How to use the kfp.dsl.ResourceOp function in kfp

def resourceop_basic():

    # Start a container. Print out env vars.
    op = dsl.ResourceOp(
        name='test-step',
        k8s_resource=json.loads(_CONTAINER_MANIFEST),
        action='create'
    )

},
					"name": "mnist-classifier",
					"type": "MODEL"
				},
				"name": "mnist-classifier",
				"replicas": 1
			}
		]
	}
}    
""")
    seldon_serving_json = seldon_serving_json_template.substitute({ 'dockerreposerving': str(docker_repo_serving),'dockertagserving': str(docker_tag_serving),'modelpvc': modelvolop.outputs["name"]})

    seldon_deployment = json.loads(seldon_serving_json)

    serve = dsl.ResourceOp(
        name='serve',
        k8s_resource=seldon_deployment,
        success_condition='status.state == Available'
    ).after(train)

def volume_op_dag():

    datasetName = "your-dataset"
    dataset = PipelineVolume(datasetName)

    step0 = dsl.ResourceOp(name="dataset-creation",k8s_resource=get_dataset_yaml(
        datasetName,
        "XXXXXXXXXXXXXXX",
        "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
        "http://your_endpoint.com",
        "bucket-name",
        ""
    ))

    step1 = dsl.ContainerOp(
        name="step1",
        image="library/bash:4.4.23",
        command=["sh", "-c"],
        arguments=["echo 1|tee /data/file1"],
        pvolumes={"/data": dataset}
    ).after(step0)

"--labels-path", labels_path,
            "--out-path", lr_prediction_path,
            "--c-param", lr_c_param,
            "--action", "train",
            "--model-path", lr_model_path,
        ],
        pvolumes={"/mnt": vectorize_step.pvolume}
    )

    try:
        seldon_config = yaml.load(open("../deploy_pipeline/seldon_production_pipeline.yaml"))
    except:
        # If this file is run from the project core directory 
        seldon_config = yaml.load(open("deploy_pipeline/seldon_production_pipeline.yaml"))

    deploy_step = dsl.ResourceOp(
        name="seldondeploy",
        k8s_resource=seldon_config,
        attribute_outputs={"name": "{.metadata.name}"})

    deploy_step.after(predict_step)

},
					"name": "mnist-classifier",
					"type": "MODEL"
				},
				"name": "mnist-classifier",
				"replicas": 1
			}
		]
	}
}    
""")
    seldon_serving_json = seldon_serving_json_template.substitute({ 'dockerreposerving': str(docker_repo_serving),'dockertagserving': str(docker_tag_serving),'modelpvc': modelvolop.outputs["name"]})

    seldon_deployment = json.loads(seldon_serving_json)

    serve = dsl.ResourceOp(
        name='serve',
        k8s_resource=seldon_deployment,
        success_condition='status.state == Available'
    ).after(build_serving)

}
							}
						]
					}
				}
			}
		}
	}
}
""")

    tfjobjson = tfjobjson_template.substitute({ 'dockerrepotraining': str(docker_repo_training),'dockertagtraining': str(docker_tag_training),'modelpvc': modelvolop.outputs["name"]})

    tfjob = json.loads(tfjobjson)

    train = dsl.ResourceOp(
        name="train",
        k8s_resource=tfjob,
        success_condition='status.replicaStatuses.Worker.succeeded == 1'
    )

    seldon_serving_json_template = Template("""
{
	"apiVersion": "machinelearning.seldon.io/v1alpha2",
	"kind": "SeldonDeployment",
	"metadata": {
		"labels": {
			"app": "seldon"
		},
		"name": "mnist-classifier"
	},
	"spec": {