code: def get_bert_embedding(self, text):
      tokenized_text = self.tokenizer.tokenize(text)
      tokenized_text = ["[CLS]"] + tokenized_text + ["[SEP]"]
      indexed_tokens = self.tokenizer.convert_tokens_to_ids(
          tokenized_text
      )
      tokens_tensor = torch.Tensor([indexed_tokens]).long()
      segments_tensor = torch.Tensor([0] * len(tokenized_text)).long()
      with torch.no_grad():
          encoded_layers, _ = self.model(
              tokens_tensor.cuda(),
              segments_tensor.cuda(),
              output_all_encoded_layers=False,
          )
      return encoded_layers.squeeze()[0]
  
imports: import argparse
  import numpy
  import torch
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling the BertFeatExtractor obj's underlying member method code with example usage: obj.get_bert_embedding(text) and returns: encoded_layers