class_docstr: An auxiliary class for storing the the words space.
class_docstr_tok: ["An","auxiliary","class","for","storing","the","the","words","space","."]
docstr: Parameters
  ----------
  matrix : iterable of numpy.ndarray
      Matrix that contains word-vectors.
  index2word : list of str
      Words which correspond to the `matrix`.
docstr_tok: ["Parameters","--","--","--","--","--","matrix",":","iterable","of","numpy.ndarray","Matrix","that","contains","word-vectors",".","index2word",":","list","of","str","Words","which","correspond","to","the","`","matrix","`","."]
code: def __init__(self, matrix, index2word):
      """
      Parameters
      ----------
      matrix : iterable of numpy.ndarray
          Matrix that contains word-vectors.
      index2word : list of str
          Words which correspond to the `matrix`.
  
      """
      self.mat = matrix
      self.index2word = index2word
  
      # build a dict to map word to index
      self.word2index = {}
      for idx, word in enumerate(self.index2word):
          self.word2index[word] = idx
  
imports: import warnings
  from collections import OrderedDict
  import numpy
  from gensim import utils
label_desc: Use image node_id 1 to create a new Space object  with example: obj = Space(matrix, index2word)