code: def setUp(self):
      super().setUp()
      self.train_X = torch.rand(10, 1, device=self.device)
      self.train_Y = (
          torch.sin(self.train_X) + torch.randn_like(self.train_X) * 0.2
      )
  
imports: import itertools
  import warnings
  import torch
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.approximate_gp import _SingleTaskVariationalGP, ApproximateGPyTorchModel, SingleTaskVariationalGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Log
  from botorch.models.utils.inducing_point_allocators import GreedyImprovementReduction, GreedyVarianceReduction
  from botorch.posteriors import GPyTorchPosterior, TransformedPosterior
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.likelihoods import GaussianLikelihood, MultitaskGaussianLikelihood
  from gpytorch.mlls import VariationalELBO
  from gpytorch.variational import IndependentMultitaskVariationalStrategy, VariationalStrategy
label_desc: Use image node_id 1 for calling the TestApproximateGP obj's underlying member method code with example usage: obj.setUp() without return types