class_docstr: TransRec is translation-based model for sequential recommendation.
  It assumes that the `prev. item` + `user`  = `next item`.
  We use the Euclidean Distance to calculate the similarity in this implementation.
class_docstr_tok: ["TransRec","is","translation-based","model","for","sequential","recommendation",".","It","assumes","that","the","`","prev",".","item","`","+","`","user","`","=","`","next","item","`",".","We","use","the","Euclidean","Distance","to","calculate","the","similarity","in","this","implementation","."]
code: def predict(self, interaction):
      user = interaction[self.USER_ID]  # [B]
      item_seq = interaction[self.ITEM_SEQ]  # [B Len]
      item_seq_len = interaction[self.ITEM_SEQ_LEN]
      test_item = interaction[self.ITEM_ID]
  
      seq_output = self.forward(user, item_seq, item_seq_len)  # [B H]
      test_item_emb = self.item_embedding(test_item)  # [B H]
      test_bias = self.bias(test_item)  # [B 1]
  
      scores = test_bias - self._l2_distance(
          seq_output, test_item_emb
      )  # [B 1]
      scores = scores.squeeze(-1)  # [B]
      return scores
  
file_path: RecBole/recbole/model/sequential_recommender/transrec.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, EmbLoss, RegLoss
  from recbole.utils import InputType
label_desc: Use image node_id 6 for calling the TransRec obj's underlying member method code with example usage: obj.predict(interaction) and returns: scores