class_docstr: A `TfmdDL` which splits a batch into equal size pieces for each worker
class_docstr_tok: ["A","`","TfmdDL","`","which","splits","a","batch","into","equal","size","pieces","for","each","worker"]
code: def after_batch(self, b):
      self.i += find_bs(b)
      return self.dl.after_batch(b)
  
imports: from __future__ import annotations
  from .basics import *
  from .callback.progress import ProgressCallback
  from torch.nn.parallel import DistributedDataParallel, DataParallel
  from .data.load import _FakeLoader, _loaders
  from .optimizer import OptimWrapper
label_desc: Use image node_id 8 for calling the DistributedDL obj's underlying member method code with example usage: obj.after_batch(b) and returns: self