efficientnetBx model.save() fails due to serialization problem with tf2.10.0
**System information**.
- Have I written custom code: derived from Keras image_classification_efficientnet_fine_tuning
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Win10Pro
- TensorFlow installed from (source or binary): binary
- TensorFlow version (use command below): tf2.10.0
- Python version: 3.10.2
- Bazel version (if compiling from source):
- GPU model and memory: NVIDIA RTX TITAN 24Gb
- Exact command to reproduce: model.save()

Describe the problem clearly here. Be sure to convey here why it's a bug in Keras or why the requested feature is needed.

**Describe the current behavior**.
model save() fails and reports a serialization problem.

**Describe the expected behavior**.
saving keras model without error.

**[Contributing](https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md)**.

- Do you want to contribute a PR? (yes/no): no

**Source code / logs**.

WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 273). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: ./models/EfficientNetB7_Naiads.h5py\\assets
INFO:tensorflow:Assets written to: ./models/EfficientNetB7_Naiads.h5py\\assets
Output exceeds the [size limit](command:workbench.action.openSettings?[). Open the full output data [in a text editor](command:workbench.action.openLargeOutput?df6c3c15-cfcc-4522-938b-e380b707df64)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In [31], line 1
----> 1 model.save('./models/EfficientNetB7_Naiads.h5py')

File e:\\02- Vision Projects\\01- Naiads Projects\\notebooks\\.venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = _process_traceback_frames(e.__traceback__)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

File C:\\Python310\\lib\\json\\encoder.py:199, in JSONEncoder.encode(self, o)
    195         return encode_basestring(o)
    196 # This doesn't pass the iterator directly to ''.join() because the
    197 # exceptions aren't as detailed.  The list call should be roughly
    198 # equivalent to the PySequence_Fast that ''.join() would do.
--> 199 chunks = self.iterencode(o, _one_shot=True)
    200 if not isinstance(chunks, (list, tuple)):
    201     chunks = list(chunks)

File C:\\Python310\\lib\\json\\encoder.py:257, in JSONEncoder.iterencode(self, o, _one_shot)
    252 else:
    253     _iterencode = _make_iterencode(
...
    255         self.key_separator, self.item_separator, self.sort_keys,
    256         self.skipkeys, _one_shot)
--> 257 return _iterencode(o, 0)

TypeError: Unable to serialize [2.0897 2.1129 2.1082] to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.
diff --git a/keras/applications/efficientnet.py b/keras/applications/efficientnet.py
index 5ea6c447e2..619499e671 100644
--- a/keras/applications/efficientnet.py
+++ b/keras/applications/efficientnet.py
@@ -364,7 +364,9 @@ def EfficientNet(
         # original implementation.
         # See https://github.com/tensorflow/tensorflow/issues/49930 for more
         # details
-        x = layers.Rescaling(1.0 / tf.math.sqrt(IMAGENET_STDDEV_RGB))(x)
+        x = layers.Rescaling(
+            [1.0 / math.sqrt(stddev) for stddev in IMAGENET_STDDEV_RGB]
+        )(x)
 
     x = layers.ZeroPadding2D(
         padding=imagenet_utils.correct_pad(x, 3), name="stem_conv_pad"
['keras/applications/efficientnet.py']