code: def test_rff_mean_abs_error(self):
      # Suppress numerical warnings
      warnings.simplefilter("ignore", NumericalWarning)
  
      train_x, train_y, test_x, test_y = make_data()
      likelihood = GaussianLikelihood()
      gp_model = GPRegressionModel(train_x, train_y, likelihood)
      mll = gpytorch.mlls.ExactMarginalLogLikelihood(
          likelihood, gp_model
      )
  
      # Optimize the model
      gp_model.train()
      likelihood.train()
  
      optimizer = optim.Adam(gp_model.parameters(), lr=0.1)
      for _ in range(30):
          optimizer.zero_grad()
          output = gp_model(train_x)
          loss = -mll(output, train_y)
          loss.backward()
          optimizer.step()
  
          # Check that we have the right LinearOperator type
          kernel = likelihood(
              gp_model(train_x)
          ).lazy_covariance_matrix.evaluate_kernel()
          self.assertIsInstance(
              kernel,
              linear_operator.operators.LowRankRootAddedDiagLinearOperator,
          )
  
      for param in gp_model.parameters():
          self.assertTrue(param.grad is not None)
          self.assertGreater(param.grad.norm().item(), 0)
  
      # Test the model
      gp_model.eval()
      likelihood.eval()
  
      test_preds = likelihood(gp_model(test_x)).mean
      mean_abs_error = torch.mean(torch.abs(test_y - test_preds))
  
      self.assertLess(mean_abs_error.squeeze().item(), 0.05)
  
imports: import os
  import random
  import unittest
  import warnings
  from math import pi
  import linear_operator
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import RFFKernel, ScaleKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.priors import SmoothedBoxPrior
  from gpytorch.utils.warnings import NumericalWarning
label_desc: Use image node_id 3 for calling the TestRFFRegression obj's underlying member method code with example usage: obj.test_rff_mean_abs_error() without return types