old_contents: import numpy as np
  
  class Glove(object):
      """Load a GloVe model and provide access to the vectors and vector space.
  
      Provides access to word vectors as if it were a dictionary:
  
          glove_instance = Glove('file')
          glove['word']
  
      Unrecognized words will return the Null vector (all 0).
  
      Also provides a way to find the closest word to a vector in the vector
      space.
  
      """
  
      def __init__(self, glove_file):
          """Set up the GloVe class by reading in a vector file.
  
          Args:
              glove_file (str): Location of the plain text GloVe vector file.
  
          """
          self.__model = {}
          self.__line_to_word = {}
          space = []
          # Load the GloVe data from a file
          with open(glove_file, 'r') as open_file:
              for line_number, line in enumerate(open_file):
                  sline = line.split()
                  key_word = sline[0]
                  vector = np.array([float(i) for i in sline[1:]])
                  self.__model[key_word] = vector
                  self.__line_to_word[line_number] = key_word
                  space.append(vector)
  
          # Set up a vector space so we can quickly find the closest vector
          self.__vector_space = np.array(space)
  
          # Null vector for unrecognized words
          self.__null_vector = np.zeros(len(vector))
  
      def __getitem__(self, key):
          """Return the vector representation of a word.
  
          Args:
              key (str): A word to locate in the vector space.
  
          Returns:
              numpy array: The location of the word in the vector space, or the
                  null (0) vector if the word is not found.
  
          """
          return self.__model.get(key, self.__null_vector)
  
      def closest_word(self, vector):
          """Return the closest word to a given vector.
  
          Args:
              vector (numpy array): A vector of the same dimension as the vector
                  space.
  
          Returns:
              str: The closest word to the input vector in the vector space.
          """
          squares = (self.__vector_space - vector)**2
          distances = np.sum(squares, axis=1)
          line_number = np.argmin(distances)
          return self.__line_to_word[line_number]
  
new_contents: import numpy as np
  
  class Glove(object):
      """Load a GloVe model and provide access to the vectors and vector space.
  
      Provides access to word vectors as if it were a dictionary:
  
          glove_instance = Glove('file')
          glove['word']
  
      Unrecognized words will return the Null vector (all 0).
  
      Also provides a way to find the closest word to a vector in the vector
      space.
  
      """
  
      def __init__(self, glove_file):
          """Set up the GloVe class by reading in a vector file.
  
          Args:
              glove_file (str): Location of the plain text GloVe vector file.
  
          """
          self.__model = {}
          self.__line_to_word = {}
          space = []
          # Load the GloVe data from a file
          with open(glove_file, 'r') as open_file:
              for line_number, line in enumerate(open_file):
                  sline = line.split()
                  key_word = sline[0]
                  vector = np.array([float(i) for i in sline[1:]])
                  self.__model[key_word] = vector
                  self.__line_to_word[line_number] = key_word
                  space.append(vector)
  
          # Set up a vector space so we can quickly find the closest vector
          self.__vector_space = np.array(space)
  
          # Null vector for unrecognized words
          self.vector_size = len(vector)
          self.__null_vector = np.zeros(self.vector_size)
  
      def __getitem__(self, key):
          """Return the vector representation of a word.
  
          Args:
              key (str): A word to locate in the vector space.
  
          Returns:
              numpy array: The location of the word in the vector space, or the
                  null (0) vector if the word is not found.
  
          """
          return self.__model.get(key, self.__null_vector)
  
      def closest_word(self, vector):
          """Return the closest word to a given vector.
  
          Args:
              vector (numpy array): A vector of the same dimension as the vector
                  space.
  
          Returns:
              str: The closest word to the input vector in the vector space.
          """
          squares = (self.__vector_space - vector)**2
          distances = np.sum(squares, axis=1)
          line_number = np.argmin(distances)
          return self.__line_to_word[line_number]
  
subject: Add vector_size to Glove class