code: def forward(self, item_seq, item_emb):
      mask = item_seq.gt(0)
  
      position_ids = torch.arange(
          item_seq.size(1), dtype=torch.long, device=item_seq.device
      )
      position_ids = position_ids.unsqueeze(0).expand_as(item_seq)
      position_embedding = self.position_embedding(position_ids)
  
      input_emb = item_emb + position_embedding
      input_emb = self.LayerNorm(input_emb)
      input_emb = self.dropout(input_emb)
  
      extended_attention_mask = self.get_attention_mask(item_seq)
  
      trm_output = self.trm_encoder(
          input_emb,
          extended_attention_mask,
          output_all_encoded_layers=True,
      )
      output = trm_output[-1]
  
      alpha = self.fn(output).to(torch.double)
      alpha = torch.where(mask.unsqueeze(-1), alpha, -9e15)
      alpha = torch.softmax(alpha, dim=1, dtype=torch.float)
      return alpha
  
imports: import numpy
  import torch
  from torch import nn
  import torch.nn.functional
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import TransformerEncoder
label_desc: Use image node_id 3 for calling the TransNet obj's underlying member method code with example usage: obj.forward(item_seq, item_emb) and returns: alpha