class_docstr: Helper class to simplify the handling of gradient buckets
class_docstr_tok: ["Helper","class","to","simplify","the","handling","of","gradient","buckets"]
docstr: Release the buffer from memory. The bucket will need to be rebuilt before use
docstr_tok: ["Release","the","buffer","from","memory",".","The","bucket","will","need","to","be","rebuilt","before","use"]
code: def collapse(self) -> None:
      """
      Release the buffer from memory. The bucket will need to be rebuilt before use
      """
      if not self._is_collapsed:
          for p in self._params:
              assert p.grad is not None
              p.grad.detach_()
              p.grad = None
  
          self.buffer = torch.zeros(
              0, dtype=self.buffer.dtype, device=self.buffer.device
          )
          self._fill = 0
          self.params_checked_in = 0
          self._is_collapsed = True
  
imports: from typing import Any, Callable, List, Optional, Union
  import torch
label_desc: Use image node_id 8 for calling the GradBucket obj's underlying member method code with example usage: obj.collapse() without return types