FA-59421 / Subscription proration billing / Open access
Trial conversion stub period: stub cycle length · case 01
Stub invoices after a trial exceed the monthly price when the stub spans a 31-day cycle.
ROOT CAUSE
The stub cycle is fixed at 30 days instead of the actual anchor-to-anchor length.
VERIFIED REPAIR
Restore the contract rule at the stub cycle length step: use `cycle = (nxt - prev).days`.
Unsuccessful approach: The attempt uses the calendar month of the trial end, which differs from the anchor-to-anchor cycle.
Case contract
Input {trial_end date, anchor_day 1..28, price}. If trial_end.day equals anchor_day, charge the full price and the next anchor is one month later. Otherwise charge price*days/cycle half-up, where days runs from trial_end to the next anchor date strictly after it and cycle is the length of the anchor-to-anchor month containing trial_end. Return [amount, next anchor ISO].
Why this case matters
Converting a trial onto a fixed billing anchor produces a stub invoice whose length depends on real month boundaries.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
import calendar
N = 1
observations = []
def solve(x):
t = datetime.date(*x['trial_end'])
a = x['anchor_day']
if t.day == a:
ny, nm = (t.year + 1, 1) if t.month == 12 else (t.year, t.month + 1)
return [x['price'], datetime.date(ny, nm, a).isoformat()]
if t.day < a:
nxt = datetime.date(t.year, t.month, a)
py, pm = (t.year - 1, 12) if t.month == 1 else (t.year, t.month - 1)
prev = datetime.date(py, pm, a)
else:
ey, em = (t.year + 1, 1) if t.month == 12 else (t.year, t.month + 1)
nxt = datetime.date(ey, em, a)
prev = datetime.date(t.year, t.month, a)
days = (nxt - t).days
cycle = 30
return [(x['price'] * days * 2 + cycle) // (2 * cycle), nxt.isoformat()]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'trial_end': [2024, 4, 27], 'anchor_day': 28, 'price': 999}, [32, '2024-04-28']), ('regression', {'trial_end': [2024, 4, 1], 'anchor_day': 28, 'price': 78891}, [68712, '2024-04-28']), ('partial-repair probe', {'trial_end': [2024, 12, 16], 'anchor_day': 28, 'price': 2900}, [1160, '2024-12-28']), ('partial-repair probe', {'trial_end': [2025, 2, 6], 'anchor_day': 15, 'price': 2900}, [842, '2025-02-15']), ('normal control', {'trial_end': [2023, 4, 14], 'anchor_day': 1, 'price': 999}, [566, '2023-05-01']), ('normal control', {'trial_end': [2023, 6, 15], 'anchor_day': 15, 'price': 999}, [999, '2023-07-15']), ('normal control', {'trial_end': [2025, 3, 15], 'anchor_day': 15, 'price': 2900}, [2900, '2025-04-15']), ('normal control', {'trial_end': [2024, 2, 8], 'anchor_day': 8, 'price': 999}, [999, '2024-03-08'])], [('regression', {'trial_end': [2024, 4, 1], 'anchor_day': 28, 'price': 78891}, [68712, '2024-04-28']), ('regression', {'trial_end': [2024, 12, 25], 'anchor_day': 1, 'price': 12600}, [2845, '2025-01-01']), ('partial-repair probe', {'trial_end': [2025, 2, 6], 'anchor_day': 15, 'price': 2900}, [842, '2025-02-15']), ('partial-repair probe', {'trial_end': [2024, 6, 4], 'anchor_day': 28, 'price': 999}, [773, '2024-06-28']), ('normal control', {'trial_end': [2024, 6, 25], 'anchor_day': 1, 'price': 