code: def __init__(self, train_x, use_inducing=False):
      variational_distribution = CholeskyVariationalDistribution(
          train_x.size(-2), batch_shape=train_x.shape[:-2]
      )
      inducing_points = (
          torch.randn(50, train_x.size(-1)) if use_inducing else train_x
      )
      strategy_cls = VariationalStrategy
      variational_strategy = strategy_cls(
          self,
          inducing_points,
          variational_distribution,
          learn_inducing_locations=use_inducing,
      )
      super(GPClassificationModel, self).__init__(variational_strategy)
      self.mean_module = gpytorch.means.ConstantMean()
      self.covar_module = gpytorch.kernels.ScaleKernel(
          gpytorch.kernels.RBFKernel()
      )
  
imports: import unittest
  import torch
  import gpytorch
  from gpytorch.models import ApproximateGP
  from gpytorch.test.model_test_case import VariationalModelTestCase
  from gpytorch.variational import CholeskyVariationalDistribution, VariationalStrategy
label_desc: Use image node_id 1 to create a new GPClassificationModel object from inherited base classes: ApproximateGP with example: obj = GPClassificationModel(train_x, use_inducing)