code: def setUp(self):
      super().setUp()
      self.model = MockModel(MockPosterior())
      self.objective = DummyMCObjective()
      self.X_observed = torch.tensor([[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]])
      self.X_pending = torch.tensor([[1.0, 3.0, 4.0]])
      self.mc_samples = 250
      self.qmc = True
      self.ref_point = [0.0, 0.0]
      self.mo_objective = DummyMCMultiOutputObjective()
      self.Y = torch.tensor(
          [[1.0, 2.0]]
      )  # (2 x 1)-dim multi-objective outcomes
      self.seed = 1
  
imports: import math
  from unittest import mock
  import torch
  from botorch.acquisition import logei, monte_carlo
  from botorch.acquisition.factory import get_acquisition_function
  from botorch.acquisition.multi_objective import logei, MCMultiOutputObjective, monte_carlo
  from botorch.acquisition.objective import MCAcquisitionObjective, ScalarizedPosteriorTransform
  from botorch.acquisition.utils import compute_best_feasible_objective
  from botorch.utils.multi_objective.box_decompositions.non_dominated import FastNondominatedPartitioning, NondominatedPartitioning
  from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior
  from gpytorch.distributions import MultivariateNormal
  from torch import Tensor
label_desc: Use image node_id 1 for calling the TestGetAcquisitionFunction obj's underlying member method code with example usage: obj.setUp() without return types