2900}, [580, '2024-07-01']), ('normal control', {'trial_end': [2023, 4, 30], 'anchor_day': 15, 'price': 44760}, [22380, '2023-05-15']), ('normal control', {'trial_end': [2024, 6, 14], 'anchor_day': 1, 'price': 999}, [566, '2024-07-01']), ('normal control', {'trial_end': [2024, 7, 15], 'anchor_day': 15, 'price': 999}, [999, '2024-08-15'])], [('regression', {'trial_end': [2025, 2, 6], 'anchor_day': 15, 'price': 2900}, [842, '2025-02-15']), ('regression', {'trial_end': [2024, 12, 25], 'anchor_day': 1, 'price': 12600}, [2845, '2025-01-01']), ('partial-repair probe', {'trial_end': [2024, 6, 4], 'anchor_day': 28, 'price': 999}, [773, '2024-06-28']), ('partial-repair probe', {'trial_end': [2024, 4, 10], 'anchor_day': 15, 'price': 2900}, [468, '2024-04-15']), ('normal control', {'trial_end': [2023, 3, 26], 'anchor_day': 26, 'price': 2900}, [2900, '2023-04-26']), ('normal control', {'trial_end': [2023, 5, 22], 'anchor_day': 22, 'price': 47810}, [47810, '2023-06-22']), ('normal control', {'trial_end': [2025, 6, 10], 'anchor_day': 10, 'price': 999}, [999, '2025-07-10']), ('normal control', {'trial_end': [2023, 12, 2], 'anchor_day': 2, 'price': 999}, [999, '2024-01-02'])], [('regression', {'trial_end': [2024, 6, 4], 'anchor_day': 28, 'price': 999}, [773, '2024-06-28']), ('regression', {'trial_end': [2025, 2, 6], 'anchor_day': 15, 'price': 2900}, [842, '2025-02-15']), ('partial-repair probe', {'trial_end': [2023, 10, 3], 'anchor_day': 16, 'price': 999}, [433, '2023-10-16']), ('partial-repair probe', {'trial_end': [2023, 6, 18], 'anchor_day': 28, 'price': 2900}, [935, '2023-06-28']), ('normal control', {'trial_end': [2023, 5, 29], 'anchor_day': 29, 'price': 15548}, [15548, '2023-06-29']), ('normal control', {'trial_end': [2024, 6, 15], 'anchor_day': 15, 'price': 8594}, [8594, '2024-07-15']), ('normal control', {'trial_end': [2023, 4, 17], 'anchor_day': 1, 'price': 2900}, [1353, '2023-05-01']), ('normal control', {'trial_end': [2023, 4, 6], 'anchor_day': 6, 'price': 999}, [999, '2023-05-06'])], [('regression', {'trial_end': [2024, 4, 10], 'anchor_day': 15, 'price': 2900}, [468, '2024-04-15']), ('regression', {'trial_end': [2023, 12, 16], 'anchor_day': 15, 'price': 2900}, [2806, '2024-01-15']), ('partial-repair probe', {'trial_end': [2025, 3, 22], 'anchor_day': 28, 'price': 58121}, [12455, '2025-03-28']), ('partial-repair probe', {'trial_end': [2024, 3, 15], 'anchor_day': 28, 'price': 999}, [448, '2024-03-28']), ('normal control', {'trial_end': [2023, 11, 28], 'anchor_day': 28, 'price': 2900}, [2900, '2023-12-28']), ('normal control', {'trial_end': [2024, 4, 11], 'anchor_day': 1, 'price': 999}, [666, '2024-05-01']), ('normal control', {'trial_end': [2023, 3, 1], 'anchor_day': 1, 'price': 7363}, [7363, '2023-04-01']), ('normal control', {'trial_end': [2025, 4, 29], 'anchor_day': 15, 'price': 2900}, [1547, '2025-05-15'])]]
for i, (label, args, expected) in enumerate(fixtures[N-1]):
check("%s %d" % (label, i), solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression 0 | [33, '2024-04-28'] | [32, '2024-04-28'] | Failed |
| regression 1 | [71002, '2024-04-28'] | [68712, '2024-04-28'] | Failed |
