code: def load_stanford(cls, filename):
          
  
          dct = {}
          vectors = array.array('d')
  
          # Read in the data.
          with io.open(filename, 'r', encoding='utf-8') as savefile:
              for i, line in enumerate(savefile):
                  tokens = line.split(' ')
  
                  word = tokens[0]
                  entries = tokens[1:]
  
                  dct[word] = i
                  vectors.extend(float(x) for x in entries)
  
          # Infer word vectors dimensions.
          no_components = len(entries)
          no_vectors = len(dct)
  
          # Set up the model instance.
          instance = Glove()
          instance.no_components = no_components
          instance.word_vectors = (np.array(vectors)
                                   .reshape(no_vectors,
                                            no_components))
          instance.word_biases = np.zeros(no_vectors)
          instance.add_dictionary(dct)
  
          return instance
docs: Load model from the output files generated by
          the C code from http://nlp.stanford.edu/projects/glove/.
  
          The entries of the word dictionary will be of type
          unicode in Python 2 and str in Python 3.