code: def test_plot_regression_categorical_missing_value():
      df = pd.DataFrame({"y": np.random.normal(size=300)})
      df.loc[100:200, "y"] += 1
      df.loc[200:300, "y"] += 2
      df["x"] = "a"
      df.loc[100:200, "x"] = "b"
      df.loc[200:300, "x"] = np.NaN
      res = plot(df, target_col="y")
      assert len(res[2][0, 0].get_yticklabels()) == 3
      assert (
          res[2][0, 0].get_yticklabels()[2].get_text() == "dabl_mi..."
      )
  
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 20 for calling a global function with example usage: test_plot_regression_categorical_missing_value() without return types