code: def test_init(self):
      mm = MockModel(MockPosterior(mean=torch.rand(2, 1)))
      mc_points = torch.rand(2, 2)
      qNIPV = qNegIntegratedPosteriorVariance(
          model=mm, mc_points=mc_points
      )
      sampler = qNIPV.sampler
      self.assertIsInstance(sampler, SobolQMCNormalSampler)
      self.assertEqual(sampler.sample_shape, torch.Size([1]))
      self.assertTrue(torch.equal(mc_points, qNIPV.mc_points))
      self.assertIsNone(qNIPV.X_pending)
      self.assertIsNone(qNIPV.posterior_transform)
      sampler = IIDNormalSampler(sample_shape=torch.Size([2]))
      qNIPV = qNegIntegratedPosteriorVariance(
          model=mm, mc_points=mc_points, sampler=sampler
      )
      self.assertIsInstance(qNIPV.sampler, IIDNormalSampler)
      self.assertEqual(qNIPV.sampler.sample_shape, torch.Size([2]))
  
file_path: botorch/test/acquisition/test_active_learning.py
imports: from unittest import mock
  import torch
  from botorch.acquisition.active_learning import PairwiseMCPosteriorVariance, qNegIntegratedPosteriorVariance
  from botorch.acquisition.objective import GenericMCObjective, ScalarizedPosteriorTransform
  from botorch.models.pairwise_gp import PairwiseGP
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from botorch.sampling.normal import IIDNormalSampler, SobolQMCNormalSampler
  from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior
  from gpytorch.distributions import MultitaskMultivariateNormal
label_desc: Use image node_id 1 for calling the TestQNegIntegratedPosteriorVariance obj's underlying member method code with example usage: obj.test_init() without return types