code: def transform_paragraph(self, paragraph, epochs=50, ignore_missing=False):
          
  
          if self.word_vectors is None:
              raise Exception('Model must be fit to transform paragraphs')
  
          if self.dictionary is None:
              raise Exception('Dictionary must be provided to '
                              'transform paragraphs')
  
          cooccurrence = collections.defaultdict(lambda: 0.0)
  
          for token in paragraph:
              try:
                  cooccurrence[self.dictionary[token]] += self.max_count / 10.0
              except KeyError:
                  if not ignore_missing:
                      raise
  
          random_state = check_random_state(self.random_state)
  
          word_ids = np.array(cooccurrence.keys(), dtype=np.int32)
          values = np.array(cooccurrence.values(), dtype=np.float64)
          shuffle_indices = np.arange(len(word_ids), dtype=np.int32)
  
          # Initialize the vector to mean of constituent word vectors
          paragraph_vector = np.mean(self.word_vectors[word_ids], axis=0)
          sum_gradients = np.ones_like(paragraph_vector)
  
          # Shuffle the coocurrence matrix
          random_state.shuffle(shuffle_indices)
          transform_paragraph(self.word_vectors,
                              self.word_biases,
                              paragraph_vector,
                              sum_gradients,
                              word_ids,
                              values,
                              shuffle_indices,
                              self.learning_rate,
                              self.max_count,
                              self.alpha,
                              epochs)
  
          return paragraph_vector
docs: Transform an iterable of tokens into its vector representation
          (a paragraph vector).
  
          Experimental. This will return something close to a tf-idf
          weighted average of constituent token vectors by fitting
          rare words (with low word bias values) more closely.