code: def __init__(self, inducing_points, distribution_cls):
      variational_distribution = distribution_cls(
          inducing_points.size(-1)
      )
      variational_strategy = gpytorch.variational.VariationalStrategy(
          self,
          inducing_points,
          variational_distribution,
          learn_inducing_locations=True,
          jitter_val=1e-4,
      )
      super(SVGPRegressionModel, self).__init__(variational_strategy)
      self.mean_module = gpytorch.means.ConstantMean()
      self.covar_module = gpytorch.kernels.ScaleKernel(
          gpytorch.kernels.RBFKernel()
      )
  
imports: import math
  import unittest
  import warnings
  from pathlib import Path
  from unittest.mock import MagicMock, patch
  import linear_operator
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.models import ApproximateGP
  from gpytorch.test.base_test_case import BaseTestCase
  from gpytorch.test.utils import least_used_cuda_device
  from gpytorch.utils.warnings import OldVersionWarning
label_desc: Use image node_id 1 to create a new SVGPRegressionModel object from inherited base classes: ApproximateGP with example: obj = SVGPRegressionModel(inducing_points, distribution_cls)