code: def _build_model():
      """Build the model to fit."""
      categorical_transformer = Pipeline(
          steps=[
              (
                  "encoder",
                  run_available_kwargs(
                      OrdinalEncoder,
                      handle_unknown="use_encoded_value",
                      unknown_value=np.nan,
                      dtype=np.float64,
                  ),
              )
          ]
      )
      preprocessor = ColumnTransformer(
          transformers=[
              ("num", "passthrough", _NUM_FEATURES),
              ("cat", categorical_transformer, _CAT_FEATURES),
          ]
      )
  
      model = Pipeline(
          steps=[
              ("preprocessing", preprocessor),
              (
                  "model",
                  run_available_kwargs(
                      HistGradientBoostingClassifier,
                      max_depth=5,
                      max_iter=200,
                      random_state=42,
                      categorical_features=[False] * len(_NUM_FEATURES)
                      + [True] * len(_CAT_FEATURES),
                  ),
              ),
          ]
      )
      return model
  
file_path: deepchecks/deepchecks/tabular/datasets/classification/lending_club.py
imports: import typing
  import warnings
  from urllib.request import urlopen
  import joblib
  import numpy
  import pandas
  import sklearn
  from category_encoders import OrdinalEncoder
  from sklearn.compose import ColumnTransformer
  from sklearn.ensemble import HistGradientBoostingClassifier
  from sklearn.pipeline import Pipeline
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.utils.function import run_available_kwargs
label_desc: Use image node_id 3 for calling a global function with example usage: _build_model() and returns: model