HARDNET cannot load the model according to documentation

[XMK233]

I run the code according to this:

import torch
model = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet68', pretrained=True)
# or any of these variants
# model = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet85', pretrained=True)
# model = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)
# model = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)
model.eval()

But I get this error:

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-8-f03545ea41ea> in <module>()
     17     return
     18 
---> 19 test()

<ipython-input-8-f03545ea41ea> in test()
      5     model_name = "HarDNet"
      6     ##
----> 7     model1 = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet68', pretrained=True)
      8 #     model2 = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet85', pretrained=True)
      9 #     model3 = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)

~\Anaconda3\lib\site-packages\torch\hub.py in load(github, model, *args, **kwargs)
    339     entry = _load_entry_from_hubconf(hub_module, model)
    340 
--> 341     model = entry(*args, **kwargs)
    342 
    343     sys.path.remove(repo_dir)

~/.cache\torch\hub\PingoLH_Pytorch-HarDNet_master/hubconf.py in hardnet68(pretrained, **kwargs)
      8     """
      9     # Call the model, load pretrained weights
---> 10     model = hardnet.HarDNet(depth_wise=False, arch=68, pretrained=pretrained)
     11     return model
     12 

~/.cache\torch\hub\PingoLH_Pytorch-HarDNet_master\hardnet.py in __init__(self, depth_wise, arch, pretrained, weight_path)
    215               checkpoint = 'https://ping-chao.com/hardnet/hardnet39ds-0e6c6fa9.pth'
    216 
--> 217             self.load_state_dict(torch.hub.load_state_dict_from_url(checkpoint, progress=False))
    218           else:
    219             postfix = 'ds' if depth_wise else ''

~\Anaconda3\lib\site-packages\torch\hub.py in load_state_dict_from_url(url, model_dir, map_location, progress)
    433         hash_prefix = HASH_REGEX.search(filename).group(1)
    434         _download_url_to_file(url, cached_file, hash_prefix, progress=progress)
--> 435     return torch.load(cached_file, map_location=map_location)

~\Anaconda3\lib\site-packages\torch\serialization.py in load(f, map_location, pickle_module, **pickle_load_args)
    385         f = f.open('rb')
    386     try:
--> 387         return _load(f, map_location, pickle_module, **pickle_load_args)
    388     finally:
    389         if new_fd:

~\Anaconda3\lib\site-packages\torch\serialization.py in _load(f, map_location, pickle_module, **pickle_load_args)
    572     unpickler = pickle_module.Unpickler(f, **pickle_load_args)
    573     unpickler.persistent_load = persistent_load
--> 574     result = unpickler.load()
    575 
    576     deserialized_storage_keys = pickle_module.load(f, **pickle_load_args)

~\Anaconda3\lib\site-packages\torch\serialization.py in persistent_load(saved_id)
    535                 obj = data_type(size)
    536                 obj._torch_load_uninitialized = True
--> 537                 deserialized_objects[root_key] = restore_location(obj, location)
    538             storage = deserialized_objects[root_key]
    539             if view_metadata is not None:

~\Anaconda3\lib\site-packages\torch\serialization.py in default_restore_location(storage, location)
    117 def default_restore_location(storage, location):
    118     for _, _, fn in _package_registry:
--> 119         result = fn(storage, location)
    120         if result is not None:
    121             return result

~\Anaconda3\lib\site-packages\torch\serialization.py in _cuda_deserialize(obj, location)
     93 def _cuda_deserialize(obj, location):
     94     if location.startswith('cuda'):
---> 95         device = validate_cuda_device(location)
     96         if getattr(obj, "_torch_load_uninitialized", False):
     97             storage_type = getattr(torch.cuda, type(obj).__name__)

~\Anaconda3\lib\site-packages\torch\serialization.py in validate_cuda_device(location)
     77 
     78     if not torch.cuda.is_available():
---> 79         raise RuntimeError('Attempting to deserialize object on a CUDA '
     80                            'device but torch.cuda.is_available() is False. '
     81                            'If you are running on a CPU-only machine, '

RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location='cpu' to map your storages to the CPU.

The code and model can be downloaded but cannot be loaded. So what is the problem here? Thanks.