code: def test_spectral_mixture_gp_mean_abs_error(self, empspect=False):
      likelihood = GaussianLikelihood(
          noise_prior=SmoothedBoxPrior(exp(-5), exp(3), sigma=0.1)
      )
      gp_model = SpectralMixtureGPModel(
          train_x, train_y, likelihood, empspect=empspect
      )
      mll = gpytorch.mlls.ExactMarginalLogLikelihood(
          likelihood, gp_model
      )
  
      # Optimize the model
      gp_model.train()
      likelihood.train()
      optimizer = optim.Adam(list(gp_model.parameters()), lr=0.01)
      optimizer.n_iter = 0
  
      if not empspect:
          gp_model.load_state_dict(good_state_dict, strict=False)
  
      for i in range(300):
          optimizer.zero_grad()
          output = gp_model(train_x)
          loss = -mll(output, train_y)
          loss.backward()
          optimizer.n_iter += 1
          optimizer.step()
  
          if i == 0:
              for param in gp_model.parameters():
                  self.assertTrue(param.grad is not None)
                  # TODO: Uncomment when we figure out why this is flaky.
                  # self.assertGreater(param.grad.norm().item(), 0.)
  
      # Test the model
      with torch.no_grad():
          gp_model.eval()
          likelihood.eval()
          test_preds = likelihood(gp_model(test_x)).mean
          mean_abs_error = torch.mean(torch.abs(test_y - test_preds))
  
      # The spectral mixture kernel should be trivially able to
      # extrapolate the sine function.
      self.assertLess(mean_abs_error.squeeze().item(), 0.02)
  
imports: import unittest
  from collections import OrderedDict
  from math import exp, pi
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import SpectralMixtureKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.priors import SmoothedBoxPrior
  from gpytorch.test.base_test_case import BaseTestCase
label_desc: Use image node_id 2 for calling the TestSpectralMixtureGPRegression obj's underlying member method code with example usage: obj.test_spectral_mixture_gp_mean_abs_error(empspect) without return types