code: def test_initialization(self):
      # test non batch case
      model = ApproximateGPyTorchModel(
          train_X=self.train_X, train_Y=self.train_Y
      )
      self.assertIsInstance(model.model, _SingleTaskVariationalGP)
      self.assertIsInstance(model.likelihood, GaussianLikelihood)
      self.assertIsInstance(
          model.model.variational_strategy, VariationalStrategy
      )
      self.assertEqual(model.num_outputs, 1)
  
      # test batch case
      stacked_y = torch.cat((self.train_Y, self.train_Y), dim=-1)
      model = ApproximateGPyTorchModel(
          train_X=self.train_X, train_Y=stacked_y, num_outputs=2
      )
      self.assertIsInstance(model.model, _SingleTaskVariationalGP)
      self.assertIsInstance(
          model.likelihood, MultitaskGaussianLikelihood
      )
      self.assertIsInstance(
          model.model.variational_strategy,
          IndependentMultitaskVariationalStrategy,
      )
      self.assertEqual(model.num_outputs, 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 2 for calling the TestApproximateGP obj's underlying member method code with example usage: obj.test_initialization() without return types