class_docstr: Parent class for checks that only use a model and no datasets.
class_docstr_tok: ["Parent","class","for","checks","that","only","use","a","model","and","no","datasets","."]
docstr: Run check.
  
  Parameters
  ----------
  model
      Model to run the check on
  {additional_run_params:2*indent}
docstr_tok: ["Run","check",".","Parameters","--","--","--","--","--","model","Model","to","run","the","check","on","{","additional_run_params:2","*","indent","}"]
code: def run(
      self, model, random_state: int = 42, with_display: bool = True
  ) -> CheckResult:
      """Run check.
  
      Parameters
      ----------
      model
          Model to run the check on
      {additional_run_params:2*indent}
      """
      with ProgressBarGroup() as progressbar_factory:
          # Currently we do not receive model into context since there are no model only checks
          context: Context = self.context_type(
              random_state=random_state, with_display=with_display
          )
          self.initialize_run(context)
  
          with progressbar_factory.create_dummy(
              name="Computing Check", unit="Check"
          ):
              result = self.compute(context)
              context.finalize_check_result(result, self)
      return result
  
imports: from typing import Any
  from deepchecks.core.check_result import CheckResult
  from deepchecks.core.checks import DatasetKind, ModelOnlyBaseCheck, SingleDatasetBaseCheck, TrainTestBaseCheck
  from deepchecks.utils.ipython import ProgressBarGroup
  from deepchecks.vision import deprecation_warnings
  from deepchecks.vision._shared_docs import docstrings
  from deepchecks.vision.context import Context
  from deepchecks.vision.vision_data import VisionData
  from deepchecks.vision.vision_data.batch_wrapper import BatchWrapper
label_desc: Use image node_id 1 for calling the ModelOnlyCheck obj's underlying member method code with example usage: obj.run(model, random_state, with_display) and returns: result