[doc] Update Dict Train Loader doc. (#6579)

* update doc

* update example

docs/source/advanced/multiple_loaders.rst

@@ -9,7 +9,7 @@ Multiple Datasets
 Lightning supports multiple dataloaders in a few ways.
 
 1. Create a dataloader that iterates multiple datasets under the hood.
-2. In the training loop you can pass multiple loaders as a dict or list/tuple and lightning 
+2. In the training loop you can pass multiple loaders as a dict or list/tuple and lightning
    will automatically combine the batches from different loaders.
 3. In the validation and test loop you also have the option to return multiple dataloaders
    which lightning will call sequentially.
@@ -75,21 +75,38 @@ For more details please have a look at :paramref:`~pytorch_lightning.trainer.tra
 
             loader_a = torch.utils.data.DataLoader(range(6), batch_size=4)
             loader_b = torch.utils.data.DataLoader(range(15), batch_size=5)
-            
+
             # pass loaders as a dict. This will create batches like this:
             # {'a': batch from loader_a, 'b': batch from loader_b}
             loaders = {'a': loader_a,
                        'b': loader_b}
 
-            # OR: 
+            # OR:
             # pass loaders as sequence. This will create batches like this:
             # [batch from loader_a, batch from loader_b]
             loaders = [loader_a, loader_b]
 
             return loaders
 
 Furthermore, Lightning also supports that nested lists and dicts (or a combination) can
-be returned 
+be returned.
+
+.. testcode::
+
+    class LitModel(LightningModule):
+
+        def train_dataloader(self):
+
+            loader_a = torch.utils.data.DataLoader(range(8), batch_size=4)
+            loader_b = torch.utils.data.DataLoader(range(16), batch_size=2)
+
+            return {'a': loader_a, 'b': loader_b}
+
+        def training_step(self, batch, batch_idx):
+            # access a dictionnary with a batch from each dataloader
+            batch_a = batch["a"]
+            batch_b = batch["b"]
+
 
 .. testcode::
 
@@ -103,12 +120,29 @@ be returned
             loader_c = torch.utils.data.DataLoader(range(64), batch_size=4)
 
             # pass loaders as a nested dict. This will create batches like this:
-            # {'loader_a_b': {'a': batch from loader a, 'b': batch from loader b},
-            #  'loader_c_d': {'c': batch from loader c, 'd': batch from loader d}}
-            loaders = {'loaders_a_b': {'a': loader_a, 'b': loader_b},
-                       'loaders_c_d': {'c': loader_c, 'd': loader_d}}
+            loaders = {
+                'loaders_a_b': {
+                    'a': loader_a,
+                    'b': loader_b
+                },
+                'loaders_c_d': {
+                    'c': loader_c,
+                    'd': loader_d
+                }
+            }
             return loaders
 
+        def training_step(self, batch, batch_idx):
+            # access the data
+            batch_a_b = batch["loaders_a_b"]
+            batch_c_d = batch["loaders_c_d"]
+
+            batch_a = batch_a_b["a"]
+            batch_b = batch_a_b["a"]
+
+            batch_c = batch_c_d["c"]
+            batch_d = batch_c_d["d"]
+
 ----------
 
 Test/Val dataloaders