diff --git a/keras/src/layers/layer.py b/keras/src/layers/layer.py
index eaff1a8376a2..4ef338b668a1 100644
--- a/keras/src/layers/layer.py
+++ b/keras/src/layers/layer.py
@@ -44,7 +44,6 @@
 from keras.src.metrics.metric import Metric
 from keras.src.ops.node import Node
 from keras.src.ops.operation import Operation
-from keras.src.saving.keras_saveable import KerasSaveable
 from keras.src.utils import python_utils
 from keras.src.utils import summary_utils
 from keras.src.utils import traceback_utils
@@ -67,7 +66,7 @@
 
 
 @keras_export(["keras.Layer", "keras.layers.Layer"])
-class Layer(BackendLayer, Operation, KerasSaveable):
+class Layer(BackendLayer, Operation):
     """This is the class from which all layers inherit.
 
     A layer is a callable object that takes as input one or more tensors and
diff --git a/keras/src/layers/preprocessing/feature_space.py b/keras/src/layers/preprocessing/feature_space.py
index 5fc5e34afafa..5f219dc1cf1c 100644
--- a/keras/src/layers/preprocessing/feature_space.py
+++ b/keras/src/layers/preprocessing/feature_space.py
@@ -6,12 +6,13 @@
 from keras.src.layers.preprocessing.tf_data_layer import TFDataLayer
 from keras.src.saving import saving_lib
 from keras.src.saving import serialization_lib
+from keras.src.saving.keras_saveable import KerasSaveable
 from keras.src.utils import backend_utils
 from keras.src.utils.module_utils import tensorflow as tf
 from keras.src.utils.naming import auto_name
 
 
-class Cross:
+class Cross(KerasSaveable):
     def __init__(self, feature_names, crossing_dim, output_mode="one_hot"):
         if output_mode not in {"int", "one_hot"}:
             raise ValueError(
@@ -23,6 +24,9 @@ def __init__(self, feature_names, crossing_dim, output_mode="one_hot"):
         self.crossing_dim = crossing_dim
         self.output_mode = output_mode
 
+    def _obj_type(self):
+        return "Cross"
+
     @property
     def name(self):
         return "_X_".join(self.feature_names)
@@ -39,7 +43,7 @@ def from_config(cls, config):
         return cls(**config)
 
 
-class Feature:
+class Feature(KerasSaveable):
     def __init__(self, dtype, preprocessor, output_mode):
         if output_mode not in {"int", "one_hot", "float"}:
             raise ValueError(
@@ -55,6 +59,9 @@ def __init__(self, dtype, preprocessor, output_mode):
         self.preprocessor = preprocessor
         self.output_mode = output_mode
 
+    def _obj_type(self):
+        return "Feature"
+
     def get_config(self):
         return {
             "dtype": self.dtype,
diff --git a/keras/src/legacy/saving/legacy_h5_format.py b/keras/src/legacy/saving/legacy_h5_format.py
index d7f3c3eb7ded..5b919f80e7c6 100644
--- a/keras/src/legacy/saving/legacy_h5_format.py
+++ b/keras/src/legacy/saving/legacy_h5_format.py
@@ -6,7 +6,6 @@
 from absl import logging
 
 from keras.src import backend
-from keras.src import optimizers
 from keras.src.backend.common import global_state
 from keras.src.legacy.saving import json_utils
 from keras.src.legacy.saving import saving_options
@@ -161,6 +160,8 @@ def load_model_from_hdf5(filepath, custom_objects=None, compile=True):
             # Set optimizer weights.
             if "optimizer_weights" in f:
                 try:
+                    from keras.src import optimizers
+
                     if isinstance(model.optimizer, optimizers.Optimizer):
                         model.optimizer.build(model._trainable_variables)
                     else:
@@ -249,6 +250,8 @@ def save_optimizer_weights_to_hdf5_group(hdf5_group, optimizer):
         hdf5_group: HDF5 group.
         optimizer: optimizer instance.
     """
+    from keras.src import optimizers
+
     if isinstance(optimizer, optimizers.Optimizer):
         symbolic_weights = optimizer.variables
     else:
diff --git a/keras/src/legacy/saving/saving_utils.py b/keras/src/legacy/saving/saving_utils.py
index 5780ad701163..1373ba11e785 100644
--- a/keras/src/legacy/saving/saving_utils.py
+++ b/keras/src/legacy/saving/saving_utils.py
@@ -4,11 +4,8 @@
 from absl import logging
 
 from keras.src import backend
-from keras.src import layers
 from keras.src import losses
 from keras.src import metrics as metrics_module
-from keras.src import models
-from keras.src import optimizers
 from keras.src import tree
 from keras.src.legacy.saving import serialization
 from keras.src.saving import object_registration
@@ -49,6 +46,9 @@ def model_from_config(config, custom_objects=None):
     global MODULE_OBJECTS
 
     if not hasattr(MODULE_OBJECTS, "ALL_OBJECTS"):
+        from keras.src import layers
+        from keras.src import models
+
         MODULE_OBJECTS.ALL_OBJECTS = layers.__dict__
         MODULE_OBJECTS.ALL_OBJECTS["InputLayer"] = layers.InputLayer
         MODULE_OBJECTS.ALL_OBJECTS["Functional"] = models.Functional
@@ -132,6 +132,8 @@ def compile_args_from_training_config(training_config, custom_objects=None):
         custom_objects = {}
 
     with object_registration.CustomObjectScope(custom_objects):
+        from keras.src import optimizers
+
         optimizer_config = training_config["optimizer_config"]
         optimizer = optimizers.deserialize(optimizer_config)
         # Ensure backwards compatibility for optimizers in legacy H5 files
diff --git a/keras/src/ops/operation.py b/keras/src/ops/operation.py
index 9529a8e689f1..5813593340e3 100644
--- a/keras/src/ops/operation.py
+++ b/keras/src/ops/operation.py
@@ -7,13 +7,14 @@
 from keras.src.api_export import keras_export
 from keras.src.backend.common.keras_tensor import any_symbolic_tensors
 from keras.src.ops.node import Node
+from keras.src.saving.keras_saveable import KerasSaveable
 from keras.src.utils import python_utils
 from keras.src.utils import traceback_utils
 from keras.src.utils.naming import auto_name
 
