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}
code: def __init__(
      self,
      train: Optional[VisionData] = None,
      test: Optional[VisionData] = None,
      random_state: int = 42,
      with_display: bool = True,
  ):
      # Validations
      if train is None and test is None:
          raise DeepchecksValueError(
              "At least one dataset must be passed to the method!"
          )
      if test is not None and train is None:
          raise DatasetValidationError(
              "Can't initialize context with only test. if you have single dataset, "
              "initialize it as train"
          )
  
      train.init_cache()
      train.name = (
          DEFAULT_DATASET_NAMES[0] if train.name is None else train.name
      )
      self._task_type = train.task_type
  
      if test is not None:
          validate_vision_data_compatibility(train, test)
          test.init_cache()
          test.name = (
              DEFAULT_DATASET_NAMES[1]
              if test.name is None
              else test.name
          )
  
      self._train = train
      self._test = test
      self._with_display = with_display
      self.random_state = random_state
      set_seeds(random_state)
  
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 1 to create a new Context object from inherited base classes: BaseContext with example: obj = Context(train, test, random_state, with_display)