Tensorflow model save/load broken after tensorflow updated.
Hi @ludiusvox ,

Google colab is not supporting TF versions <=2.7 versions. 

With latest versions of Tensorflow you need `tf_keras` package and set os.environ["TF_USE_LEGACY_KERAS"] = "1".

Also you can test with keras 3.x version package by installing `pip install keras --upgrade` and `import keras` directly instead of `from tensorflow import keras `.

Please try both the approaches and let us know the outcome. Thanks!

 Thanks for the advice I will work on it.  Let me save this for later. Also if I am working off of Google collab and on my local machube my GPU needs specific version of TF for the 4080GTX cannot use brand new CUDA or CuDNN and therefore the models portability is in question. For each specific version of tensorflow we have tested configurations which can be found [here](https://www.tensorflow.org/install/source#gpu). Thanks. 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?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62809">Yes</a>
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### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.6

### Custom code

Yes

### OS platform and distribution

google collab

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I saved my models from early december as "FullDataModel.keras", when I tried to load in mid january it couldn't compile and google collab I noticed a about a week ago that there was an update in tensorflow.  So to get my models to load, I had to load a current version of the google colab backend and retrain, and then save the model, and reload.  Why is the keras.load_model("") not backwards compatible, why is there no try/catch blocks in loading and saving models to load different models from different versions of tensorflow.

### Standalone code to reproduce the issue

```shell
Try to load an older TF model into a recent (Jan 2024) that was saved in 2023 of a CNN.
```


### Relevant log output

```shell
---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-8-06e83b693b52> in <cell line: 54>()
     52 
     53 # Load the model
---> 54 model = load_model(model_path)
     55 
     56 

6 frames

/usr/local/lib/python3.10/dist-packages/keras/src/engine/base_layer.py in load_own_variables(self, store)
   3529         all_vars = self._trainable_weights + self._non_trainable_weights
   3530         if len(store.keys()) != len(all_vars):
-> 3531             raise ValueError(
   3532                 f"Layer '{self.name}' expected {len(all_vars)} variables, "
   3533                 "but received "

ValueError: Layer 'conv2d' expected 2 variables, but received 0 variables during loading. Expected: ['conv2d/kernel:0', 'conv2d/bias:0']
```
