How to use the kfp.gcp.use_gcp_secret function in kfp

training.set_gpu_limit(1)

    prediction = dataflow_tf_predict_op(
        data_file_pattern=evaluation,
        schema=schema,
        target_column=target,
        model=training.output,
        run_mode=predict_mode,
        gcp_project=project,
        predictions_dir=output_template
    ).apply(gcp.use_gcp_secret('user-gcp-sa'))

    confusion_matrix = confusion_matrix_op(
        predictions=prediction.output,
        output_dir=output_template
    ).apply(gcp.use_gcp_secret('user-gcp-sa'))

training_output_dir=output_template
    ).apply(gcp.use_gcp_secret('user-gcp-sa'))

    if use_gpu:
        training.image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:0517114dc2b365a4a6d95424af6157ead774eff3',
        training.set_gpu_limit(1)

    prediction = dataflow_tf_predict_op(
        data_file_pattern=evaluation,
        schema=schema,
        target_column=target,
        model=training.output,
        run_mode=predict_mode,
        gcp_project=project,
        predictions_dir=output_template
    ).apply(gcp.use_gcp_secret('user-gcp-sa'))

    confusion_matrix = confusion_matrix_op(
        predictions=prediction.output,
        output_dir=output_template
    ).apply(gcp.use_gcp_secret('user-gcp-sa'))

server_name=tf_server_name
        )
    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))

'runner', value='gs:///tmp'),
    input_path=kfp.dsl.PipelineParam(
        'path', value='gs:///'),
    output_path=kfp.dsl.PipelineParam(
        'path', value='gs:///output/output'),
    window=kfp.dsl.PipelineParam('window', value=280),
    period=kfp.dsl.PipelineParam('period', value=1)):
  """Defines the pipeline."""
  sentiment_analysis_task = SentimentAnalysisOp(
      'SentimentAnalysis',
      project, # To authenticate.
      gcp_temp_location,
      input_path,
      output_path,
      window, period).apply(
        kfp.gcp.use_gcp_secret()) # To apply gcp service account secret.

train_batch_size=1024,
        eval_batch_size=1024,
        steps_per_eval=250,
        train_steps=10000,
        num_train_images=218593,
        num_eval_images=54648,
        num_label_classes=10):
    output_dir = os.path.join(str(output), '{{workflow.name}}')
    preprocess_staging = os.path.join(output_dir, 'staging')
    preprocess_output = os.path.join(output_dir, 'preprocessed_output')
    train_output = os.path.join(output_dir, 'model')
    preprocess = resnet_preprocess_op(project_id, preprocess_output, preprocess_staging, train_csv,
                                      validation_csv, labels, train_batch_size, eval_batch_size).apply(gcp.use_gcp_secret())
    train = resnet_train_op(project_id, preprocess_output, train_output, region, depth, train_batch_size,
                            eval_batch_size, steps_per_eval, train_steps, num_train_images, num_eval_images,
                            num_label_classes, tf_version).apply(gcp.use_gcp_secret())
    train.after(preprocess)
    export_output = os.path.join(str(train.outputs['job_dir']), 'export')
    deploy = resnet_deploy_op(export_output, model, version, project_id, region,
                              tf_version).apply(gcp.use_gcp_secret())