text: """
  This is an example dag for using the Kubernetes Executor.
  """
  import os
  
  from airflow import DAG
  from airflow.operators.python_operator import PythonOperator
  from airflow.utils.dates import days_ago
  
  args = {
      'owner': 'airflow',
  }
  
  with DAG(
      dag_id='k8s_executor',
      default_args=args,
      schedule_interval=None,
      start_date=days_ago(2),
      tags=['example', 'example2'],
  ) as dag:
  
      affinity = {
          'podAntiAffinity': {
              'requiredDuringSchedulingIgnoredDuringExecution': [
                  {
                      'topologyKey': 'kubernetes.io/hostname',
                      'labelSelector': {
                          'matchExpressions': [{'key': 'app', 'operator': 'In', 'values': ['airflow']}]
                      },
                  }
              ]
          }
      }
  
      tolerations = [{'key': 'dedicated', 'operator': 'Equal', 'value': 'airflow'}]
  
      def use_zip_binary():
          """
          Checks whether Zip is installed.
          :return: True if it is installed, False if not.
          :rtype: bool
          """
          return_code = os.system("zip")
          if return_code != 0:
              raise SystemError("The zip binary is missing")
  
      def print_stuff():
          print("hell, Carl!")
  
      # You don't have to use any special KubernetesExecutor configuration if you don't want to
      start_task = PythonOperator(
          task_id="start_task", 
          python_callable=print_stuff
      )
  
      # But you can if you want to
      one_task = PythonOperator(
          task_id="one_task",
          python_callable=print_stuff,
          executor_config={"KubernetesExecutor": {"image": "airflow/ci:latest"}},
      )
  
      # Use the zip binary, which is only found in this special docker image
      two_task = PythonOperator(
          task_id="two_task",
          python_callable=use_zip_binary,
          executor_config={"KubernetesExecutor": {"image": "airflow/ci_zip:latest"}},
      )
  
      # Limit resources on this operator/task with node affinity & tolerations
      three_task = PythonOperator(
          task_id="three_task",
          python_callable=print_stuff,
          executor_config={
              "KubernetesExecutor": {
                  "request_memory": "128Mi",
                  "limit_memory": "128Mi",
                  "tolerations": tolerations,
                  "affinity": affinity,
              }
          },
      )
  
      # Add arbitrary labels to worker pods
      four_task = PythonOperator(
          task_id="four_task",
          python_callable=print_stuff,
          executor_config={
              "KubernetesExecutor": {
                  "labels": {"foo": "bar"}
              }
          },
      )
  
      start_task >> [one_task, two_task, three_task, four_task]