code: def test_type_hints(add, feature_type, target_type):
      X = (
          pd.DataFrame(np.random.randint(4, size=100), columns=["feat"])
          + add
      )
      X["target"] = np.random.uniform(size=100)
      plot(
          X,
          type_hints={"feat": feature_type, "target": target_type},
          target_col="target",
      )
      # get title of figure
      text = plt.gcf()._suptitle.get_text()
      assert feature_type.capitalize() in text
      ax = plt.gca()
      # one of the labels is 'target' iif regression
      labels = ax.get_ylabel() + ax.get_xlabel()
      assert ("target" in labels) == (target_type == "continuous")
      plt.close("all")
  
imports: import pytest
  import numpy
  import pandas
  import matplotlib.pyplot
  import warnings
  import itertools
  from sklearn.datasets import make_regression, make_blobs, load_digits, fetch_openml, load_diabetes
  from sklearn.preprocessing import KBinsDiscretizer
  from dabl.preprocessing import clean, detect_types, guess_ordinal
  from dabl.plot.supervised import plot, plot_classification_categorical, plot_classification_continuous, plot_regression_categorical, plot_regression_continuous
  from dabl.utils import data_df_from_bunch
  from dabl import set_config
label_desc: Use image node_id 2 for calling a global function with example usage: test_type_hints(add, feature_type, target_type) without return types