code: def test_regression_error(
      self,
      cuda=False,
      mll_cls=gpytorch.mlls.VariationalELBO,
      distribution_cls=gpytorch.variational.CholeskyVariationalDistribution,
  ):
      train_x, train_y = train_data(cuda=cuda)
      likelihood = GaussianLikelihood()
      model = SVGPRegressionModel(
          torch.linspace(0, 1, 25), distribution_cls
      )
      mll = mll_cls(likelihood, model, num_data=len(train_y))
      if cuda:
          likelihood = likelihood.cuda()
          model = model.cuda()
          mll = mll.cuda()
  
      # Find optimal model hyperparameters
      model.train()
      likelihood.train()
      optimizer = optim.Adam(
          [
              {"params": model.parameters()},
              {"params": likelihood.parameters()},
          ],
          lr=0.01,
      )
  
      for _ in range(200):
          optimizer.zero_grad()
          output = model(train_x)
          loss = -mll(output, train_y)
          loss.backward()
          optimizer.step()
  
      for param in model.parameters():
          self.assertTrue(param.grad is not None)
          self.assertGreater(param.grad.norm().item(), 0)
      for param in likelihood.parameters():
          self.assertTrue(param.grad is not None)
          self.assertGreater(param.grad.norm().item(), 0)
  
      # Set back to eval mode
      model.eval()
      likelihood.eval()
      test_preds = likelihood(model(train_x)).mean.squeeze()
      mean_abs_error = torch.mean(torch.abs(train_y - test_preds) / 2)
      self.assertLess(mean_abs_error.item(), 0.014)
  
      if (
          distribution_cls
          is gpytorch.variational.CholeskyVariationalDistribution
      ):
          # finally test fantasization
          # we only will check that tossing the entire training set into the model will reduce the mae
          model.likelihood = likelihood
          fant_model = model.get_fantasy_model(train_x, train_y)
          fant_preds = fant_model.likelihood(
              fant_model(train_x)
          ).mean.squeeze()
          updated_abs_error = torch.mean(
              torch.abs(train_y - fant_preds) / 2
          )
          # TODO: figure out why this error is worse than before
          self.assertLess(updated_abs_error.item(), 0.15)
  
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 for calling the TestSVGPRegression obj's underlying member method code with example usage: obj.test_regression_error(cuda, mll_cls, distribution_cls) without return types