class_docstr: This is an extension of SASRec, which concatenates item representations and item attribute representations
  as the input to the model.
class_docstr_tok: ["This","is","an","extension","of","SASRec",",","which","concatenates","item","representations","and","item","attribute","representations","as","the","input","to","the","model","."]
code: def full_sort_predict(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      item_seq_len = interaction[self.ITEM_SEQ_LEN]
      seq_output = self.forward(item_seq, item_seq_len)
      test_items_emb = self.item_embedding.weight
      scores = torch.matmul(
          seq_output, test_items_emb.transpose(0, 1)
      )  # [B, item_num]
      return scores
  
file_path: RecBole/recbole/model/sequential_recommender/sasrecf.py
imports: import torch
  from torch import nn
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import TransformerEncoder, FeatureSeqEmbLayer
  from recbole.model.loss import BPRLoss
  from recbole.utils import FeatureType
label_desc: Use image node_id 6 for calling the SASRecF obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: scores