class_docstr: A module to provide a shortcut
  from (the output of one non-final BertLayer in BertEncoder) to (cross-entropy computation in BertForSequenceClassification)
class_docstr_tok: ["A","module","to","provide","a","shortcut","from","(","the","output","of","one","non-final","BertLayer","in","BertEncoder",")","to","(","cross-entropy","computation","in","BertForSequenceClassification",")"]
code: def forward(self, encoder_outputs):
      # Pooler
      pooler_input = encoder_outputs[0]
      pooler_output = self.pooler(pooler_input)
      # "return" pooler_output
  
      # BertModel
      bmodel_output = (pooler_input, pooler_output) + encoder_outputs[
          1:
      ]
      # "return" bmodel_output
  
      # Dropout and classification
      pooled_output = bmodel_output[1]
  
      pooled_output = self.dropout(pooled_output)
      logits = self.classifier(pooled_output)
  
      return logits, pooled_output
  
imports: import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from transformers.models.bert.modeling_bert import BERT_INPUTS_DOCSTRING, BERT_START_DOCSTRING, BertEmbeddings, BertLayer, BertPooler, BertPreTrainedModel
label_desc: Use image node_id 2 for calling the BertHighway obj's underlying member method code with example usage: obj.forward(encoder_outputs) and returns: logits,  pooled_output