def METHOD_NAME():
    circuits = [circ, 3 * circ]
    true_values = [-2.0, -2.0]
    num_qubits = len(qreg)
    p0 = 0.2
    p1 = 0.1
    inverse_confusion_matrix = generate_inverse_confusion_matrix(
        num_qubits, p0=p0, p1=p1
    )
    @rem_decorator(inverse_confusion_matrix=inverse_confusion_matrix)
    def noisy_readout_batched(circuits) -> List[MeasurementResult]:
        return [noisy_readout_executor(c, p0=p0, p1=p1) for c in circuits]
    noisy_executor = partial(noisy_readout_executor, p0=p0, p1=p1)
    base_values = [
        raw_execute(c, noisy_executor, observable) for c in circuits
    ]
    rem_values = Executor(noisy_readout_batched).evaluate(circuits, observable)
    for true_val, base, rem_val in zip(true_values, base_values, rem_values):
        assert abs(true_val - rem_val) < abs(true_val - base)
        assert np.isclose(true_val, rem_val, atol=0.05)