code: def test_predict_recommend(self):
      def gen_rand_user_item_feature(user_num, item_num, class_num):
          user_id = random.randint(1, user_num)
          item_id = random.randint(1, item_num)
          rating = random.randint(1, class_num)
          sample = Sample.from_ndarray(
              np.array([user_id, item_id]), np.array([rating])
          )
          return UserItemFeature(user_id, item_id, sample)
  
      model = NeuralCF(200, 80, 5)
      data = self.sc.parallelize(range(0, 50)).map(
          lambda i: gen_rand_user_item_feature(200, 80, 5)
      )
      predictions = model.predict_user_item_pair(data).collect()
      print(predictions[0])
      recommended_items = model.recommend_for_user(
          data, max_items=3
      ).collect()
      print(recommended_items[0])
      recommended_users = model.recommend_for_item(
          data, max_users=4
      ).collect()
      print(recommended_users[0])
  
file_path: analytics-zoo/pyzoo/test/zoo/models/recommendation/test_neuralcf.py
imports: import pytest
  import random
  from zoo.pipeline.api.keras.layers import *
  from zoo.models.recommendation import UserItemFeature
  from zoo.models.recommendation import NeuralCF
  from test.zoo.pipeline.utils.test_utils import ZooTestCase
label_desc: Use image node_id 4 for calling the TestNeuralCF obj's underlying member method code with example usage: obj.test_predict_recommend() and returns: UserItemFeature