class_docstr: Starts a Cloud Data Fusion pipeline. Works for both batch and stream pipelines.
  
  .. seealso::
      For more information on how to use this operator, take a look at the guide:
      :ref:`howto/operator:CloudDataFusionStartPipelineOperator`
  
  :param pipeline_name: Your pipeline name.
  :param pipeline_type: Optional pipeline type (BATCH by default).
  :param instance_name: The name of the instance.
  :param success_states: If provided the operator will wait for pipeline to be in one of
      the provided states.
  :param pipeline_timeout: How long (in seconds) operator should wait for the pipeline to be in one of
      ``success_states``. Works only if ``success_states`` are provided.
  :param location: The Cloud Data Fusion location in which to handle the request.
  :param runtime_args: Optional runtime args to be passed to the pipeline
  :param namespace: If your pipeline belongs to a Basic edition instance, the namespace ID
      is always default. If your pipeline belongs to an Enterprise edition instance, you
      can create a namespace.
  :param api_version: The version of the api that will be requested for example 'v3'.
  :param gcp_conn_id: The connection ID to use when fetching connection info.
  :param impersonation_chain: Optional service account to impersonate using short-term
      credentials, or chained list of accounts required to get the access_token
      of the last account in the list, which will be impersonated in the request.
      If set as a string, the account must grant the originating account
      the Service Account Token Creator IAM role.
      If set as a sequence, the identities from the list must grant
      Service Account Token Creator IAM role to the directly preceding identity, with first
      account from the list granting this role to the originating account (templated).
  :param asynchronous: Flag to return after submitting the pipeline ID to the Data Fusion API.
      This is useful for submitting long-running pipelines and
      waiting on them asynchronously using the CloudDataFusionPipelineStateSensor
  :param deferrable: Run operator in the deferrable mode. Is not related to asynchronous parameter. While
      asynchronous parameter gives a possibility to wait until pipeline reaches terminate state using
      sleep() method, deferrable mode checks for the state using asynchronous calls. It is not possible to
      use both asynchronous and deferrable parameters at the same time.
  :param poll_interval: Polling period in seconds to check for the status. Used only in deferrable mode.
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docstr: Callback for when the trigger fires - returns immediately.
  
  Relies on trigger to throw an exception, otherwise it assumes execution was successful.
code: def execute_complete(self, context: Context, event: dict[str, Any]):
      """
      Callback for when the trigger fires - returns immediately.
  
      Relies on trigger to throw an exception, otherwise it assumes execution was successful.
      """
      if event["status"] == "error":
          raise AirflowException(event["message"])
      self.log.info(
          "%s completed with response %s ",
          self.task_id,
          event["message"],
      )
      return event["pipeline_id"]
  
imports: from __future__ import annotations
  import time
  from typing import TYPE_CHECKING, Any, Sequence
  from google.api_core.retry import exponential_sleep_generator
  from googleapiclient.errors import HttpError
  from airflow.configuration import conf
  from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning
  from airflow.providers.google.cloud.hooks.datafusion import SUCCESS_STATES, DataFusionHook, PipelineStates
  from airflow.providers.google.cloud.links.datafusion import DataFusionInstanceLink, DataFusionPipelineLink, DataFusionPipelinesLink
  from airflow.providers.google.cloud.operators.cloud_base import GoogleCloudBaseOperator
  from airflow.providers.google.cloud.triggers.datafusion import DataFusionStartPipelineTrigger
  from airflow.providers.google.cloud.utils.datafusion import DataFusionPipelineType
  from airflow.providers.google.cloud.utils.helpers import resource_path_to_dict
label_desc: Use image node_id 3 for calling the CloudDataFusionStartPipelineOperator obj's underlying member method code with example usage: obj.execute_complete(context, event) and returns: event