text: from datetime import datetime, timedelta
  
  import airflow
  from airflow import DAG
  from airflow.contrib.operators.kubernetes_pod_operator import KubernetesPodOperator
  from airflow.utils.dates import days_ago
  
  # Define your docker image and the AWS role that will run the image (based on your airflow-repo)
  IMAGE = "593291632749.dkr.ecr.eu-west-1.amazonaws.com/airflow-oral-pqs:v1.0.3"
  ROLE = "airflow_oral_pqs"
  
  # Task arguments
  task_args = {
      "depends_on_past": False,
      "email_on_failure": True,
      "owner": "samnlindsay",
      "email": ["sam.lindsay@digital.justice.gov.uk"],
  }
  
  # # # Define your DAG
  # Some notes:
  # setting - (start_date=datetime.now() and schedule_interval=None) is a way to set up you tag so it can only be triggered manually
  # To actually put it on a schedule you can set something like:
  # start_date=datetime(2018, 8, 1), schedule_interval=timedelta(days=1)
  dag = DAG(
      "oral_pqs",
      default_args=task_args,
      description="Go get some oral PQs and put them in S3",
      start_date=datetime(2018, 11, 28),
      schedule_interval=None
  )
  
  task_id = "oral-pqs-api-call"
  task1 = KubernetesPodOperator(
      dag=dag,
      namespace="airflow",
      image=IMAGE,
      env_vars={
          "SCRIPT_NAME": "Oral_PQs_api_call.py",
          "script1_var": '100'
      },
      labels={"app": dag.dag_id},
      name=task_id,
      in_cluster=True,
      task_id=task_id,
      get_logs=True,
      annotations={"iam.amazonaws.com/role": ROLE},
  )