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: Get the pre-trained word embeddings
  
  Args:
      word (str): the word to extract from the models embedding
  
  Returns:
      :obj:`np.ndarray`: the word embeddings
docstr_tok: ["Get","the","pre-trained","word","embeddings","Args",":","word","(","str",")",":","the","word","to","extract","from","the","models","embedding","Returns",":",":","obj",":","`","np.ndarray","`",":","the","word","embeddings"]
code: def get_word_embedding(self, word):
      """
      Get the pre-trained word embeddings
  
      Args:
          word (str): the word to extract from the models embedding
  
      Returns:
          :obj:`np.ndarray`: the word embeddings
      """
      if not self.model:
          return np.full((300,), -1)
      if word in self.model.vocab:
          vec = np.array(self.model.word_vec(word))
      else:
          vec = np.full((300,), -1)
      return vec
  
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 3 for calling the Word2Vec obj's underlying member method code with example usage: obj.get_word_embedding(word) and returns: vec, np