code: def __init__(self, inducing_points, distribution_cls):
      variational_distribution = distribution_cls(
          inducing_points.size(-1)
      )
      variational_strategy = (
          gpytorch.variational.UnwhitenedVariationalStrategy(
              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()
      )
  
file_path: gpytorch/test/examples/test_unwhitened_svgp_regression.py
imports: import math
  import unittest
  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
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)