code: def test_dspp(self):
      model = ThreeLayerDSPP(
          train_x.shape,
          num_inducing=num_inducing_pts,
          hidden_dim=hidden_dim,
          Q=num_quadrature_sites,
      )
  
      model.train()
  
      adam = torch.optim.Adam(
          [{"params": model.parameters()}],
          lr=initial_lr,
          betas=(0.9, 0.999),
      )
      objective = gpytorch.mlls.DeepPredictiveLogLikelihood(
          model.likelihood, model, num_data=train_n, beta=0.05
      )
  
      epochs_iter = range(num_epochs)
      losses = []
  
      for i in epochs_iter:
          adam.zero_grad()
          output = model(train_x)
          loss = -objective(output, train_y)
          loss.backward()
          adam.step()
          losses.append(loss.item())
  
      model.eval()
      preds = model.likelihood(model(test_x))
      self.assertLess((preds.mean - test_y).abs().mean().item(), 0.5)
      self.assertLess(preds.variance.mean().item(), 0.5)
  
imports: import unittest
  import torch
  import gpytorch
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.means import ConstantMean, LinearMean
  from gpytorch.models.deep_gps.dspp import DSPP, DSPPLayer
  from gpytorch.test.base_test_case import BaseTestCase
  from gpytorch.variational import MeanFieldVariationalDistribution, VariationalStrategy
label_desc: Use image node_id 1 for calling the TestSGPRRegression obj's underlying member method code with example usage: obj.test_dspp() without return types