class_docstr: Word2Vec service
  
      Args:
          word2vec_model_path (str): the local path to the Word2Vec pre-trained model
class_docstr_tok: ["Word2Vec","service","Args",":","word2vec_model_path","(","str",")",":","the","local","path","to","the","Word2Vec","pre-trained","model"]
code: def __init__(self, word2vec_model_path):
      self.word2vec_model_path = word2vec_model_path
      self.model = self.load_word2vec_model_from_path()
  
imports: import nltk
  import nltk.corpus
  import numpy
  import requests
  from gensim.models.keyedvectors import KeyedVectors
  from nltk.corpus import wordnet
  from nltk.stem.snowball import SnowballStemmer
  from nlp_architect.utils.generic import license_prompt
label_desc: Use image node_id 1 to create a new Word2Vec object  with example: obj = Word2Vec(word2vec_model_path)