              self.c = <Transition*>self.mem.realloc(self.c, self._size * sizeof(self.c[0]))
  
          self.c[self.n_moves] = self.init_transition(self.n_moves, action, label)
          self.n_moves += 1
          return 1
 
</s> remove             for label_str in sorted(label_strs):
 </s> add             for label_str in label_strs: </s> remove         if g_act == MISSING:
 </s> add         if g_act == MISSING or g_act == ISNT: </s> remove                             actions[LEFT][label] = True
 </s> add                             if (LEFT, label) not in seen_actions:
                                actions[LEFT].append(label)
        print("Add action", action, self.strings[label], self.n_moves)
              self.c = <Transition*>self.mem.realloc(self.c, self._size * sizeof(self.c[0]))
  
          self.c[self.n_moves] = self.init_transition(self.n_moves, action, label)
        print("Add action", action, self.strings[label], self.n_moves)
          self.n_moves += 1
          return 1
keep keep add keep keep
 WIP on add_label bug during NER training

Currently when a new label is introduced to NER during training,
it causes the labels to be read in in an unexpected order. This
invalidates the model. 
spacy/syntax/transition_system.pyx