code: def test_overflow_predict():
      no_users, no_items = (1000, 1000)
  
      train = sp.rand(no_users, no_items, format="csr", random_state=42)
  
      model = LightFM(loss="warp")
  
      model.fit(train)
  
      with pytest.raises((ValueError, OverflowError)):
          print(
              model.predict(
                  1231241241231241414,
                  np.arange(no_items),
                  user_features=sp.identity(no_users),
              )
          )
  
imports: import numpy
  import pytest
  import scipy.sparse
  from lightfm.lightfm import LightFM
label_desc: Use image node_id 17 for calling a global function with example usage: test_overflow_predict() without return types