code: def test_regression_error(self):
      train_x, train_y = train_data()
      likelihood = GaussianLikelihood()
      inducing_points = (
          torch.linspace(0, 1, 25).unsqueeze(-1).repeat(2, 1, 1)
      )
      model = SVGPRegressionModel(inducing_points)
      mll = gpytorch.mlls.VariationalELBO(
          likelihood, model, num_data=train_y.size(-1)
      )
  
      # Find optimal model hyperparameters
      model.train()
      likelihood.train()
      optimizer = optim.Adam(
          [
              {"params": model.parameters()},
              {"params": likelihood.parameters()},
          ],
          lr=0.01,
      )
      for _ in range(180):
          optimizer.zero_grad()
          output = model(train_x)
          loss = -mll(output, train_y)
          loss = loss.sum()
          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[0, :] - test_preds[0, :]) / 2
      )
      mean_abs_error2 = torch.mean(
          torch.abs(train_y[1, :] - test_preds[1, :]) / 2
      )
      self.assertLess(mean_abs_error.item(), 0.012)
      self.assertLess(mean_abs_error2.item(), 1e-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 3 for calling the TestSVGPRegression obj's underlying member method code with example usage: obj.test_regression_error() without return types