code: def test_training_schedules():
      model = LightFM(
          no_components=10,
          learning_schedule="adagrad",
          random_state=SEED,
      )
      model.fit_partial(train, epochs=0)
  
      assert (model.item_embedding_gradients == 1).all()
      assert (model.item_embedding_momentum == 0).all()
      assert (model.item_bias_gradients == 1).all()
      assert (model.item_bias_momentum == 0).all()
  
      assert (model.user_embedding_gradients == 1).all()
      assert (model.user_embedding_momentum == 0).all()
      assert (model.user_bias_gradients == 1).all()
      assert (model.user_bias_momentum == 0).all()
  
      model.fit_partial(train, epochs=1)
  
      assert (model.item_embedding_gradients > 1).any()
      assert (model.item_embedding_momentum == 0).all()
      assert (model.item_bias_gradients > 1).any()
      assert (model.item_bias_momentum == 0).all()
  
      assert (model.user_embedding_gradients > 1).any()
      assert (model.user_embedding_momentum == 0).all()
      assert (model.user_bias_gradients > 1).any()
      assert (model.user_bias_momentum == 0).all()
  
      model = LightFM(
          no_components=10,
          learning_schedule="adadelta",
          random_state=SEED,
      )
      model.fit_partial(train, epochs=0)
  
      assert (model.item_embedding_gradients == 0).all()
      assert (model.item_embedding_momentum == 0).all()
      assert (model.item_bias_gradients == 0).all()
      assert (model.item_bias_momentum == 0).all()
  
      assert (model.user_embedding_gradients == 0).all()
      assert (model.user_embedding_momentum == 0).all()
      assert (model.user_bias_gradients == 0).all()
      assert (model.user_bias_momentum == 0).all()
  
      model.fit_partial(train, epochs=1)
  
      assert (model.item_embedding_gradients > 0).any()
      assert (model.item_embedding_momentum > 0).any()
      assert (model.item_bias_gradients > 0).any()
      assert (model.item_bias_momentum > 0).any()
  
      assert (model.user_embedding_gradients > 0).any()
      assert (model.user_embedding_momentum > 0).any()
      assert (model.user_bias_gradients > 0).any()
      assert (model.user_bias_momentum > 0).any()
  
imports: import pickle
  import numpy
  import scipy.sparse
  from scipy import stats
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import KFold, RandomizedSearchCV
  from lightfm.lightfm import LightFM
  from lightfm.datasets import fetch_movielens
  from lightfm.evaluation import auc_score, precision_at_k
label_desc: Use image node_id 33 for calling a global function with example usage: test_training_schedules() without return types