code: def test_concatenate_pending_points(self):
      c = BMIMTestClass()
      # test if no pending points
      c.X_pending = None
      X = torch.rand(1, 2)
      self.assertTrue(torch.equal(c.dummy_method(X), X))
      # basic test
      X_pending = torch.rand(2, 2)
      c.X_pending = X_pending
      X_expected = torch.cat([X, X_pending], dim=-2)
      self.assertTrue(torch.equal(c.dummy_method(X), X_expected))
      # batch test
      X = torch.rand(2, 1, 2)
      X_expected = torch.cat([X, X_pending.expand(2, 2, 2)], dim=-2)
      self.assertTrue(torch.equal(c.dummy_method(X), X_expected))
  
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 TestConcatenatePendingPoints obj's underlying member method code with example usage: obj.test_concatenate_pending_points() without return types