code: def __init__(self, inducing_points):
      variational_distribution = CholeskyVariationalDistribution(
          inducing_points.size(-2), batch_shape=torch.Size([2])
      )
      variational_strategy = VariationalStrategy(
          self,
          inducing_points,
          variational_distribution,
          learn_inducing_locations=True,
          jitter_val=1e-3,
      )
      super(SVGPRegressionModel, self).__init__(variational_strategy)
      self.mean_module = gpytorch.means.ConstantMean()
      self.covar_module = gpytorch.kernels.ScaleKernel(
          gpytorch.kernels.RBFKernel(
              lengthscale_prior=gpytorch.priors.SmoothedBoxPrior(
                  0.001, 1.0, sigma=0.1
              )
          )
      )
  
imports: import os
  import random
  import unittest
  from math import pi
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.models import ApproximateGP
  from gpytorch.test.utils import least_used_cuda_device
  from gpytorch.variational import CholeskyVariationalDistribution, VariationalStrategy
label_desc: Use image node_id 1 to create a new SVGPRegressionModel object from inherited base classes: ApproximateGP with example: obj = SVGPRegressionModel(inducing_points)