### 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)

assert deserialized.partial_kwargs == {
        "op_args": [],
        "op_kwargs": {"arg1": [1, 2, {"a": "b"}]},
        "retry_delay": timedelta(seconds=30),
    }
    # Ensure the serialized operator can also be correctly pickled, to ensure
    # correct interaction between DAG pickling and serialization. This is done
    # here so we don't need to duplicate tests between pickled and non-pickled
    # DAGs everywhere else.
    ads(pickle.dumps(deserialized))
    assert pickled.op_kwargs_expand_input == _ExpandInputRef(
        "list-of-dicts",
        _XComRef({"task_id": "op1", "key": XCOM_RETURN_KEY}),
    )
    assert pickled.partial_kwargs == {
        "op_args": [],
        "op_kwargs": {"arg1": [1, 2, {"a": "b"}]},
        "retry_delay": timedelta(seconds=30),
    }
def test_mapped_task_group_serde():
    from airflow.decorators.task_group import task_group
    from airflow.models.expandinput import DictOfListsExpandInput
    from airflow.utils.task_group import MappedTaskGroup
    with DAG("test-dag", start_date=datetime(2020, 1, 1)) as dag:
        @task_group
        def tg(a: str) -> None:
            BaseOperator(task_id="op1")
            with pytest.raises(NotImplementedError) as ctx:
                BashOperator.partial(task_id="op2").expand(bash_command=["ls", a])
            assert str(ctx.value) == "operator expansion in an expanded task group is not yet supported"