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 to_detach(self, b, cpu=True, gather=True):
      b = self._to_detach(b, cpu, gather)
  
      def _inner(b):
          if b.ndim > 0:
              # for each rank, compute overflow of read idxs vs self.n and accumulate them to unpad totals after gathering
              n = sum(
                  [
                      min(
                          0,
                          max(
                              -len(b) // self.world_size,
                              self.n
                              - (
                                  self.i
                                  + r * self.n_padded // self.world_size
                              ),
                          ),
                      )
                      for r in range(self.world_size)
                  ]
              )
              b = b[: n or None]
          return b
  
      return (
          apply(_inner, b)
          if gather
          and all(hasattr(self, o) for o in ("i", "n", "n_padded"))
          else 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 11 for calling the DistributedDL obj's underlying member method code with example usage: obj.to_detach(b, cpu, gather) and returns: unknown, b