code: def _make_model_and_likelihood(
      self,
      num_inducing=16,
      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, inducing_points):
              variational_distribution = distribution_cls(
                  num_inducing, batch_shape=batch_shape
              )
              variational_strategy = strategy_cls(
                  self,
                  inducing_points,
                  variational_distribution,
                  learn_inducing_locations=True,
              )
              super().__init__(variational_strategy)
              if constant_mean:
                  self.mean_module = gpytorch.means.ConstantMean(
                      batch_shape=batch_shape + torch.Size([2])
                  )
                  self.mean_module.initialize(constant=1.0)
              else:
                  self.mean_module = gpytorch.means.ZeroMean()
              self.covar_module = gpytorch.kernels.ScaleKernel(
                  gpytorch.kernels.RBFKernel(
                      batch_shape=batch_shape + torch.Size([2])
                  ),
                  batch_shape=batch_shape + torch.Size([2]),
              )
  
          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
  
      inducing_points = torch.randn(num_inducing, 2).repeat(
          *inducing_batch_shape, 1, 1
      )
      return (
          _SVGPRegressionModel(inducing_points),
          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 TestBatchDecoupledVariationalGPBatchDim 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