code: def test_clamp():
      def _convert_to_float(x):
          return float(x) if x is not None else None
  
      def _convert_to_batch_tensor(x, data):
          return x * torch.ones_like(data) if x is not None else None
  
      def _convert_to_single_tensor(x, data):
          return x * torch.ones_like(data[0]) if x is not None else None
  
      for min, max in [(-1, None), (None, 1), (-1, 1)]:
          data = 3 * torch.randn((11, 12, 13))
          case1 = clamp(data, min, max)
          case2 = clamp(
              data, _convert_to_float(min), _convert_to_float(max)
          )
          case3 = clamp(
              data,
              _convert_to_batch_tensor(min, data),
              _convert_to_batch_tensor(max, data),
          )
          case4 = clamp(
              data,
              _convert_to_single_tensor(min, data),
              _convert_to_single_tensor(max, data),
          )
  
          assert torch.all(case1 == case2)
          assert torch.all(case2 == case3)
          assert torch.all(case3 == case4)
  
imports: import warnings
  import numpy
  import torch
  import torchvision.transforms.functional
  from advertorch.utils import torch_allclose
  from advertorch.utils import clamp
  from advertorch.utils import CIFAR10_MEAN
  from advertorch.utils import CIFAR10_STD
  from advertorch.utils import MNIST_MEAN
  from advertorch.utils import MNIST_STD
  from advertorch.utils import NormalizeByChannelMeanStd
  from advertorch.utils import PerImageStandardize
  from advertorch.utils import torch_flip
  from advertorch_examples.utils import bchw2bhwc
  from advertorch_examples.utils import bhwc2bchw
label_desc: Use image node_id 6 for calling a global function with example usage: test_clamp() and returns: unknown, unknown, unknown