code: def test_negative_ordinal():
      # check that a low card int with negative values is plotted correctly
      data = pd.DataFrame(
          [
              np.random.randint(0, 10, size=1000) - 5,
              np.random.randint(0, 2, size=1000),
          ]
      ).T
      # ensure first column is low_card_int
      # FIXME this is a weird example for ordinal, as it's uniform?
      assert (
          detect_types(data).T.idxmax()
          == ["low_card_int_ordinal", "categorical"]
      ).all()
      # smoke test
      plot(data, target_col=1)
  
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 11 for calling a global function with example usage: test_negative_ordinal() without return types