code: def train_data(cuda=False):
      train_x = torch.linspace(0, 1, 260).unsqueeze(-1)
      train_y_cos = torch.cos(
          train_x * (2 * pi)
      ).squeeze() + 0.01 * torch.randn(260)
      train_y_sin = torch.sin(
          train_x * (2 * pi)
      ).squeeze() + 0.01 * torch.randn(260)
  
      # Make train_x (2 x 260 x 1) and train_y (2 x 260)
      train_x = (
          torch.cat([train_x, train_x], dim=1)
          .transpose(-2, 1)
          .unsqueeze(-1)
      )
      train_y = torch.cat(
          [train_y_cos.unsqueeze(-1), train_y_sin.unsqueeze(-1)], dim=1
      ).transpose(-2, -1)
      if cuda:
          return train_x.cuda(), train_y.cuda()
      else:
          return train_x, train_y
  
imports: import os
  import random
  import unittest
  from math import pi
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.models import ApproximateGP
  from gpytorch.test.utils import least_used_cuda_device
  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