doc: dynamictaskmapping pythonoperator op_kwargs (apache#39242)

Co-authored-by: raphaelauv <raphaelauv@users.noreply.github.com>

docs/apache-airflow/authoring-and-scheduling/dynamic-task-mapping.rst

@@ -291,6 +291,24 @@ Sometimes an upstream needs to specify multiple arguments to a downstream operat
 
 This produces two task instances at run-time printing ``1`` and ``2`` respectively.
 
+Also it's possible to mix ``expand_kwargs`` with most of the operators arguments like the ``op_kwargs`` of the PythonOperator
+
+.. code-block:: python
+
+ def print_args(x, y):
+ print(x)
+ print(y)
+ return x + y
+
+
+ PythonOperator.partial(task_id="task-1", python_callable=print_args).expand_kwargs(
+ [
+ {"op_kwargs": {"x": 1, "y": 2}, "show_return_value_in_logs": True},
+ {"op_kwargs": {"x": 3, "y": 4}, "show_return_value_in_logs": False},
+ ]
+ )
+
+
 Similar to ``expand``, you can also map against a XCom that returns a list of dicts, or a list of XComs each returning a dict. Re-using the S3 example above, you can use a mapped task to perform "branching" and copy files to different buckets:
 
 .. code-block:: python