code: def test_plot_regression_missing_categories():
      df = pd.DataFrame(
          {"cat_col": np.random.choice(["a", "b", "c", "d"], size=100)}
      )
      df["target"] = np.NaN
      counts = df.cat_col.value_counts()
      df.loc[df.cat_col == "a", "target"] = np.random.normal(
          size=counts["a"]
      )
      df.loc[df.cat_col == "b", "target"] = np.random.normal(
          1, size=counts["b"]
      )
      axes = plot(df, target_col="target")
      ticklabels = axes[-1][0, 0].get_yticklabels()
      assert [label.get_text() for label in ticklabels] == ["a", "b"]
  
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 21 for calling a global function with example usage: test_plot_regression_missing_categories() without return types