code: def encode(self, sentences):
      inputs = self.tokenizer(
          sentences, padding=True, truncation=True, return_tensors="pt"
      )
      with torch.no_grad():
          outputs = self.model(**inputs, output_hidden_states=False)
          sequence_output = outputs[0]
  
      embeddings = torch.sum(
          sequence_output * inputs["attention_mask"].unsqueeze(-1),
          dim=1,
      ) / torch.clamp(
          torch.sum(inputs["attention_mask"], dim=1, keepdims=True),
          min=1e-9,
      )
  
      return embeddings
  
file_path: textflint/textflint/generation/validator/sentence_encoding.py
imports: import torch
  from transformers import AutoModel, AutoTokenizer
  from .validator import Validator
label_desc: Use image node_id 4 for calling the SentenceEncoding obj's underlying member method code with example usage: obj.encode(sentences) and returns: embeddings