def METHOD_NAME(self, float_model, img_data):
    ''' Adding artificial bias and testing if bias persists after bias
        correction. This test case changes the bias of a quantized submodule
    '''
    artificial_model = copy.deepcopy(float_model)
    artificial_model.qconfig = default_qconfig
    torch.ao.quantization.prepare(artificial_model, inplace=True)
    for data in img_data:
        artificial_model(data[0])
    torch.ao.quantization.convert(artificial_model, inplace=True)
    # manually changing bias
    for name, submodule in artificial_model.named_modules():
        if type(submodule) in _supported_modules:
            x = get_param(submodule, 'bias')
            weight = get_param(submodule, 'weight')
            if x is not None:
                submodule.set_weight_bias(weight, x.data * 3)
    bias_correction(float_model, artificial_model, img_data, target_modules=_supported_modules_quantized)
    # Trims off the shadow module,
    for name, submodule in artificial_model.named_modules():
        if isinstance(submodule, ns.Shadow):
            parent_name, child_name = parent_child_names(name)
            parent = get_module(artificial_model, parent_name)
            parent._modules[child_name] = submodule.orig_module
    for name, artificial_submodule in artificial_model.named_modules():
        if type(artificial_submodule) in _supported_modules_quantized:
            submodule = get_module(float_model, name)
            float_bias = get_param(submodule, 'bias')
            artificial_bias = get_param(artificial_submodule, 'bias')
            self.assertTrue(self.compute_sqnr(float_bias, artificial_bias) > 30,
                            "Correcting quantized bias produced too much noise, sqnr score too low")