code: def full_sort_predict(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      item_seq_len = interaction[self.ITEM_SEQ_LEN]
      item_seq = self.reconstruct_test_data(item_seq, item_seq_len)
      seq_output = self.forward(item_seq)
      seq_output = self.gather_indexes(
          seq_output, item_seq_len - 1
      )  # [B H]
      test_items_emb = self.item_embedding.weight[
          : self.n_items
      ]  # delete masked token
      scores = (
          torch.matmul(seq_output, test_items_emb.transpose(0, 1))
          + self.output_bias
      )  # [B, item_num]
      return scores
  
file_path: RecBole/recbole/model/sequential_recommender/bert4rec.py
imports: import random
  import torch
  from torch import nn
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import TransformerEncoder
label_desc: Use image node_id 8 for calling the BERT4Rec obj's underlying member method code with example usage: obj.full_sort_predict(interaction) and returns: scores