def METHOD_NAME(enable_quantization):
    input_size = [1, 1, 8, 8]
    model = BasicConvTestModel()
    config = _get_config_for_algo(input_size, enable_quantization)
    compressed_model, compression_ctrl = create_compressed_model_and_algo_for_test(model, config)
    sparsity_ctrl = compression_ctrl
    if enable_quantization:
        for ctrl in compression_ctrl.child_ctrls:
            if isinstance(ctrl, BaseSparsityAlgoController):
                sparsity_ctrl = ctrl
    compressed_model.conv.weight.data = torch.ones_like(compressed_model.conv.weight.data)
    assert sparsity_ctrl.sparsified_module_info[0].operand.binary_mask.shape[0] == 2
    sparsity_ctrl.sparsified_module_info[0].operand.binary_mask[0] = 0
    input_tensor = torch.Tensor(generate_lazy_sweep_data(input_size))
    x_nncf = compressed_model(input_tensor)
    inference_model = compression_ctrl.strip()
    x_torch = inference_model(input_tensor)
    check_quantizer_operators(inference_model)
    nonzero_inference_model = torch.count_nonzero(inference_model.conv.weight.data)
    nonzero_binary_mask = torch.count_nonzero(compressed_model.conv.pre_ops["0"].operand.binary_mask)
    assert nonzero_inference_model == 4
    assert nonzero_inference_model == nonzero_binary_mask
    assert torch.all(torch.isclose(x_nncf, x_torch)), f"{x_nncf.view(-1)} != {x_torch.view(-1)}"