code: def test_kernel_output_more_features_than_data(self):
      train_x = torch.randn(50, 3)
      train_y = torch.randn(50)
      test_x = torch.randn(500, 3)
      model = TestModel(train_x, train_y)
  
      # Make sure that the prior kernel is the correct type
      model.train()
      output = model(train_x).lazy_covariance_matrix.evaluate_kernel()
      self.assertIsInstance(
          output, linear_operator.operators.RootLinearOperator
      )
      self.assertNotIsInstance(
          output, linear_operator.operators.LowRankRootLinearOperator
      )
  
      # Make sure that the prior predictive kernel is the correct type
      model.train()
      output = model.likelihood(
          model(train_x)
      ).lazy_covariance_matrix.evaluate_kernel()
      self.assertIsInstance(
          output, linear_operator.operators.AddedDiagLinearOperator
      )
      self.assertNotIsInstance(
          output,
          linear_operator.operators.LowRankRootAddedDiagLinearOperator,
      )
  
      # Make sure we're calling the correct prediction strategy
      _wrapped_ps = MagicMock(
          wraps=gpytorch.models.exact_prediction_strategies.RFFPredictionStrategy
      )
      with patch(
          "gpytorch.models.exact_prediction_strategies.RFFPredictionStrategy",
          new=_wrapped_ps,
      ) as ps_mock:
          model.eval()
          output = model.likelihood(model(test_x))
          _ = (
              output.mean + output.variance
          )  # Compute something to break through any lazy evaluations
          self.assertTrue(ps_mock.called)
  
imports: import unittest
  from unittest.mock import MagicMock, patch
  import linear_operator
  import torch
  import gpytorch
  from gpytorch.kernels import RFFKernel
  from gpytorch.test.base_kernel_test_case import BaseKernelTestCase
label_desc: Use image node_id 8 for calling the TestRFFKernel obj's underlying member method code with example usage: obj.test_kernel_output_more_features_than_data() without return types