code: def test_multitask_gp_mean_abs_error(self, cuda=False):
      train_x, train_y = self._get_data(cuda=cuda)
      likelihood = MultitaskGaussianLikelihood(num_tasks=2)
      model = MultitaskGPModel(train_x, train_y, likelihood)
  
      if cuda:
          model.cuda()
  
      # Find optimal model hyperparameters
      model.train()
      likelihood.train()
  
      # Use the adam optimizer
      optimizer = torch.optim.Adam(
          model.parameters(), lr=0.1
      )  # Includes GaussianLikelihood parameters
  
      # "Loss" for GPs - the marginal log likelihood
      mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)
  
      n_iter = 50
      for _ in range(n_iter):
          # Zero prev backpropped gradients
          optimizer.zero_grad()
          # Make predictions from training data
          # Again, note feeding duplicated x_data and indices indicating which task
          output = model(train_x)
          # TODO: Fix this view call!!
          loss = -mll(output, train_y)
          loss.backward()
          optimizer.step()
  
      # Test the model
      model.eval()
      likelihood.eval()
  
      test_x = torch.linspace(
          0,
          1,
          51,
          device=torch.device("cuda") if cuda else torch.device("cpu"),
      )
      test_y1 = torch.sin(test_x * (2 * pi))
      test_y2 = torch.cos(test_x * (2 * pi))
      test_preds = likelihood(model(test_x)).mean
      mean_abs_error_task_1 = torch.mean(
          torch.abs(test_y1 - test_preds[:, 0])
      )
      mean_abs_error_task_2 = torch.mean(
          torch.abs(test_y2 - test_preds[:, 1])
      )
  
      self.assertLess(mean_abs_error_task_1.squeeze().item(), 0.05)
      self.assertLess(mean_abs_error_task_2.squeeze().item(), 0.05)
  
imports: import os
  import random
  import unittest
  from math import pi
  import torch
  import gpytorch
  from gpytorch.distributions import MultitaskMultivariateNormal
  from gpytorch.kernels import GridInterpolationKernel, MultitaskKernel, RBFKernel
  from gpytorch.likelihoods import MultitaskGaussianLikelihood
  from gpytorch.means import ConstantMean, MultitaskMean
  from gpytorch.test.utils import least_used_cuda_device
label_desc: Use image node_id 4 for calling the TestKroneckerMultiTaskKISSGPRegression obj's underlying member method code with example usage: obj.test_multitask_gp_mean_abs_error(cuda) without return types