code: def forward(self, x):
      clf_tokens_mask = (
          x.transpose(0, 1).contiguous().to("cpu")
          == self.tokenizer.vocab["[CLS]"]
      )
      hidden_states = self.transformer(x)
      clf_tokens_states = (
          hidden_states * clf_tokens_mask.unsqueeze(-1).float()
      ).sum(dim=0)
      clf_logits = self.classification_head(clf_tokens_states)
  
      return clf_logits
  
imports: import torch
  from pytorch_pretrained_bert import BertTokenizer
  from transfer_nlp.plugins.config import register_plugin
label_desc: Use image node_id 3 for calling the TransformerWithClfHeadAndAdapters obj's underlying member method code with example usage: obj.forward(x) and returns: clf_logits