Update RPC tutorial with new calls to dist autograd and optmizer

intermediate_source/rpc_tutorial.rst

@@ -598,19 +598,20 @@ accumulate to the same set of ``Tensors``.
 
         # train for 10 iterations
         for epoch in range(10):
-            # create distributed autograd context
             for data, target in get_next_batch():
-                with dist_autograd.context():
+                # create distributed autograd context
+                with dist_autograd.context() as context_id:
                     hidden[0].detach_()
                     hidden[1].detach_()
                     output, hidden = model(data, hidden)
                     loss = criterion(output, target)
                     # run distributed backward pass
-                    dist_autograd.backward([loss])
+                    dist_autograd.backward(context_id, [loss])
                     # run distributed optimizer
-                    opt.step()
-                    # not necessary to zero grads as each iteration creates a different
-                    # distributed autograd context which hosts different grads
+                    opt.step(context_id)
+                    # not necessary to zero grads since they are
+                    # accumulated into the distributed autograd context
+                    # which is reset every iteration.
             print("Training epoch {}".format(epoch))