code: def test_predict_not_fitted():
      model = LightFM()
  
      with pytest.raises(ValueError):
          model.predict(np.arange(10), np.arange(10))
  
      with pytest.raises(ValueError):
          model.predict_rank(1)
  
      with pytest.raises(ValueError):
          model.get_user_representations()
  
      with pytest.raises(ValueError):
          model.get_item_representations()
  
imports: import numpy
  import pytest
  import scipy.sparse
  from lightfm.lightfm import LightFM
label_desc: Use image node_id 14 for calling a global function with example usage: test_predict_not_fitted() without return types