Fix issue with Masking layer with Tensor as mask_value (#20791)

* Fix issue with Masking layer with Tensor as `mask_value`

* fix formatting

keras/src/layers/core/masking.py

@@ -2,6 +2,7 @@
 from keras.src import ops
 from keras.src.api_export import keras_export
 from keras.src.layers.layer import Layer
+from keras.src.saving.serialization_lib import deserialize_keras_object
 
 
 @keras_export("keras.layers.Masking")
@@ -45,6 +46,9 @@ class Masking(Layer):
 
     def __init__(self, mask_value=0.0, **kwargs):
         super().__init__(**kwargs)
+        # `mask_value` can be a serialized tensor, hence verify it
+        if isinstance(mask_value, dict) and mask_value.get("config", None):
+            mask_value = deserialize_keras_object(mask_value)
         self.mask_value = mask_value
         self.supports_masking = True
         self.built = True

keras/src/layers/core/masking_test.py

@@ -3,7 +3,9 @@
 
 from keras.src import layers
 from keras.src import models
+from keras.src import ops
 from keras.src import testing
+from keras.src.saving import load_model
 
 
 class MaskingTest(testing.TestCase):
@@ -57,3 +59,22 @@ def call(self, inputs, mask=None):
             ]
         )
         model(x)
+
+    @pytest.mark.requires_trainable_backend
+    def test_masking_with_tensor(self):
+        model = models.Sequential(
+            [
+                layers.Masking(mask_value=ops.convert_to_tensor([0.0])),
+                layers.LSTM(1),
+            ]
+        )
+        x = np.array(
+            [
+                [[0.0, 0.0], [1.0, 2.0], [0.0, 0.0]],
+                [[2.0, 2.0], [0.0, 0.0], [2.0, 1.0]],
+            ]
+        )
+        model(x)
+        model.save("model.keras")
+        reload_model = load_model("model.keras")
+        reload_model(x)