dummy and unused b/c of mocking
          X = torch.zeros(2, 2, 1, device=self.device, dtype=dtype)
  
          # test batch mode
          sampler = IIDNormalSampler(sample_shape=torch.Size([2]))
          acqf = qUpperConfidenceBound(
              model=mm, beta=0.5, sampler=sampler
          )
          res = acqf(X)
          self.assertEqual(res[0].item(), 1.0)
          self.assertEqual(res[1].item(), 0.0)
  
          # test batch mode
          sampler = IIDNormalSampler(
              sample_shape=torch.Size([2]), seed=12345
          )
          acqf = qUpperConfidenceBound(
              model=mm, beta=0.5, sampler=sampler
          )
          res = acqf(X)  # 1-dim batch
          self.assertEqual(res[0].item(), 1.0)
          self.assertEqual(res[1].item(), 0.0)
          self.assertEqual(
              acqf.sampler.base_samples.shape, torch.Size([2, 1, 2, 1])
          )
          bs = acqf.sampler.base_samples.clone()
          acqf(X)
          self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
          res = acqf(X.expand(2, -1, 1))  # 2-dim batch
          self.assertEqual(res[0].item(), 1.0)
          self.assertEqual(res[1].item(), 0.0)
          # the base samples should have the batch dim collapsed
          self.assertEqual(
              acqf.sampler.base_samples.shape, torch.Size([2, 1, 2, 1])
          )
          bs = acqf.sampler.base_samples.clone()
          acqf(X.expand(2, -1, 1))
          self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
  
          # test batch mode, qmc
          sampler = SobolQMCNormalSampler(sample_shape=torch.Size([2]))
          acqf = qUpperConfidenceBound(
              model=mm, beta=0.5, sampler=sampler
          )
          res = acqf(X)
          self.assertEqual(res[0].item(), 1.0)
          self.assertEqual(res[1].item(), 0.0)
          self.assertEqual(
              acqf.sampler.base_samples.shape, torch.Size([2, 1, 2, 1])
          )
          bs = acqf.sampler.base_samples.clone()
          acqf(X)
          self.assertTrue(torch.equal(acqf.sampler.base_samples, bs))
  
          # basic test for X_pending and warning
          acqf.set_X_pending()
          self.assertIsNone(acqf.X_pending)
          acqf.set_X_pending(None)
          self.assertIsNone(acqf.X_pending)
          acqf.set_X_pending(X)
          self.assertTrue(torch.equal(acqf.X_pending, X))
          mm._posterior._samples = torch.zeros(
              2, 4, 1, device=self.device, dtype=dtype
          )
          res = acqf(X)
          X2 = torch.zeros(
              1,
              1,
              1,
              device=self.device,
              dtype=dtype,
              requires_grad=True,
          )
          with warnings.catch_warnings(
              record=True
          ) as ws, settings.debug(True):
              acqf.set_X_pending(X2)
              self.assertEqual(acqf.X_pending, X2)
              self.assertEqual(
                  sum(
                      issubclass(w.category, BotorchWarning) for w in ws
                  ),
                  1,
              )
  
imports: import warnings
  from copy import deepcopy
  from functools import partial
  from itertools import product
  from math import pi
  from unittest import mock
  import torch
  from botorch import settings
  from botorch.acquisition.monte_carlo import MCAcquisitionFunction, qExpectedImprovement, qNoisyExpectedImprovement, qProbabilityOfImprovement, qSimpleRegret, qUpperConfidenceBound, SampleReducingMCAcquisitionFunction
  from botorch.acquisition.objective import ConstrainedMCObjective, GenericMCObjective, IdentityMCObjective, ScalarizedPosteriorTransform
  from botorch.acquisition.utils import prune_inferior_points
  from botorch.exceptions import BotorchWarning, UnsupportedError
  from botorch.models import SingleTaskGP
  from botorch.sampling.normal import IIDNormalSampler, SobolQMCNormalSampler
  from botorch.utils.low_rank import sample_cached_cholesky
  from botorch.utils.test_helpers import DummyNonScalarizingPosteriorTransform
  from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior
  from botorch.utils.transforms import standardize
  from torch import Tensor
label_desc: Use image node_id 2 for calling the TestQUpperConfidenceBound obj's underlying member method code with example usage: obj.test_q_upper_confidence_bound_batch() without return types