text: #!/usr/bin/env python3
  
  import unittest
  
  import torch
  
  import gpytorch
  from gpytorch.test.variational_test_case import VariationalTestCase
  
  
  class TestCiqVariationalGP(VariationalTestCase, unittest.TestCase):
      @property
      def batch_shape(self):
          return torch.Size([])
  
      @property
      def distribution_cls(self):
          return gpytorch.variational.CholeskyVariationalDistribution
  
      @property
      def mll_cls(self):
          return gpytorch.mlls.VariationalELBO
  
      @property
      def strategy_cls(self):
          return gpytorch.variational.CiqVariationalStrategy
  
      def test_training_iteration(self, *args, **kwargs):
          cg_mock, cholesky_mock, ciq_mock = super().test_training_iteration(*args, **kwargs)
          self.assertFalse(cg_mock.called)
          self.assertFalse(cholesky_mock.called)
          self.assertEqual(ciq_mock.call_count, 4)  # One for each forward pass, one for each backward pass
  
      def test_eval_iteration(self, *args, **kwargs):
          cg_mock, cholesky_mock, ciq_mock = super().test_eval_iteration(*args, **kwargs)
          self.assertFalse(cg_mock.called)
          self.assertFalse(cholesky_mock.called)
          self.assertEqual(ciq_mock.call_count, 2)  # One for each evaluation call
  
  
  class TestMeanFieldCiqVariationalGP(TestCiqVariationalGP):
      @property
      def distribution_cls(self):
          return gpytorch.variational.MeanFieldVariationalDistribution
  
  
  class TestDeltaCiqVariationalGP(TestCiqVariationalGP):
      @property
      def distribution_cls(self):
          return gpytorch.variational.DeltaVariationalDistribution
  
  
  class TestNgdCiqVariationalGP(TestCiqVariationalGP):
      @property
      def distribution_cls(self):
          return gpytorch.variational.NaturalVariationalDistribution
  
  
  if __name__ == "__main__":
      unittest.main()
  