code: def full_sort_topk(uid_series, model, test_data, k, device=None):
      """Calculate the top-k items' scores and ids for each user in uid_series.
  
      Note:
          The score of [pad] and history items will be set into -inf.
  
      Args:
          uid_series (numpy.ndarray): User id series.
          model (AbstractRecommender): Model to predict.
          test_data (FullSortEvalDataLoader): The test_data of model.
          k (int): The top-k items.
          device (torch.device, optional): The device which model will run on. Defaults to ``None``.
              Note: ``device=None`` is equivalent to ``device=torch.device('cpu')``.
  
      Returns:
          tuple:
              - topk_scores (torch.Tensor): The scores of topk items.
              - topk_index (torch.Tensor): The index of topk items, which is also the internal ids of items.
      """
      scores = full_sort_scores(uid_series, model, test_data, device)
      return torch.topk(scores, k)
  
imports: import numpy
  import torch
  from recbole.data.interaction import Interaction
label_desc: Use image node_id 2 for calling a global function with example usage: full_sort_topk(uid_series, model, test_data, k, device) and returns: torch