server: python backend error: c_python_backend_utils.TritonModelException: Tensor is stored in GPU and cannot be converted to NumPy

Description I am currently using the Python Backend BLS function and called another tensorrt model using the pb_utils.inferencerequest interface and the call succeeded, but the result is stored on the GPU，and I can’t find how to copy the interface from the GPU.

Triton Information 22.01

Are you using the Triton container or did you build it yourself? no

Expected behavior Can python backend copy InferenceRequest results directly to the CPU？

Here is my debugging information：

(Pdb)
> /fas_repo/bls_model/1/fas_pipe.py(60)face_detect()
-> inputs=[images],
(Pdb)
> /fas_repo/bls_model/1/fas_pipe.py(61)face_detect()
-> requested_output_names=self.outputs_0)
(Pdb)
> /fas_repo/bls_model/1/fas_pipe.py(58)face_detect()
-> infer_request = pb_utils.InferenceRequest(
(Pdb)
> /fas_repo/bls_model/1/fas_pipe.py(62)face_detect()
-> infer_response = infer_request.exec()
(Pdb)
> /fas_repo/bls_model/1/fas_pipe.py(65)face_detect()
-> confs = pb_utils.get_output_tensor_by_name(infer_response, 'class')
(Pdb)
> /fas_repo/bls_model/1/fas_pipe.py(66)face_detect()
-> locs = pb_utils.get_output_tensor_by_name(infer_response, 'bbox')
(Pdb) p confs
<c_python_backend_utils.Tensor object at 0x7f08f1716130>
(Pdb) dir(confs)
['__class__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'as_numpy', 'from_dlpack', 'is_cpu', 'name', 'to_dlpack', 'triton_dtype']
(Pdb) p confs.is_cpu()
False
(Pdb) p confs.as_numpy()
*** c_python_backend_utils.TritonModelException: Tensor is stored in GPU and cannot be converted to NumPy.
(Pdb)

This is the code that I sent the request：

        ......
        import pdb
        pdb.set_trace()
        images = pb_utils.Tensor("images", preprocessed_imgs)
        infer_request = pb_utils.InferenceRequest(
            model_name=self.model_name0,
            inputs=[images],
            requested_output_names=self.outputs_0)
        infer_response = infer_request.exec()
        #if infer_response.has_error():
        #    return False
        confs = pb_utils.get_output_tensor_by_name(infer_response, 'class')
        locs = pb_utils.get_output_tensor_by_name(infer_response, 'bbox')
        ......