code: def test_plot_regression_categoricals_scatter():
      rng = np.random.RandomState(0)
      data = pd.DataFrame(
          rng.normal(scale=4, size=(1000, 2)),
          columns=["cont1", "cont2"],
      )
      data["cat1"] = 1 - 2 * rng.randint(0, 2, size=1000)
      data["cat2"] = 1 - 2 * rng.randint(0, 2, size=1000)
      data["y"] = (
          data.cat1 * (data.cont1 + 2) ** 2
          - 10 * data.cat1
          + data.cont1 * 0.5
          + data.cat2 * data.cont2 * 3
      )
      figs = plot(data, target_col="y", find_scatter_categoricals=True)
      ax1, ax2 = figs[1][0]
      assert ax1.get_xlabel() == "cont2"
      assert ax2.get_xlabel() == "cont1"
      assert ax1.get_legend().get_title().get_text() == "cat2"
      assert ax2.get_legend().get_title().get_text() == "cat1"
  
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 23 for calling a global function with example usage: test_plot_regression_categoricals_scatter() without return types