code: def test_gp_draw_multi_output(self):
      for dtype in (torch.float, torch.double):
          tkwargs = {"device": self.device, "dtype": dtype}
          model, _, _ = _get_model(**tkwargs, multi_output=True)
          mean = model.mean_module.raw_constant.detach().clone()
          gp = GPDraw(model)
          # test initialization
          self.assertIsNone(gp.Xs)
          self.assertIsNone(gp.Ys)
          # make sure model is actually deepcopied
          model.mean_module.constant = float("inf")
          self.assertTrue(
              torch.equal(gp._model.mean_module.raw_constant, mean)
          )
          # test basic functionality
          test_X1 = torch.rand(1, 1, **tkwargs, requires_grad=True)
          Y1 = gp(test_X1)
          self.assertEqual(Y1.shape, torch.Size([1, 2]))
          Y1[:, 1].backward()
          self.assertIsNotNone(test_X1.grad)
          initial_base_samples = gp._base_samples
          with torch.no_grad():
              Y2 = gp(torch.rand(1, 1, **tkwargs))
          self.assertEqual(Y2.shape, torch.Size([1, 2]))
          new_base_samples = gp._base_samples
          self.assertTrue(
              torch.equal(
                  initial_base_samples, new_base_samples[..., :1, :]
              )
          )
          # evaluate in batch mode (need a new model for this!)
          model = model, _, _ = _get_model(**tkwargs, multi_output=True)
          gp = GPDraw(model)
          with torch.no_grad():
              Y_batch = gp(torch.rand(2, 1, 1, **tkwargs))
          self.assertEqual(Y_batch.shape, torch.Size([2, 1, 2]))
  
imports: from itertools import product
  from math import pi
  from unittest import mock
  import torch
  from botorch.models.converter import batched_to_model_list
  from botorch.models.deterministic import DeterministicModel
  from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.model import ModelList
  from botorch.models.multitask import MultiTaskGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.utils.gp_sampling import get_deterministic_model, get_deterministic_model_list, get_deterministic_model_multi_samples, get_gp_samples, get_weights_posterior, GPDraw, RandomFourierFeatures
  from botorch.utils.testing import BotorchTestCase
  from botorch.utils.transforms import is_ensemble
  from gpytorch.kernels import MaternKernel, PeriodicKernel, RBFKernel, ScaleKernel
  from torch.distributions import MultivariateNormal
label_desc: Use image node_id 2 for calling the TestGPDraw obj's underlying member method code with example usage: obj.test_gp_draw_multi_output() without return types