Adding TensorFlow Hub KerasLayer to Sequential Model Raises ValueError
After investigating the code, I found a potential cause of the issue in `tensorflow_hub/keras_layer.py` lines 26-31:
```python
# Use Keras 2.
version_fn = getattr(tf.keras, "version", None)
if version_fn and version_fn().startswith("3."):
  import tf_keras as keras
else:
  keras = tf.keras
```
Depending on the Keras version, the module might either import `tf_keras` or directly use `tf.keras`, the former causes the `isinstance(layer, Layer)` check in `Sequential.add` to return `False` for `hub.KerasLayer`, even though it inherits from `keras.layers.Layer` Hi @ruddyscent ,

TFHub has dependency on `tf_keras` package (i.e Keras2) as per the setup.py of TFHub.

https://github.com/tensorflow/hub/blob/ff72e25fef44bd67d5c14fb5328aa44e303e3404/tensorflow_hub/pip_package/setup.py#L31

Since TF2.16 comes with Keras3 the problem arises. As a workaround, you can install tf_keras package and set environment variable TF_USE_LEGACY_KERAS=1 to ensure Keras2 will be used with tf.keras.  > After investigating the code, I found a potential cause of the issue in `tensorflow_hub/keras_layer.py` lines 26-31:
> 
> ```python
> # Use Keras 2.
> version_fn = getattr(tf.keras, "version", None)
> if version_fn and version_fn().startswith("3."):
>   import tf_keras as keras
> else:
>   keras = tf.keras
> ```
> 
> Depending on the Keras version, the module might either import `tf_keras` or directly use `tf.keras`, the latter causes the `isinstance(layer, Layer)` check in `Sequential.add` to return `False` for `hub.KerasLayer`, even though it inherits from `keras.layers.Layer`

Hi, When I tried with TF2.16v on Colab environment the error stack seems generated from the _ensure_keras_2_importable() function from `tensorflow_hub/__init__.py` as per attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/5585bf7974198ca45bacdb9e84db91d4/63849_tf2-16.ipynb). > ```python
> # Use Keras 2.
> version_fn = getattr(tf.keras, "version", None)
> if version_fn and version_fn().startswith("3."):
>   import tf_keras as keras
> else:
>   keras = tf.keras
> ```

Even this code seems confusing to me. If version_fn results in Keras3 then it tries to import tf_keras to use Keras2 else it assumes Keras2 is alreday there(i.e TF<=2.15v) hence it takes keras=tf.keras. 

But `version_fn = getattr(tf.keras, "version", None)` returns None making this to use tf.keras (i.e Keras3) which ic not compatible with TF_Hub.
 > > ```python
> > # Use Keras 2.
> > version_fn = getattr(tf.keras, "version", None)
> > if version_fn and version_fn().startswith("3."):
> >   import tf_keras as keras
> > else:
> >   keras = tf.keras
> > ```
> 
> Even this code seems confusing to me. If version_fn results in Keras3 then it tries to import tf_keras to use Keras2 else it assumes Keras2 is alreday there(i.e TF<=2.15v) hence it takes keras=tf.keras.
> 
> But `version_fn = getattr(tf.keras, "version", None)` returns None making this to use tf.keras (i.e Keras3) which ic not compatible with TF_Hub.

Actually, `version_fn` is not None, that code works as intended, which is to use Keras 2

`tf.keras.Sequential` is on Keras 3. and `hub.KerasLayer` is on Keras 2, so I believe due to the version difference, it causes `isinstance(layer, Layer)` to fail and return False > ```python
> # Use Keras 2.
> version_fn = getattr(tf.keras, "version", None)
> if version_fn and version_fn().startswith("3."):
>   import tf_keras as keras
> ```

From the code above if version_fn is not None and if it starts with "3" (which means Keras 3 found) in that case it is importing tf_keras package as keras. Please note that tf_keras package is for Keras2. This indicates TFHub supports only Keras2 package.

The else part it assumes that version_fn doesn't starts with "3" which case it assumes Keras2 installed with TF package and it marks keras=tf.keras. But for any case if version_fn becomes None and if Keras3 installed with TF then tf.keras will become Keras3 which might a problem. 

