class_docstr: Orders instances in a round-robin fashion, where we take one instance from every dataset in
  turn. When one dataset runs out, we continue iterating round-robin through the rest.
  
  Registered as a `MultiTaskScheduler` with name "roundrobin".
class_docstr_tok: ["Orders","instances","in","a","round-robin","fashion",",","where","we","take","one","instance","from","every","dataset","in","turn",".","When","one","dataset","runs","out",",","we","continue","iterating","round-robin","through","the","rest",".","Registered","as","a","`","MultiTaskScheduler","`","with","name","``","roundrobin","''","."]
code: def __init__(self, batch_size: int, drop_last: bool = False):
      super().__init__()
      self.batch_size = batch_size
      self.drop_last = drop_last
  
imports: from collections import defaultdict
  from typing import Any, Dict, Iterable, Union, List, Mapping
  import more_itertools
  from allennlp.common.registrable import Registrable
  from allennlp.data.instance import Instance
label_desc: Use image node_id 1 to create a new RoundRobinScheduler object from inherited base classes: MultiTaskScheduler with example: obj = RoundRobinScheduler(batch_size, drop_last)