class_docstr: Constraint using similarity between sentence encodings of x and x_adv
  where the text embeddings are created using BERT, trained on NLI data, and
  fine- tuned on the STS benchmark dataset.
  
  Available models can be found here: https://huggingface.co/sentence-transformers
class_docstr_tok: ["Constraint","using","similarity","between","sentence","encodings","of","x","and","x_adv","where","the","text","embeddings","are","created","using","BERT",",","trained","on","NLI","data",",","and","fine-","tuned","on","the","STS","benchmark","dataset",".","Available","models","can","be","found","here",":","https",":","\/\/huggingface.co\/sentence-transformers"]
code: def encode(self, sentences):
      return self.model.encode(sentences)
  
imports: from textattack.constraints.semantics.sentence_encoders import SentenceEncoder
  from textattack.shared import utils
label_desc: Use image node_id 2 for calling the BERT obj's underlying member method code with example usage: obj.encode(sentences) and returns: self