code: def test_train_test_split_dask_dataframe(
      xy_classification_pandas, train_size, test_size
  ):
      X, y = xy_classification_pandas
      kwargs = {"shuffle": True} if DASK_2130 else {}
  
      (
          X_train,
          X_test,
          y_train,
          y_test,
      ) = dask_ml.model_selection.train_test_split(
          X, y, train_size=train_size, test_size=test_size, **kwargs
      )
      assert isinstance(X_train, dd.DataFrame)
      assert isinstance(y_train, dd.Series)
  
      assert (y_train.size + y_test.size).compute() == len(y)
  
imports: import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  from sklearn.datasets import fetch_20newsgroups, make_regression
  import dask_ml.model_selection
  from dask_ml._compat import DASK_2130
label_desc: Use image node_id 13 for calling a global function with example usage: test_train_test_split_dask_dataframe(xy_classification_pandas, train_size, test_size) without return types