code: def test_pass_customized_prior(self, get_model_mock: Mock) -> None:
      x = [torch.zeros(2, 2)]
      y = [torch.zeros(2, 1)]
      yvars = [torch.ones(2, 1)]
      kwarg = {
          "prior": {
              "covar_module_prior": {
                  "lengthscale_prior": GammaPrior(12.0, 2.0),
                  "outputscale_prior": GammaPrior(2.0, 12.0),
              },
          }
      }
      model = get_and_fit_model(
          Xs=x,
          Ys=y,
          Yvars=yvars,
          task_features=[],
          fidelity_features=[],
          metric_names=["L2NormMetric"],
          state_dict=None,
          refit_model=False,
          **kwarg,  # pyre-ignore
      )
      self.assertIsInstance(model, SingleTaskGP)
      self.assertIsInstance(
          model.likelihood, FixedNoiseGaussianLikelihood
      )
  
      self.assertEqual(
          model.covar_module.base_kernel.lengthscale_prior.concentration,
          12.0,
      )
      self.assertEqual(
          model.covar_module.base_kernel.lengthscale_prior.rate, 2.0
      )
      self.assertEqual(
          model.covar_module.outputscale_prior.concentration, 2.0
      )
      self.assertEqual(model.covar_module.outputscale_prior.rate, 12.0)
  
      model = get_and_fit_model(
          Xs=x + x,
          Ys=y + y,
          Yvars=yvars + yvars,
          task_features=[1],
          fidelity_features=[],
          metric_names=["L2NormMetric", "L2NormMetric2"],
          state_dict=None,
          refit_model=False,
          **kwarg,  # pyre-ignore
      )
      for m in model.models:
          self.assertIs(type(m), MultiTaskGP)
          self.assertIsInstance(
              m.likelihood, FixedNoiseGaussianLikelihood
          )
          self.assertEqual(
              m.covar_module.base_kernel.lengthscale_prior.concentration,
              12.0,
          )
          self.assertEqual(
              m.covar_module.base_kernel.lengthscale_prior.rate, 2.0
          )
          self.assertEqual(
              m.covar_module.outputscale_prior.concentration, 2.0
          )
          self.assertEqual(m.covar_module.outputscale_prior.rate, 12.0)
  
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 3 for calling the BotorchDefaultsTest obj's underlying member method code with example usage: obj.test_pass_customized_prior(get_model_mock) without return types