code: def native_layer_norm_backward_default(func, *args, **kwargs):
      _, new_kwargs = normalize_function(
          func,
          args=args,
          kwargs=kwargs,
          normalize_to_only_use_kwargs=True,
      )
      grad_out = new_kwargs.pop("grad_out")
      inp = new_kwargs.pop("input")
      d_input, d_gamma, d_beta = func(
          grad_out._values, inp._values, **new_kwargs
      )
      if d_input is None:
          return (None, d_gamma, d_beta)
  
      return (
          NestedTensor(d_input, **extract_kwargs(inp)),
          d_gamma,
          d_beta,
      )
  
imports: import functools
  import math
  import operator
  import torch
  from torch.nested._internal.sdpa import jagged_scaled_dot_product_attention
  from .nested_tensor import NestedTensor
  from typing import *
  from torch.fx.operator_schemas import normalize_function
label_desc: Use image node_id 42 for calling a global function with example usage: native_layer_norm_backward_default(func) and returns: NestedTensor,  d_gamma,  d_beta, None,  d_gamma,  d_beta