code: def handle_sizes_for_dynamic_shapes(func, args, kwargs):
      def f(args, kwargs, extra_args, extra_kwargs):
          if extra_args:
              for i, t in extra_args:
                  args[i] = t.size()
          if extra_kwargs:
              for k, t in extra_kwargs.items():
                  kwargs[k] = t.size()
  
          return func(*args, **kwargs)
  
      extra_args = []
      extra_kwargs = {}
      for i, arg in enumerate(args):
          if isinstance(arg, torch.Size):
              extra_args.append((i, torch.empty(arg, device="cpu")))
      for key, value in kwargs.items():
          if isinstance(value, torch.Size):
              extra_kwargs[key] = torch.empty(value, device="cpu")
  
      return f, args, kwargs, extra_args, extra_kwargs
  
imports: import torch
  from torch.fx.experimental.proxy_tensor import make_fx
  from torch.testing._internal.common_methods_invocations import wrapper_set_seed
  import torch.utils._pytree
label_desc: Use image node_id 2 for calling a global function with example usage: handle_sizes_for_dynamic_shapes(func, args, kwargs) and returns: f,  args,  kwargs,  extra_args,  extra_kwargs, func