code: def get_nn_functional_compiled_fn_and_inputs(
      name, self_size, args, variant_name="", *extra_args
  ):
      test_name = "test_nn_" + name
  
      if variant_name != "":
          test_name = test_name + "_" + variant_name
  
      no_grad = variant_name == "inplace"
  
      self_variable = create_input((self_size,))[0][0]
      kwargs = None
  
      # need to record this because methods can change the size (e.g. unsqueeze)
      args_variable, kwargs_variable = create_input(args)
  
      self_tensor = deepcopy(self_variable.data)
      args_tensor = deepcopy(unpack_variables(args_variable))
  
      f_args_variable = (self_variable,) + args_variable
      f_args_tensor = (self_tensor,) + args_tensor
      with torch._jit_internal._disable_emit_hooks():
          script_fn, inputs = gen_script_fn_and_args(
              name, "nn_functional", *f_args_variable
          )
      return script_fn, inputs
  
imports: from torch.jit.annotations import BroadcastingList2, BroadcastingList3
  import torch.nn.functional
  import torch
  import torch.cuda
  import torch.jit
  import torch.jit._logging
  import torch.jit.frontend
  from torch.testing._internal.common_nn import module_tests, new_module_tests
  from torch.testing._internal.common_utils import is_iterable_of_tensors, noncontiguous_like
  import collections
  from copy import deepcopy
  from typing import Any, Dict, List, Union
  import math
  from torch import inf
label_desc: Use image node_id 11 for calling a global function with example usage: get_nn_functional_compiled_fn_and_inputs(name, _size, args, variant_name) and returns: script_fn,  inputs