Explain the users how they can check if python code is top-level (#34006

)

Many users have problem with it. Adding the way how they can
check it easily.

(cherry picked from commit 9702a14)

docs/apache-airflow/best-practices.rst

@@ -176,6 +176,96 @@ Good example:
 
 In the Bad example, NumPy is imported each time the DAG file is parsed, which will result in suboptimal performance in the DAG file processing. In the Good example, NumPy is only imported when the task is running.
 
+Since it is not always obvious, see the next chapter to check how my code is "top-level" code.
+
+How to check if my code is "top-level" code
+-------------------------------------------
+
+In order to understand whether your code is "top-level" or not you need to understand a lot of
+intricacies of how parsing Python works. In general, when Python parses the python file it executes
+the code it sees, except (in general) internal code of the methods that it does not execute.
+
+It has a number of special cases that are not obvious - for example top-level code also means
+any code that is used to determine default values of methods.
+
+However, there is an easy way to check whether your code is "top-level" or not. You simply need to
+parse your code and see if the piece of code gets executed.
+
+Imagine this code:
+
+.. code-block:: python
+
+  from airflow import DAG
+  from airflow.operators.python import PythonOperator
+  import pendulum
+
+
+  def get_task_id():
+      return "print_array_task"  # <- is that code going to be executed?
+
+
+  def get_array():
+      return [1, 2, 3]  # <- is that code going to be executed?
+
+
+  with DAG(
+      dag_id="example_python_operator",
+      schedule=None,
+      start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
+      catchup=False,
+      tags=["example"],
+  ) as dag:
+
+      operator = PythonOperator(
+          task_id=get_task_id(),
+          python_callable=get_array,
+          dag=dag,
+      )
+
+What you can do check it, add to your code you want to check some print statements and run
+``python <my_dag_file>.py``.
+
+
+.. code-block:: python
+
+  from airflow import DAG
+  from airflow.operators.python import PythonOperator
+  import pendulum
+
+
+  def get_task_id():
+      print("Executing 1")
+      return "print_array_task"  # <- is that code going to be executed? YES
+
+
+  def get_array():
+      print("Executing 2")
+      return [1, 2, 3]  # <- is that code going to be executed? NO
+
+
+  with DAG(
+      dag_id="example_python_operator",
+      schedule=None,
+      start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
+      catchup=False,
+      tags=["example"],
+  ) as dag:
+
+      operator = PythonOperator(
+          task_id=get_task_id(),
+          python_callable=get_array,
+          dag=dag,
+      )
+
+When you execute that code you will see:
+
+.. code-block:: bash
+
+    root@cf85ab34571e:/opt/airflow# python /files/test_python.py
+    Executing 1
+
+This means that the ``get_array`` is not executed as top-level code, but ``get_task_id`` is.
+
 .. _best_practices/dynamic_dag_generation:
 
 Dynamic DAG Generation