class_docstr: GRU4Rec is a model that incorporate RNN for recommendation.
  
  Note:
  
      Regarding the innovation of this article,we can only achieve the data augmentation mentioned
      in the paper and directly output the embedding of the item,
      in order that the generation method we used is common to other sequential models.
class_docstr_tok: ["GRU4Rec","is","a","model","that","incorporate","RNN","for","recommendation",".","Note",":","Regarding","the","innovation","of","this","article",",","we","can","only","achieve","the","data","augmentation","mentioned","in","the","paper","and","directly","output","the","embedding","of","the","item",",","in","order","that","the","generation","method","we","used","is","common","to","other","sequential","models","."]
code: def calculate_loss(self, interaction):
      loss, prediction_prob = self.calculate_loss_prob(interaction)
      return loss
  
file_path: RecBole/recbole/model/sequential_recommender/gru4reccpr.py
imports: import torch
  from torch import nn
  from torch.nn.init import xavier_uniform_, xavier_normal_
  import torch.nn.functional
  import math
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
  import sys
label_desc: Use image node_id 6 for calling the GRU4RecCPR obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss