class_docstr: Contains all the data + properties the user has passed to a check/suite, and validates it seamlessly.
  
  Parameters
  ----------
  train : Optional[VisionData] , default: None
      VisionData object, representing data the model was fitted on
  test : Optional[VisionData] , default: None
      VisionData object, representing data the models predicts on
  {additional_run_params:indent}
docstr: Return the relevant VisionData by given kind.
docstr_tok: ["Return","the","relevant","VisionData","by","given","kind","."]
code: def get_data_by_kind(self, kind: DatasetKind):
      """Return the relevant VisionData by given kind."""
      if kind == DatasetKind.TRAIN:
          return self.train
      elif kind == DatasetKind.TEST:
          return self.test
      else:
          raise DeepchecksValueError(f"Unexpected dataset kind {kind}")
  
file_path: deepchecks/deepchecks/vision/context.py
imports: from typing import Optional
  from deepchecks.core import CheckFailure, CheckResult, DatasetKind
  from deepchecks.core.context import BaseContext
  from deepchecks.core.errors import DatasetValidationError, DeepchecksNotSupportedError, DeepchecksValueError
  from deepchecks.utils.plot import DEFAULT_DATASET_NAMES
  from deepchecks.vision._shared_docs import docstrings
  from deepchecks.vision.vision_data import TaskType, VisionData
  from deepchecks.vision.vision_data.utils import set_seeds, validate_vision_data_compatibility
label_desc: Use image node_id 5 for calling the Context obj's underlying member method code with example usage: obj.get_data_by_kind(kind) and returns: self, self