@model_validator(mode="after")
def check_instruments(self) -> BenchConfigExtended:
    if not self.instruments:
        raise ValueError("At least one instrument must be defined in 'instruments'.")
    return self

def calculate_std_dev(self, reading_value: float, range_value: float | None = None) -> float:
    """
    Calculates the standard deviation (sigma) for a given reading.
    This is a simplified example; real datasheets can be more complex.
    Assumes reading_value is positive for typical instrument readings.
    """
    if reading_value < 0:
        # Or handle as per specific instrument/measurement context
        # For now, using absolute value for calculation if negative readings are possible and meaningful
        # reading_value = abs(reading_value)
        pass  # Assuming reading_value is typically positive or magnitude.

    variance = 0.0
    if self.percent_reading is not None:
        if self.percent_reading < 0:
            raise ValueError("percent_reading must be non-negative.")
        variance += (self.percent_reading * reading_value) ** 2

    if self.offset_value is not None:
        if self.offset_value < 0:
            raise ValueError("offset_value must be non-negative.")
        variance += self.offset_value**2

    if self.percent_range is not None and range_value is not None:
        if self.percent_range < 0:
            raise ValueError("percent_range must be non-negative.")
        if range_value <= 0:  # Range should be positive
            raise ValueError("range_value must be positive for percent_range calculation.")
        variance += (self.percent_range * range_value) ** 2

    if variance < 0.0:  # Should not happen with non-negative inputs and squaring
        raise ValueError("Calculated variance is negative, check inputs and logic.")
    if variance == 0.0:  # No spec provided, or spec results in zero uncertainty
        return 0.0  # Or raise an error, or return a very small number if appropriate

    return math.sqrt(variance)