How to use the kfserving.KFServingClient function in kfserving

if framework != 'custom' and default_model_uri:
        default_model_spec = EndpointSpec(framework, default_model_uri)
    elif framework == 'custom' and default_custom_model_spec:
        default_model_spec = customEndpointSpec(default_custom_model_spec)
    
    # Create Canary deployment if canary model uri is provided.
    if framework != 'custom' and canary_model_uri:
        canary_model_spec = EndpointSpec(framework, canary_model_uri)
        kfsvc = InferenceService(metadata, default_model_spec, canary_model_spec, canary_model_traffic)
    elif framework == 'custom' and canary_custom_model_spec:
        canary_model_spec = customEndpointSpec(canary_custom_model_spec)
        kfsvc = InferenceService(metadata, default_model_spec, canary_model_spec, canary_model_traffic)
    else:
        kfsvc = InferenceService(metadata, default_model_spec)

    KFServing = KFServingClient()

    if action == 'create':
        KFServing.create(kfsvc, watch=True, timeout_seconds=120)
    elif action == 'update':
        KFServing.patch(model_name, kfsvc)
    elif action == 'rollout':
        KFServing.rollout_canary(model_name, canary=canary_model_spec, percent=canary_model_traffic, namespace=namespace, watch=True, timeout_seconds=120)
    elif action == 'promote':
        KFServing.promote(model_name, namespace=namespace, watch=True, timeout_seconds=120)
    elif action == 'delete':
        KFServing.delete(model_name, namespace=namespace)
    else:
        raise("Error: No matching action: " + action)

    model_status = KFServing.get(model_name, namespace=namespace)
    return model_status