allow user to control optimizer step for every optimizer

* added custom hook for user defined optimizer step

* refactored to allow multiple optimizers different training_step

* refactored to allow multiple optimizers different training_step

* refactored to allow multiple optimizers different training_step

* refactored to allow multiple optimizers different training_step

* refactored to allow multiple optimizers different training_step

* pep8

pytorch_lightning/root_module/root_module.py

@@ -66,6 +66,20 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
         """
         raise NotImplementedError
 
+    def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i):
+        """
+        Do something instead of the standard optimizer behavior
+        :param epoch_nb:
+        :param batch_nb:
+        :param optimizer:
+        :param optimizer_i:
+        :return:
+        """
+        optimizer.step()
+
+        # clear gradients
+        optimizer.zero_grad()
+
     @data_loader
     def tng_dataloader(self):
         """