Make MultiOptimizer serializable

The current implementation of MultiOptimizer puts grads and vars
inside the optimizer_specs variable. Since the grads and vars are
not serializable, any model that uses MultiOptimizer class cannot
be saved or checkpointed. This PR will only extract the necessary
optimizer_specs that are useful for re-instantiating the
MultiOptimizer instance in get_config() method, leading to a
serializable MultiOptimizer implementation.

tensorflow_addons/optimizers/discriminative_layer_training.py

@@ -124,7 +124,13 @@ def apply_gradients(self, grads_and_vars, **kwargs):
 
     def get_config(self):
         config = super(MultiOptimizer, self).get_config()
-        config.update({"optimizer_specs": self.optimizer_specs})
+        optimizer_specs_without_gv = []
+        for optimizer_spec in self.optimizer_specs:
+            optimizer_specs_without_gv.append({
+                "optimizer": optimizer_spec["optimizer"],
+                "weights": optimizer_spec["weights"]
+            })
+        config.update({"optimizer_specs": optimizer_specs_without_gv})
         return config
 
     @classmethod