def METHOD_NAME():
    width_handler, supernet = create_two_conv_width_supernet(elasticity_params={"width": BASIC_ELASTIC_WIDTH_PARAMS})
    device = get_model_device(supernet)
    ACTIVE_WIDTH = 1
    x = torch.ones(supernet.INPUT_SIZE).to(device)
    width_handler.activate_subnet_for_config({0: ACTIVE_WIDTH})
    actual_output = supernet(x)
    conv1 = supernet.conv1
    ref_weights = conv1.weight[:ACTIVE_WIDTH, :, :, :]
    ref_bias = conv1.bias[:ACTIVE_WIDTH]
    o1 = do_conv2d(conv1, x, weight=ref_weights, bias=ref_bias)
    ref_output = supernet.last_conv(o1)
    assert torch.equal(actual_output, ref_output)