class_docstr: Parent class for checks that only use one dataset.
class_docstr_tok: ["Parent","class","for","checks","that","only","use","one","dataset","."]
docstr: Run check.
  
  Parameters
  ----------
  dataset: TextData
      Dataset representing data an estimator was fitted on
  model: object, default: None
      Dummy model object for compatibility with SingleDatasetBaseCheck
  with_display : bool , default: True
      flag that determines if checks will calculate display (redundant in some checks).
  predictions: Union[TTextPred, None] , default: None
      predictions on dataset
  probabilities: Union[TTextProba, None] , default: None
      probabilities on dataset
  model_classes: Optional[List], default: None
      For classification: list of classes known to the model
  random_state : int, default 42
      A seed to set for pseudo-random functions, primarily sampling.
  
  {prediction_formats:2*indent}
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code: def run(
      self,
      dataset: TextData,
      model=None,  # pylint: disable=unused-argument
      with_display: bool = True,
      predictions: Optional[TTextPred] = None,
      probabilities: Optional[TTextProba] = None,
      model_classes: Optional[List] = None,
      random_state: int = 42,
  ) -> CheckResult:
      """Run check.
  
      Parameters
      ----------
      dataset: TextData
          Dataset representing data an estimator was fitted on
      model: object, default: None
          Dummy model object for compatibility with SingleDatasetBaseCheck
      with_display : bool , default: True
          flag that determines if checks will calculate display (redundant in some checks).
      predictions: Union[TTextPred, None] , default: None
          predictions on dataset
      probabilities: Union[TTextProba, None] , default: None
          probabilities on dataset
      model_classes: Optional[List], default: None
          For classification: list of classes known to the model
      random_state : int, default 42
          A seed to set for pseudo-random functions, primarily sampling.
  
      {prediction_formats:2*indent}
      """
      assert self.context_type is not None
      context = self.context_type(  # pylint: disable=not-callable
          train_dataset=dataset,
          with_display=with_display,
          train_pred=predictions,
          train_proba=probabilities,
          model_classes=model_classes,
          random_state=random_state,
      )
      result = self.run_logic(context, dataset_kind=DatasetKind.TRAIN)
      context.finalize_check_result(result, self, DatasetKind.TRAIN)
      return result
  
file_path: deepchecks/deepchecks/nlp/base_checks.py
imports: import abc
  from typing import List, Optional
  from deepchecks.core.check_result import CheckResult
  from deepchecks.core.checks import DatasetKind, SingleDatasetBaseCheck, TrainTestBaseCheck
  from deepchecks.nlp._shared_docs import docstrings
  from deepchecks.nlp.context import Context, TTextPred, TTextProba
  from deepchecks.nlp.text_data import TextData
label_desc: Use image node_id 1 for calling the SingleDatasetCheck obj's underlying member method code with example usage: obj.run(dataset, model, with_display, predictions, probabilities, model_classes, random_state) and returns: result