code: def test_task_feature(self, get_model_mock):
      x = [torch.zeros(2, 2)]
      y = [torch.zeros(2, 1)]
      yvars = [torch.ones(2, 1)]
      get_and_fit_model(
          Xs=x,
          Ys=y,
          Yvars=yvars,
          task_features=[1],
          fidelity_features=[],
          metric_names=["L2NormMetric"],
          state_dict=None,
          refit_model=False,
      )
      # Check that task feature was correctly passed to _get_model
      self.assertEqual(
          get_model_mock.mock_calls[0][2]["task_feature"], 1
      )
  
      # check error on multiple task features
      with self.assertRaises(NotImplementedError):
          get_and_fit_model(
              Xs=x,
              Ys=y,
              Yvars=yvars,
              task_features=[0, 1],
              fidelity_features=[],
              metric_names=["L2NormMetric"],
              state_dict=None,
              refit_model=False,
          )
  
      # check error on multiple fidelity features
      with self.assertRaises(NotImplementedError):
          get_and_fit_model(
              Xs=x,
              Ys=y,
              Yvars=yvars,
              task_features=[],
              fidelity_features=[-1, -2],
              metric_names=["L2NormMetric"],
              state_dict=None,
              refit_model=False,
          )
  
      # check error on botch task and fidelity feature
      with self.assertRaises(NotImplementedError):
          get_and_fit_model(
              Xs=x,
              Ys=y,
              Yvars=yvars,
              task_features=[1],
              fidelity_features=[-1],
              metric_names=["L2NormMetric"],
              state_dict=None,
              refit_model=False,
          )
  
imports: from copy import deepcopy
  from unittest import mock
  from unittest.mock import Mock
  import torch
  from ax.models.torch.botorch_defaults import _get_acquisition_func, _get_customized_covar_module, _get_model, get_and_fit_model, get_warping_transform, NO_FEASIBLE_POINTS_MESSAGE
  from ax.utils.common.testutils import TestCase
  from ax.utils.common.typeutils import checked_cast, not_none
  from ax.utils.testing.mock import fast_botorch_optimize
  from botorch.acquisition.logei import qLogExpectedImprovement, qLogNoisyExpectedImprovement
  from botorch.acquisition.monte_carlo import qExpectedImprovement, qNoisyExpectedImprovement, qProbabilityOfImprovement, qSimpleRegret
  from botorch.acquisition.objective import ConstrainedMCObjective
  from botorch.acquisition.penalized import L1PenaltyObjective, PenalizedMCObjective
  from botorch.exceptions.errors import UnsupportedError
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.gp_regression_fidelity import SingleTaskMultiFidelityGP
  from botorch.models.multitask import MultiTaskGP
  from botorch.models.transforms.input import Warp
  from botorch.utils.constraints import get_outcome_constraint_transforms
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.likelihoods.gaussian_likelihood import FixedNoiseGaussianLikelihood, GaussianLikelihood
  from gpytorch.module import Module
  from gpytorch.priors import GammaPrior
  from gpytorch.priors.lkj_prior import LKJCovariancePrior
  from gpytorch.priors.prior import Prior
label_desc: Use image node_id 2 for calling the BotorchDefaultsTest obj's underlying member method code with example usage: obj.test_task_feature(get_model_mock) without return types