code: def test_batch_forward_then_nonbatch_forward_eval(self):
      batch_data = self.create_batch_test_data()
      likelihood, labels = self.create_batch_likelihood_and_labels()
      model = self.create_model(batch_data, labels, likelihood)
      model.eval()
      output = model(batch_data)
  
      # Smoke test derivatives working
      output.mean.sum().backward()
  
      self.assertTrue(output.lazy_covariance_matrix.dim() == 3)
      self.assertTrue(
          output.lazy_covariance_matrix.size(-1) == batch_data.size(-2)
      )
      self.assertTrue(
          output.lazy_covariance_matrix.size(-2) == batch_data.size(-2)
      )
  
      # Create non-batch data
      data = self.create_test_data()
      output = model(data)
      self.assertTrue(output.lazy_covariance_matrix.dim() == 3)
      self.assertTrue(
          output.lazy_covariance_matrix.size(-1) == data.size(-2)
      )
      self.assertTrue(
          output.lazy_covariance_matrix.size(-2) == data.size(-2)
      )
  
      # Smoke test derivatives working
      output.mean.sum().backward()
  
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 9 for calling the TestExactGP obj's underlying member method code with example usage: obj.test_batch_forward_then_nonbatch_forward_eval() without return types