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 whether there is test dataset defined.
docstr_tok: ["Return","whether","there","is","test","dataset","defined","."]
code: def have_test(self):
      """Return whether there is test dataset defined."""
      return self._test is not None
  
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 3 for calling the Context obj's underlying member method code with example usage: obj.have_test() and returns: unknown