Fix SequentialLR initialization (#72856)
What was happening is that when we have multiple learning rate schedulers, the order in which they are being initialized is not being taken into account. This is a problem if they were being initialized in sequential order (as one might intuitively do).
Each scheduler calls `step()` on initialization and sets the `lr` in its optimizer's `params_groups`. However, this means that step 0 will be using the `lr` that was set by the very last scheduler (in the case of initializing schedulers sequentially) instead of the first scheduler.
The fix in this PR, addresses the above bug by performing a call to the appropriate scheduler on initialization after decrementing the `last_epoch` values in order to keep them the same post-step. This will ensure that the correct scheduler is the one setting the `lr` values for the optimizer's `param_groups`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72856
Approved by: https://github.com/jbschlosser
