Fix _save_tpu: use _maybe_convert_to_cpu instead of to cpu.
Author: baoleai
Pull request: yes

# What does this PR do?

When saving a checkpoint, we execute a `model.to('cpu')` transition and subsequently revert it with `model.to('xla')`. While these operations do not directly affect the model, the optimizer remains connected to the model parameters as they were before being moved to 'cpu'. Consequently, after saving checkpoint, there's a misalignment between the optimizer's parameters and those within the model. Following `optimizer.step()`, executing `zero_grad()` sets the gradients to None.  Consequently, the parameters associated with that optimizer will not receive gradient updates anymore (the optimizer is no longer associated with the model) and keep to None. Therefore, when performing all_reduce, a situation of all_reduce([]) may occur, leading to a core dump in torch_xla.
This commit utilizes `_maybe_convert_to_cpu` to replace the operations of `to('cpu')`.


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Fixes https://github.com/pytorch/xla/issues/6620

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