def _get_module_names_for_setting_qconfig(model: _nn.Module, mod_name: str) -> _Tuple[str, ...]:
        """
        When layers are fused and we want to skip quantization for a convolution
        or linear layer, we need to set the qconfig for the layer being fused as None
        as well.
        """
        try:
            submod = model.get_submodule(mod_name)
        except AttributeError:
            return (mod_name,)

        if isinstance(submod, _torch.nn.Conv2d) or isinstance(submod, _torch.nn.ConvTranspose2d):
            return mod_name, f"{mod_name}.conv"
        elif isinstance(submod, _torch.nn.Linear):
            return mod_name, f"{mod_name}.linear"
        return (mod_name,)

        When layers are fused and we want to skip quantization for a convolution
        or linear layer, we need to set the qconfig for the layer being fused as None
        as well.
        
coremltools/optimize/torch/quantization/_qconfig_mapping.py