Add the SuperMLP class

tests/test_super_model.py

@@ -48,3 +48,29 @@ class TestSuperLinear(unittest.TestCase):
         output_shape = (32, abstract_child["_out_features"].value)
         outputs = model(inputs)
         self.assertEqual(tuple(outputs.shape), output_shape)
+
+    def test_super_mlp(self):
+        hidden_features = spaces.Categorical(12, 24, 36)
+        out_features = spaces.Categorical(12, 24, 36)
+        mlp = super_core.SuperMLP(10, hidden_features, out_features)
+        print(mlp)
+        self.assertTrue(mlp.fc1._out_features, mlp.fc2._in_features)
+
+        abstract_space = mlp.abstract_search_space
+        print("The abstract search space for SuperMLP is:\n{:}".format(abstract_space))
+        self.assertEqual(
+            abstract_space["fc1"]["_out_features"],
+            abstract_space["fc2"]["_in_features"],
+        )
+        self.assertTrue(
+            abstract_space["fc1"]["_out_features"]
+            is abstract_space["fc2"]["_in_features"]
+        )
+
+        abstract_space.clean_last_sample()
+        abstract_child = abstract_space.random(reuse_last=True)
+        print("The abstract child program is:\n{:}".format(abstract_child))
+        self.assertEqual(
+            abstract_child["fc1"]["_out_features"].value,
+            abstract_child["fc2"]["_in_features"].value,
+        )