class_docstr: Constraint using similarity between sentence encodings of x and x_adv
  where the text embeddings are created using InferSent.
docstr: Retrieves the InferSent model.
  
  Returns:
      The pretrained InferSent model.
code: def get_infersent_model(self):
      """Retrieves the InferSent model.
  
      Returns:
          The pretrained InferSent model.
      """
      infersent_version = 2
      model_folder_path = utils.download_from_s3(InferSent.MODEL_PATH)
      model_path = os.path.join(
          model_folder_path, f"infersent{infersent_version}.pkl"
      )
      params_model = {
          "bsize": 64,
          "word_emb_dim": 300,
          "enc_lstm_dim": 2048,
          "pool_type": "max",
          "dpout_model": 0.0,
          "version": infersent_version,
      }
      infersent = InferSentModel(params_model)
      infersent.load_state_dict(torch.load(model_path))
      word_embedding_path = utils.download_from_s3(
          InferSent.WORD_EMBEDDING_PATH
      )
      w2v_path = os.path.join(
          word_embedding_path, "fastText", "crawl-300d-2M.vec"
      )
      infersent.set_w2v_path(w2v_path)
      infersent.build_vocab_k_words(K=100000)
      return infersent
  
file_path: TextAttack/textattack/constraints/semantics/sentence_encoders/infer_sent/infer_sent.py
imports: import os
  import torch
  from textattack.constraints.semantics.sentence_encoders import SentenceEncoder
  from textattack.shared import utils
  from .infer_sent_model import InferSentModel
label_desc: Use image node_id 2 for calling the InferSent obj's underlying member method code with example usage: obj.get_infersent_model() and returns: infersent