code: def test_that_we_dont_get_redundant_inducing_points(self):
      train_X = torch.rand(15, 1, device=self.device)
      stacked_train_X = torch.cat((train_X, train_X), dim=0)
      num_inducing = 20
      inducing_points_1 = self.ipa.allocate_inducing_points(
          inputs=stacked_train_X,
          covar_module=MaternKernel(),
          num_inducing=num_inducing,
          input_batch_shape=torch.Size([]),
      )
      # should not have 20 inducing points when 15 singular dimensions
      # are passed
      self.assertLess(inducing_points_1.shape[-2], num_inducing)
  
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 5 for calling the TestGreedyVarianceReduction obj's underlying member method code with example usage: obj.test_that_we_dont_get_redundant_inducing_points() without return types