How to use the kfp.onprem.mount_pvc function in kfp

# This pipeline automatically injects the Kubeflow TFX image if the
  # environment variable 'KUBEFLOW_TFX_IMAGE' is defined. Currently, the tfx
  # cli tool exports the environment variable to pass to the pipelines.
  tfx_image = os.environ.get('KUBEFLOW_TFX_IMAGE', None)

  runner_config = kubeflow_dag_runner.KubeflowDagRunnerConfig(
      kubeflow_metadata_config=metadata_config,
      # Specify custom docker image to use.
      tfx_image=tfx_image,
      pipeline_operator_funcs=(
          # If running on K8s Engine (GKE) on Google Cloud Platform (GCP),
          # kubeflow_dag_runner.get_default_pipeline_operator_funcs() provides
          # default configurations specifically for GKE on GCP, such as secrets.
          [
              onprem.mount_pvc(_persistent_volume_claim, _persistent_volume,
                               _persistent_volume_mount)
          ]))

  kubeflow_dag_runner.KubeflowDagRunner(config=runner_config).run(
      _create_pipeline(
          pipeline_name=_pipeline_name,
          pipeline_root=_pipeline_root,
          data_root=_data_root,
          module_file=_module_file,
          serving_model_dir=_serving_model_dir,
          # 0 means auto-detect based on on the number of CPUs available during
          # execution time.
          direct_num_workers=0))

tf_server_name = 'taxi-cab-classification-model-{{workflow.uid}}'

    if platform != 'GCP':
        vop = dsl.VolumeOp(
            name="create_pvc",
            resource_name="pipeline-pvc",
            modes=dsl.VOLUME_MODE_RWM,
            size="1Gi"
        )
    
        checkout = dsl.ContainerOp(
            name="checkout",
            image="alpine/git:latest",
            command=["git", "clone", "https://github.com/kubeflow/pipelines.git", str(output) + "/pipelines"],
        ).apply(onprem.mount_pvc(vop.outputs["name"], 'local-storage', output))
        checkout.after(vop)

    validation = dataflow_tf_data_validation_op(
        inference_data=train,
        validation_data=evaluation,
        column_names=column_names,
        key_columns=key_columns,
        gcp_project=project,
        run_mode=mode,
        validation_output=output_template,
    )
    if platform != 'GCP':
        validation.after(checkout)

    preprocess = dataflow_tf_transform_op(
        training_data_file_pattern=train,

else:
        deploy = kubeflow_deploy_op(
            cluster_name=project,
            model_dir=str(training.output) + '/export/export',
            pvc_name='users-pvc',
#            pvc_name=vop.outputs["name"],
            server_name=tf_server_name,
            service_type='NodePort',
        )

    steps = [validation, preprocess, training, analysis, prediction, cm, roc, deploy]
    for step in steps:
        if platform == 'GCP':
            step.apply(gcp.use_gcp_secret('user-gcp-sa'))
        else:
            step.apply(onprem.mount_pvc('users-pvc', 'local-storage', output))
#            step.apply(onprem.mount_pvc(vop.outputs["name"], 'local-storage', output))

# KF Pipelines using Kubeflow. If installing KF Pipelines using the
  # lightweight deployment option, you may need to override the defaults.
  metadata_config = kubeflow_dag_runner.get_default_kubeflow_metadata_config()

  # This pipeline automatically injects the Kubeflow TFX image if the
  # environment variable 'KUBEFLOW_TFX_IMAGE' is defined. Currently, the tfx
  # cli tool exports the environment variable to pass to the pipelines.
  tfx_image = os.environ.get('KUBEFLOW_TFX_IMAGE', None)

  runner_config = kubeflow_dag_runner.KubeflowDagRunnerConfig(
      kubeflow_metadata_config=metadata_config,
      # Specify custom docker image to use.
      tfx_image=tfx_image,
      pipeline_operator_funcs=(
          kubeflow_dag_runner.get_default_pipeline_operator_funcs() + [
              onprem.mount_pvc(_persistent_volume_claim, _persistent_volume,
                               _persistent_volume_mount)
          ]))

  kubeflow_dag_runner.KubeflowDagRunner(config=runner_config).run(
      _create_pipeline(
          pipeline_name=_pipeline_name,
          pipeline_root=_pipeline_root,
          data_root=_data_root,
          module_file=_module_file,
          serving_model_dir=_serving_model_dir,
          direct_num_workers=_beam_num_workers))

else:
        deploy = kubeflow_deploy_op(
            cluster_name=project,
            model_dir=str(training.output) + '/export/export',
            pvc_name='users-pvc',
#            pvc_name=vop.outputs["name"],
            server_name=tf_server_name,
            service_type='NodePort',
        )

    steps = [validation, preprocess, training, analysis, prediction, cm, roc, deploy]
    for step in steps:
        if platform == 'GCP':
            step.apply(gcp.use_gcp_secret('user-gcp-sa'))
        else:
            step.apply(onprem.mount_pvc('users-pvc', 'local-storage', output))
#            step.apply(onprem.mount_pvc(vop.outputs["name"], 'local-storage', output))