code: def forward(
      self, token_ids, segment_ids, attention_mask, DEBUG=False
  ):
      if DEBUG:
          import pdb
  
          pdb.set_trace()
      try:
          output_bert, output_pooler, _ = self.bert_model(
              token_ids, segment_ids, attention_mask
          )
      except RuntimeError as e:
          print(token_ids.size())
          print(segment_ids.size())
          print(attention_mask.size())
          print(e)
          import pdb
  
          pdb.set_trace()
          output_bert, output_pooler, _ = self.bert_model(
              token_ids, segment_ids, attention_mask
          )
  
      if self.additional_linear is not None:
          # embeddings = (batch_size, embedding_size)
          embeddings = output_pooler
      else:
          # embeddings = (batch_size, embedding_size)
          embeddings = output_bert[:, 0, :]
  
      # in case of dimensionality reduction
      if self.additional_linear is not None:
          result = self.additional_linear(self.dropout(embeddings))
      else:
          result = embeddings
  
      return result
  
imports: from torch import nn
  import torch
label_desc: Use image node_id 2 for calling the BertEncoder obj's underlying member method code with example usage: obj.forward(token_ids, segment_ids, attention_mask, DEBUG) and returns: result