code: def checkpoint(func, inputs, params, flag):
      """
      Evaluate a function without caching intermediate activations, allowing for
      reduced memory at the expense of extra compute in the backward pass.
      :param func: the function to evaluate.
      :param inputs: the argument sequence to pass to `func`.
      :param params: a sequence of parameters `func` depends on but does not
                     explicitly take as arguments.
      :param flag: if False, disable gradient checkpointing.
      """
      if flag:
          from torch.utils.checkpoint import (
              checkpoint as torch_checkpoint,
          )
  
          return torch_checkpoint(func, *inputs)
          # args = tuple(inputs) + tuple(params)
          # return CheckpointFunction.apply(func, len(inputs), *args)
      else:
          return func(*inputs)
  
imports: import math
  import numpy
  import torch
  import torch.nn
  from einops import repeat
  from ldm.util import instantiate_from_config
label_desc: Use image node_id 6 for calling a global function with example usage: checkpoint(func, inputs, params, flag) and returns: torch_checkpoint, func