code: def __init__(self, grid_size=16, grid_bounds=([-1, 1],)):
      variational_distribution = CholeskyVariationalDistribution(
          num_inducing_points=16, batch_shape=torch.Size([2])
      )
      variational_strategy = (
          AdditiveGridInterpolationVariationalStrategy(
              self,
              grid_size=grid_size,
              grid_bounds=grid_bounds,
              num_dim=2,
              variational_distribution=variational_distribution,
          )
      )
      super(GPClassificationModel, self).__init__(variational_strategy)
      self.mean_module = ConstantMean(
          constant_prior=SmoothedBoxPrior(-1e-5, 1e-5)
      )
      self.covar_module = ScaleKernel(
          RBFKernel(
              ard_num_dims=1,
              lengthscale_prior=SmoothedBoxPrior(
                  exp(-5), exp(6), sigma=0.1
              ),
          ),
          outputscale_prior=SmoothedBoxPrior(
              exp(-5), exp(6), sigma=0.1
          ),
      )
  
imports: import os
  import random
  import unittest
  from math import exp
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import RBFKernel, ScaleKernel
  from gpytorch.likelihoods import BernoulliLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.models import ApproximateGP
  from gpytorch.priors import SmoothedBoxPrior
  from gpytorch.variational import AdditiveGridInterpolationVariationalStrategy, CholeskyVariationalDistribution
label_desc: Use image node_id 1 to create a new GPClassificationModel object from inherited base classes: ApproximateGP with example: obj = GPClassificationModel(grid_size, grid_bounds)