code: def test_classification_error(self):
      train_x, train_y = train_data()
      likelihood = BernoulliLikelihood()
      model = GPClassificationModel(train_x)
      mll = gpytorch.mlls.VariationalELBO(
          likelihood, model, num_data=len(train_y)
      )
  
      # Find optimal model hyperparameters
      model.train()
      likelihood.train()
      optimizer = optim.Adam(model.parameters(), lr=0.1)
      optimizer.n_iter = 0
      for _ in range(75):
          optimizer.zero_grad()
          output = model(train_x)
          loss = -mll(output, train_y)
          loss.backward()
          optimizer.n_iter += 1
          optimizer.step()
  
      for param in model.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.round()
      mean_abs_error = torch.mean(torch.abs(train_y - test_preds) / 2)
      self.assertLess(mean_abs_error.item(), 1e-5)
  
file_path: gpytorch/test/examples/test_simple_gp_classification.py
imports: import os
  import random
  import unittest
  from math import pi
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.likelihoods import BernoulliLikelihood
  from gpytorch.models import ApproximateGP
  from gpytorch.variational import CholeskyVariationalDistribution, UnwhitenedVariationalStrategy
label_desc: Use image node_id 3 for calling the TestSimpleGPClassification obj's underlying member method code with example usage: obj.test_classification_error() without return types