code: def compile(self):
      if self.flow._flow_decorators.get(
          "trigger"
      ) or self.flow._flow_decorators.get("trigger_on_finish"):
          raise AirflowException(
              "Deploying flows with @trigger or @trigger_on_finish decorator(s) "
              "to Airflow is not supported currently."
          )
  
      # Visit every node of the flow and recursively build the state machine.
      def _visit(node, workflow, exit_node=None):
          kube_deco = dict(
              [
                  deco
                  for deco in node.decorators
                  if deco.name == "kubernetes"
              ][0].attributes
          )
          if kube_deco:
              # Only guard against use_tmpfs and tmpfs_size as these determine if tmpfs is enabled.
              for attr in [
                  "use_tmpfs",
                  "tmpfs_size",
                  "persistent_volume_claims",
                  "image_pull_policy",
              ]:
                  if kube_deco[attr]:
                      raise AirflowException(
                          "The decorator attribute *%s* is currently not supported on Airflow "
                          "for the @kubernetes decorator on step *%s*"
                          % (attr, node.name)
                      )
  
          parent_is_foreach = (
              any(  # Any immediate parent is a foreach node.
                  self.graph[n].type == "foreach" for n in node.in_funcs
              )
          )
          state = AirflowTask(
              node.name, is_mapper_node=parent_is_foreach
          ).set_operator_args(**self._to_job(node))
          if node.type == "end":
              workflow.add_state(state)
  
          # Continue linear assignment within the (sub)workflow if the node
          # doesn't branch or fork.
          elif node.type in ("start", "linear", "join", "foreach"):
              workflow.add_state(state)
              _visit(
                  self.graph[node.out_funcs[0]],
                  workflow,
              )
  
          elif node.type == "split":
              workflow.add_state(state)
              for func in node.out_funcs:
                  _visit(
                      self.graph[func],
                      workflow,
                  )
          else:
              raise AirflowException(
                  "Node type *%s* for  step *%s* "
                  "is not currently supported by "
                  "Airflow." % (node.type, node.name)
              )
  
          return workflow
  
      # set max active tasks here , For more info check here :
      # https://airflow.apache.org/docs/apache-airflow/stable/_api/airflow/models/dag/index.html#airflow.models.dag.DAG
      airflow_dag_args = (
          {}
          if self.max_workers is None
          else dict(max_active_tasks=self.max_workers)
      )
      airflow_dag_args[
          "is_paused_upon_creation"
      ] = self.is_paused_upon_creation
  
      # workflow timeout should only be enforced if a dag is scheduled.
      if (
          self.workflow_timeout is not None
          and self.schedule is not None
      ):
          airflow_dag_args["dagrun_timeout"] = dict(
              seconds=self.workflow_timeout
          )
  
      appending_sensors = self._collect_flow_sensors()
      workflow = Workflow(
          dag_id=self.name,
          default_args=self._create_defaults(),
          description=self.description,
          schedule_interval=self.schedule,
          # `start_date` is a mandatory argument even though the documentation lists it as optional value
          # Based on the code, Airflow will throw a `AirflowException` when `start_date` is not provided
          # to a DAG : https://github.com/apache/airflow/blob/0527a0b6ce506434a23bc2a6f5ddb11f492fc614/airflow/models/dag.py#L2170
          start_date=datetime.now(),
          tags=self.tags,
          file_path=self._file_path,
          graph_structure=self.graph_structure,
          metadata=dict(
              contains_foreach=self.contains_foreach,
              flow_name=self.flow.name,
          ),
          **airflow_dag_args,
      )
      workflow = _visit(self.graph["start"], workflow)
  
      workflow.set_parameters(self.parameters)
      if len(appending_sensors) > 0:
          for s in appending_sensors:
              workflow.add_state(s)
          workflow.graph_structure.insert(
              0, [[s.name] for s in appending_sensors]
          )
      return self._to_airflow_dag_file(workflow.to_dict())
  
imports: import json
  import os
  import random
  import string
  import sys
  from datetime import datetime, timedelta
  from io import BytesIO
  import metaflow.util
  from metaflow import current
  from metaflow.decorators import flow_decorators
  from metaflow.exception import MetaflowException
  from metaflow.includefile import FilePathClass
  from metaflow.metaflow_config import AIRFLOW_KUBERNETES_CONN_ID, AIRFLOW_KUBERNETES_KUBECONFIG_CONTEXT, AIRFLOW_KUBERNETES_KUBECONFIG_FILE, AIRFLOW_KUBERNETES_STARTUP_TIMEOUT_SECONDS, AWS_SECRETS_MANAGER_DEFAULT_REGION, AZURE_STORAGE_BLOB_SERVICE_ENDPOINT, CARD_AZUREROOT, CARD_GSROOT, CARD_S3ROOT, DATASTORE_SYSROOT_AZURE, DATASTORE_SYSROOT_GS, DATASTORE_SYSROOT_S3, DATATOOLS_S3ROOT, DEFAULT_SECRETS_BACKEND_TYPE, KUBERNETES_SECRETS, KUBERNETES_SERVICE_ACCOUNT, S3_ENDPOINT_URL, SERVICE_HEADERS, SERVICE_INTERNAL_URL
  from metaflow.metaflow_config_funcs import config_values
  from metaflow.parameters import DelayedEvaluationParameter, JSONTypeClass, deploy_time_eval
  from metaflow.plugins.cards.card_modules import chevron
  from metaflow.plugins.kubernetes.kubernetes import Kubernetes
  from metaflow.plugins.timeout_decorator import get_run_time_limit_for_task
  from metaflow.util import compress_list, dict_to_cli_options, get_username
  from .None import airflow_utils
  from .airflow_utils import AIRFLOW_MACROS, TASK_ID_XCOM_KEY, AirflowTask, Workflow
  from .exception import AirflowException
  from .sensors import SUPPORTED_SENSORS
label_desc: Use image node_id 16 for calling the Airflow obj's underlying member method code with example usage: obj.compile() and returns: self, workflow