code: def unbind_int(func, *args, **kwargs):
      # Note that this specializes on the length of the offsets
      _, new_kwargs = normalize_function(
          func,
          args=args,
          kwargs=kwargs,
          normalize_to_only_use_kwargs=True,
      )
  
      dim = new_kwargs["dim"]
      if dim != 0:
          raise RuntimeError(
              "unbind(): only supported for NestedTensor on dim=0"
          )
  
      inp = new_kwargs.pop("input")
      values = inp.values()
      offsets = inp.offsets()
      lengths = inp.lengths()
  
      if inp._ragged_idx != 1:
          raise RuntimeError(
              "unbind(): only supported for NestedTensor when jagged dimension is 1"
          )
  
      if lengths is None:
          return torch.split(values, offsets.diff().tolist())
      return [
          values[offsets[i] : (offsets[i] + lengths[i])]
          for i in range(lengths.shape[0])
      ]
  
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 27 for calling a global function with example usage: unbind_int(func) and returns: unknown, torch