### 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)

executor_config={"pod_override": executor_config_pod},
            doc_md="### Task Tutorial Documentation",
        )
        return {"simple_dag": dag}
def make_user_defined_macro_filter_dag():
    """Make DAGs with user defined macros and filters using locally defined methods.
    For Webserver, we do not include ``user_defined_macros`` & ``user_defined_filters``.
    The examples here test:
        (1) functions can be successfully displayed on UI;
        (2) templates with function macros have been rendered before serialization.
    """
    def compute_next_execution_date(dag, execution_date):
        return dag.following_schedule(execution_date)
    default_args = {"start_date": datetime(2019, 7, 10)}
    dag = DAG(
        "user_defined_macro_filter_dag",
        default_args=default_args,
        user_defined_macros={
            "next_execution_date": compute_next_execution_date,
        },
        user_defined_filters={"hello": lambda name: f"Hello {name}"},
        catchup=False,
    )
    BashOperator(
        task_id="echo",
        bash_command='echo "{{ next_execution_date(dag, execution_date) }}"',
        dag=dag,
    )
    return {dag.dag_id: dag}
def collect_dags(dag_folder=None):