code: def test_regression_error_cuda(self):
      if not torch.cuda.is_available():
          return
      with least_used_cuda_device():
          train_x, train_y = train_data(cuda=True)
          likelihood = GaussianLikelihood().cuda()
          inducing_points = (
              torch.linspace(0, 1, 25).unsqueeze(-1).repeat(2, 1, 1)
          )
          model = SVGPRegressionModel(inducing_points).cuda()
          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(150):
              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(), 1e-1)
          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 5 for calling the TestSVGPRegression obj's underlying member method code with example usage: obj.test_regression_error_cuda() without return types