docstr: calculate the the force of repeat
docstr_tok: ["calculate","the","the","force","of","repeat"]
code: def forward(self, all_memory, last_memory, item_seq, mask=None):
      """
      calculate the the force of repeat
      """
      all_memory = self.dropout(self.Ur(all_memory))
  
      last_memory = self.dropout(self.Wr(last_memory))
      last_memory = last_memory.unsqueeze(1)
      last_memory = last_memory.repeat(1, self.seq_len, 1)
  
      output_er = self.tanh(last_memory + all_memory)
  
      output_er = self.Vr(output_er).squeeze(2)
  
      if mask is not None:
          output_er.masked_fill_(mask, -1e9)
  
      output_er = nn.Softmax(dim=-1)(output_er)
  
      batch_size, b_len = item_seq.size()
      repeat_recommendation_decoder = torch.zeros(
          [batch_size, self.num_item], device=self.device
      )
      repeat_recommendation_decoder.scatter_add_(1, item_seq, output_er)
  
      return repeat_recommendation_decoder.to(self.device)
  
imports: import torch
  from torch import nn
  from torch.nn import functional
  from torch.nn.init import xavier_normal_, constant_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.utils import InputType
label_desc: Use image node_id 2 for calling the Repeat_Recommendation_Decoder obj's underlying member method code with example usage: obj.forward(all_memory, last_memory, item_seq, mask) and returns: repeat_recommendation_decoder