code: def test_cache_across_lazy_threshold(self):
      x = self.create_test_data()
      likelihood, labels = self.create_likelihood_and_labels()
      model = self.create_model(x, labels, likelihood)
      model.eval()
      model(x)  # populate caches
  
      with settings.max_eager_kernel_size(
          2 * N_PTS - 1
      ), settings.fast_pred_var(True):
          # now we'll cross the threshold and use lazy tensors
          new_x = self.create_test_data()
          _, new_y = self.create_likelihood_and_labels()
          model = model.get_fantasy_model(new_x, new_y)
          predicted = model(self.create_test_data())
  
          # the main purpose of the test was to ensure there was no error, but we can verify shapes too
          self.assertEqual(predicted.mean.shape, torch.Size([N_PTS]))
          self.assertEqual(
              predicted.variance.shape, torch.Size([N_PTS])
          )
  
imports: import unittest
  import torch
  import gpytorch
  from gpytorch import settings
  from gpytorch.kernels import GridInterpolationKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.mlls import ExactMarginalLogLikelihood
  from gpytorch.models import ExactGP
  from gpytorch.models.exact_prediction_strategies import InterpolatedPredictionStrategy
  from gpytorch.test.model_test_case import BaseModelTestCase
label_desc: Use image node_id 2 for calling the TestSumExactGP obj's underlying member method code with example usage: obj.test_cache_across_lazy_threshold() without return types