code: def train_data(cuda=False):
      train_x = torch.linspace(0, 1, 260)
      train_y = torch.cos(train_x * (2 * math.pi)).gt(0).float()
      if cuda:
          return train_x.cuda(), train_y.cuda()
      else:
          return train_x, train_y
  
imports: import math
  import unittest
  from unittest.mock import MagicMock, patch
  import linear_operator
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.likelihoods import BernoulliLikelihood
  from gpytorch.models import ApproximateGP
  from gpytorch.test.base_test_case import BaseTestCase
  from gpytorch.variational import CholeskyVariationalDistribution, VariationalStrategy
label_desc: Use image node_id 1 for calling a global function with example usage: train_data(cuda) and returns: train_x,  train_y, train_x,  train_y