code: def data_implicit_factorization():
      interactions = movielens.get_movielens_dataset("100K")
  
      train, test = random_train_test_split(
          interactions, random_state=RANDOM_STATE
      )
  
      model = ImplicitFactorizationModel(
          loss="bpr",
          n_iter=1,
          batch_size=1024,
          learning_rate=1e-2,
          l2=1e-6,
          random_state=RANDOM_STATE,
          use_cuda=CUDA,
      )
      model.fit(train)
  
      return train, test, model
  
file_path: spotlight/tests/test_evaluation_metrics.py
imports: import os
  import numpy
  import pytest
  from spotlight.evaluation import precision_recall_score, sequence_precision_recall_score
  from spotlight.cross_validation import random_train_test_split, user_based_train_test_split
  from spotlight.datasets import movielens
  from spotlight.factorization.implicit import ImplicitFactorizationModel
  from spotlight.sequence.implicit import ImplicitSequenceModel
label_desc: Use image node_id 1 for calling a global function with example usage: data_implicit_factorization() and returns: train,  test,  model