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running into an error I haven’t seen before when registering flows.  Can someone point me in the right direction?

```   prefect.utilities.exceptions.ClientError: [{'path': ['create_flow_from_compressed_string'], 'message': '1 validation error for FlowSchema\nrun_config\n  value is not a valid dict (type=type_error.dict)', 'extensions': {'code': 'INTERNAL_SERVER_ERROR'}}]```

Hey <@U01SBD76XRV>, I haven’t seen this either but looks like your RunConfig is not a valid dict? Could you post your RunConfig?

sure, i subclassed it, maybe that is what it h8s

```class KubernetesRunConfig(KubernetesRun):
    """A KubernetesRun class that gives some warnings for some common prefect config 'gotchas'"""

    def __init__(self, *args, **kwargs):
        if "image_pull_policy" not in kwargs:
            # this is the k8s default anyway, but its nice to be explicit so you can see it in the UI
            kwargs.update(image_pull_policy="IfNotPresent")

        if "job_template" not in kwargs and "job_template_path" not in kwargs:
            kwargs.update(job_template=_ETL_NODE_TEMPLATE)

        if "labels" not in kwargs or not any(label.startswith("k8s-") for label in kwargs["labels"]):
            warnings.warn(
                "Registering config with no k8s agent label.  "
                "Your label must match the agent you want to run on or your flow will never start!"
            )

        if "image" not in kwargs:
            warnings.warn(
                "No image passed.  Is this intentional?  "
                "If you don't pass an image the flow run will use the base prefect image."
            )
        super().__init__(*args, **kwargs)```


```    run_config=KubernetesRunConfig(labels=[settings.PREFECT_AGENT_LABEL], image=settings.PREFECT_IMAGE),```

I get a different error when trying to use this RunConfig: `ValueError: Flow could not be deserialized successfully. Error was: TypeError('not all arguments converted during string formatting')`. Will look into a bit more later

I dug through the code and I don’t think you can subclass RunConfig because it looks for the appropriate schema for the RunConfig and uses that to serialize. Code <https://github.com/PrefectHQ/prefect/blob/cd80333af08eabf9e24bfa3a57c4f928fd49b770/src/prefect/serialization/run_config.py#L67-L74|here> . I am confirming with the team.

this needs to be implemented to support subclassing
```class MyUberSchema(OneOfSchema):
    type_schemas = {"foo": FooSchema, "bar": BarSchema}

    def get_obj_type(self, obj):
        if isinstance(obj, Foo):
            return "foo"
        elif isinstance(obj, Bar):
            return "bar"
        else:
            raise Exception("Unknown object type: {}".format(obj.__class__.__name__))```

<@U01SBD76XRV> You might be able to accomplish this with a metaclass or by just naming your subclass to collide with the Prefect one

I'm not sure if we're going to explicitly support subclassing runconfigs yet

yeah true, thanks for the suggestion.  Yeah, I just snagged that from the marshmallow-oneof docs

but i get why you wouldn’t want to support that