class_docstr: Set expansion module, given a trained np2vec model.
class_docstr_tok: ["Set","expansion","module",",","given","a","trained","np2vec","model","."]
docstr: Compute cosine similarity between a seed terms and a term.
  Args:
      seed_id: seed term id's
      term_id: the term id
  
  Returns:
      Similarity between the seed terms and the term
docstr_tok: ["Compute","cosine","similarity","between","a","seed","terms","and","a","term",".","Args",":","seed_id",":","seed","term","id's","term_id",":","the","term","id","Returns",":","Similarity","between","the","seed","terms","and","the","term"]
code: def seed2term_similarity(self, seed_id, term_id):
      """
      Compute cosine similarity between a seed terms and a term.
      Args:
          seed_id: seed term id's
          term_id: the term id
  
      Returns:
          Similarity between the seed terms and the term
  
      """
      logger.info(
          "calculate similarity for: %s , %s", str(seed_id), term_id
      )
      res = self.np2vec_model.n_similarity(seed_id, list(term_id))
      logger.info("similarity result: %s", str(res))
      return res
  
file_path: nlp-architect/solutions/set_expansion/set_expand.py
imports: import logging
  import sys
  from argparse import ArgumentParser
  from os import path
  from nlp_architect.models.np2vec import NP2vec
  from nlp_architect.utils.io import validate_existing_filepath, check_size, load_json_file
label_desc: Use image node_id 11 for calling the SetExpand obj's underlying member method code with example usage: obj.seed2term_similarity(seed_id, term_id) and returns: res