| partial-repair probe 2 | [1160, '2024-12-28'] | [1160, '2024-12-28'] | Passed |
| partial-repair probe 3 | [870, '2025-02-15'] | [842, '2025-02-15'] | Failed |
| normal control 4 | [566, '2023-05-01'] | [566, '2023-05-01'] | Passed |
| normal control 5 | [999, '2023-07-15'] | [999, '2023-07-15'] | Passed |
| normal control 6 | [2900, '2025-04-15'] | [2900, '2025-04-15'] | Passed |
| normal control 7 | [999, '2024-03-08'] | [999, '2024-03-08'] | Passed |
SHA-256 / 11e98f2c2b4c154529d50c45a6fa7377b88ba5a0d9587f3adf49e83f9f8fd765
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
import calendar
N = 1
observations = []
def solve(x):
t = datetime.date(*x['trial_end'])
a = x['anchor_day']
if t.day == a:
ny, nm = (t.year + 1, 1) if t.month == 12 else (t.year, t.month + 1)
return [x['price'], datetime.date(ny, nm, a).isoformat()]
if t.day < a:
nxt = datetime.date(t.year, t.month, a)
py, pm = (t.year - 1, 12) if t.month == 1 else (t.year, t.month - 1)
prev = datetime.date(py, pm, a)
else:
ey, em = (t.year + 1, 1) if t.month == 12 else (t.year, t.month + 1)
nxt = datetime.date(ey, em, a)
prev = datetime.date(t.year, t.month, a)
days = (nxt - t).days
cycle = (datetime.date(t.year + t.month // 12, t.month % 12 + 1, 1) - datetime.date(t.year, t.month, 1)).days
return [(x['price'] * days * 2 + cycle) // (2 * cycle), nxt.isoformat()]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'trial_end': [2024, 4, 27], 'anchor_day': 28, 'price': 999}, [32, '2024-04-28']), ('regression', {'trial_end': [2024, 4, 1], 'anchor_day': 28, 'price': 78891}, [68712, '2024-04-28']), ('partial-repair probe', {'trial_end': [2024, 12, 16], 'anchor_day': 28, 'price': 2900}, [1160, '2024-12-28']), ('partial-repair probe', {'trial_end': [2025, 2, 6], 'anchor_day': 15, 'price': 2900}, [842, '2025-02-15']), ('normal control', {'trial_end': [2023, 4, 14], 'anchor_day': 1, 'price': 999}, [566, '2023-05-01']), ('normal control', {'trial_end': [2023, 6, 15], 'anchor_day': 15, 'price': 999}, [999, '2023-07-15']), ('normal control', {'trial_end': [2025, 3, 15], 'anchor_day': 15, 'price': 2900}, [2900, '2025-04-15']), ('normal control', {'trial_end': [2024, 2, 8], 'anchor_day': 8, 'price': 999}, [999, '2024-03-08'])], [('regression', {'trial_end': [2024, 4, 1], 'anchor_day': 28, 'price': 78891}, [68712, '2024-04-28']), ('regression', {'trial_end': [2024, 12, 25], 'anchor_day': 1, 'price': 12600}, [2845, '2025-01-01']), ('partial-repair probe', {'trial_end': [2025, 2, 6], 'anchor_day': 15, 'price': 2900}, [842, '2025-02-15']), ('partial-repair probe', {'trial_end': [2024, 6, 4], 'anchor_day': 28, 'price': 999}, [773, '2024-06-28']), ('normal control', {'trial_end': [2024, 6, 25], 'anchor_day': 1, 'price': 2900}, [580, '2024-07-01']), ('normal control', {'trial_end': [2023, 4, 30], 'anchor_day': 15, 'price': 44760}, [22380, '2023-05-15']), ('normal control', {'trial_end': [2024, 6, 14], 'anchor_day': 1, 'price': 999}, [566, '2024-07-01']), ('normal control', {'trial_end': [2024, 7, 15], 'anchor_day': 15, 'price': 999}, [999, '2024-08-15'])], [('regression', {'trial_end': [2025, 2, 6], 'anchor_day': 15, 'price': 