code: def test_BotorchModelOneOutcome(self) -> None:
      (
          Xs1,
          Ys1,
          Yvars1,
          bounds,
          tfs,
          feature_names,
          metric_names,
      ) = get_torch_test_data(
          dtype=torch.float, cuda=False, constant_noise=True
      )
      for use_input_warping, use_loocv_pseudo_likelihood in product(
          (True, False), (True, False)
      ):
          model = BotorchModel(
              use_input_warping=use_input_warping,
              use_loocv_pseudo_likelihood=use_loocv_pseudo_likelihood,
          )
          with mock.patch(FIT_MODEL_MO_PATH) as _mock_fit_model:
              model.fit(
                  datasets=[
                      SupervisedDataset(
                          X=Xs1[0],
                          Y=Ys1[0],
                          Yvar=Yvars1[0],
                          feature_names=feature_names,
                          outcome_names=metric_names,
                      )
                  ],
                  metric_names=metric_names[:1],
                  search_space_digest=SearchSpaceDigest(
                      feature_names=feature_names,
                      bounds=bounds,
                      task_features=tfs,
                  ),
              )
              _mock_fit_model.assert_called_once()
              if use_loocv_pseudo_likelihood:
                  mll_cls = LeaveOneOutPseudoLikelihood
              else:
                  mll_cls = ExactMarginalLogLikelihood
              self.assertIsInstance(
                  _mock_fit_model.mock_calls[0][1][0],
                  mll_cls,
              )
          X = torch.rand(2, 3, dtype=torch.float)
          f_mean, f_cov = model.predict(X)
          self.assertTrue(f_mean.shape == torch.Size([2, 1]))
          self.assertTrue(f_cov.shape == torch.Size([2, 1, 1]))
          if use_input_warping:
              self.assertTrue(hasattr(model.model, "input_transform"))
              self.assertIsInstance(model.model.input_transform, Warp)
          else:
              self.assertFalse(hasattr(model.model, "input_transform"))
  
imports: import dataclasses
  from itertools import chain, product
  from typing import Any, cast, Dict
  from unittest import mock
  import numpy
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.exceptions.core import DataRequiredError
  from ax.models.torch.botorch import BotorchModel, get_feature_importances_from_botorch_model, get_rounding_func
  from ax.models.torch.botorch_defaults import get_and_fit_model, get_chebyshev_scalarization, recommend_best_out_of_sample_point
  from ax.models.torch.utils import sample_simplex
  from ax.models.torch_base import TorchOptConfig
  from ax.utils.common.testutils import TestCase
  from ax.utils.testing.mock import fast_botorch_optimize
  from ax.utils.testing.torch_stubs import get_torch_test_data
  from botorch.acquisition.utils import get_infeasible_cost
  from botorch.models import ModelListGP, SingleTaskGP
  from botorch.models.transforms.input import Warp
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.objective import get_objective_weights_transform
  from gpytorch.likelihoods import _GaussianLikelihoodBase
  from gpytorch.likelihoods.gaussian_likelihood import FixedNoiseGaussianLikelihood
  from gpytorch.mlls import ExactMarginalLogLikelihood, LeaveOneOutPseudoLikelihood
  from gpytorch.priors import GammaPrior
  from gpytorch.priors.lkj_prior import LKJCovariancePrior
label_desc: Use image node_id 7 for calling the BotorchModelTest obj's underlying member method code with example usage: obj.test_BotorchModelOneOutcome() without return types