code: def test_train_test_split_errors(setup):
      pytest.raises(ValueError, train_test_split)
  
      pytest.raises(
          ValueError, train_test_split, range(3), train_size=1.1
      )
  
      pytest.raises(
          ValueError,
          train_test_split,
          range(3),
          test_size=0.6,
          train_size=0.6,
      )
      pytest.raises(
          ValueError,
          train_test_split,
          range(3),
          test_size=np.float32(0.6),
          train_size=np.float32(0.6),
      )
      pytest.raises(
          ValueError, train_test_split, range(3), test_size="wrong_type"
      )
      pytest.raises(
          ValueError,
          train_test_split,
          range(3),
          test_size=2,
          train_size=4,
      )
      pytest.raises(
          TypeError, train_test_split, range(3), some_argument=1.1
      )
      pytest.raises(ValueError, train_test_split, range(3), range(42))
      pytest.raises(
          ValueError,
          train_test_split,
          range(10),
          shuffle=False,
          stratify=True,
      )
  
      with pytest.raises(
          ValueError,
          match=r"train_size=11 should be either positive and "
          r"smaller than the number of samples 10 or a "
          r"float in the \(0, 1\) range",
      ):
          train_test_split(range(10), train_size=11, test_size=1)
  
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 1 for calling a global function with example usage: test_train_test_split_errors(setup) without return types