code: def as_keyed_vectors(self):
          
  
          edge_generator = combinations_with_replacement(self.kv.index2word, r=2)
  
          if not self.quiet:
              vocab_size = len(self.kv.vocab)
              total_size = reduce(lambda x, y: x * y, range(1, vocab_size + 2)) / \
                           (2 * reduce(lambda x, y: x * y, range(1, vocab_size)))
  
              edge_generator = tqdm(edge_generator, desc='Generating edge features', total=total_size)
  
          # Generate features
          tokens = []
          features = []
          for edge in edge_generator:
              token = str(tuple(sorted(edge)))
              embedding = self._embed(edge)
  
              tokens.append(token)
              features.append(embedding)
  
          # Build KV instance
          edge_kv = KeyedVectors(vector_size=self.kv.vector_size)
          edge_kv.add(
              entities=tokens,
              weights=features)
  
          return edge_kv
docs: Generated a KeyedVectors instance with all
          :return: