hello.
I am trying to quantize my custom model and apply nnapi.
nnapi prototype “(Prototype) Convert MobileNetV2 to NNAPI — PyTorch Tutorials 1.7.1 documentation  1”
I followed the example provided here.
First, as in mobilenet v2, a quant pair was created, inherited as quantizable_my_model, and the pretrained weight was loaded, and the example was followed.
But nnapi_model = torch.backends._nnapi.prepare.convert_model_to_nnapi(MY_MODEL,MY_TENSOR)
The following error occurred in.
RuntimeError:
Module'NnapiModule' has no attribute'weights' (This attribute exists on the Python module, but we failed to convert Python type:'list' to a TorchScript type.):
  File "/home/ubuntu/anaconda3/envs/nnapi_2/lib/python3.6/site-packages/torch/backends/_nnapi/prepare.py", line 35
    def init(self):
        assert self.comp is None
        self.weights = [w.contiguous() for w in self.weights]
                                                ~~~~~~~~~~~~ <--- HERE
        comp = torch.classes._nnapi.Compilation()
        comp.init(self.ser_model, self.weights)

def make_custom_nnapi(output_dir_path):

    example_img = torch.rand(1, 3, 256, 256, device="cpu")
    example_val = torch.rand(1, 10, 2, device="cpu")
    example_jac = torch.rand(1, 10, 2, 2, device="cpu")

    MY_MODEL = quantizable_MY_MODEL() # This function just loads a model that inherits a pair of quantstub and dequantstub. and this function load pretrained weight

    MY_MODEL.fuse_model() # fuse_model() is equal with mobilenetv2

    MY_MODEL.qconfig = torch.quantization.get_default_qconfig('fbgemm')

    MY_MODEL_fp32_prepared = torch.quantization.prepare(MY_MODEL)
    MY_MODEL_fp32_prepared(example_img)
    MY_MODEL_int8 = torch.quantization.convert(MY_MODEL_fp32_prepared)
    example_img.nnapi_nhwc = True


    with torch.no_grad():
        MY_MODEL_traced = torch.jit.trace(MY_MODEL_int8, example_img)
    nnapi_model = torch.backends._nnapi.prepare.convert_model_to_nnapi(MY_MODEL_traced,example_img) #error occured this line

class QuantizableMYMODEL(MYMODEL2Q):
    def __init__(self, *args, **kwargs):
        """
        MobileNet V2 main class
        Args:
           Inherits args from floating point MobileNetV2
        """
        super(QuantizableMYMODEL, self).__init__(*args, **kwargs)
        self.quant = QuantStub()
        self.dequant = DeQuantStub()

    def forward(self, x):
        x = self.quant(x)
        # x = self._forward_impl(x)
        x = self.dequant(x)
        return x

    


def quantizable_MY_MODEL(pretrained=False, progress=True, quantize=False,**kwargs):
    """
    Constructs a MobileNetV2 architecture from
    `"MobileNetV2: Inverted Residuals and Linear Bottlenecks"
    <https://arxiv.org/abs/1801.04381>`_.
    Note that quantize = True returns a quantized model with 8 bit
    weights. Quantized models only support inference and run on CPUs.
    GPU inference is not yet supported
    Args:
     pretrained (bool): If True, returns a model pre-trained on ImageNet.
     progress (bool): If True, displays a progress bar of the download to stderr
     quantize(bool): If True, returns a quantized model, else returns a float model
    """

    config_path=PATH TO CONFIG
    checkpoint_path=PATH TO CHEKCPOINT
    checkpoint = torch.load(checkpoint_path, map_location=torch.device("cpu"))

    with open(config_path) as f:
        config = yaml.load(f)

    model = Quantizable_MY_MODEL(
        **config["model_params"]["MYMODEL_params"],
        **config["model_params"]["common_params"]
    )
    model.load_state_dict(checkpoint["MYMODEL"])
    model.eval()
    # _replace_relu(model)

    return model

This is my source code, Hope this helps solve this problem. Thank you.
Don’t mind about the syntax of the uppercase letters
Please help me what to do.