code: def _get_single_embedding(model, text, device):
      """Get the bert embedding for a single sentence
      :param text: The current sentence
      :type text: str
      :param device: A pytorch device
      :type device: torch.device
      :param model: a pytorch model
      :type model: torch.nn
      :return: A bert embedding of the single sentence
      :rtype: torch.embedding
      """
      tokenizer = BertTokenizer.from_pretrained(Language.ENGLISH)
      words = (
          [BertTokens.CLS] + tokenizer.tokenize(text) + [BertTokens.SEP]
      )
      tokenized_ids = tokenizer.convert_tokens_to_ids(words)
      token_tensor = torch.tensor([tokenized_ids], device=device)
      embedding = model.bert.embeddings(token_tensor)[0]
      return embedding, words
  
imports: import logging
  import torch
  from pytorch_pretrained_bert import BertTokenizer
  from interpret_text.experimental.common.utils_bert import Language, Tokenizer
  from interpret_text.experimental.common.constants import BertTokens
label_desc: Use image node_id 1 for calling a global function with example usage: _get_single_embedding(model, text, device) and returns: embedding,  words