code: def test_float_classification_target():
      # check we can plot even if we do classification with a float target
      X, y = make_blobs()
      data = pd.DataFrame(X)
      data["target"] = y.astype(float)
      types = detect_types(data)
      assert types.categorical["target"]
      plot(data, target_col="target")
      # same with "actual float" - we need to specify classification for that :-/
      data["target"] = y.astype(float) + 0.2
      plot(
          data,
          target_col="target",
          type_hints={"target": "categorical"},
      )
      plt.close("all")
  
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 3 for calling a global function with example usage: test_float_classification_target() without return types