def __init__(self, pipeline_operator_funcs: Optional[List[OpFunc]]=None, tfx_image: Optional[Text]=None, kubeflow_metadata_config: Optional[kubeflow_pb2.KubeflowMetadataConfig]=None, supported_launcher_classes: List[Type[base_component_launcher.BaseComponentLauncher]]=None, **kwargs):
    'Creates a KubeflowDagRunnerConfig object.\n\n    The user can use pipeline_operator_funcs to apply modifications to\n    ContainerOps used in the pipeline. For example, to ensure the pipeline\n    steps mount a GCP secret, and a Persistent Volume, one can create config\n    object like so:\n\n      from kfp import gcp, onprem\n      mount_secret_op = gcp.use_secret(\'my-secret-name)\n      mount_volume_op = onprem.mount_pvc(\n        "my-persistent-volume-claim",\n        "my-volume-name",\n        "/mnt/volume-mount-path")\n\n      config = KubeflowDagRunnerConfig(\n        pipeline_operator_funcs=[mount_secret_op, mount_volume_op]\n      )\n\n    Args:\n      pipeline_operator_funcs: A list of ContainerOp modifying functions that\n        will be applied to every container step in the pipeline.\n      tfx_image: The TFX container image to use in the pipeline.\n      kubeflow_metadata_config: Runtime configuration to use to connect to\n        Kubeflow metadata.\n      supported_launcher_classes: A list of component launcher classes that are\n        supported by the current pipeline. List sequence determines the order in\n        which launchers are chosen for each component being run.\n      **kwargs: keyword args for PipelineConfig.\n    '
    supported_launcher_classes = (supported_launcher_classes or [in_process_component_launcher.InProcessComponentLauncher, kubernetes_component_launcher.KubernetesComponentLauncher])
    super(KubeflowDagRunnerConfig, self).__init__(supported_launcher_classes=supported_launcher_classes, **kwargs)
    self.pipeline_operator_funcs = (pipeline_operator_funcs or get_default_pipeline_operator_funcs())
    self.tfx_image = (tfx_image or DEFAULT_KUBEFLOW_TFX_IMAGE)
    self.kubeflow_metadata_config = (kubeflow_metadata_config or get_default_kubeflow_metadata_config())
Creates a KubeflowDagRunnerConfig object.

The user can use pipeline_operator_funcs to apply modifications to
ContainerOps used in the pipeline. For example, to ensure the pipeline
steps mount a GCP secret, and a Persistent Volume, one can create config
object like so:

  from kfp import gcp, onprem
  mount_secret_op = gcp.use_secret('my-secret-name)
  mount_volume_op = onprem.mount_pvc(
    "my-persistent-volume-claim",
    "my-volume-name",
    "/mnt/volume-mount-path")

  config = KubeflowDagRunnerConfig(
    pipeline_operator_funcs=[mount_secret_op, mount_volume_op]
  )

Args:
  pipeline_operator_funcs: A list of ContainerOp modifying functions that
    will be applied to every container step in the pipeline.
  tfx_image: The TFX container image to use in the pipeline.
  kubeflow_metadata_config: Runtime configuration to use to connect to
    Kubeflow metadata.
  supported_launcher_classes: A list of component launcher classes that are
    supported by the current pipeline. List sequence determines the order in
    which launchers are chosen for each component being run.
  **kwargs: keyword args for PipelineConfig.
def __init__(self, pipeline_operator_funcs: Optional[List[OpFunc]]=None, tfx_image: Optional[Text]=None, kubeflow_metadata_config: Optional[kubeflow_pb2.KubeflowMetadataConfig]=None, supported_launcher_classes: List[Type[base_component_launcher.BaseComponentLauncher]]=None, **kwargs):
    'Creates a KubeflowDagRunnerConfig object.\n\n    The user can use pipeline_operator_funcs to apply modifications to\n    ContainerOps used in the pipeline. For example, to ensure the pipeline\n    steps mount a GCP secret, and a Persistent Volume, one can create config\n    object like so:\n\n      from kfp import gcp, onprem\n      mount_secret_op = gcp.use_secret(\'my-secret-name)\n      mount_volume_op = onprem.mount_pvc(\n        "my-persistent-volume-claim",\n        "my-volume-name",\n        "/mnt/volume-mount-path")\n\n      config = KubeflowDagRunnerConfig(\n        pipeline_operator_funcs=[mount_secret_op, mount_volume_op]\n      )\n\n    Args:\n      pipeline_operator_funcs: A list of ContainerOp modifying functions that\n        will be applied to every container step in the pipeline.\n      tfx_image: The TFX container image to use in the pipeline.\n      kubeflow_metadata_config: Runtime configuration to use to connect to\n        Kubeflow metadata.\n      supported_launcher_classes: A list of component launcher classes that are\n        supported by the current pipeline. List sequence determines the order in\n        which launchers are chosen for each component being run.\n      **kwargs: keyword args for PipelineConfig.\n    '
    supported_launcher_classes = (supported_launcher_classes or [in_process_component_launcher.InProcessComponentLauncher, kubernetes_component_launcher.KubernetesComponentLauncher])
    super(KubeflowDagRunnerConfig, self).__init__(supported_launcher_classes=supported_launcher_classes, **kwargs)
    self.pipeline_operator_funcs = (pipeline_operator_funcs or get_default_pipeline_operator_funcs())
    self.tfx_image = (tfx_image or DEFAULT_KUBEFLOW_TFX_IMAGE)
    self.kubeflow_metadata_config = (kubeflow_metadata_config or get_default_kubeflow_metadata_config())