2900}, [842, '2025-02-15']), ('regression', {'trial_end': [2024, 12, 25], 'anchor_day': 1, 'price': 12600}, [2845, '2025-01-01']), ('partial-repair probe', {'trial_end': [2024, 6, 4], 'anchor_day': 28, 'price': 999}, [773, '2024-06-28']), ('partial-repair probe', {'trial_end': [2024, 4, 10], 'anchor_day': 15, 'price': 2900}, [468, '2024-04-15']), ('normal control', {'trial_end': [2023, 3, 26], 'anchor_day': 26, 'price': 2900}, [2900, '2023-04-26']), ('normal control', {'trial_end': [2023, 5, 22], 'anchor_day': 22, 'price': 47810}, [47810, '2023-06-22']), ('normal control', {'trial_end': [2025, 6, 10], 'anchor_day': 10, 'price': 999}, [999, '2025-07-10']), ('normal control', {'trial_end': [2023, 12, 2], 'anchor_day': 2, 'price': 999}, [999, '2024-01-02'])], [('regression', {'trial_end': [2024, 6, 4], 'anchor_day': 28, 'price': 999}, [773, '2024-06-28']), ('regression', {'trial_end': [2025, 2, 6], 'anchor_day': 15, 'price': 2900}, [842, '2025-02-15']), ('partial-repair probe', {'trial_end': [2023, 10, 3], 'anchor_day': 16, 'price': 999}, [433, '2023-10-16']), ('partial-repair probe', {'trial_end': [2023, 6, 18], 'anchor_day': 28, 'price': 2900}, [935, '2023-06-28']), ('normal control', {'trial_end': [2023, 5, 29], 'anchor_day': 29, 'price': 15548}, [15548, '2023-06-29']), ('normal control', {'trial_end': [2024, 6, 15], 'anchor_day': 15, 'price': 8594}, [8594, '2024-07-15']), ('normal control', {'trial_end': [2023, 4, 17], 'anchor_day': 1, 'price': 2900}, [1353, '2023-05-01']), ('normal control', {'trial_end': [2023, 4, 6], 'anchor_day': 6, 'price': 999}, [999, '2023-05-06'])], [('regression', {'trial_end': [2024, 4, 10], 'anchor_day': 15, 'price': 2900}, [468, '2024-04-15']), ('regression', {'trial_end': [2023, 12, 16], 'anchor_day': 15, 'price': 2900}, [2806, '2024-01-15']), ('partial-repair probe', {'trial_end': [2025, 3, 22], 'anchor_day': 28, 'price': 58121}, [12455, '2025-03-28']), ('partial-repair probe', {'trial_end': [2024, 3, 15], 'anchor_day': 28, 'price': 999}, [448, '2024-03-28']), ('normal control', {'trial_end': [2023, 11, 28], 'anchor_day': 28, 'price': 2900}, [2900, '2023-12-28']), ('normal control', {'trial_end': [2024, 4, 11], 'anchor_day': 1, 'price': 999}, [666, '2024-05-01']), ('normal control', {'trial_end': [2023, 3, 1], 'anchor_day': 1, 'price': 7363}, [7363, '2023-04-01']), ('normal control', {'trial_end': [2025, 4, 29], 'anchor_day': 15, 'price': 2900}, [1547, '2025-05-15'])]]
for i, (label, args, expected) in enumerate(fixtures[N-1]):
check("%s %d" % (label, i), solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression 0 | [33, '2024-04-28'] | [32, '2024-04-28'] | Failed |
| regression 1 | [71002, '2024-04-28'] | [68712, '2024-04-28'] | Failed |
| partial-repair probe 2 | [1123, '2024-12-28'] | [1160, '2024-12-28'] | Failed |
| partial-repair probe 3 | [932, '2025-02-15'] | [842, '2025-02-15'] | Failed |
| normal control 4 | [566, '2023-05-01'] | [566, '2023-05-01'] | Passed |
| normal control 5 | [999, '2023-07-15'] | [999, '2023-07-15'] | Passed |
| normal control 6 | [2900, '2025-04-15'] | [2900, '2025-04-15'] | Passed |
| normal control 7 | [999, '2024-03-08'] | [999, '2024-03-08'] | Passed |
