Docs: highlight key optimization code lines with :emphasize-lines:

docs/source-pytorch/common/optimization.rst

@@ -34,6 +34,7 @@ since Lightning automates that for you.
 Under the hood, Lightning does the following:
 
 .. code-block:: python
+    :emphasize-lines: 4-8,10,12
 
     for epoch in epochs:
         for batch in data:
@@ -130,7 +131,8 @@ configured in your :meth:`~lightning.pytorch.core.LightningModule.configure_opti
 You can access your own optimizer with ``optimizer.optimizer``. However, if you use your own optimizer
 to perform a step, Lightning won't be able to support accelerators, precision and profiling for you.
 
-.. testcode:: python
+.. code-block:: python
+    :emphasize-lines: 9,20-21
 
     # function hook in LightningModule
     def optimizer_step(
@@ -168,6 +170,7 @@ relying on a different API from Native PyTorch ones, you should override the :me
 If you are using native PyTorch schedulers, there is no need to override this hook since Lightning will handle it automatically by default.
 
 .. code-block:: python
+    :emphasize-lines: 5-6,10-11
 
     from timm.scheduler import TanhLRScheduler
 
@@ -202,7 +205,8 @@ the arguments along with your optimizer.
 
 For example, here we will apply a stronger gradient clipping after a certain number of epochs:
 
-.. testcode:: python
+.. code-block:: python
+    :emphasize-lines: 2,6
 
     def configure_gradient_clipping(self, optimizer, gradient_clip_val, gradient_clip_algorithm):
         if self.current_epoch > 5:
@@ -221,6 +225,7 @@ distributed setting into consideration so you don't have to derive it manually.
 :class:`~torch.optim.lr_scheduler.OneCycleLR` scheduler, which requires pre-computed ``total_steps`` during initialization.
 
 .. code-block:: python
+    :emphasize-lines: 4
 
     def configure_optimizers(self):
         optimizer = ...