code: def _get_data(self, cuda=False, num_data=11, add_noise=False):
      device = torch.device("cuda") if cuda else torch.device("cpu")
      # Simple training data: let's try to learn a sine function
      train_x = torch.linspace(0, 1, num_data, device=device)
      train_y = torch.sin(train_x * (2 * pi))
      if add_noise:
          train_y.add_(torch.randn_like(train_x).mul_(0.1))
      test_x = torch.linspace(0, 1, 51, device=device)
      test_y = torch.sin(test_x * (2 * pi))
      return train_x, test_x, train_y, test_y
  
imports: import unittest
  from math import exp, pi
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import RBFKernel, ScaleKernel
  from gpytorch.likelihoods import FixedNoiseGaussianLikelihood
  from gpytorch.likelihoods.noise_models import FixedGaussianNoise
  from gpytorch.means import ConstantMean
  from gpytorch.priors import SmoothedBoxPrior
  from gpytorch.test.base_test_case import BaseTestCase
  from gpytorch.test.utils import least_used_cuda_device
label_desc: Use image node_id 1 for calling the TestFixedNoiseFantasies obj's underlying member method code with example usage: obj._get_data(cuda, num_data, add_noise) and returns: train_x,  test_x,  train_y,  test_y