class_docstr: Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
  checkpoint for all other args.
class_docstr_tok: ["Wraps","a","module",".","When","forward","(",")","is","called",",","passes","kwargs","that","require_grad","through","torch.checkpoint","(",")","and","bypasses","checkpoint","for","all","other","args","."]
code: def __init__(self, wrap):
      super().__init__()
      self.wrap = wrap
  
imports: import functools
  import math
  import os
  import fsspec
  import torch
  import torch.nn
  import torch.nn.functional
  import torchaudio
  from transformers import LogitsWarper
  from TTS.tts.layers.tortoise.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
label_desc: Use image node_id 1 to create a new CheckpointedLayer object from inherited base classes: nn with example: obj = CheckpointedLayer(wrap)