code: def test_check_input():
      # Train for single iter.
      interactions = movielens.get_movielens_dataset("100K")
  
      train, test = random_train_test_split(
          interactions, random_state=RANDOM_STATE
      )
  
      model = ExplicitFactorizationModel(
          loss="regression",
          n_iter=1,
          batch_size=1024,
          learning_rate=1e-3,
          l2=1e-6,
          use_cuda=CUDA,
      )
      model.fit(train)
  
      # Modify data to make imcompatible with original model.
      train.user_ids[0] = train.user_ids.max() + 1
      with pytest.raises(ValueError):
          model.fit(train)
  
file_path: spotlight/tests/factorization/test_explicit.py
imports: import os
  import numpy
  import pytest
  from spotlight.cross_validation import random_train_test_split
  from spotlight.datasets import movielens
  from spotlight.evaluation import rmse_score
  from spotlight.factorization.explicit import ExplicitFactorizationModel
  from spotlight.factorization.representations import BilinearNet
  from spotlight.layers import BloomEmbedding
label_desc: Use image node_id 4 for calling a global function with example usage: test_check_input() without return types