code: def normalize_op_input_output2(
      f, args, kwargs, output_process_fn_grad=None, requires_grad=True
  ):
      flat_args, args_spec = tree_flatten(args)
      diff_argnums = tuple(
          i
          for i, arg in enumerate(flat_args)
          if diff_arg(arg, requires_grad=requires_grad)
      )
      assert len(diff_argnums) > 0
      primals = tuple(flat_args[i] for i in diff_argnums)
  
      @functools.wraps(f)
      def wrapped(*primals):
          _args = list(flat_args)
          for num, arg in zip(diff_argnums, primals):
              _args[num] = arg
          _args = tree_unflatten(_args, args_spec)
          result = f(*_args, **kwargs)
          if output_process_fn_grad is not None:
              result = output_process_fn_grad(result)
          if isinstance(result, tuple):
              # TODO We should check that the integer outputs also agree
              result = tuple(
                  r
                  for r in result
                  if isinstance(r, Tensor)
                  and (r.is_floating_point() or r.is_complex())
              )
              assert len(result) > 0
          return result
  
      return wrapped, primals
  
imports: from collections import defaultdict
  from torch import Tensor
  import torch.autograd
  from torch._decomp import core_aten_decompositions, decomposition_table
  from torch.utils._python_dispatch import TorchDispatchMode
  from torch.utils._pytree import tree_map, tree_flatten, tree_unflatten
  from torch.utils import _pytree
  from torch.testing import make_tensor
  from torch.testing._internal.common_cuda import tf32_off
  from torch.testing._internal.common_utils import unMarkDynamoStrictTest
  from torch.testing._internal.common_utils import is_iterable_of_tensors, IS_WINDOWS, TestCase, skipIfCrossRef, suppress_warnings, TEST_WITH_ASAN, TEST_WITH_SLOW, run_tests, skipIfTorchDynamo
  from torch.testing._internal.common_modules import module_db, modules
  from torch.testing._internal.common_device_type import onlyNativeDeviceTypes, ops, instantiate_device_type_tests, onlyCPU, onlyCUDA
  from torch.testing._internal.common_methods_invocations import op_db, skip, skipOps, xfail
  from torch._dispatch.python import enable_python_dispatcher
  from torch._ops import DispatchKey
  import itertools
  import functools
  from functools import partial
  import unittest
label_desc: Use image node_id 9 for calling a global function with example usage: normalize_op_input_output2(f, args, kwargs, output_process_fn_grad, requires_grad) and returns: wrapped,  primals, result