def METHOD_NAME(
    db, inflexible_devices, setup_sources
) -> dict[Sensor, list[int | float]]:
    """
    Set up inflexible devices and forecasts.
    """
    # 2 days of test data
    time_slots = initialize_index(
        start=pd.Timestamp("2015-01-01").tz_localize("Europe/Amsterdam"),
        end=pd.Timestamp("2015-01-03").tz_localize("Europe/Amsterdam"),
        resolution="15T",
    )
    # PV (8 hours at zero capacity, 8 hours at 90% capacity, and again 8 hours at zero capacity)
    headroom = 0.1  # 90% of nominal capacity
    pv_sensor = inflexible_devices["PV power sensor"]
    capacity = pv_sensor.get_attribute("capacity_in_mw")
    pv_values = (
        [0] * (8 * 4) + [(1 - headroom) * capacity] * (8 * 4) + [0] * (8 * 4)
    ) * (len(time_slots) // (24 * 4))
    add_as_beliefs(db, pv_sensor, pv_values, time_slots, setup_sources["Seita"])
    # Residual demand (1 MW continuously)
    residual_demand_sensor = inflexible_devices["residual demand power sensor"]
    residual_demand_values = [-1] * len(time_slots)
    add_as_beliefs(
        db,
        residual_demand_sensor,
        residual_demand_values,
        time_slots,
        setup_sources["Seita"],
    )
    return {
        pv_sensor: pv_values,
        residual_demand_sensor: residual_demand_values,
    }