code: def load_data(
      data_format: str = "Dataset", as_train_test: bool = True
  ) -> t.Union[t.Tuple, t.Union[Dataset, pd.DataFrame]]:
      """Load and returns the Adult dataset (classification).
  
      Parameters
      ----------
      data_format : str, default: 'Dataset'
          Represent the format of the returned value. Can be 'Dataset'|'Dataframe'
          'Dataset' will return the data as a Dataset object
          'Dataframe' will return the data as a pandas Dataframe object
  
      as_train_test : bool, default: True
          If True, the returned data is splitted into train and test exactly like the toy model
          was trained. The first return value is the train data and the second is the test data.
          In order to get this model, call the load_fitted_model() function.
          Otherwise, returns a single object.
  
      Returns
      -------
      dataset : Union[deepchecks.Dataset, pd.DataFrame]
          the data object, corresponding to the data_format attribute.
      train, test : Tuple[Union[deepchecks.Dataset, pd.DataFrame],Union[deepchecks.Dataset, pd.DataFrame]
          tuple if as_train_test = True. Tuple of two objects represents the dataset splitted to train and test sets.
      """
      if not as_train_test:
          dataset = pd.read_csv(
              _FULL_DATA_URL, names=_FEATURES + [_target]
          )
          dataset["income"] = dataset["income"].str.replace(
              ".", "", regex=True
          )  # fix label inconsistency
  
          if data_format == "Dataset":
              dataset = Dataset(
                  dataset, label=_target, cat_features=_CAT_FEATURES
              )
              return dataset
          elif data_format == "Dataframe":
              return dataset
          else:
              raise ValueError(
                  'data_format must be either "Dataset" or "Dataframe"'
              )
      else:
          train = pd.read_csv(
              _TRAIN_DATA_URL, names=_FEATURES + [_target]
          )
          test = pd.read_csv(
              _TEST_DATA_URL, skiprows=1, names=_FEATURES + [_target]
          )
          test[_target] = test[_target].str[:-1]
  
          if data_format == "Dataset":
              train = Dataset(
                  train, label=_target, cat_features=_CAT_FEATURES
              )
              test = Dataset(
                  test, label=_target, cat_features=_CAT_FEATURES
              )
              return train, test
          elif data_format == "Dataframe":
              return train, test
          else:
              raise ValueError(
                  'data_format must be either "Dataset" or "Dataframe"'
              )
  
file_path: deepchecks/deepchecks/tabular/datasets/classification/adult.py
imports: import typing
  from urllib.request import urlopen
  import joblib
  import pandas
  import sklearn
  from category_encoders import OrdinalEncoder
  from sklearn.compose import ColumnTransformer
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import Pipeline
  from deepchecks.tabular.dataset import Dataset
label_desc: Use image node_id 1 for calling a global function with example usage: load_data(data_format, as_train_test) and returns: dataset, train,  test, dataset, train,  test