Mirrored strategy model.load_weights() failure
Hi @SamKnightGit ,

This is fixed in Keras3. Please verify the attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/206895e35a67e2c6a082e0284b3c1703/62831_keras3ipynb.ipynb#scrollTo=fzlL6NqA5Hul). Verified that works after upgrading to Keras3. Thanks for the help! 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/62831">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/62831">No</a>
 To confirm, will the fix for this come in a patch release for tf 2.15? Or will an upgrade to tf 2.16 be necessary when it is released? Hi @SamKnightGit ,


This seems fixed with tf-nightly as Keras3 will be imported.I have tested with tf-nightly and used keras as tf.keras and it works fine. Please refer attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/e7b4bb67b3f8f5d1312ba2b9056394dd/62831_tf-nightly_r2.ipynb). Thanks @SuryanarayanaY, do you know what the timeline is for a fix for this issue? Is waiting for the tf 2.16 release (with keras 3) the only option or will there be a patch for this released to tf 2.15. Perhaps I should create a tf-keras issue detailing the bug to make a patch release for tf 2.15 possible? Hi @SamKnightGit ,

As this got fixed in master,I doubt to cherry pick it as it will work with Keras3 and TF2.15v as backend also. However if you want this to cherry pick it for tf-keras=2.15v, you may raise a request at tf-keras repo and let's hear from there. 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/62831">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/62831">No</a>

### Issue type

Bug

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

No -- appears to be fixed in Nightly

### Source

binary

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

Linux Ubuntu 23.04

### Mobile device

_No response_

### Python version

3.11

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

12.2

### GPU model and memory

_No response_

### Current behavior?

Expected:
`load_weights` succeeds when using `MirroredStrategy`

Actual:
`load_weights` raises an obscure error: "TypeError: unsupported operand type(s) for /: 'Dataset' and 'int'"

Note that this error does **not** occur when using `OneDeviceStrategy` and appears to have been fixed in `nightly` (example code runs successfully).

### Standalone code to reproduce the issue

```shell
[Colab Link](https://colab.research.google.com/drive/1EBxNuuPVq7fBEI4erxAS0ru0jiPXGTJj?usp=sharing)

Code:

import os
import tensorflow as tf
from tensorflow import keras
print("TensorFlow version:", tf.__version__)

strategy = tf.distribute.MirroredStrategy()
# strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
MODEL_WEIGHTS_PATH = './model.weights.h5'
if os.path.exists(MODEL_WEIGHTS_PATH):
  os.remove(MODEL_WEIGHTS_PATH)

with strategy.scope():
  model = tf.keras.Sequential([tf.keras.layers.Dense(1, input_shape=(1,))])
  model.compile(loss='mse', optimizer='sgd')

train_dataset = tf.data.Dataset.from_tensors(([1.], [1.])).repeat(100).batch(10)
val_dataset = tf.data.Dataset.from_tensors(([2.], [2.])).repeat(20).batch(10)

model.fit(
  train_dataset,
  epochs=10,
  validation_data=val_dataset,
)
model.save_weights(MODEL_WEIGHTS_PATH)
model.load_weights(MODEL_WEIGHTS_PATH)
print("Weights loaded successfully!")
model.summary()
```
```


### Relevant log output

```shell
TypeError                                 Traceback (most recent call last)
Cell In[8], line 25
     19 model.fit(
     20   train_dataset,
     21   epochs=10,
     22   validation_data=val_dataset,
     23 )
     24 model.save_weights(MODEL_WEIGHTS_PATH)
---> 25 model.load_weights(MODEL_WEIGHTS_PATH)
     26 print("Weights loaded successfully!")
     27 model.summary()

File ~/miniconda3/envs/tf214/lib/python3.11/site-packages/keras/src/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 ~/miniconda3/envs/tf214/lib/python3.11/site-packages/tensorflow/python/distribute/values_util.py:214, in on_read_assign_cross_replica(var, value, read_value)
    212 if var.aggregation == vs.VariableAggregation.SUM:
    213   strategy = var._distribute_strategy  # pylint: disable=protected-access
--> 214   tensor = math_ops.cast(tensor / strategy.num_replicas_in_sync,
    215                          var.dtype)
    216 return assign_on_each_device(var, assign_on_device, tensor,
    217                              read_value)

TypeError: unsupported operand type(s) for /: 'Dataset' and 'int'
```
