Yes, here’s the reproducer:
#! /usr/bin/python3                                                                                                                                                                                                                    

import torch
import torch.backends._nnapi.prepare

class TestNNAPI(torch.nn.Module):
    def __init__(self):
        super(TestNNAPI, self).__init__()
        self.conv = torch.nn.Conv2d(1, 6,  kernel_size=3, padding=1, stride=1)
        self.quant = torch.quantization.QuantStub()
        self.dequant = torch.quantization.DeQuantStub()

    def forward(self, x):
        x = self.quant(x)
        x = self.conv(x)
        x = self.dequant(x)
        res = x + torch.ones(1)
        return res

nnapi_model = TestNNAPI()
nnapi_model.eval()
nnapi_model.qconfig = torch.quantization.get_default_qconfig('qnnpack')
torch.backends.quantized.engine = 'qnnpack'

testinput = torch.rand(6, 1, 600, 800)
testinput.nnapi_nhwc = True

torch.quantization.prepare(nnapi_model, inplace=True)
nnapi_model(testinput)
torch.quantization.convert(nnapi_model, inplace=True)

traced_nnapi_model = torch.jit.trace(nnapi_model, testinput)
traced_nnapi_model = torch.backends._nnapi.prepare.convert_model_to_nnapi(traced_nnapi_model, testinput)

If I remove the quantization.prepare/convert , the script runs fine.