docstr: calculate the force of explore
docstr_tok: ["calculate","the","force","of","explore"]
code: def forward(self, all_memory, last_memory, item_seq, mask=None):
      """
      calculate the force of explore
      """
      all_memory_values, last_memory_values = all_memory, last_memory
  
      all_memory = self.dropout(self.Ue(all_memory))
  
      last_memory = self.dropout(self.We(last_memory))
      last_memory = last_memory.unsqueeze(1)
      last_memory = last_memory.repeat(1, self.seq_len, 1)
  
      output_ee = self.tanh(all_memory + last_memory)
      output_ee = self.Ve(output_ee).squeeze(-1)
  
      if mask is not None:
          output_ee.masked_fill_(mask, -1e9)
  
      output_ee = output_ee.unsqueeze(-1)
  
      alpha_e = nn.Softmax(dim=1)(output_ee)
      alpha_e = alpha_e.repeat(1, 1, self.hidden_size)
      output_e = (alpha_e * all_memory_values).sum(dim=1)
      output_e = torch.cat([output_e, last_memory_values], dim=1)
      output_e = self.dropout(self.matrix_for_explore(output_e))
  
      item_seq_first = item_seq[:, 0].unsqueeze(1).expand_as(item_seq)
      item_seq_first = item_seq_first.masked_fill(item_seq > 0, 0)
      item_seq_first.requires_grad_(False)
      output_e.scatter_add_(
          1,
          item_seq + item_seq_first,
          float("-inf") * torch.ones_like(item_seq),
      )
      explore_recommendation_decoder = nn.Softmax(1)(output_e)
  
      return explore_recommendation_decoder
  
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 Explore_Recommendation_Decoder obj's underlying member method code with example usage: obj.forward(all_memory, last_memory, item_seq, mask) and returns: explore_recommendation_decoder