class_docstr: Contains all the data + properties the user has passed to a check/suite, and validates it seamlessly.
  
  Parameters
  ----------
  train: Union[Dataset, pd.DataFrame, None] , default: None
      Dataset or DataFrame object, representing data an estimator was fitted on
  test: Union[Dataset, pd.DataFrame, None] , default: None
      Dataset or DataFrame object, representing data an estimator predicts on
  model: Optional[BasicModel] , default: None
      A scikit-learn-compatible fitted estimator instance
  {additional_context_params:indent}
docstr: Return feature importance timeout.
code: def feature_importance_timeout(self) -> t.Optional[int]:
      """Return feature importance timeout."""
      return self._feature_importance_timeout
  
imports: import typing
  import numpy
  import pandas
  from deepchecks.core.context import BaseContext
  from deepchecks.core.errors import DatasetValidationError, DeepchecksNotSupportedError, DeepchecksValueError, ModelValidationError
  from deepchecks.tabular._shared_docs import docstrings
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.tabular.metric_utils import DeepcheckScorer, get_default_scorers, init_validate_scorers
  from deepchecks.tabular.metric_utils.scorers import validate_proba
  from deepchecks.tabular.utils.feature_importance import calculate_feature_importance_or_none, validate_feature_importance
  from deepchecks.tabular.utils.task_inference import get_all_labels, infer_classes_from_model, infer_task_type_by_class_number, infer_task_type_by_labels
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.tabular.utils.validation import ensure_predictions_proba, ensure_predictions_shape, model_type_validation, validate_model
  from deepchecks.utils.docref import doclink
  from deepchecks.utils.logger import get_logger
  from deepchecks.utils.plot import DEFAULT_DATASET_NAMES
  from deepchecks.utils.typing import BasicModel
label_desc: Use image node_id 8 for calling the Context obj's underlying member method code with example usage: obj.feature_importance_timeout() and returns: self