In brand new cluster using k3d locally, I first installed keda:
```bash
helm install keda \
    --namespace keda kedacore/keda \
    --version "v1.5.0"
```

Next, I installed airflow using this config:

```yaml
executor: CeleryExecutor
defaultAirflowTag: 2.0.0-python3.7
airflowVersion: 2.0.0
workers:
  keda:
    enabled: true 
  persistence:
    enabled: false
pgbouncer:
  enabled: true
```

I think this should create a scaled object `airflow-worker`.  

But it does not.

@turbaszek and @dimberman you may have insight ...

chart/tests/helm_template_generator.py
chart = jmespath.search("metadata.labels.chart", instance)
    if chart and 'postgresql' in chart:
        return
    validate = create_validator(instance.get("apiVersion"), instance.get("kind"))
    validate.validate(instance)
def render_chart(name="RELEASE-NAME", values=None, show_only=None):
    """
    Function that renders a helm chart into dictionaries. For helm chart testing only
    """
    values = values or {}
    with NamedTemporaryFile() as tmp_file:
        content = yaml.dump(values)
        tmp_file.write(content.encode())
        tmp_file.flush()
        command = ["helm", "template", name, sys.path[0], '--values', tmp_file.name]
        if show_only:
            for i in show_only:
                command.extend(["--show-only", i])
        templates = subprocess.check_output(command)
        k8s_objects = yaml.load_all(templates)
        k8s_objects = [k8s_object for k8s_object in k8s_objects if k8s_object]  
        for k8s_object in k8s_objects:
            validate_k8s_object(k8s_object)
        return k8s_objects
def prepare_k8s_lookup_dict(k8s_objects) -> Dict[Tuple[str, str], Dict[str, Any]]: