Some k8s infrastructure may require custom annotations and/or labels to
be applied to pods in the cluster (ex: custom autoscalers). Sematic
already supports control over this for runner and standalone job pods,
but doesn't for pods created via the Ray integration. This PR adds that
support. The integration works by adding helm-configurable annotations
and labels. The integration works by allowing you to specify annotations
or labels to apply to ALL pods created by Sematic's ray integration. You
can specify different configurations for pods with/without GPUs but
otherwise the configurations must be universal. These configurations
must be specified at the helm level, and NOT by pipeline authors.

Testing
-------

In addition to the unit test, manually applied a Ray cluster yaml
created with these new configurations to confirm the resulting pods had
the expected annotations and labels.

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Co-authored-by: Josh Bauer <josh@sematic.dev>

Apparently `tuple` was missing an implementation for `can_cast_type`. It
likely went unnoticed because `can_cast` has a quick check to return
`True` if the source and destination types are the same python object
(`a is b` check), which would work for most cases. But for other cases
you might want it to work, the implementation was missing & causing
problems. This PR adds an implementation.

Co-authored-by: Josh Bauer <josh@sematic.dev>

Release v0.40.0

Co-authored-by: Josh Bauer <josh@sematic.dev>