code: def test_train_test_split_sparse(setup):
      # check that train_test_split converts scipy sparse matrices
      # to csr, as stated in the documentation
      X = np.arange(100).reshape((10, 10))
      sparse_types = [sps.csr_matrix, sps.csc_matrix, sps.coo_matrix]
      for InputFeatureType in sparse_types:
          X_s = InputFeatureType(X)
          for x in (X_s, mt.tensor(X_s, chunk_size=(2, 5))):
              X_train, X_test = train_test_split(x)
              assert isinstance(X_train.fetch(), SparseNDArray)
              assert isinstance(X_test.fetch(), SparseNDArray)
  
imports: import itertools
  import numpy
  import pandas
  import pytest
  from ....None import dataframe
  from ....None import tensor
  from ....dataframe.core import DATAFRAME_TYPE
  from ....lib.sparse import SparseNDArray
  from ...utils.validation import _num_samples
  from ..None import train_test_split, KFold
label_desc: Use image node_id 6 for calling a global function with example usage: test_train_test_split_sparse(setup) without return types