code: def test_derivative_gp_futures(self):
      model = GPWithDerivatives(train_X, train_Y)
      mll = gpytorch.mlls.sum_marginal_log_likelihood.ExactMarginalLogLikelihood(
          model.likelihood, model
      )
  
      mll.train()
      mll.eval()
  
      # get a posterior to fill in caches
      model(torch.randn(num_train_samples).reshape(-1, 1))
  
      new_x = torch.randn((1, 1, dim))
      new_y = torch.randn((num_fantasies, 1, 1, 1 + dim))
  
      # just check that this can run without error
      model.get_fantasy_model(new_x, new_y)
  
file_path: gpytorch/test/examples/test_derivative_gp_fantasy.py
imports: import unittest
  from math import pi
  import torch
  import gpytorch
  from gpytorch.distributions import MultitaskMultivariateNormal
  from gpytorch.kernels import ScaleKernel, RBFKernelGrad
  from gpytorch.likelihoods import MultitaskGaussianLikelihood
  from gpytorch.means import ConstantMeanGrad
  from gpytorch.test.base_test_case import BaseTestCase
label_desc: Use image node_id 1 for calling the TestDerivativeGPFutures obj's underlying member method code with example usage: obj.test_derivative_gp_futures() without return types