code: def embedding_default(func, *args, **kwargs):
      _, new_kwargs = normalize_function(
          func,
          args=args,
          kwargs=kwargs,
          normalize_to_only_use_kwargs=True,
      )
  
      # guaranteed this is non-empty if we got here
      indices = new_kwargs.pop("indices")
      weight = new_kwargs.pop("weight")
  
      return NestedTensor(
          func(weight, indices._values, **new_kwargs),
          **extract_kwargs(indices),
      )
  
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 48 for calling a global function with example usage: embedding_default(func) and returns: NestedTensor