can't save compiled model as .tf
@Imacder Keras separates the concerns of saving your model architecture and saving your model weights.
The model structure can be described and saved using two different formats: JSON and YAML. You can save a model with model.save() or [keras.models.save_model()](https://www.tensorflow.org/api_docs/python/tf/keras/saving/save_model) (which is equivalent). You can load it back with [keras.models.load_model()](https://www.tensorflow.org/api_docs/python/tf/keras/saving/load_model). Please make sure to follow this guide here to save a compiled model, 
Thank you! This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
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### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.13.0

### Custom code

Yes

### OS platform and distribution

Mac mini m1

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

m1 gpu and 8GB Ram

### Current behavior?

can't save compile model as a .tf file.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf


class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
    def __init__(self, d_model, f=0.5, warmup_steps=4_000):
        super().__init__()

        self.d_model = d_model
        self.d_model = tf.cast(self.d_model, tf.float32)

        self.warmup_steps = warmup_steps
        self.f = f

    def __call__(self, step):
        step = tf.cast(step, dtype=tf.float32)
        arg1 = tf.math.rsqrt(step)
        arg2 = step * (self.warmup_steps ** -1.5)

        return self.f * tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)

    def get_config(self):
        config = {
            "d_model": self.d_model,
            "warmup_steps": self.warmup_steps,
            "f": self.f
        }
        return config


model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(784,)),
    tf.keras.layers.Dense(10)
])
learning_rate = CustomSchedule(1024)
optimizer = tf.keras.optimizers.legacy.Adam(learning_rate, beta_1=0.9, beta_2=0.98,
                                            epsilon=1e-5)
model.compile(
    optimizer=optimizer,
    loss="mse", metrics=['accuracy'])
model.save("model.tf")
```


### Relevant log output

```shell
/opt/homebrew/Caskroom/miniconda/base/envs/pythonProject/bin/python /Users/albert/PycharmProjects/pythonProject/AI/test.py 
Traceback (most recent call last):
  File "/Users/albert/PycharmProjects/pythonProject/AI/test.py", line 40, in <module>
    model.save("model.tf")
  File "/opt/homebrew/Caskroom/miniconda/base/envs/pythonProject/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/opt/homebrew/Caskroom/miniconda/base/envs/pythonProject/lib/python3.10/json/encoder.py", line 199, in encode
    chunks = self.iterencode(o, _one_shot=True)
  File "/opt/homebrew/Caskroom/miniconda/base/envs/pythonProject/lib/python3.10/json/encoder.py", line 257, in iterencode
    return _iterencode(o, 0)
TypeError: Unable to serialize 1024.0 to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.
```
