class_docstr: NLP Model 
  Glove is a wrapper for loading pretrained word 
  embedding vectors hosted at http://nlp.stanford.edu/data/glove.6B.zip.
  
  References:
      .. [#] `Pennington, Jeffrey, Richard Socher, and Christopher Manning,
          “Glove: Global Vectors for Word Representation”,
          In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)`_
docstr: Embedding Vector
  
  Args:
      word (str): a word for which the corresponding embedding is desired
  
  Returns:
      a numpy float array of above dimension
  
  raises ValueError: if word is not in the vocab
docstr_tok: ["Embedding","Vector","Args",":","word","(","str",")",":","a","word","for","which","the","corresponding","embedding","is","desired","Returns",":","a","numpy","float","array","of","above","dimension","raises","ValueError",":","if","word","is","not","in","the","vocab"]
code: def embedding(self, word: str):
      """
      Embedding Vector
  
      Args:
          word (str): a word for which the corresponding embedding is desired
  
      Returns:
          a numpy float array of above dimension
  
      raises ValueError: if word is not in the vocab
      """
  
      if word not in self._vocab:
          raise ValueError(f"word '{word}' not in vocab")
  
      return self._embeddings[self._vocab[word]]
  
imports: import os
  import numpy
label_desc: Use image node_id 3 for calling the Glove obj's underlying member method code with example usage: obj.embedding(word) and returns: self