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"]
docstr: Load Word2Vec model
  
  Returns:
      the Word2Vec model
docstr_tok: ["Load","Word2Vec","model","Returns",":","the","Word2Vec","model"]
code: def load_word2vec_model_from_path(self):
      """
      Load Word2Vec model
  
      Returns:
          the Word2Vec model
      """
      word_embeddings_model = KeyedVectors.load_word2vec_format(
          self.word2vec_model_path, binary=True
      )
      if not word_embeddings_model:
          return None
      return word_embeddings_model
  
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 2 for calling the Word2Vec obj's underlying member method code with example usage: obj.load_word2vec_model_from_path() and returns: word_embeddings_model, None