code: def test_end_to_end(self):
      for dtype in (torch.float, torch.double):
          tkwargs = {"dtype": dtype, "device": self.device}
          mars = MARS(
              alpha=0.5,
              n_w=3,
              chebyshev_weights=[0.5, 0.5],
              ref_point=[1.0, 1.0],
              baseline_Y=torch.randn(5, 2, **tkwargs),
          )
          samples = torch.randn(5, 9, 2, **tkwargs)
          mars_vals = mars(samples)
          self.assertEqual(mars_vals.shape, torch.Size([5, 3]))
          self.assertEqual(mars_vals.dtype, dtype)
          self.assertEqual(mars_vals.device.type, self.device.type)
  
imports: import warnings
  from typing import Optional
  import torch
  from botorch import settings
  from botorch.acquisition.multi_objective.multi_output_risk_measures import IndependentCVaR, IndependentVaR, MARS, MultiOutputExpectation, MultiOutputRiskMeasureMCObjective, MultiOutputWorstCase, MVaR
  from botorch.acquisition.multi_objective.objective import IdentityMCMultiOutputObjective, WeightedMCMultiOutputObjective
  from botorch.exceptions.errors import UnsupportedError
  from botorch.exceptions.warnings import BotorchWarning
  from botorch.models.deterministic import GenericDeterministicModel
  from botorch.models.transforms.input import InputPerturbation
  from botorch.utils.multi_objective.pareto import is_non_dominated
  from botorch.utils.testing import BotorchTestCase
  from torch import Tensor
label_desc: Use image node_id 5 for calling the TestMARS obj's underlying member method code with example usage: obj.test_end_to_end() without return types