@xta0, I can replicate the error on iOS using the code below. Not sure if this is a regression because I don’t see how backward is called anywhere.
Python code:
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 392)
        self.fc2 = nn.Linear(392, 10)

    def forward(self, x):
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        return x

n = Net()
X = th.randn(3, 28 * 28)


simple_net = th.jit.trace(n, X)

iOS C++ Code
auto qengines = at::globalContext().supportedQEngines();
if (std::find(qengines.begin(), qengines.end(), at::QEngine::QNNPACK) != qengines.end()) {
    at::globalContext().setQEngine(at::QEngine::QNNPACK);
}
_impl = torch::jit::load(filePath.UTF8String);
_impl.eval();

std::vector<torch::jit::IValue> modelArgs;

at::Tensor tensor = torch::from_blob(data, trainingDataVectorShape, at::kFloat);
modelArgs.push_back(tensor);

auto result = _impl.forward(modelArgs);


Resulting error:
libc++abi.dylib: terminating with uncaught exception of type torch::jit::ErrorReport: 
Unknown builtin op: aten::_batch_norm_impl_index_backward.
Could not find any similar ops to aten::_batch_norm_impl_index_backward. This op may not exist or may not be currently supported in TorchScript.
:
  File "<string>", line 19

            def backward(grad_output):
                dinput, dweight, dbias = torch._batch_norm_impl_index_backward(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
                    impl_idx, input, grad_output, weight, running_mean, running_var,
                    save1, save2, training, eps, [True, has_weight, has_bias], reserve)