code: def test_plot_regression_correlation():
      df = pd.DataFrame({"y": np.random.normal(size=1000)})
      df["x1"] = df["y"] + np.random.normal(scale=0.1, size=1000)
      df["x2"] = df["x1"] + np.random.normal(scale=0.1, size=1000)
      with pytest.warns(
          UserWarning,
          match=r"Not plotting highly correlated \(0.*\) feature x2",
      ):
          res = plot_regression_continuous(df, target_col="y")
      assert res.shape == (1, 1)
      with warnings.catch_warnings(record=True) as w:
          res = plot_regression_continuous(
              df, target_col="y", prune_correlations_threshold=0
          )
          assert len(w) == 0
      assert res.shape == (1, 2)
  
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 22 for calling a global function with example usage: test_plot_regression_correlation() without return types