code: def predict(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      item_seq_len = interaction[self.ITEM_SEQ_LEN]
      test_item = interaction[self.ITEM_ID]
      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_item_emb = self.item_embedding(test_item)
      scores = (torch.mul(seq_output, test_item_emb)).sum(
          dim=1
      ) + self.output_bias[
          test_item
      ]  # [B]
      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 7 for calling the BERT4Rec obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores