code: def make_data():
      train_x = torch.linspace(0, 1, 1000)
      train_y = (
          torch.sin(train_x * (4 * pi))
          + torch.randn(train_x.size()) * 0.2
      )
      test_x = torch.linspace(0.02, 1, 51)
      test_y = torch.sin(test_x * (4 * pi))
      return train_x, train_y, test_x, test_y
  
imports: import os
  import random
  import unittest
  from math import exp, pi
  import torch
  from torch.utils.data import DataLoader, TensorDataset
  import gpytorch
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import RBFKernel, ScaleKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.priors import SmoothedBoxPrior
label_desc: Use image node_id 1 for calling a global function with example usage: make_data() and returns: train_x,  train_y,  test_x,  test_y