TF-Keras mixed precision training leads to autograph errors
@lgeiger I was not able to replicate the issue on colab using TF [v2.16.1](https://colab.research.google.com/gist/sushreebarsa/60005f6240f6632694003f18c99fdf96/66374.ipynb#scrollTo=CWYFhjxzepie) and tf-[nightly](https://colab.research.google.com/gist/sushreebarsa/6f4d7c4abfaa6ef4d951b7ab2251de4b/66374.ipynb) as well. Please have a look at the attached gists and confirm the same?
Thank you! @sushreebarsa As mentioned in the issue this is an issue with TF-Keras, I added `os.environ["TF_USE_LEGACY_KERAS"] = "1"` to the example code as well to match the colab which should enable you to reproduce this issue.
 Fixed in #67189 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/66374">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66374">No</a>

### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

2.16.1, 2.17.0.dev20240423

### Custom code

No

### OS platform and distribution

Linux, Colab

### Mobile device

_No response_

### Python version

3.10, 3.11

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

12.3 / 8.9.7.29

### GPU model and memory

NVIDIA Tesla T4

### Current behavior?

Since TF version 2.16 mixed precision training is fails to compile with autograph and throws warnings when running a minimal mixed precision training example:

```python
import os

os.environ["TF_USE_LEGACY_KERAS"] = "1"

import tensorflow as tf
from tensorflow import keras

keras.mixed_precision.set_global_policy('mixed_float16')
inputs = keras.Input(shape=(784,))
x = keras.layers.Dense(10)(inputs)
outputs = keras.layers.Activation('softmax', dtype='float32')(x)

model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(loss='sparse_categorical_crossentropy', optimizer=keras.optimizers.RMSprop())

(x_train, y_train), _ = keras.datasets.mnist.load_data()
x_train = x_train.reshape(60000, 784).astype('float32') / 255

_ = model.fit(x_train, y_train, batch_size=128, epochs=1, steps_per_epoch=1, verbose=0)
```

TF Autograph doesn't transform the `create_autocast_variable` function and throws the following warnings:
```
WARNING:tensorflow:AutoGraph could not transform <function create_autocast_variable at 0x7a263a673400> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: <gast.gast.Expr object at 0x7a25b0be2e00>
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
```

Autograph should be able to transform the function and should not throw any warnings like it did in TF 2.15.
Unfortunately we're currently unable to upgrade to Keras 3 due to other issues, so it would be good to be able to get this patched in TF-Keras as well.

I'm not sure if this issue is caused by Autograph or by TF-Keras.

### Standalone code to reproduce the issue

See the following notebooks: This [wasn't an issue in TF 2.15](https://colab.research.google.com/drive/1iW1V_a7vXYvZ0wjAwlbNYz2ORjaFWCeW?usp=sharing), but [fails in TF 2.16](https://colab.research.google.com/drive/1Zzz-ydhptsvjFAs8B4TMAo20jqA2Y07Z?usp=sharing) and still [fails in TF Nightly](https://colab.research.google.com/drive/16qW1wbEIEFJ_4H7TM8XE5RzuQIdCRTZS?usp=sharing).


### Relevant log output

_No response_