code: def test_get_feature_importances_from_botorch_model(self) -> None:
      tkwargs = {"dtype": torch.double}
      train_X = torch.rand(5, 3, **tkwargs)  # pyre-ignore [6]
      train_Y = train_X.sum(dim=-1, keepdim=True)
      simple_gp = SingleTaskGP(train_X=train_X, train_Y=train_Y)
      simple_gp.covar_module.base_kernel.lengthscale = torch.tensor(
          [1, 3, 5], **tkwargs  # pyre-ignore [6]
      )
      importances = get_feature_importances_from_botorch_model(
          simple_gp
      )
      self.assertTrue(
          np.allclose(importances, np.array([15 / 23, 5 / 23, 3 / 23]))
      )
      self.assertEqual(importances.shape, (1, 1, 3))
      # Model with no base kernel
      simple_gp.covar_module.base_kernel = None  # pyre-ignore [16]
      with self.assertRaisesRegex(
          NotImplementedError,
          "Failed to extract lengthscales from `m.covar_module.base_kernel`",
      ):
          get_feature_importances_from_botorch_model(simple_gp)
  
      with self.assertRaisesRegex(
          RuntimeError,
          "Cannot calculate feature_importances without a fitted model",
      ):
          get_feature_importances_from_botorch_model(None)
  
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 10 for calling the BotorchModelTest obj's underlying member method code with example usage: obj.test_get_feature_importances_from_botorch_model() without return types