code: def native_layer_norm_default(func, *args, **kwargs):
      _, new_kwargs = normalize_function(
          func,
          args=args,
          kwargs=kwargs,
          normalize_to_only_use_kwargs=True,
      )
  
      inp = new_kwargs.pop("input")
      normalized_shape = new_kwargs["normalized_shape"]
  
      # Ensure we're not trying to normalize over the ragged dim
      if inp.dim() < 3 or (inp.dim() - len(normalized_shape)) < 2:
          raise RuntimeError(
              "layer_norm(): normalizing over ragged dim not supported for nested tensors"
          )
  
      output, mean, std = func(inp._values, **new_kwargs)
      return (NestedTensor(output, **extract_kwargs(inp)), mean, std)
  
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 41 for calling a global function with example usage: native_layer_norm_default(func) and returns: NestedTensor,  mean,  std