refactor: simplify test dipole sel_type parameters and remove redundant class

Co-authored-by: njzjz <9496702+njzjz@users.noreply.github.com>

Author: Copilot

source/tests/consistent/fitting/test_dipole.py

@@ -61,7 +61,7 @@
     (True, False),  # resnet_dt
     ("float64", "float32"),  # precision
     (True, False),  # mixed_types
-    ([], [0, 1]),  # sel_type
+    ([], [0]),  # sel_type
 )
 class TestDipole(CommonTest, DipoleFittingTest, unittest.TestCase):
     @property
@@ -241,119 +241,3 @@ def atol(self) -> float:
             return 1e-4
         else:
             raise ValueError(f"Unknown precision: {precision}")
-
-
-class TestDipoleSelTypeBehavior(unittest.TestCase):
-    """Test sel_type behavior specifically, without cross-backend consistency."""
-
-    def setUp(self) -> None:
-        self.ntypes = 2
-        self.natoms = np.array([6, 6, 2, 4], dtype=np.int32)
-
-    def test_tf_sel_type_all_types(self):
-        """Test that TF dipole fitting creates networks for all selected types."""
-        if not INSTALLED_TF:
-            self.skipTest("TensorFlow not available")
-
-        sel_type = [0, 1]  # Select all types
-
-        tf_obj = DipoleFittingTF(
-            ntypes=self.ntypes,
-            dim_descrpt=20,
-            embedding_width=30,
-            neuron=[5, 5, 5],
-            sel_type=sel_type,
-        )
-
-        # Verify sel_type is set correctly
-        self.assertEqual(set(tf_obj.sel_type), set(sel_type))
-
-        # Verify sel_mask is correct
-        expected_mask = np.array([i in sel_type for i in range(self.ntypes)])
-        np.testing.assert_array_equal(tf_obj.sel_mask, expected_mask)
-
-    def test_tf_sel_type_partial(self):
-        """Test that TF dipole fitting works with partial type selection."""
-        if not INSTALLED_TF:
-            self.skipTest("TensorFlow not available")
-
-        sel_type = [0]  # Select only type 0
-
-        tf_obj = DipoleFittingTF(
-            ntypes=self.ntypes,
-            dim_descrpt=20,
-            embedding_width=30,
-            neuron=[5, 5, 5],
-            sel_type=sel_type,
-        )
-
-        # Verify sel_type is set correctly
-        self.assertEqual(set(tf_obj.sel_type), set(sel_type))
-
-        # Verify sel_mask is correct
-        expected_mask = np.array([i in sel_type for i in range(self.ntypes)])
-        np.testing.assert_array_equal(tf_obj.sel_mask, expected_mask)
-
-    def test_dp_exclude_types_behavior(self):
-        """Test that DP dipole fitting excludes the correct types."""
-        sel_type = [0]  # Select only type 0
-        all_types = list(range(self.ntypes))
-        exclude_types = [t for t in all_types if t not in sel_type]
-
-        dp_obj = DipoleFittingDP(
-            ntypes=self.ntypes,
-            dim_descrpt=20,
-            embedding_width=30,
-            neuron=[5, 5, 5],
-            exclude_types=exclude_types,
-        )
-
-        # Verify exclude_types is set correctly
-        self.assertEqual(set(dp_obj.exclude_types), set(exclude_types))
-
-        # Verify get_sel_type returns the correct types
-        selected_types = dp_obj.get_sel_type()
-        self.assertEqual(set(selected_types), set(sel_type))
-
-    def test_serialization_with_excluded_types(self):
-        """Test that sel_type is correctly stored in DipoleFittingSeA."""
-        if not INSTALLED_TF:
-            self.skipTest("TensorFlow not available")
-
-        # Test with excluding one type
-        sel_type = [0]  # Only select type 0, exclude type 1
-
-        tf_obj = DipoleFittingTF(
-            ntypes=self.ntypes,
-            dim_descrpt=20,
-            embedding_width=30,
-            neuron=[5, 5, 5],
-            sel_type=sel_type,
-        )
-
-        # Verify that sel_type is correctly stored
-        self.assertEqual(tf_obj.sel_type, sel_type)
-
-        # Verify that sel_mask reflects the excluded types
-        expected_mask = np.array([True, False])  # Only type 0 is selected
-        np.testing.assert_array_equal(tf_obj.sel_mask, expected_mask)
-
-    def test_network_collection_none_handling(self):
-        """Test that NetworkCollection properly handles None networks."""
-        from deepmd.dpmodel.utils.network import (
-            NetworkCollection,
-        )
-
-        # Create a NetworkCollection with some None entries
-        collection = NetworkCollection(ndim=1, ntypes=2)
-
-        # Test that None values can be set
-        collection[0] = None
-        collection[1] = None
-
-        # Test serialization with None values
-        serialized = collection.serialize()
-        self.assertIn("networks", serialized)
-        networks = serialized["networks"]
-        self.assertEqual(len(networks), 2)
-        self.assertTrue(all(net is None for net in networks))