code: def _getTensors(self):
      X = torch.tensor(
          [[-2, 1, 3], [0.5, -0.5, 1.0]], device=self.device
      )
      X_null_two = torch.tensor(
          [[-2, 1, 3], [0.5, -0.5, 1.0]], device=self.device
      )
      X_expected = torch.tensor(
          [[-1, 1, -2], [-1, -0.5, -2]], device=self.device
      )
      X_expected_null_two = torch.tensor(
          [[-1, 1, 3], [-1, -0.5, 1.0]], device=self.device
      )
      return X, X_null_two, X_expected, X_expected_null_two
  
imports: from __future__ import annotations
  import warnings
  import torch
  from botorch import settings
  from botorch.acquisition.fixed_feature import FixedFeatureAcquisitionFunction
  from botorch.acquisition.monte_carlo import qExpectedImprovement, qNoisyExpectedImprovement
  from botorch.acquisition.multi_objective.max_value_entropy_search import qMultiObjectiveMaxValueEntropy
  from botorch.acquisition.multi_objective.monte_carlo import qExpectedHypervolumeImprovement, qNoisyExpectedHypervolumeImprovement
  from botorch.exceptions import BotorchError
  from botorch.exceptions.warnings import BotorchWarning
  from botorch.models import ModelListGP, SingleTaskGP
  from botorch.models.transforms.input import Warp
  from botorch.optim.utils import columnwise_clamp, fix_features, get_X_baseline
  from botorch.sampling.normal import IIDNormalSampler
  from botorch.utils.multi_objective.box_decompositions.non_dominated import FastNondominatedPartitioning
  from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior
label_desc: Use image node_id 1 for calling the TestFixFeatures obj's underlying member method code with example usage: obj._getTensors() and returns: X,  X_null_two,  X_expected,  X_expected_null_two