code: def test_custom_inducing_point_init(self):
      train_X_0 = torch.rand(15, 1, device=self.device)
      train_X_1 = torch.rand(15, 1, device=self.device)
      train_X_2 = torch.rand(15, 1, device=self.device)
      train_X_3 = torch.rand(15, 1, device=self.device)
  
      model_from_previous_step = SingleTaskVariationalGP(
          train_X=train_X_0, inducing_points=5
      )
  
      model_1 = SingleTaskVariationalGP(
          train_X=train_X_1,
          inducing_points=5,
          inducing_point_allocator=GreedyImprovementReduction(
              model_from_previous_step, maximize=True
          ),
      )
      model_1.init_inducing_points(train_X_2)
      model_1_inducing = (
          model_1.model.variational_strategy.inducing_points
      )
  
      model_2 = SingleTaskVariationalGP(
          train_X=train_X_2,
          inducing_points=5,
          inducing_point_allocator=GreedyImprovementReduction(
              model_from_previous_step, maximize=True
          ),
      )
      model_2_inducing = (
          model_2.model.variational_strategy.inducing_points
      )
  
      model_3 = SingleTaskVariationalGP(
          train_X=train_X_3,
          inducing_points=5,
          inducing_point_allocator=GreedyImprovementReduction(
              model_from_previous_step, maximize=False
          ),
      )
      model_3.init_inducing_points(train_X_2)
      model_3_inducing = (
          model_3.model.variational_strategy.inducing_points
      )
  
      self.assertEqual(model_1_inducing.shape, (5, 1))
      self.assertEqual(model_2_inducing.shape, (5, 1))
      self.assertAllClose(model_1_inducing, model_2_inducing)
      self.assertFalse(model_1_inducing[0, 0] == model_3_inducing[0, 0])
  
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 7 for calling the TestSingleTaskVariationalGP obj's underlying member method code with example usage: obj.test_custom_inducing_point_init() without return types