code: def forward(self, item_seq, item_seq_len):
      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)
  
      item_emb = self.item_embedding(item_seq)
      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)
      # extended_attention_mask = self.get_bi_attention_mask(item_seq)
  
      trm_output = self.item_encoder(
          input_emb,
          extended_attention_mask,
          output_all_encoded_layers=True,
      )
      output = trm_output[-1]
      output = self.gather_indexes(output, item_seq_len - 1)
  
      return output
  
imports: import random
  import numpy
  import torch
  from torch import nn
  import torch.nn.functional
  import math
  import torch.nn.functional
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
  from recbole.data.interaction import Interaction
label_desc: Use image node_id 7 for calling the FEARec obj's underlying member method code with example usage: obj.forward(item_seq, item_seq_len) and returns: output