class_docstr: It is an extension of GRU4Rec, which concatenates item and its corresponding
  pre-trained knowledge graph embedding feature as the input.
class_docstr_tok: ["It","is","an","extension","of","GRU4Rec",",","which","concatenates","item","and","its","corresponding","pre-trained","knowledge","graph","embedding","feature","as","the","input","."]
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 n_items]
      return scores
  
file_path: RecBole/recbole/model/sequential_recommender/gru4reckg.py
imports: import torch
  from torch import nn
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.loss import BPRLoss
label_desc: Use image node_id 5 for calling the GRU4RecKG obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: scores