code: def test_variational_setUp(self):
      for dtype in [torch.float, torch.double]:
          train_X = torch.rand(10, 1, device=self.device, dtype=dtype)
          train_y = torch.randn(10, 3, device=self.device, dtype=dtype)
  
          for ty, num_out in [[train_y, 3], [train_y, 1], [None, 3]]:
              batched_model = SingleTaskVariationalGP(
                  train_X,
                  train_Y=ty,
                  num_outputs=num_out,
                  learn_inducing_points=False,
              ).to(self.device)
              mll = VariationalELBO(
                  batched_model.likelihood,
                  batched_model.model,
                  num_data=10,
              )
  
              with torch.enable_grad():
                  loss = -mll(
                      batched_model.likelihood(batched_model(train_X)),
                      train_y,
                  ).sum()
                  loss.backward()
  
              # ensure that inducing points do not require grad
              model_var_strat = batched_model.model.variational_strategy
              self.assertEqual(
                  model_var_strat.base_variational_strategy.inducing_points.grad,
                  None,
              )
  
              # but that the covariance does have a gradient
              self.assertIsNotNone(
                  batched_model.model.covar_module.raw_outputscale.grad
              )
  
              # check that we always have three outputs
              self.assertEqual(batched_model._num_outputs, 3)
              self.assertIsInstance(
                  batched_model.likelihood, MultitaskGaussianLikelihood
              )
  
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 3 for calling the TestSingleTaskVariationalGP obj's underlying member method code with example usage: obj.test_variational_setUp() without return types