code: def test_with_learned_objective_train_data_1d_batch(self) -> None:
      train_batch_shape = [1]
      test_batch_shapes = [[], [1], [2]]
      for test_batch_shape in test_batch_shapes:
          self._base_test_with_learned_objective(
              train_batch_shape=torch.Size(train_batch_shape),
              # Batched inputs `X_baseline` are currently unsupported by
              # prune_inferior_points
              prune_baseline=False,
              test_batch_shape=torch.Size(test_batch_shape),
          )
  
imports: from itertools import product
  from typing import Dict
  from warnings import catch_warnings, simplefilter
  import torch
  from botorch.acquisition.input_constructors import get_acqf_input_constructor
  from botorch.acquisition.logei import qLogExpectedImprovement, qLogNoisyExpectedImprovement
  from botorch.acquisition.monte_carlo import qExpectedImprovement, qNoisyExpectedImprovement, qProbabilityOfImprovement
  from botorch.acquisition.objective import LearnedObjective
  from botorch.exceptions.warnings import InputDataWarning
  from botorch.fit import fit_gpytorch_mll
  from botorch.models import SingleTaskGP
  from botorch.sampling.normal import SobolQMCNormalSampler
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
label_desc: Use image node_id 5 for calling the TestObjectiveAndConstraintIntegration obj's underlying member method code with example usage: obj.test_with_learned_objective_train_data_1d_batch() without return types