code: def test_label_truncation():
      a = (
          "a_really_long_name_that_would_mess_up_the_layout_a_lot"
          "_by_just_being_very_long"
      )
      b = (
          "the_target_that_has_an_equally_long_name_which_would_"
          "mess_up_everything_as_well_but_in_different_places"
      )
      df = pd.DataFrame({a: np.random.uniform(0, 1, 1000)})
      df[b] = df[a] + np.random.uniform(0, 0.1, 1000)
      res = plot_regression_continuous(df, target_col=b)
  
      assert res[0, 0].get_ylabel() == "the_target_that_h..."
      assert (
          res[0, 0].get_xlabel()
          == "a_really_long_name_that_would_mess_up_the_layou..."
      )
  
      set_config(truncate_labels=False)
      res = plot_regression_continuous(df, target_col=b)
  
      assert res[0, 0].get_ylabel() == b
      assert res[0, 0].get_xlabel() == a
      set_config(truncate_labels=True)
  
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 24 for calling a global function with example usage: test_label_truncation() without return types