Preserve tensor learning rates in OneCycle (#8205)
Follow up #8202:
When an optimizer starts with a tensor learning rate, OneCycle
initialization replaces it with a Python scalar while applying
`cycle_min_lr`. This loses the caller's tensor identity, shape, and
dtype before later scheduler updates can preserve them.
This PR initializes OneCycle learning rates through the existing
tensor-aware update helper, matching the path used by subsequent
scheduler steps.
Signed-off-by: Masahiro Tanaka <mtanaka@anyscale.com>
