code: def test_train_on_single_set_test_on_batch(self):
      # We're manually going to set the hyperparameters to something they shouldn't be
      likelihood = GaussianLikelihood()
      gp_model = ExactGPModel(train_x1, train_y1, likelihood)
      mll = gpytorch.ExactMarginalLogLikelihood(likelihood, gp_model)
  
      # Find optimal model hyperparameters
      gp_model.train()
      likelihood.train()
      optimizer = optim.Adam(gp_model.parameters(), lr=0.1)
      optimizer.n_iter = 0
      for _ in range(75):
          optimizer.zero_grad()
          output = gp_model(train_x1)
          loss = -mll(output, train_y1).sum()
          loss.backward()
          optimizer.step()
  
          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()
  
      # First test on non-batch
      non_batch_predictions = likelihood(gp_model(test_x1))
      preds1 = non_batch_predictions.mean
      mean_abs_error1 = torch.mean(torch.abs(test_y1 - preds1))
      self.assertLess(mean_abs_error1.squeeze().item(), 0.1)
  
      # Make predictions for both sets of test points, and check MAEs.
      batch_predictions = likelihood(gp_model(test_x12))
      preds1 = batch_predictions.mean[0]
      preds2 = batch_predictions.mean[1]
      mean_abs_error1 = torch.mean(torch.abs(test_y1 - preds1))
      mean_abs_error2 = torch.mean(torch.abs(test_y2 - preds2))
      self.assertLess(mean_abs_error1.squeeze().item(), 0.1)
      self.assertLess(mean_abs_error2.squeeze().item(), 0.1)
  
      # Smoke test for non-batch mode derivatives failing
      test_x_param = torch.nn.Parameter(test_x1.data)
      batch_predictions = likelihood(gp_model(test_x_param))
      batch_predictions.mean.sum().backward()
      self.assertTrue(test_x_param.grad is not None)
  
      # Smoke test for batch mode derivatives failing
      test_x_param = torch.nn.Parameter(test_x12.data)
      batch_predictions = likelihood(gp_model(test_x_param))
      batch_predictions.mean.sum().backward()
      self.assertTrue(test_x_param.grad is not None)
  
imports: import math
  import os
  import random
  import unittest
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import RBFKernel, ScaleKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.means import ConstantMean
label_desc: Use image node_id 3 for calling the TestBatchGPRegression obj's underlying member method code with example usage: obj.test_train_on_single_set_test_on_batch() without return types