code: def forward(self, item_seq):
      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, bidirectional=True
      )
      trm_output = self.trm_encoder(
          input_emb,
          extended_attention_mask,
          output_all_encoded_layers=True,
      )
      ffn_output = self.output_ffn(trm_output[-1])
      ffn_output = self.output_gelu(ffn_output)
      output = self.output_ln(ffn_output)
      return output
  
file_path: RecBole/recbole/model/sequential_recommender/bert4rec.py
imports: import random
  import torch
  from torch import nn
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import TransformerEncoder
label_desc: Use image node_id 4 for calling the BERT4Rec obj's underlying member method code with example usage: obj.forward(item_seq) and returns: output