code: def test_qMS_init(self):
      d = 2
      q = 1
      num_data = 3
      q_batch_sizes = [1, 1, 1]
      num_fantasies = [2, 2, 1]
      t_batch_size = [2]
      for dtype in (torch.float, torch.double):
          bounds = torch.tensor(
              [[0], [1]], device=self.device, dtype=dtype
          )
          bounds = bounds.repeat(1, d)
          train_X = torch.rand(
              num_data, d, device=self.device, dtype=dtype
          )
          train_Y = torch.rand(
              num_data, 1, device=self.device, dtype=dtype
          )
          model = SingleTaskGP(train_X, train_Y)
  
          # exactly one of samplers or num_fantasies
          with self.assertRaises(UnsupportedError):
              qMultiStepLookahead(
                  model=model,
                  batch_sizes=q_batch_sizes,
                  valfunc_cls=[qExpectedImprovement] * 4,
                  valfunc_argfacs=[make_best_f] * 4,
                  inner_mc_samples=[2] * 4,
              )
  
          # cannot use qMS as its own valfunc_cls
          with self.assertRaises(UnsupportedError):
              qMultiStepLookahead(
                  model=model,
                  batch_sizes=q_batch_sizes,
                  valfunc_cls=[qMultiStepLookahead] * 4,
                  valfunc_argfacs=[make_best_f] * 4,
                  num_fantasies=num_fantasies,
                  inner_mc_samples=[2] * 4,
              )
  
          # construct using samplers
          samplers = [
              SobolQMCNormalSampler(sample_shape=torch.Size([nf]))
              for nf in num_fantasies
          ]
          qMS = qMultiStepLookahead(
              model=model,
              batch_sizes=q_batch_sizes,
              valfunc_cls=[qExpectedImprovement] * 4,
              valfunc_argfacs=[make_best_f] * 4,
              inner_mc_samples=[2] * 4,
              samplers=samplers,
          )
          self.assertEqual(qMS.num_fantasies, num_fantasies)
  
          # use default valfunc_cls, valfun_argfacs, inner_mc_samples
          qMS = qMultiStepLookahead(
              model=model,
              batch_sizes=q_batch_sizes,
              samplers=samplers,
          )
          self.assertEqual(len(qMS._valfunc_cls), 4)
          self.assertEqual(len(qMS.inner_samplers), 4)
          self.assertEqual(len(qMS._valfunc_argfacs), 4)
  
          # _construct_inner_samplers error catching tests below
          # AnalyticAcquisitionFunction with MCAcquisitionObjective
          with self.assertRaises(UnsupportedError):
              qMultiStepLookahead(
                  model=model,
                  objective=IdentityMCObjective(),
                  batch_sizes=q_batch_sizes,
                  valfunc_cls=[ExpectedImprovement] * 4,
                  valfunc_argfacs=[make_best_f] * 4,
                  num_fantasies=num_fantasies,
              )
          # AnalyticAcquisitionFunction and q > 1
          with self.assertRaises(UnsupportedError):
              qMultiStepLookahead(
                  model=model,
                  batch_sizes=[2, 2, 2],
                  valfunc_cls=[ExpectedImprovement] * 4,
                  valfunc_argfacs=[make_best_f] * 4,
                  num_fantasies=num_fantasies,
                  inner_mc_samples=[2] * 4,
              )
          # AnalyticAcquisitionFunction and inner_mc_samples
          with self.assertWarns(Warning):
              qMultiStepLookahead(
                  model=model,
                  batch_sizes=q_batch_sizes,
                  valfunc_cls=[ExpectedImprovement] * 4,
                  valfunc_argfacs=[make_best_f] * 4,
                  num_fantasies=num_fantasies,
                  inner_mc_samples=[2] * 4,
              )
  
          # test warmstarting
          qMS = qMultiStepLookahead(
              model=model,
              batch_sizes=q_batch_sizes,
              samplers=samplers,
          )
          q_prime = qMS.get_augmented_q_batch_size(q)
          eval_X = torch.rand(
              t_batch_size + [q_prime, d],
              device=self.device,
              dtype=dtype,
          )
          warmstarted_X = warmstart_multistep(
              acq_function=qMS,
              bounds=bounds,
              num_restarts=5,
              raw_samples=10,
              full_optimizer=eval_X,
          )
          self.assertEqual(
              warmstarted_X.shape, torch.Size([5, q_prime, d])
          )
  
      with self.assertRaisesRegex(
          UnsupportedError,
          "`qMultiStepLookahead` got a non-MC `objective`. This is not supported."
          " Use `posterior_transform` and `objective=None` instead.",
      ):
          qMultiStepLookahead(
              model=model, batch_sizes=q_batch_sizes, objective="cat"
          )
  
file_path: botorch/test/acquisition/test_multi_step_lookahead.py
imports: import torch
  from botorch.acquisition import ExpectedImprovement, qExpectedImprovement, qMultiStepLookahead
  from botorch.acquisition.multi_step_lookahead import make_best_f, warmstart_multistep
  from botorch.acquisition.objective import IdentityMCObjective
  from botorch.exceptions.errors import UnsupportedError
  from botorch.models import SingleTaskGP
  from botorch.sampling import SobolQMCNormalSampler
  from botorch.utils.testing import BotorchTestCase
label_desc: Use image node_id 1 for calling the TestMultiStepLookahead obj's underlying member method code with example usage: obj.test_qMS_init() without return types