code: def test_dataframe_warns_about_chunks(fit_intercept):
      rng = np.random.RandomState(42)
      n, d = 20, 5
      kwargs = dict(npartitions=4)
      X = dd.from_pandas(
          pd.DataFrame(rng.uniform(size=(n, d))), **kwargs
      )
      y = dd.from_pandas(pd.Series(rng.choice(2, size=n)), **kwargs)
      clf = LogisticRegression(fit_intercept=fit_intercept)
      msg = "does not support dask dataframes.*might be resolved with"
      with pytest.raises(TypeError, match=msg):
          clf.fit(X, y)
      clf.fit(X.values, y.values)
      clf.fit(X.to_dask_array(), y.to_dask_array())
      clf.fit(
          X.to_dask_array(lengths=True), y.to_dask_array(lengths=True)
      )
  
imports: import dask.array
  import dask.dataframe
  import numpy
  import numpy.linalg
  import pandas
  import pytest
  import sklearn.linear_model
  from dask.dataframe.utils import assert_eq
  from dask_glm.regularizers import Regularizer
  from sklearn.pipeline import make_pipeline
  import dask_ml.linear_model
  from dask_ml.datasets import make_classification, make_counts, make_regression
  from dask_ml.linear_model import LinearRegression, LogisticRegression, PoissonRegression
  from dask_ml.linear_model.utils import add_intercept
  from dask_ml.model_selection import GridSearchCV
label_desc: Use image node_id 19 for calling a global function with example usage: test_dataframe_warns_about_chunks(fit_intercept) without return types