code: def __init__(self, train_x):
      variational_distribution = CholeskyVariationalDistribution(
          train_x.size(0)
      )
      variational_strategy = UnwhitenedVariationalStrategy(
          self,
          train_x,
          variational_distribution,
          learn_inducing_locations=False,
      )
      super(GPClassificationModel, self).__init__(variational_strategy)
      self.mean_module = gpytorch.means.ConstantMean()
      self.covar_module = gpytorch.kernels.ScaleKernel(
          gpytorch.kernels.RBFKernel()
      )
  
file_path: gpytorch/test/examples/test_simple_gp_classification.py
imports: import os
  import random
  import unittest
  from math import pi
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.likelihoods import BernoulliLikelihood
  from gpytorch.models import ApproximateGP
  from gpytorch.variational import CholeskyVariationalDistribution, UnwhitenedVariationalStrategy
label_desc: Use image node_id 1 to create a new GPClassificationModel object from inherited base classes: ApproximateGP with example: obj = GPClassificationModel(train_x)