Update definition of optimizer in introduction_guide.rst (#10822)

Co-authored-by: rohitgr7 <[email protected]>

Author: Code-Cornelius

docs/source/starter/introduction_guide.rst

@@ -122,7 +122,7 @@ equivalent to a pure PyTorch Module except it has added functionality. However,
     torch.Size([1, 10])
 
 
-Now we add the training_step which has all our training loop logic
+Now we add the ``training_step`` which has all our training loop logic:
 
 .. testcode::
 
@@ -133,11 +133,12 @@ Now we add the training_step which has all our training loop logic
             loss = F.nll_loss(logits, y)
             return loss
 
+
 Optimizer
 ---------
 
-Next, we choose which optimizer to use for training our model.
-In PyTorch, the optimizer is created as follows:
+Next we choose which optimizer to use for training our system.
+In PyTorch, we do it as follows:
 
 .. code-block:: python
 
@@ -146,23 +147,40 @@ In PyTorch, the optimizer is created as follows:
     optimizer = Adam(LitMNIST().parameters(), lr=1e-3)
 
 
-In Lightning, the code above is moved within the :func:`~pytorch_lightning.core.LightningModule.configure_optimizers` method of the LightningModule.
+In Lightning, the same code is re-organized within the :meth:`~pytorch_lightning.core.lightning.LightningModule.configure_optimizers` method.
 
 .. testcode::
 
     class LitMNIST(LightningModule):
         def configure_optimizers(self):
             return Adam(self.parameters(), lr=1e-3)
 
-.. note:: The LightningModule is subclassing :class:`~torch.nn.Module` and therefore, you can access its children parameters directly with ``self.parameters()``.
+.. note:: The ``LightningModule`` is subclassing :class:`~torch.nn.Module` and therefore, you can access its children parameters directly with ``self.parameters()``.
 
 If you have multiple optimizers, you can configure them as follows:
 
 .. testcode::
 
     class LitMNIST(LightningModule):
         def configure_optimizers(self):
-            return Adam(self.generator(), lr=1e-3), Adam(self.discriminator(), lr=1e-3)
+            return Adam(self.generator.parameters(), lr=1e-3), Adam(self.discriminator.parameters(), lr=1e-3)
+
+If you have LR Schedulers you can return them too:
+
+.. testcode::
+
+    from torch.optim.lr_scheduler import CosineAnnealingLR
+
+
+    class LitMNIST(LightningModule):
+        def configure_optimizers(self):
+            opt = Adam(self.parameters(), lr=1e-3)
+            scheduler = CosineAnnealingLR(opt, T_max=10)
+            return [opt], [scheduler]
+
+
+For more available configurations, please checkout the :meth:`~pytorch_lightning.core.lightning.LightningModule.configure_optimizers` method.
+
 
 Data
 ----
@@ -402,8 +420,8 @@ Training
 So far we defined 4 key ingredients in pure PyTorch but organized the code with the LightningModule.
 
 1. Model.
-2. Training data.
-3. Optimizer.
+2. Optimizer.
+3. Training data.
 4. What happens in the training loop.
 
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