updated multiple val dataset docs

docs/LightningModule/RequiredTrainerInterface.md

@@ -52,7 +52,7 @@ class CoolModel(pl.LightningModule):
         y_hat = self.forward(x)
         return {'loss': F.cross_entropy(y_hat, y)(y_hat, y)}
 
-    def validation_step(self, batch, batch_nb, dataloader_i):
+    def validation_step(self, batch, batch_nb):
         # OPTIONAL
         x, y = batch
         y_hat = self.forward(x)
@@ -215,7 +215,7 @@ This is most likely the same as your training_step. But unlike training step, th
 |---|---|
 |  data_batch | The output of your dataloader. A tensor, tuple or list  |
 |  batch_nb | Integer displaying which batch this is  |
-|  dataloader_i | Integer displaying which dataloader this is  |
+|  dataloader_i | Integer displaying which dataloader this is (only if multiple val datasets used)  |
 
 **Return**   
 
@@ -226,6 +226,7 @@ This is most likely the same as your training_step. But unlike training step, th
 **Example**
 
 ``` {.python}
+# CASE 1: A single validation dataset
 def validation_step(self, data_batch, batch_nb):
     x, y, z = data_batch
     
@@ -246,7 +247,17 @@ def validation_step(self, data_batch, batch_nb):
     
     # return an optional dict
     return output
-```
+```   
+
+If you pass in multiple validation datasets, validation_step will have an additional argument.
+
+```python
+# CASE 2: multiple validation datasets
+def validation_step(self, data_batch, batch_nb, dataset_idx):
+    # dataset_idx tells you which dataset this is.   
+```   
+
+The ```dataset_idx``` corresponds to the order of datasets returned in ```val_dataloader```.    
 
 --- 
 ### validation_end
@@ -371,6 +382,9 @@ def val_dataloader(self):
     return [loader_a, loader_b, ..., loader_n]   
 ```
 
+In the case where you return multiple val_dataloaders, the validation_step will have an arguement ```dataset_idx```
+which matches the order here.    
+
 --- 
 ### test_dataloader