code: def _make_model_and_likelihood(
      self,
      num_inducing=32,
      batch_shape=torch.Size([]),
      inducing_batch_shape=torch.Size([]),
      strategy_cls=gpytorch.variational.NNVariationalStrategy,
      distribution_cls=gpytorch.variational.MeanFieldVariationalDistribution,
      constant_mean=True,
  ):
      class _VNNGPRegressionModel(gpytorch.models.ApproximateGP):
          def __init__(self, inducing_points, k, training_batch_size):
              variational_distribution = distribution_cls(
                  num_inducing, batch_shape=batch_shape
              )
              variational_strategy = strategy_cls(
                  self,
                  inducing_points,
                  variational_distribution,
                  k=k,
                  training_batch_size=training_batch_size,
              )
              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)
              return gpytorch.distributions.MultivariateNormal(
                  mean_x, covar_x
              )
  
          def __call__(self, x, prior=False, **kwargs):
              if x is not None:
                  if x.dim() == 1:
                      x = x.unsqueeze(-1)
              return self.variational_strategy(
                  x=x, prior=False, **kwargs
              )
  
      k = 3
      d = 2
      training_batch_size = 4
      inducing_points = torch.randn(
          *inducing_batch_shape, num_inducing, d
      )
      return (
          _VNNGPRegressionModel(
              inducing_points, k, training_batch_size
          ),
          self.likelihood_cls(),
      )
  
imports: import unittest
  import torch
  import gpytorch
  from gpytorch.test.variational_test_case import VariationalTestCase
label_desc: Use image node_id 7 for calling the TestVNNGP 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: _VNNGPRegressionModel,  self, gpytorch, self