SHA-256 / 7b676b326d421b13d54de7fe3eff2e74d7149b6eeda3564aca63fdc66facaf76
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
import calendar
N = 1
observations = []
def solve(x):
t = datetime.date(*x['trial_end'])
a = x['anchor_day']
if t.day == a:
ny, nm = (t.year + 1, 1) if t.month == 12 else (t.year, t.month + 1)
return [x['price'], datetime.date(ny, nm, a).isoformat()]
if t.day < a:
nxt = datetime.date(t.year, t.month, a)
py, pm = (t.year - 1, 12) if t.month == 1 else (t.year, t.month - 1)
prev = datetime.date(py, pm, a)
else:
ey, em = (t.year + 1, 1) if t.month == 12 else (t.year, t.month + 1)
nxt = datetime.date(ey, em, a)
prev = datetime.date(t.year, t.month, a)
days = (nxt - t).days
cycle = (nxt - prev).days
return [(x['price'] * days * 2 + cycle) // (2 * cycle), nxt.isoformat()]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'trial_end': [2024, 4, 27], 'anchor_day': 28, 'price': 999}, [32, '2024-04-28']), ('regression', {'trial_end': [2024, 4, 1], 'anchor_day': 28, 'price': 78891}, [68712, '2024-04-28']), ('partial-repair probe', {'trial_end': [2024, 12, 16], 'anchor_day': 28, 'price': 2900}, [1160, '2024-12-28']), ('partial-repair probe', {'trial_end': [2025, 2, 6], 'anchor_day': 15, 'price': 2900}, [842, '2025-02-15']), ('normal control', {'trial_end': [2023, 4, 14], 'anchor_day': 1, 'price': 999}, [566, '2023-05-01']), ('normal control', {'trial_end': [2023, 6, 15], 'anchor_day': 15, 'price': 999}, [999, '2023-07-15']), ('normal control', {'trial_end': [2025, 3, 15], 'anchor_day': 15, 'price': 2900}, [2900, '2025-04-15']), ('normal control', {'trial_end': [2024, 2, 8], 'anchor_day': 8, 'price': 999}, [999, '2024-03-08'])], [('regression', {'trial_end': [2024, 4, 1], 'anchor_day': 28, 'price': 78891}, [68712, '2024-04-28']), ('regression', {'trial_end': [2024, 12, 25], 'anchor_day': 1, 'price': 12600}, [2845, '2025-01-01']), ('partial-repair probe', {'trial_end': [2025, 2, 6], 'anchor_day': 15, 'price': 2900}, [842, '2025-02-15']), ('partial-repair probe', {'trial_end': [2024, 6, 4], 'anchor_day': 28, 'price': 999}, [773, '2024-06-28']), ('normal control', {'trial_end': [2024, 6, 25], 'anchor_day': 1, 'price': 2900}, [580, '2024-07-01']), ('normal control', {'trial_end': [2023, 4, 30], 'anchor_day': 15, 'price': 44760}, [22380, '2023-05-15']), ('normal control', {'trial_end': [2024, 6, 14], 'anchor_day': 1, 'price': 999}, [566, '2024-07-01']), ('normal control', {'trial_end': [2024, 7, 15], 'anchor_day': 15, 'price': 999}, [999, '2024-08-15'])], [('regression', {'trial_end': [2025, 2, 6], 'anchor_day': 15, 'price': 2900}, [842, '2025-02-15']), ('regression', {'trial_end': [2024, 12, 25], 'anchor_day': 1, 'price': 12600}, [2845, '2025-01-01']), ('partial-repair probe', {'trial_end': [2024, 6, 4], 'anchor_day': 28, 'price': 999}, [773, '2024-06-28']), ('partial-repair probe', {'trial_end': [2024, 4, 10], 'anchor_day': 15, 'price': 2900}, [468, '2024-04-15']), ('normal control', {'trial_end': [2023, 3, 26], 'anchor_day': 26, 'price': 2900}, [2900, '2023-04-26']), ('normal control', {'trial_end': [2023, 5, 22], 'anchor_day': 