code: def load_fitted_model(pretrained=True):
      """Load and return a fitted classification model.
  
      Returns
      -------
      model : Joblib
          The model/pipeline that was trained on the adult dataset.
  
      """
      if sklearn.__version__ == _MODEL_VERSION and pretrained:
          with urlopen(_MODEL_URL) as f:
              model = joblib.load(f)
      else:
          model = _build_model()
          train, _ = load_data()
          model.fit(
              train.data[train.features], train.data[train.label_name]
          )
      return model
  
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 2 for calling a global function with example usage: load_fitted_model(pretrained) and returns: model