class_docstr: Start a Java Cloud Dataflow batch job; the parameters of the operation will be passed to the job.
  
  This class is deprecated.
  
  Please use :class:`providers.apache.beam.operators.beam.BeamRunJavaPipelineOperator`.
  
  Example usage:
  
  .. code-block:: python
  
      default_args = {
          "owner": "airflow",
          "depends_on_past": False,
          "start_date": (2016, 8, 1),
          "email": ["alex@vanboxel.be"],
          "email_on_failure": False,
          "email_on_retry": False,
          "retries": 1,
          "retry_delay": timedelta(minutes=30),
          "dataflow_default_options": {
              "project": "my-gcp-project",
              "zone": "us-central1-f",
              "stagingLocation": "gs://bucket/tmp/dataflow/staging/",
          },
      }
  
      dag = DAG("test-dag", default_args=default_args)
  
      task = DataflowCreateJavaJobOperator(
          gcp_conn_id="gcp_default",
          task_id="normalize-cal",
          jar="{{var.value.gcp_dataflow_base}}pipeline-ingress-cal-normalize-1.0.jar",
          options={
              "autoscalingAlgorithm": "BASIC",
              "maxNumWorkers": "50",
              "start": "{{ds}}",
              "partitionType": "DAY",
          },
          dag=dag,
      )
  
  
  .. seealso::
      For more detail on job submission have a look at the reference:
      https://cloud.google.com/dataflow/pipelines/specifying-exec-params
  
  .. seealso::
      For more information on how to use this operator, take a look at the guide:
      :ref:`howto/operator:DataflowCreateJavaJobOperator`
  
  :param jar: The reference to a self executing Dataflow jar (templated).
  :param job_name: The 'jobName' to use when executing the Dataflow job
      (templated). This ends up being set in the pipeline options, so any entry
      with key ``'jobName'`` in ``options`` will be overwritten.
  :param dataflow_default_options: Map of default job options.
  :param options: Map of job specific options.The key must be a dictionary.
  
      The value can contain different types:
  
      * If the value is None, the single option - ``--key`` (without value) will be added.
      * If the value is False, this option will be skipped
      * If the value is True, the single option - ``--key`` (without value) will be added.
      * If the value is list, the many options will be added for each key.
        If the value is ``['A', 'B']`` and the key is ``key`` then the ``--key=A --key=B`` options
        will be left
      * Other value types will be replaced with the Python textual representation.
  
      When defining labels (``labels`` option), you can also provide a dictionary.
  
  :param project_id: Optional, the Google Cloud project ID in which to start a job.
      If set to None or missing, the default project_id from the Google Cloud connection is used.
  :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.
  
      Step two is started just after step one has finished, so if you have wait_until_finished in your
      pipeline code, step two will not start until the process stops. When this process stops,
      steps two will run, but it will only execute one iteration as the job will be in a terminal state.
  
      If you in your pipeline do not call the wait_for_pipeline method but pass wait_until_finish=True
      to the operator, the second loop will wait for the job's terminal state.
  
      If you in your pipeline do not call the wait_for_pipeline method, and pass wait_until_finish=False
      to the operator, the second loop will check once is job not in terminal state and exit the loop.
  :param expected_terminal_state: The expected terminal state of the operator on which the corresponding
      Airflow task succeeds. When not specified, it will be determined by the hook.
  
  Note that both
  ``dataflow_default_options`` and ``options`` will be merged to specify pipeline
  execution parameter, and ``dataflow_default_options`` is expected to save
  high-level options, for instances, project and zone information, which
  apply to all dataflow operators in the DAG.
  
  It's a good practice to define dataflow_* parameters in the default_args of the dag
  like the project, zone and staging location.
  
  .. code-block:: python
  
     default_args = {
         "dataflow_default_options": {
             "zone": "europe-west1-d",
             "stagingLocation": "gs://my-staging-bucket/staging/",
         }
     }
  
  You need to pass the path to your dataflow as a file reference with the ``jar``
  parameter, the jar needs to be a self executing jar (see documentation here:
  https://beam.apache.org/documentation/runners/dataflow/#self-executing-jar).
  Use ``options`` to pass on options to your job.
  
  .. code-block:: python
  
     t1 = DataflowCreateJavaJobOperator(
         task_id="dataflow_example",
         jar="{{var.value.gcp_dataflow_base}}pipeline/build/libs/pipeline-example-1.0.jar",
         options={
             "autoscalingAlgorithm": "BASIC",
             "maxNumWorkers": "50",
             "start": "{{ds}}",
             "partitionType": "DAY",
             "labels": {"foo": "bar"},
         },
         gcp_conn_id="airflow-conn-id",
         dag=my_dag,
     )
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code: def on_kill(self) -> None:
      self.log.info("On kill.")
      if self.job_id:
          self.dataflow_hook.cancel_job(
              job_id=self.job_id,
              project_id=self.project_id
              or self.dataflow_hook.project_id,
          )
  
imports: from __future__ import annotations
  import copy
  import re
  import uuid
  import warnings
  from contextlib import ExitStack
  from enum import Enum
  from functools import cached_property
  from typing import TYPE_CHECKING, Any, Sequence
  from airflow.configuration import conf
  from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning
  from airflow.providers.apache.beam.hooks.beam import BeamHook, BeamRunnerType
  from airflow.providers.google.cloud.hooks.dataflow import DEFAULT_DATAFLOW_LOCATION, DataflowHook, process_line_and_extract_dataflow_job_id_callback
  from airflow.providers.google.cloud.hooks.gcs import GCSHook
  from airflow.providers.google.cloud.links.dataflow import DataflowJobLink
  from airflow.providers.google.cloud.operators.cloud_base import GoogleCloudBaseOperator
  from airflow.providers.google.cloud.triggers.dataflow import TemplateJobStartTrigger
  from airflow.version import version
label_desc: Use image node_id 3 for calling the DataflowCreateJavaJobOperator obj's underlying member method code with example usage: obj.on_kill() without return types