code: def test_get_user_item_pairs_example(device, n_items_type):
      user_ids = np.array([10, 11, 12])
  
      n_items = 4
      if n_items_type == "np.int64":
          n_items = np.int64(n_items)
  
      expected_users = torch.tensor(
          [10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 12],
          device=device,
      )
      expected_items = torch.tensor(
          [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], device=device
      )
  
      actual_users, actual_items = _get_user_item_pairs(
          user_ids=user_ids, n_items=n_items, device=device
      )
  
      assert torch.equal(actual_users, expected_users)
      assert torch.equal(actual_items, expected_items)
  
imports: import copy
  from functools import partial
  import re
  from unittest import mock
  import numpy
  import pytest
  from sklearn.metrics import roc_auc_score
  import torch
  import torchmetrics
  from collie.metrics import _get_evaluate_in_batches_device, _get_labels, _get_user_item_pairs, auc, evaluate_in_batches, explicit_evaluate_in_batches, get_preds, mapk, mrr
label_desc: Use image node_id 2 for calling a global function with example usage: test_get_user_item_pairs_example(device, n_items_type) without return types