code: def test_model_type_hints(monkeypatch, type_hints, Model):
      type_hints = {
          "a": "continuous",
          "b": "categorical",
          "c": "useless",
      }
      X = pd.DataFrame(
          {
              "a": [0, 1, 0, 1, 0, 0, 1, 0, 1, 1] * 2,
              "b": [0.1, 0.2, 0.3, 0.1, 0.1, 0.2, 0.2, 0.1, 0.1, 0.3]
              * 2,
              "c": ["a", "b", "a", "b", "a", "a", "b", "b", "a", "b"]
              * 2,
              "target": [1, 2, 2, 1, 1, 2, 1, 2, 1, 2] * 2,
          }
      )
  
      monkeypatch.setattr(
          Model, "_get_estimators", mock_get_estimators_dummy
      )
  
      sc = Model(type_hints=type_hints)
      sc.fit(X, target_col="target")
  
      ct = sc.est_[0].ct_
  
      for col_name in type_hints:
          if type_hints[col_name] == "useless":
              # if a column was annotated as useless it's not used
              assert (
                  len(get_columns_by_name(ct, type_hints[col_name]))
                  == 0
              )
          else:
              # if a column was annotated as continuous,
              # it's in the continuous part
              # same for categorical
              assert col_name in get_columns_by_name(
                  ct, type_hints[col_name]
              )
  
imports: import pytest
  import pandas
  import numpy
  from sklearn.datasets import load_iris, make_blobs, load_digits, fetch_openml
  from sklearn.svm import LinearSVC
  from sklearn.linear_model import LogisticRegression
  from sklearn.dummy import DummyClassifier
  from dabl.datasets import load_titanic
  from dabl.models import SimpleClassifier, SimpleRegressor, AnyClassifier
label_desc: Use image node_id 10 for calling a global function with example usage: test_model_type_hints(monkeypatch, type_hints, Model) without return types