code: def unsqueeze_default(func, *args, **kwargs):
      _, new_kwargs = normalize_function(
          func,
          args=args,
          kwargs=kwargs,
          normalize_to_only_use_kwargs=True,
      )
  
      inp = new_kwargs.pop("input")
      values = inp._values
  
      # Account for collapsed jagged dim
      dim = new_kwargs["dim"]
      new_kwargs["dim"] = _wrap_jagged_dim(
          len(inp._size) + 1, dim, "unsqueeze"
      )
      return NestedTensor(
          func(values, **new_kwargs), **extract_kwargs(inp)
      )
  
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 29 for calling a global function with example usage: unsqueeze_default(func) and returns: NestedTensor