code: def sample_inputs_numpy_nms(
      opinfo, device, dtype, requires_grad, **kwargs
  ):
      make_arg = functools.partial(
          make_tensor, device=device, dtype=dtype
      )
      N = 64
      xs = make_arg([N], low=0, high=28)
      dx = make_arg([N], low=0, high=4)
      ys = make_arg([N], low=0, high=28)
      dy = make_arg([N], low=0, high=4)
      boxes = torch.stack(
          [xs, ys, xs + dx, ys + dy], dim=1
      ).requires_grad_(requires_grad)
      scores = make_arg([N], low=0, high=1, requires_grad=requires_grad)
      iou_threshold = make_arg([], low=0, high=1).item()
  
      yield SampleInput(boxes, args=(scores, iou_threshold))
  
imports: import torch
  import functools
  from torch.testing import make_tensor
  from torch.testing._internal.opinfo.core import OpInfo, SampleInput
  from torch.testing._internal.common_dtype import all_types_and
  import numpy
  from torch.testing._internal.autograd_function_db import sample_inputs_numpy_cube, sample_inputs_numpy_mul, sample_inputs_numpy_sort, sample_inputs_numpy_take
  from torch import Tensor
  from torch.types import Number
  from typing import *
  import torch._custom_ops
label_desc: Use image node_id 52 for calling a global function with example usage: sample_inputs_numpy_nms(opinfo, device, dtype, requires_grad) without return types