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 __init__(self, config):
      super().__init__()
      self.pooler = BertPooler(config)
      self.dropout = nn.Dropout(config.hidden_dropout_prob)
      self.classifier = nn.Linear(config.hidden_size, config.num_labels)
  
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 1 to create a new BertHighway object from inherited base classes: nn with example: obj = BertHighway(config)