class_docstr: Deep Interest Network utilizes the attention mechanism to get the weight of each user's behavior according
  to the target items, and finally gets the user representation.
  
  Note:
      In the official source code, unlike the paper, user features and context features are not input into DNN.
      We just migrated and changed the official source code.
      But You can get user features embedding from user_feat_list.
      Besides, in order to compare with other models, we use AUC instead of GAUC to evaluate the model.
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code: def calculate_loss(self, interaction):
      label = interaction[self.LABEL_FIELD]
      item_seq = interaction[self.ITEM_SEQ]
      user = interaction[self.USER_ID]
      item_seq_len = interaction[self.ITEM_SEQ_LEN]
      next_items = interaction[self.POS_ITEM_ID]
      output = self.forward(user, item_seq, item_seq_len, next_items)
      loss = self.loss(output, label)
      return loss
  
file_path: RecBole/recbole/model/sequential_recommender/din.py
imports: import torch
  import torch.nn
  from torch.nn.init import xavier_normal_, constant_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import MLPLayers, SequenceAttLayer, ContextSeqEmbLayer
  from recbole.utils import InputType, FeatureType
label_desc: Use image node_id 4 for calling the DIN obj's underlying member method code with example usage: obj.calculate_loss(interaction) and returns: loss