code: def setUp(self):
      super().setUp()
      train_X = torch.rand(10, 1, device=self.device)
      train_y = torch.sin(train_X) + torch.randn_like(train_X) * 0.2
  
      self.previous_model = SingleTaskVariationalGP(
          train_X=train_X, likelihood=GaussianLikelihood()
      ).to(self.device)
  
      mll = VariationalELBO(
          self.previous_model.likelihood,
          self.previous_model.model,
          num_data=10,
      )
      loss = -mll(
          self.previous_model.likelihood(self.previous_model(train_X)),
          train_y,
      ).sum()
      loss.backward()
  
      self.ipa = GreedyImprovementReduction(
          self.previous_model, maximize=True
      )
  
imports: import gc
  import torch
  from botorch.exceptions.errors import UnsupportedError
  from botorch.models.approximate_gp import SingleTaskVariationalGP
  from botorch.models.utils.inducing_point_allocators import _pivoted_cholesky_init, ExpectedImprovementQualityFunction, GreedyImprovementReduction, GreedyVarianceReduction, UnitQualityFunction
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.mlls import VariationalELBO
label_desc: Use image node_id 1 for calling the TestGreedyImprovementReduction obj's underlying member method code with example usage: obj.setUp() without return types