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]
      seq_output = self.forward(item_seq, item_seq_len)
      test_item_emb = self.item_embedding(test_item)
      scores = torch.mul(seq_output, test_item_emb).sum(dim=1)  # [B]
      return scores
  
imports: import random
  import numpy
  import torch
  from torch import nn
  import torch.nn.functional
  import math
  import torch.nn.functional
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
  from recbole.data.interaction import Interaction
label_desc: Use image node_id 14 for calling the FEARec obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores