code: def test_risk_measure_mc_objective(self):
      # abstract raises
      with self.assertRaises(TypeError):
          RiskMeasureMCObjective(n_w=3)
  
      for dtype in (torch.float, torch.double):
          samples = torch.tensor(
              [[[1.0], [0.5], [2.0], [3.0], [1.0], [5.0]]],
              device=self.device,
              dtype=dtype,
          )
          obj = NotSoAbstractRiskMeasure(n_w=3)
          # MO samples without weights
          with self.assertRaises(RuntimeError):
              obj(torch.ones(3, 2, device=self.device, dtype=dtype))
          # test _prepare_samples
          expected_samples = torch.tensor(
              [[[1.0, 0.5, 2.0], [3.0, 1.0, 5.0]]],
              device=self.device,
              dtype=dtype,
          )
          prepared_samples = obj._prepare_samples(samples)
          self.assertTrue(
              torch.equal(prepared_samples, expected_samples)
          )
          # test batches
          samples = torch.rand(
              5, 3, 6, 1, device=self.device, dtype=dtype
          )
          expected_samples = samples.view(5, 3, 2, 3)
          prepared_samples = obj._prepare_samples(samples)
          self.assertTrue(
              torch.equal(prepared_samples, expected_samples)
          )
          # negating with preprocessing function.
          obj = NotSoAbstractRiskMeasure(
              n_w=3,
              preprocessing_function=LinearMCObjective(
                  weights=torch.tensor(
                      [-1.0], device=self.device, dtype=dtype
                  )
              ),
          )
          prepared_samples = obj._prepare_samples(samples)
          self.assertTrue(
              torch.equal(prepared_samples, -expected_samples)
          )
          # MO with weights
          obj = NotSoAbstractRiskMeasure(
              n_w=2,
              preprocessing_function=LinearMCObjective(
                  weights=torch.tensor(
                      [1.0, 2.0], device=self.device, dtype=dtype
                  )
              ),
          )
          samples = torch.tensor(
              [
                  [
                      [1.0, 2.0],
                      [0.5, 0.7],
                      [2.0, 1.5],
                      [3.0, 4.0],
                      [1.0, 0.0],
                      [5.0, 3.0],
                  ]
              ],
              device=self.device,
              dtype=dtype,
          )
          expected_samples = torch.tensor(
              [[[5.0, 1.9], [5.0, 11.0], [1.0, 11.0]]],
              device=self.device,
              dtype=dtype,
          )
          prepared_samples = obj._prepare_samples(samples)
          self.assertTrue(
              torch.equal(prepared_samples, expected_samples)
          )
  
imports: from typing import Optional
  import torch
  from botorch.acquisition.objective import LinearMCObjective
  from botorch.acquisition.risk_measures import CVaR, Expectation, RiskMeasureMCObjective, VaR, WorstCase
  from botorch.utils.testing import BotorchTestCase
  from torch import Tensor
label_desc: Use image node_id 1 for calling the TestRiskMeasureMCObjective obj's underlying member method code with example usage: obj.test_risk_measure_mc_objective() without return types