class_docstr: Base class for all models
class_docstr_tok: ["Base","class","for","all","models"]
docstr: full sort prediction function.
  Given users, calculate the scores between users and all candidate items.
  
  Args:
      interaction (Interaction): Interaction class of the batch.
  
  Returns:
      torch.Tensor: Predicted scores for given users and all candidate items,
      shape: [n_batch_users * n_candidate_items]
docstr_tok: ["full","sort","prediction","function",".","Given","users",",","calculate","the","scores","between","users","and","all","candidate","items",".","Args",":","interaction","(","Interaction",")",":","Interaction","class","of","the","batch",".","Returns",":","torch.Tensor",":","Predicted","scores","for","given","users","and","all","candidate","items",",","shape",":","[","n_batch_users","*","n_candidate_items","]"]
code: def full_sort_predict(self, interaction):
      r"""full sort prediction function.
      Given users, calculate the scores between users and all candidate items.
  
      Args:
          interaction (Interaction): Interaction class of the batch.
  
      Returns:
          torch.Tensor: Predicted scores for given users and all candidate items,
          shape: [n_batch_users * n_candidate_items]
      """
      raise NotImplementedError
  
imports: from logging import getLogger
  import numpy
  import torch
  import torch.nn
  from recbole.model.layers import FMEmbedding, FMFirstOrderLinear, FLEmbedding
  from recbole.utils import ModelType, InputType, FeatureSource, FeatureType, set_color
label_desc: Use image node_id 4 for calling the AbstractRecommender obj's underlying member method code with example usage: obj.full_sort_predict(interaction) without return types