class_docstr: Constraint using similarity between sentence encodings of x and x_adv
  where the text embeddings are created using the Multilingual Universal
  Sentence Encoder.
code: def __init__(
      self, threshold=0.8, large=False, metric="angular", **kwargs
  ):
      super().__init__(threshold=threshold, metric=metric, **kwargs)
      tensorflow_text._load()
      if large:
          tfhub_url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3"
      else:
          tfhub_url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
  
      # TODO add QA SET. Details at: https://tfhub.dev/google/universal-sentence-encoder-multilingual-qa/3
      self._tfhub_url = tfhub_url
      self.model = hub.load(tfhub_url)
  
file_path: TextAttack/textattack/constraints/semantics/sentence_encoders/universal_sentence_encoder/multilingual_universal_sentence_encoder.py
imports: from textattack.constraints.semantics.sentence_encoders import SentenceEncoder
  from textattack.shared.utils import LazyLoader
label_desc: Use image node_id 1 to create a new MultilingualUniversalSentenceEncoder object from inherited base classes: SentenceEncoder with example: obj = MultilingualUniversalSentenceEncoder(threshold, large, metric)