 
 @keras_export("keras.Operation")
-class Operation:
+class Operation(KerasSaveable):
     def __init__(self, name=None):
         if name is None:
             name = auto_name(self.__class__.__name__)
@@ -311,6 +312,9 @@ def _get_node_attribute_at_index(self, node_index, attr, attr_name):
         else:
             return values
 
+    def _obj_type(self):
+        return "Operation"
+
     # Hooks for backend layer classes
     def _post_build(self):
         """Can be overridden for per backend post build actions."""
diff --git a/keras/src/saving/saving_lib.py b/keras/src/saving/saving_lib.py
index 3d19e81ddec6..01d0b0bbb031 100644
--- a/keras/src/saving/saving_lib.py
+++ b/keras/src/saving/saving_lib.py
@@ -16,14 +16,9 @@
 
 from keras.src import backend
 from keras.src.backend.common import global_state
-from keras.src.layers.layer import Layer
-from keras.src.losses.loss import Loss
-from keras.src.metrics.metric import Metric
-from keras.src.optimizers.optimizer import Optimizer
 from keras.src.saving.serialization_lib import ObjectSharingScope
 from keras.src.saving.serialization_lib import deserialize_keras_object
 from keras.src.saving.serialization_lib import serialize_keras_object
-from keras.src.trainers.compile_utils import CompileMetrics
 from keras.src.utils import dtype_utils
 from keras.src.utils import file_utils
 from keras.src.utils import io_utils
@@ -1584,32 +1579,60 @@ def get_attr_skipset(obj_type):
             "_self_unconditional_dependency_names",
         ]
     )
+    if obj_type == "Operation":
+        from keras.src.ops.operation import Operation
+
+        ref_obj = Operation()
+        skipset.update(dir(ref_obj))
     if obj_type == "Layer":
+        from keras.src.layers.layer import Layer
+
         ref_obj = Layer()
         skipset.update(dir(ref_obj))
     elif obj_type == "Functional":
+        from keras.src.layers.layer import Layer
+
         ref_obj = Layer()
         skipset.update(dir(ref_obj) + ["operations", "_operations"])
     elif obj_type == "Sequential":
+        from keras.src.layers.layer import Layer
+
         ref_obj = Layer()
         skipset.update(dir(ref_obj) + ["_functional"])
     elif obj_type == "Metric":
+        from keras.src.metrics.metric import Metric
+        from keras.src.trainers.compile_utils import CompileMetrics
+
         ref_obj_a = Metric()
         ref_obj_b = CompileMetrics([], [])
         skipset.update(dir(ref_obj_a) + dir(ref_obj_b))
     elif obj_type == "Optimizer":
+        from keras.src.optimizers.optimizer import Optimizer
+
         ref_obj = Optimizer(1.0)
         skipset.update(dir(ref_obj))
         skipset.remove("variables")
     elif obj_type == "Loss":
+        from keras.src.losses.loss import Loss
+
         ref_obj = Loss()
         skipset.update(dir(ref_obj))
+    elif obj_type == "Cross":
+        from keras.src.layers.preprocessing.feature_space import Cross
+
+        ref_obj = Cross((), 1)
+        skipset.update(dir(ref_obj))
+    elif obj_type == "Feature":
+        from keras.src.layers.preprocessing.feature_space import Feature
+
+        ref_obj = Feature("int32", lambda x: x, "int")
+        skipset.update(dir(ref_obj))
     else:
         raise ValueError(
             f"get_attr_skipset got invalid {obj_type=}. "
             "Accepted values for `obj_type` are "
             "['Layer', 'Functional', 'Sequential', 'Metric', "
-            "'Optimizer', 'Loss']"
+            "'Optimizer', 'Loss', 'Cross', 'Feature']"
         )
 
     global_state.set_global_attribute(
diff --git a/keras/src/saving/serialization_lib.py b/keras/src/saving/serialization_lib.py
index 53b88f389407..180176698d76 100644
--- a/keras/src/saving/serialization_lib.py
+++ b/keras/src/saving/serialization_lib.py
@@ -12,6 +12,7 @@
 from keras.src.api_export import keras_export
 from keras.src.backend.common import global_state
 from keras.src.saving import object_registration
+from keras.src.saving.keras_saveable import KerasSaveable
 from keras.src.utils import python_utils
 from keras.src.utils.module_utils import tensorflow as tf
 
@@ -32,6 +33,7 @@
 
 LOADING_APIS = frozenset(
     {
+        "keras.config.enable_unsafe_deserialization",
         "keras.models.load_model",
         "keras.preprocessing.image.load_img",
         "keras.saving.load_model",
@@ -817,8 +819,13 @@ def _retrieve_class_or_fn(
             try:
                 mod = importlib.import_module(module)
                 obj = vars(mod).get(name, None)
-                if obj is not None:
+                if isinstance(obj, type) and issubclass(obj, KerasSaveable):
                     return obj
+                else:
+                    raise ValueError(
+                        f"Could not deserialize '{module}.{name}' because "
+                        "it is not a KerasSaveable subclass"
+                    )
             except ModuleNotFoundError:
                 raise TypeError(
                     f"Could not deserialize {obj_type} '{name}' because "