class_docstr: DIEN has an interest extractor layer to capture temporal interests from history behavior sequence,and an
  interest evolving layer to capture interest evolving process that is relative to the target item. At interest
  evolving layer, attention mechanism is embedded intothe sequential structure novelly, and the effects of relative
  interests are strengthened during interest evolution.
code: def predict(self, interaction):
      item_seq = interaction[self.ITEM_SEQ]
      neg_item_seq = interaction[self.NEG_ITEM_SEQ]
      user = interaction[self.USER_ID]
      item_seq_len = interaction[self.ITEM_SEQ_LEN]
      next_items = interaction[self.POS_ITEM_ID]
      scores, _ = self.forward(
          user, item_seq, neg_item_seq, item_seq_len, next_items
      )
      return self.sigmoid(scores)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import xavier_normal_, constant_
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence, PackedSequence
  from recbole.utils import ModelType, InputType, FeatureType
  from recbole.model.layers import FMEmbedding, MLPLayers, ContextSeqEmbLayer, SequenceAttLayer
  from recbole.model.abstract_recommender import SequentialRecommender
label_desc: Use image node_id 5 for calling the DIEN obj's underlying member method code with example usage: obj.predict(interaction) and returns: self