code: def test_inducing_points_shape_and_repeatability(self):
      for train_X in [
          torch.rand(15, 1, device=self.device),  # single task
          torch.rand(2, 15, 1, device=self.device),  # batched inputs
      ]:
          inducing_points_1 = self.ipa.allocate_inducing_points(
              inputs=train_X,
              covar_module=MaternKernel(),
              num_inducing=5,
              input_batch_shape=torch.Size([]),
          )
  
          inducing_points_2 = self.ipa.allocate_inducing_points(
              inputs=train_X,
              covar_module=MaternKernel(),
              num_inducing=5,
              input_batch_shape=torch.Size([]),
          )
  
          if len(train_X) == 3:  # batched inputs
              self.assertEqual(inducing_points_1.shape, (2, 5, 1))
              self.assertEqual(inducing_points_2.shape, (2, 5, 1))
          else:
              self.assertEqual(inducing_points_1.shape, (5, 1))
              self.assertEqual(inducing_points_2.shape, (5, 1))
          self.assertAllClose(inducing_points_1, inducing_points_2)
  
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 4 for calling the TestGreedyVarianceReduction obj's underlying member method code with example usage: obj.test_inducing_points_shape_and_repeatability() without return types