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 initialized & validated scorers if provided or default scorers otherwise.
  
  Parameters
  ----------
  scorers : Union[List[str], Dict[str, Union[str, Callable]]], default: None
      List of scorers to use. If None, use default scorers.
      Scorers can be supplied as a list of scorer names or as a dictionary of names and functions.
  use_avg_defaults : bool, default True
      If no scorers were provided, for classification, determines whether to use default scorers that return
      an averaged metric, or default scorers that return a metric per class.
  Returns
  -------
  List[DeepcheckScorer]
      A list of initialized & validated scorers.
code: def get_scorers(
      self,
      scorers: t.Union[
          t.Mapping[str, t.Union[str, t.Callable]], t.List[str]
      ] = None,
      use_avg_defaults=True,
  ) -> t.List[DeepcheckScorer]:
      """Return initialized & validated scorers if provided or default scorers otherwise.
  
      Parameters
      ----------
      scorers : Union[List[str], Dict[str, Union[str, Callable]]], default: None
          List of scorers to use. If None, use default scorers.
          Scorers can be supplied as a list of scorer names or as a dictionary of names and functions.
      use_avg_defaults : bool, default True
          If no scorers were provided, for classification, determines whether to use default scorers that return
          an averaged metric, or default scorers that return a metric per class.
      Returns
      -------
      List[DeepcheckScorer]
          A list of initialized & validated scorers.
      """
      scorers = scorers or get_default_scorers(
          self.task_type, use_avg_defaults
      )
      return init_validate_scorers(
          scorers,
          self.model,
          self.train,
          self.model_classes,
          self.observed_classes,
      )
  
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 13 for calling the Context obj's underlying member method code with example usage: obj.get_scorers(scorers, use_avg_defaults) and returns: init_validate_scorers