code: def test_plot_regression_with_target_outliers():
      df = pd.DataFrame(
          data={
              "feature": np.random.randint(low=1, high=100, size=200),
              # target values are bound between 50 and 100
              "target": np.random.randint(low=50, high=100, size=200),
          }
      )
      # append single outlier record with target value 0
      df = pd.concat(
          [df, pd.DataFrame({"feature": [50], "target": [0]})],
          ignore_index=True,
      )
  
      with pytest.warns(
          UserWarning, match="Dropped 1 outliers in column target."
      ):
          plot_regression_continuous(df, target_col="target")
  
      with pytest.warns(
          UserWarning, match="Dropped 1 outliers in column target."
      ):
          plot_regression_categorical(df, target_col="target")
  
      res = plot(df, target_col="target")
      assert len(res) == 3
      ax = res[0]
      # ensure outlier at 0 was removed
      assert ax.get_xticks()[0] == 40
  
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 19 for calling a global function with example usage: test_plot_regression_with_target_outliers() without return types