code: def test_input_dtypes():
      dtypes = (np.int32, np.int64, np.float32, np.float64)
  
      no_users, no_items = (10, 100)
      no_features = 20
  
      for dtype in dtypes:
          train = sp.coo_matrix((no_users, no_items), dtype=dtype)
  
          user_features = sp.coo_matrix(
              (no_users, no_features), dtype=dtype
          )
          item_features = sp.coo_matrix(
              (no_items, no_features), dtype=dtype
          )
  
          model = LightFM()
          model.fit_partial(
              train,
              user_features=user_features,
              item_features=item_features,
          )
  
          model.predict(
              np.random.randint(0, no_users, 10).astype(np.int32),
              np.random.randint(0, no_items, 10).astype(np.int32),
              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 5 for calling a global function with example usage: test_input_dtypes() without return types