code: def _make_model_and_likelihood(
      self,
      num_inducing=8,
      batch_shape=torch.Size([]),
      inducing_batch_shape=torch.Size([]),
      strategy_cls=gpytorch.variational.VariationalStrategy,
      distribution_cls=gpytorch.variational.CholeskyVariationalDistribution,
      constant_mean=True,
  ):
      class _SVGPRegressionModel(gpytorch.models.ApproximateGP):
          def __init__(self):
              variational_distribution = distribution_cls(
                  num_inducing**2, batch_shape=batch_shape
              )
              variational_strategy = strategy_cls(
                  self,
                  num_inducing,
                  [(-3, 3), (-3, 3)],
                  variational_distribution,
              )
              super().__init__(variational_strategy)
              if constant_mean:
                  self.mean_module = gpytorch.means.ConstantMean()
                  self.mean_module.initialize(constant=1.0)
              else:
                  self.mean_module = gpytorch.means.ZeroMean()
              self.covar_module = gpytorch.kernels.ScaleKernel(
                  gpytorch.kernels.RBFKernel()
              )
  
          def forward(self, x):
              mean_x = self.mean_module(x)
              covar_x = self.covar_module(x)
              latent_pred = gpytorch.distributions.MultivariateNormal(
                  mean_x, covar_x
              )
              return latent_pred
  
      return _SVGPRegressionModel(), self.likelihood_cls()
  
imports: import unittest
  import torch
  import gpytorch
  from gpytorch.test.variational_test_case import VariationalTestCase
label_desc: Use image node_id 1 for calling the TestGridVariationalGP obj's underlying member method code with example usage: obj._make_model_and_likelihood(num_inducing, batch_shape, inducing_batch_shape, strategy_cls, distribution_cls, constant_mean) and returns: _SVGPRegressionModel,  self, latent_pred