Add load_state_dict hint doc about invoke order work with lr_scheduler (#149942)

Fixes #119168

## Test Result

![image](https://github.com/user-attachments/assets/edb8124c-f103-475a-b903-20fbc71fdea6)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/149942
Approved by: https://github.com/janeyx99

Co-authored-by: Jane (Yuan) Xu <31798555+janeyx99@users.noreply.github.com>

torch/optim/optimizer.py

@@ -858,6 +858,10 @@ class Optimizer:
             state_dict (dict): optimizer state. Should be an object returned
                 from a call to :meth:`state_dict`.
 
+        .. warning::
+            Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`,
+            as calling it beforehand will overwrite the loaded learning rates.
+
         .. note::
             The names of the parameters (if they exist under the "param_names" key of each param group
             in :meth:`state_dict`) will not affect the loading process.
@@ -868,6 +872,18 @@ class Optimizer:
             If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override
             the current names, if present, in the optimizer state. If they do not exist in loaded state dict,
             the optimizer ``param_names`` will remain unchanged.
+
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> model = torch.nn.Linear(10, 10)
+            >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4)
+            >>> scheduler1 = torch.optim.lr_scheduler.LinearLR(optim, start_factor=0.1, end_factor=1, total_iters=20)
+            >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR(optim, T_max=80, eta_min=3e-5)
+            >>> lr = torch.optim.lr_scheduler.SequentialLR(optim, schedulers=[scheduler1, scheduler2], milestones=[20])
+            >>> lr.load_state_dict(torch.load('./save_seq.pt'))
+            >>> # now load the optimizer checkpoint after loading the LRScheduler
+            >>> optim.load_state_dict(torch.load('./save_optim.pt'))
+
         """
         # shallow copy, to be consistent with module API
         state_dict = state_dict.copy()