class_docstr: Run Pipeline job.
  
  :param project_id: Required. The ID of the Google Cloud project that the service belongs to.
  :param region: Required. The ID of the Google Cloud region that the service belongs to.
  :param display_name: Required. The user-defined name of this Pipeline.
  :param template_path: Required. The path of PipelineJob or PipelineSpec JSON or YAML file. It can be
      a local path, a Google Cloud Storage URI (e.g. "gs://project.name"), an Artifact Registry URI
      (e.g. "https://us-central1-kfp.pkg.dev/proj/repo/pack/latest"), or an HTTPS URI.
  :param job_id: Optional. The unique ID of the job run. If not specified, pipeline name + timestamp
      will be used.
  :param pipeline_root: Optional. The root of the pipeline outputs. If not set, the staging bucket set
      in aiplatform.init will be used. If that's not set a pipeline-specific artifacts bucket will be
      used.
  :param parameter_values: Optional. The mapping from runtime parameter names to its values that
      control the pipeline run.
  :param input_artifacts: Optional. The mapping from the runtime parameter name for this artifact to
      its resource id. For example: "vertex_model":"456". Note: full resource name
      ("projects/123/locations/us-central1/metadataStores/default/artifacts/456") cannot be used.
  :param enable_caching: Optional. Whether to turn on caching for the run.
      If this is not set, defaults to the compile time settings, which are True for all tasks by
      default, while users may specify different caching options for individual tasks.
      If this is set, the setting applies to all tasks in the pipeline. Overrides the compile time
      settings.
  :param encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer managed
      encryption key used to protect the job. Has the form:
      ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
      The key needs to be in the same region as where the compute resource is created. If this is set,
      then all resources created by the PipelineJob will be encrypted with the provided encryption key.
      Overrides encryption_spec_key_name set in aiplatform.init.
  :param labels: Optional. The user defined metadata to organize PipelineJob.
  :param failure_policy: Optional. The failure policy - "slow" or "fast". Currently, the default of a
      pipeline is that the pipeline will continue to run until no more tasks can be executed, also
      known as PIPELINE_FAILURE_POLICY_FAIL_SLOW (corresponds to "slow"). However, if a pipeline is set
      to PIPELINE_FAILURE_POLICY_FAIL_FAST (corresponds to "fast"), it will stop scheduling any new
      tasks when a task has failed. Any scheduled tasks will continue to completion.
  :param service_account: Optional. Specifies the service account for workload run-as account. Users
      submitting jobs must have act-as permission on this run-as account.
  :param network: Optional. The full name of the Compute Engine network to which the job should be
      peered. For example, projects/12345/global/networks/myVPC.
      Private services access must already be configured for the network. If left unspecified, the
      network set in aiplatform.init will be used. Otherwise, the job is not peered with any network.
  :param create_request_timeout: Optional. The timeout for the create request in seconds.
  :param experiment: Optional. The Vertex AI experiment name or instance to associate to this
      PipelineJob. Metrics produced by the PipelineJob as system.Metric Artifacts will be associated as
      metrics to the current Experiment Run. Pipeline parameters will be associated as parameters to
      the current Experiment Run.
  :param gcp_conn_id: The connection ID to use connecting to Google Cloud.
  :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).
code: def execute(self, context: Context):
      self.log.info("Running Pipeline job")
      self.hook = PipelineJobHook(
          gcp_conn_id=self.gcp_conn_id,
          impersonation_chain=self.impersonation_chain,
      )
      result = self.hook.run_pipeline_job(
          project_id=self.project_id,
          region=self.region,
          display_name=self.display_name,
          template_path=self.template_path,
          job_id=self.job_id,
          pipeline_root=self.pipeline_root,
          parameter_values=self.parameter_values,
          input_artifacts=self.input_artifacts,
          enable_caching=self.enable_caching,
          encryption_spec_key_name=self.encryption_spec_key_name,
          labels=self.labels,
          failure_policy=self.failure_policy,
          service_account=self.service_account,
          network=self.network,
          create_request_timeout=self.create_request_timeout,
          experiment=self.experiment,
      )
  
      pipeline_job = result.to_dict()
      pipeline_job_id = self.hook.extract_pipeline_job_id(pipeline_job)
      self.log.info(
          "Pipeline job was created. Job id: %s", pipeline_job_id
      )
  
      self.xcom_push(
          context, key="pipeline_job_id", value=pipeline_job_id
      )
      VertexAIPipelineJobLink.persist(
          context=context,
          task_instance=self,
          pipeline_id=pipeline_job_id,
      )
      return pipeline_job
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Any, Sequence
  from google.api_core.exceptions import NotFound
  from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
  from google.cloud.aiplatform_v1.types import PipelineJob
  from airflow.providers.google.cloud.hooks.vertex_ai.pipeline_job import PipelineJobHook
  from airflow.providers.google.cloud.links.vertex_ai import VertexAIPipelineJobLink, VertexAIPipelineJobListLink
  from airflow.providers.google.cloud.operators.cloud_base import GoogleCloudBaseOperator
label_desc: Use image node_id 2 for calling the RunPipelineJobOperator obj's underlying member method code with example usage: obj.execute(context) and returns: pipeline_job