code: def test_verify_output_shape(self):
      # output shape matching t-batch shape of X
      self.assertTrue(
          _verify_output_shape(
              acqf=None, X=torch.ones(3, 2, 1), output=torch.ones(3)
          )
      )
      # output shape is [], t-batch shape of X is [1]
      X = torch.ones(1, 1, 1)
      self.assertTrue(
          _verify_output_shape(acqf=None, X=X, output=torch.tensor(1))
      )
      # shape mismatch and cls does not have model attribute
      acqf = BMIMTestClass()
      with self.assertWarns(RuntimeWarning):
          self.assertTrue(
              _verify_output_shape(acqf=acqf, X=X, output=X)
          )
      # shape mismatch and cls.model does not define batch shape
      acqf.model = NotSoAbstractBaseModel()
      with self.assertWarns(RuntimeWarning):
          self.assertTrue(
              _verify_output_shape(acqf=acqf, X=X, output=X)
          )
      # Output matches model batch shape.
      acqf.model._batch_shape = torch.Size([3, 5])
      self.assertTrue(
          _verify_output_shape(acqf=acqf, X=X, output=torch.empty(3, 5))
      )
      # Output has additional dimensions beyond model batch shape.
      for X_batch in [(2, 3, 5), (2, 1, 5), (2, 1, 1)]:
          self.assertTrue(
              _verify_output_shape(
                  acqf=acqf,
                  X=torch.empty(*X_batch, 1, 1),
                  output=torch.empty(2, 3, 5),
              )
          )
  
imports: from typing import Any
  import torch
  from botorch.models import GenericDeterministicModel, HigherOrderGP, ModelList, PairwiseGP, SaasFullyBayesianSingleTaskGP, SingleTaskGP
  from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP
  from botorch.models.gp_regression_fidelity import SingleTaskMultiFidelityGP
  from botorch.models.gp_regression_mixed import MixedSingleTaskGP
  from botorch.models.model import Model
  from botorch.models.multitask import MultiTaskGP
  from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior
  from botorch.utils.transforms import _verify_output_shape, concatenate_pending_points, is_ensemble, is_fully_bayesian, match_batch_shape, normalize, normalize_indices, standardize, t_batch_mode_transform, unnormalize
  from torch import Tensor
label_desc: Use image node_id 1 for calling the TestBatchModeTransform obj's underlying member method code with example usage: obj.test_verify_output_shape() without return types