Currently (as implemented in #17414), timetables are serialised by their classes’ full import path. This works most of the time, but not in some cases, including:

* Nested in class or function
* Declared directly in a DAG file without a valid import name (e.g. `12345.py`)

It’s fundamentally impossible to fix some of the cases (e.g. function-local class declaration) due to how Python works, but by requiring the user to explicitly register the timetable class, we can at least expose that problem so users don’t attempt to do that.

However, since the timetable actually would work a lot of times without any additional mechanism, I’m also wondering if we should _require_ registration.

1. Always require registration. A DAG using an unregistered timetable class fails to serialise.
2. Only require registration when the timetable class has wonky import path. “Normal” classes work out of the box without registering, and user sees a serialisation error asking for registration otherwise.
3. Don’t require registration. If a class cannot be correctly serialised, tell the user we can’t do it and the timetable must be declared another way.
airflow/serialization/serialized_objects.py
from airflow.models.dag import DAG, create_timetable
from airflow.providers_manager import ProvidersManager
from airflow.serialization.enums import DagAttributeTypes as DAT, Encoding
from airflow.serialization.helpers import serialize_template_field
from airflow.serialization.json_schema import Validator, load_dag_schema
from airflow.settings import json
from airflow.timetables.base import Timetable
from airflow.utils.code_utils import get_python_source
from airflow.utils.module_loading import as_importable_string, import_string
from airflow.utils.task_group import TaskGroup
try:
    
    from kubernetes.client import models as k8s
    from airflow.kubernetes.pod_generator import PodGenerator
    
    HAS_KUBERNETES = True
except ImportError:
    HAS_KUBERNETES = False
if TYPE_CHECKING:
    from airflow.ti_deps.deps.base_ti_dep import BaseTIDep
log = logging.getLogger(__name__)
_OPERATOR_EXTRA_LINKS: Set[str] = {
    "airflow.operators.trigger_dagrun.TriggerDagRunLink",
    "airflow.sensors.external_task.ExternalTaskSensorLink",
    
    "airflow.operators.dagrun_operator.TriggerDagRunLink",
    "airflow.sensors.external_task_sensor.ExternalTaskSensorLink",
}
@cache
def get_operator_extra_links():