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**Description**

Related to the discussion here: https://github.com/apache/airflow/pull/8553#discussion_r416136954

Allow user to define expected terminal state. e.g. `JOB_STATE_CANCELLED` could be not treated as failure but expected terminal state.


<!-- A short description of your feature -->


:param location: Job location.
    :param gcp_conn_id: The connection ID to use connecting to Google Cloud.
    :param poll_sleep: The time in seconds to sleep between polling Google
        Cloud Platform for the dataflow job status while the job is in the
        JOB_STATE_RUNNING state.
    :param job_class: The name of the dataflow job class to be executed, it
        is often not the main class configured in the dataflow jar file.
    :param multiple_jobs: If pipeline creates multiple jobs then monitor all jobs
    :param check_if_running: before running job, validate that a previous run is not in process
        if job is running finish with nothing, WaitForRun= wait until job finished and the run job)
        ``jar``, ``options``, and ``job_name`` are templated so you can use variables in them.
    :param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
        successfully cancelled when task is being killed.
    :param wait_until_finished: (Optional)
        If True, wait for the end of pipeline execution before exiting.
        If False, only submits job.
        If None, default behavior.
        The default behavior depends on the type of pipeline:
        * for the streaming pipeline, wait for jobs to start,
        * for the batch pipeline, wait for the jobs to complete.
        .. warning::
            You cannot call ``PipelineResult.wait_until_finish`` method in your pipeline code for the operator
            to work properly. i. e. you must use asynchronous execution. Otherwise, your pipeline will
            always wait until finished. For more information, look at:
            `Asynchronous execution
            <https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_10>`__
        The process of starting the Dataflow job in Airflow consists of two steps:
        * running a subprocess and reading the stderr/stderr log for the job id.
        * loop waiting for the end of the job ID from the previous step.
          This loop checks the status of the job.