### What do you see as an issue?


The [documentation for Dynamic Task Mapping](https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/dynamic-task-mapping.html#simple-mapping
) does not include an example of a "reduce" task (e.g. `sum_it` in the [examples](https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/dynamic-task-mapping.html#simple-mapping)) using the classic (or non-TaskFlow) operators. It only includes an example that uses the TaskFlow operators.

When I attempted to write a "reduce" task using classic operators for my DAG, I found that there wasn't an obvious approach.

### Solving the problem

We should add an example of a "reduce" task that uses the classic (non-TaskFlow) operators.

For example, for the given `sum_it` example:

```
"""Example DAG demonstrating the usage of dynamic task mapping reduce using classic
operators.
"""
from __future__ import annotations

from datetime import datetime

from airflow import DAG
from airflow.decorators import task
from airflow.operators.python import PythonOperator


def add_one(x: int):
    return x + 1

def sum_it(values):
    total = sum(values)
    print(f"Total was {total}")


with DAG(dag_id="example_dynamic_task_mapping_reduce", start_date=datetime(2022, 3, 4)):
    add_one_task = PythonOperator.partial(
        task_id="add_one",
        python_callable=add_one,
    ).expand(
        op_kwargs=[
            {"x": 1},
            {"x": 2},
            {"x": 3},
        ]
    )

    sum_it_task = PythonOperator(
        task_id="sum_it",
        python_callable=sum_it,
        op_kwargs={"values": add_one_task.output},
    )
```

### Anything else

_No response_

### Are you willing to submit PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

def poke(self, context: Context):
                return False
        op = DummySensor(task_id="dummy", mode=mode, poke_interval=23)
        blob = SerializedBaseOperator.serialize_operator(op)
        assert "deps" in blob
        serialized_op = SerializedBaseOperator.deserialize_operator(blob)
        assert serialized_op.reschedule == (mode == "reschedule")
        assert op.deps == serialized_op.deps
    @pytest.mark.parametrize("mode", ["poke", "reschedule"])
    def test_serialize_mapped_sensor_has_reschedule_dep(self, mode):
        from airflow.sensors.base import BaseSensorOperator
        class DummySensor(BaseSensorOperator):
            def poke(self, context: Context):
                return False
        op = DummySensor.partial(task_id="dummy", mode=mode).expand(poke_interval=[23])
        blob = SerializedBaseOperator.serialize_mapped_operator(op)
        assert "deps" in blob
        assert "airflow.ti_deps.deps.ready_to_reschedule.ReadyToRescheduleDep" in blob["deps"]
    @pytest.mark.parametrize(
        "passed_success_callback, expected_value",
        [
            ({"on_success_callback": lambda x: print("hi")}, True),
            ({}, False),
        ],
    )
    def test_dag_on_success_callback_roundtrip(self, passed_success_callback, expected_value):