              golds = [golds]
          for multitask in self._multitasks:
              multitask.update(docs, golds, drop=drop, sgd=sgd)
          cuda_stream = util.get_cuda_stream()
          states, golds, max_steps = self._init_gold_batch(docs, golds)
          (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
                                                                              drop)
          todo = [(s, g) for (s, g) in zip(states, golds)
                  if not s.is_final() and g is not None]
          if not todo:
 
</s> remove             docs, cuda_stream, 0.0)
 </s> add             docs, cuda_stream, drop) </s> remove                 for batch in minibatch(train_docs, size=batch_sizes):
                    batch = [(d, g) for (d, g) in batch if len(d) < max_doc_len]
 </s> add                 for batch in minibatch_by_words(train_docs, size=batch_sizes): </s> remove             vectors = state2vec(token_ids[:n_states])
            scores = vec2scores(vectors)
 </s> add             vectors, _ = state2vec.begin_update(token_ids[:n_states], drop)
            scores, _ = vec2scores.begin_update(vectors, drop=drop) </s> remove     if not output_path.exists():
        output_path.mkdir()
 </s> add  </s> remove 
 </s> add                     words_seen += sum(len(doc) for doc in docs)
        # Chop sequences into lengths of this many transitions, to make the
        # batch uniform length.
        cut_gold = numpy.random.choice(range(20, 100))
        states, golds, max_steps = self._init_gold_batch(docs, golds, max_length=cut_gold)
              golds = [golds]
          for multitask in self._multitasks:
              multitask.update(docs, golds, drop=drop, sgd=sgd)
          cuda_stream = util.get_cuda_stream()
        # Chop sequences into lengths of this many transitions, to make the
        # batch uniform length.
        cut_gold = numpy.random.choice(range(20, 100))
        states, golds, max_steps = self._init_gold_batch(docs, golds, max_length=cut_gold)
          (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
                                                                              drop)
          todo = [(s, g) for (s, g) in zip(states, golds)
                  if not s.is_final() and g is not None]
          if not todo:
keep keep keep keep replace keep keep keep keep keep
 Revert "Merge branch 'develop' of https://github.com/explosion/spaCy into develop"

This reverts commit c9ba3d3c2dc7067cf8bd55f878cec45a8c6d73d4, reversing
changes made to 92c26a35d425d4e8ca1b805ea776ea10f5ded3df. 
spacy/syntax/nn_parser.pyx