code: def __init__(self, inducing_points, base_inducing_points):
      base_variational_distribution = (
          gpytorch.variational.MeanFieldVariationalDistribution(
              base_inducing_points.size(-1)
          )
      )
      variational_strategy = (
          gpytorch.variational.BatchDecoupledVariationalStrategy(
              self,
              base_inducing_points,
              base_variational_distribution,
              learn_inducing_locations=True,
              mean_var_batch_dim=-1,
          )
      )
      super(SVGPRegressionModel, self).__init__(variational_strategy)
      self.mean_module = gpytorch.means.ConstantMean(
          batch_shape=torch.Size([2])
      )
      self.covar_module = gpytorch.kernels.ScaleKernel(
          gpytorch.kernels.RBFKernel(batch_shape=torch.Size([2])),
          batch_shape=torch.Size([2]),
      )
  
file_path: gpytorch/test/examples/test_batch_decoupled_ppgpr_regression.py
imports: import math
  import unittest
  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
label_desc: Use image node_id 1 to create a new SVGPRegressionModel object from inherited base classes: ApproximateGP with example: obj = SVGPRegressionModel(inducing_points, base_inducing_points)