code: def split_by_type(tensors):
      buckets = OrderedDict()
      for tensor in tensors:
          tp = tensor.type()
          if tp not in buckets:
              buckets[tp] = []
          buckets[tp].append(tensor)
      return buckets
  
imports: from collections import OrderedDict
  import copy
  import importlib
  from itertools import chain
  import torch
  import torch.distributed
  from torch.nn.modules import Module
  from torch.autograd import Variable
  from ..multi_tensor_apply import multi_tensor_applier
label_desc: Use image node_id 6 for calling a global function with example usage: split_by_type(tensors) and returns: buckets