class_docstr: Provides [FairScale]
  (https://fairscale.readthedocs.io/en/latest/api/nn/checkpoint/checkpoint_activations.html)'s
  activation/gradient checkpointing functionality.
  
  The parameters and their defaults are the same as they are in FairScale, and
  any of them can be overriden on a per-module basis by passing the corresponding parameter
  to `.wrap_module()`.
  
  This can also be used in conjunction with the
  :class:`allennlp.nn.parallel.fairscale_fsdp_accelerator.FairScaleFsdpAccelerator`.
  See the [T5 implementation](/api/modules/transformer/t5/) for an example
  of how to use the two together.
code: def __init__(self, offload_to_cpu: Optional[bool] = True) -> None:
      self._offload_to_cpu = offload_to_cpu
  
file_path: allennlp/allennlp/nn/checkpoint/fairscale_checkpoint_wrapper.py
imports: from typing import Optional
  from fairscale.nn.checkpoint import checkpoint_wrapper
  import torch.nn
  from allennlp.nn.checkpoint.checkpoint_wrapper import CheckpointWrapper
label_desc: Use image node_id 1 to create a new FairScaleCheckpointWrapper object from inherited base classes: CheckpointWrapper with example: obj = FairScaleCheckpointWrapper(offload_to_cpu)