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 two term id's.
  Args:
      term_id_1: first term id
      term_id_2: second term id
  
  Returns:
      Similarity between the first and second term id's
docstr_tok: ["Compute","cosine","similarity","between","two","term","id","'s",".","Args",":","term_id_1",":","first","term","id","term_id_2",":","second","term","id","Returns",":","Similarity","between","the","first","and","second","term","id","'s"]
code: def term2term_similarity(self, term_id_1, term_id_2):
      """
      Compute cosine similarity between two term id's.
      Args:
          term_id_1: first term id
          term_id_2: second term id
  
      Returns:
          Similarity between the first and second term id's
  
      """
      logger.info(
          "calculate similarity for: %s , %s", term_id_1, term_id_2
      )
      res = self.np2vec_model.similarity(term_id_1, term_id_2)
      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 10 for calling the SetExpand obj's underlying member method code with example usage: obj.term2term_similarity(term_id_1, term_id_2) and returns: res