code: def test_get_user_item_pairs_unordered(device, n_items_type):
      user_ids = np.array([1, 16, 33, 22])
  
      n_items = 2
      if n_items_type == "np.int64":
          n_items = np.int64(n_items)
  
      expected_users = torch.tensor(
          [1, 1, 16, 16, 33, 33, 22, 22], device=device
      )
      expected_items = torch.tensor(
          [0, 1, 0, 1, 0, 1, 0, 1], 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 3 for calling a global function with example usage: test_get_user_item_pairs_unordered(device, n_items_type) without return types