22, 'price': 47810}, [47810, '2023-06-22']), ('normal control', {'trial_end': [2025, 6, 10], 'anchor_day': 10, 'price': 999}, [999, '2025-07-10']), ('normal control', {'trial_end': [2023, 12, 2], 'anchor_day': 2, 'price': 999}, [999, '2024-01-02'])], [('regression', {'trial_end': [2024, 6, 4], 'anchor_day': 28, 'price': 999}, [773, '2024-06-28']), ('regression', {'trial_end': [2025, 2, 6], 'anchor_day': 15, 'price': 2900}, [842, '2025-02-15']), ('partial-repair probe', {'trial_end': [2023, 10, 3], 'anchor_day': 16, 'price': 999}, [433, '2023-10-16']), ('partial-repair probe', {'trial_end': [2023, 6, 18], 'anchor_day': 28, 'price': 2900}, [935, '2023-06-28']), ('normal control', {'trial_end': [2023, 5, 29], 'anchor_day': 29, 'price': 15548}, [15548, '2023-06-29']), ('normal control', {'trial_end': [2024, 6, 15], 'anchor_day': 15, 'price': 8594}, [8594, '2024-07-15']), ('normal control', {'trial_end': [2023, 4, 17], 'anchor_day': 1, 'price': 2900}, [1353, '2023-05-01']), ('normal control', {'trial_end': [2023, 4, 6], 'anchor_day': 6, 'price': 999}, [999, '2023-05-06'])], [('regression', {'trial_end': [2024, 4, 10], 'anchor_day': 15, 'price': 2900}, [468, '2024-04-15']), ('regression', {'trial_end': [2023, 12, 16], 'anchor_day': 15, 'price': 2900}, [2806, '2024-01-15']), ('partial-repair probe', {'trial_end': [2025, 3, 22], 'anchor_day': 28, 'price': 58121}, [12455, '2025-03-28']), ('partial-repair probe', {'trial_end': [2024, 3, 15], 'anchor_day': 28, 'price': 999}, [448, '2024-03-28']), ('normal control', {'trial_end': [2023, 11, 28], 'anchor_day': 28, 'price': 2900}, [2900, '2023-12-28']), ('normal control', {'trial_end': [2024, 4, 11], 'anchor_day': 1, 'price': 999}, [666, '2024-05-01']), ('normal control', {'trial_end': [2023, 3, 1], 'anchor_day': 1, 'price': 7363}, [7363, '2023-04-01']), ('normal control', {'trial_end': [2025, 4, 29], 'anchor_day': 15, 'price': 2900}, [1547, '2025-05-15'])]]
for i, (label, args, expected) in enumerate(fixtures[N-1]):
check("%s %d" % (label, i), solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression 0 | [32, '2024-04-28'] | [32, '2024-04-28'] | Passed |
| regression 1 | [68712, '2024-04-28'] | [68712, '2024-04-28'] | Passed |
| partial-repair probe 2 | [1160, '2024-12-28'] | [1160, '2024-12-28'] | Passed |
| partial-repair probe 3 | [842, '2025-02-15'] | [842, '2025-02-15'] | Passed |
| normal control 4 | [566, '2023-05-01'] | [566, '2023-05-01'] | Passed |
| normal control 5 | [999, '2023-07-15'] | [999, '2023-07-15'] | Passed |
| normal control 6 | [2900, '2025-04-15'] | [2900, '2025-04-15'] | Passed |
| normal control 7 | [999, '2024-03-08'] | [999, '2024-03-08'] | Passed |
SHA-256 / a2c56f9dd3397cdc5d4c143530b07587df7c83483c3be84a15d16a51b4ac7f2d
Verification & scope
A deterministic teaching model of a stipulated billing rule. It makes no claim to reproduce any billing provider's exact behaviour and is not billing software. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.
Observations recorded using Python 3.12.14 at 2026-09-29T14:46:36.184616+00:00.
Case digest / c5afbc96cc4aebdb40a6154fd277b64ddaa9d11c898ee0b75b68a52aa2da413c