code: def test_get_preds_implicit(
      model, test_implicit_predicted_scores, device
  ):
      n_users, n_items = test_implicit_predicted_scores.shape
      user_ids = np.arange(n_users)
      model.return_value = test_implicit_predicted_scores.view(-1)
      actual_preds = get_preds(
          model=model, user_ids=user_ids, n_items=n_items, device=device
      )
  
      assert torch.equal(actual_preds, test_implicit_predicted_scores)
  
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 4 for calling a global function with example usage: test_get_preds_implicit(model, test_implicit_predicted_scores, device) without return types