code: def test_t_batch_mode_transform(self):
      c = BMIMTestClass()
      # test with q != 1
      # non-batch
      X = torch.rand(3, 2)
      Xout = c.q_method(X)
      self.assertTrue(torch.equal(Xout, X.unsqueeze(0)))
      # test with expected_q = 1
      with self.assertRaises(AssertionError):
          c.q1_method(X)
      # batch
      X = X.unsqueeze(0)
      Xout = c.q_method(X)
      self.assertTrue(torch.equal(Xout, X))
      # test with expected_q = 1
      with self.assertRaises(AssertionError):
          c.q1_method(X)
  
      # test with q = 1
      X = torch.rand(1, 2)
      Xout = c.q_method(X)
      self.assertTrue(torch.equal(Xout, X.unsqueeze(0)))
      # test with expected_q = 1
      Xout = c.q1_method(X)
      self.assertTrue(torch.equal(Xout, X.unsqueeze(0)))
      # batch
      X = X.unsqueeze(0)
      Xout = c.q_method(X)
      self.assertTrue(torch.equal(Xout, X))
      # test with expected_q = 1
      Xout = c.q1_method(X)
      self.assertTrue(torch.equal(Xout, X))
  
      # test single-dim
      X = torch.zeros(1)
      with self.assertRaises(ValueError):
          c.q_method(X)
  
      # test with kwargs
      X = torch.rand(1, 2)
      with self.assertRaises(AssertionError):
          c.kw_method(X)
      Xout = c.kw_method(X, dummy_arg=5)
      self.assertTrue(torch.equal(Xout, X.unsqueeze(0)))
  
      # test assert_output_shape
      X = torch.rand(5, 1, 2)
      with self.assertWarns(RuntimeWarning):
          c.wrong_shape_method(X)
      Xout = c.correct_shape_method(X)
      self.assertEqual(Xout.shape, X.shape[:-2])
      # test when output shape is torch.Size()
      Xout = c.correct_shape_method(torch.rand(1, 2))
      self.assertEqual(Xout.shape, torch.Size())
      # test with model batch shape
      c.model = MockModel(MockPosterior(mean=X))
      with self.assertRaises(AssertionError):
          c.broadcast_batch_shape_method(X)
      c.model = MockModel(
          MockPosterior(mean=X.repeat(2, *[1] * X.dim()))
      )
      Xout = c.broadcast_batch_shape_method(X)
      self.assertEqual(Xout.shape, c.model.batch_shape)
  
      # test with non-tensor argument
      X = ((3, 4), {"foo": True})
      Xout = c.q_method(X)
      self.assertEqual(X, Xout)
  
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 2 for calling the TestBatchModeTransform obj's underlying member method code with example usage: obj.test_t_batch_mode_transform() without return types