code: def from_text(cls, fname, vocabUnicodeSize=78, desired_vocab=None, encoding="utf-8"):
          
          with open(fname, "rb") as fin:
              header = fin.readline()
              vocab_size, vector_size = list(map(int, header.split()))
  
              vocab = np.empty(vocab_size, dtype="<U%s" % vocabUnicodeSize)
              vectors = np.empty((vocab_size, vector_size), dtype=np.float)
              for i, line in enumerate(fin):
                  line = line.decode(encoding).rstrip()
                  parts = line.split(" ")
                  word = parts[0]
                  include = desired_vocab is None or word in desired_vocab
                  if include:
                      vector = np.array(parts[1:], dtype=np.float)
                      vocab[i] = word
                      vectors[i] = unitvec(vector)
  
              if desired_vocab is not None:
                  vectors = vectors[vocab != "", :]
                  vocab = vocab[vocab != ""]
          return cls(vocab=vocab, vectors=vectors)
docs: Create a WordVectors class based on a word2vec text file
  
          Parameters
          ----------
          fname : path to file
          vocabUnicodeSize: the maximum string length (78, by default)
          desired_vocab: if set, this will ignore any word and vector that
                         doesn't fall inside desired_vocab.
  
          Returns
          -------
          WordVectors instance