code: def _built_in_importance(
      model: t.Any,
      dataset: t.Union["tabular.Dataset", pd.DataFrame],
  ) -> t.Tuple[t.Optional[pd.Series], t.Optional[str]]:
      """Get feature importance member if present in model."""
      features = (
          dataset.features
          if isinstance(dataset, tabular.Dataset)
          else dataset.columns
      )
  
      if hasattr(model, "feature_importances_"):  # Ensembles
          if model.feature_importances_ is None:
              return None, None
          normalized_feature_importance_values = (
              model.feature_importances_
              / model.feature_importances_.sum()
          )
          return (
              pd.Series(
                  normalized_feature_importance_values, index=features
              ),
              "feature_importances_",
          )
  
      if hasattr(model, "coef_"):  # Linear models
          if model.coef_ is None:
              return None, None
          coef = np.abs(model.coef_.flatten())
          coef = coef / coef.sum()
          return pd.Series(coef, index=features), "coef_"
  
      return None, None
  
imports: import time
  import typing
  import warnings
  import numpy
  import pandas
  from sklearn.inspection import permutation_importance
  from sklearn.pipeline import Pipeline
  from deepchecks import tabular
  from deepchecks.core import errors
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.tabular.metric_utils.scorers import DeepcheckScorer, get_default_scorers, init_validate_scorers
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.tabular.utils.validation import validate_model
  from deepchecks.utils.logger import get_logger
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 3 for calling a global function with example usage: _built_in_importance(model, dataset) and returns: None,  None, pd,  str, pd,  str, None,  None, None,  None