code: def checkpoint(func, inputs, params, flag):
      """
      Evaluate a function without caching intermediate activations.
  
      Parameters:
          func (function): The function to be evaluated. This should be a callable object.
          inputs (sequence): The arguments to pass to `func`. This is a sequence of inputs that `func` will be called with.
          params (sequence): A sequence of parameters that `func` depends on but does not explicitly take as arguments.
                             These are additional parameters required by `func`.
          flag (bool): If set to False, disables gradient checkpointing. If True, enables gradient checkpointing which
                       allows for memory savings at the cost of extra compute during the backward pass.
  
      Returns:
          The result of evaluating `func` with the given inputs and parameters, with reduced memory usage during the forward pass.
      """
  
      if flag:
          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 apex.contrib.group_norm import GroupNorm
  from einops import repeat
  from torch._dynamo import disable
  from torch.cuda.amp import custom_bwd, custom_fwd
label_desc: Use image node_id 6 for calling a global function with example usage: checkpoint(func, inputs, params, flag) and returns: CheckpointFunction, func