### 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)

BaseHook.get_connection = self.backup_base_hook_get_connection
    def test_serialization(self):
        """Serialization and deserialization should work for every DAG and Operator."""
        dags = collect_dags()
        serialized_dags = {}
        for _, v in dags.items():
            dag = SerializedDAG.to_dict(v)
            SerializedDAG.validate_schema(dag)
            serialized_dags[v.dag_id] = dag
        
        self.validate_serialized_dag(serialized_dags["simple_dag"], serialized_simple_dag_ground_truth)
    @pytest.mark.parametrize(
        "timetable, serialized_timetable",
        [
            (
                cron_timetable("0 0 * * *"),
                {
                    "__type": "airflow.timetables.interval.CronDataIntervalTimetable",
                    "__var": {"expression": "0 0 * * *", "timezone": "UTC"},
                },
            ),
            (
                CustomSerializationTimetable("foo"),
                CUSTOM_TIMETABLE_SERIALIZED,
            ),
        ],
    )
    @pytest.mark.usefixtures("timetable_plugin")
    def test_dag_serialization_to_timetable(self, timetable, serialized_timetable):