code: def test_not_enough_features_fails():
      no_users, no_items = (10, 100)
      no_features = 20
  
      train = sp.coo_matrix((no_users, no_items), dtype=np.int32)
  
      user_features = sp.csr_matrix(
          (no_users - 1, no_features), dtype=np.int32
      )
      item_features = sp.csr_matrix(
          (no_items - 1, no_features), dtype=np.int32
      )
      model = LightFM()
      with pytest.raises(Exception):
          model.fit_partial(
              train,
              user_features=user_features,
              item_features=item_features,
          )
  
imports: import numpy
  import pytest
  import scipy.sparse
  from lightfm.lightfm import LightFM
label_desc: Use image node_id 6 for calling a global function with example usage: test_not_enough_features_fails() without return types