@Aloqeely , Could you please import TF2.16 and confirm what will be the version_fn output ? Here you go
```python
import tensorflow as tf

version_fn = getattr(tf.keras, "version", None)
print("TF Version: " + tf.__version__)
print("TF Keras Version: " + version_fn())
```
Output:
```
TF Version: 2.16.1
TF Keras Version: 3.0.5
```
 > Hi @ruddyscent ,
> 
> TFHub has dependency on `tf_keras` package (i.e Keras2) as per the setup.py of TFHub.
> 
> https://github.com/tensorflow/hub/blob/ff72e25fef44bd67d5c14fb5328aa44e303e3404/tensorflow_hub/pip_package/setup.py#L31
> 
> Since TF2.16 comes with Keras3 the problem arises. As a workaround, you can install tf_keras package and set environment variable TF_USE_LEGACY_KERAS=1 to ensure Keras2 will be used with tf.keras.

If this workaround fixes ruddyscent's problem, then I think we should mark this issue as resolved, because the problem is tensorflow hub not supporting Keras 3, so it is not relevant to this repository. > 3.0.5

This means when tf.keras version is 3.x then Hub suggesting to import tf_keras as keras which is a Keras2 package. For that we should install with tf_keras package using `pip install tf_keras`. But from the error log the package is having keras/src which seems to be from Keras3 package for me. > This means when tf.keras version is 3.x then Hub suggesting to import tf_keras as keras which is a Keras2 package. For that we should install with tf_keras package using `pip install tf_keras`. But from the error log the package is having keras/src which seems to be from Keras3 package for me.

Yep, hub does that.
The error log is caused by `tf.keras.Sequential` (Keras 3, hence the error is produced from keras/src) after it received a `hub.KerasLayer` (Keras 2) win10, no gpu, no colab

tensorflow_hub를 사용하시려면 다음과 같이 해보세요
import tensorflow as tf
import tensorflow_hub as hub
import tf_keras as keras  """tensorflow_hub 설치시 자동으로 설치됩니다."

...
...
model = keras.Sequential([               """"tf.keras --> keras"""
      hub,KerasLayer(url, input_shape=(size, size, 3)
...
...
imgfile = keras.utils.get_file('image.jpg', 'https://..........')     """"tf.keras --> keras"""
작동이 잘됩니다.
 Thank you, @sj-yoon, for your assistance. 

The solution provided by @SuryanarayanaY works well for my situation. I had the same issue, and I could fix it with the solution provided by @SuryanarayanaY. Thanks 

Others can check my notebook for steps to fix it at this [page](https://www.kaggle.com/code/alishohadaee/dl-transfer-learning-on-images).
### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.16.1

### Custom code

No

### OS platform and distribution

Ubuntu 22.04.3 LTS

### Mobile device

_No response_

### Python version

Python 3.11.0rc1

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

12.3.0

### GPU model and memory

_No response_

### Current behavior?

I can execute the following code without any issues in TensorFlow 2.15.0 and TensorFlow Hub 1.16.1. However, when I upgrade the TensorFlow version to 2.16.0 or above, I encounter an error stating that `KerasLayer` cannot be added to the Sequential model.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import tensorflow_hub as hub

image_size = 224
URL = "https://tfhub.dev/tensorflow/efficientnet/b0/feature-vector/1"

model = tf.keras.Sequential([
        hub.KerasLayer(URL, input_shape=(image_size, image_size, 3))
])
```


### Relevant log output

```shell
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[29], line 1
----> 1 model = tf.keras.Sequential([
      2         feature_extractor,
      3         tf.keras.layers.Dense(2, activation = 'softmax')
      4 ])
      6 model.build([None, image_size, image_size, 3])
      7 model.summary()

File /usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py:70, in Sequential.__init__(self, layers, trainable, name)
     68 if layers:
     69     for layer in layers:
---> 70         self.add(layer, rebuild=False)
     71     self._maybe_rebuild()

File /usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py:92, in Sequential.add(self, layer, rebuild)
     90         layer = origin_layer
     91 if not isinstance(layer, Layer):
---> 92     raise ValueError(
     93         "Only instances of `keras.Layer` can be "
     94         f"added to a Sequential model. Received: {layer} "
     95         f"(of type {type(layer)})"
     96     )
     97 if not self._is_layer_name_unique(layer):
...
    101         "the name of a layer in this model. Update the `name` argument "
    102         "to pass a unique name."
    103     )

ValueError: Only instances of `keras.Layer` can be added to a Sequential model. Received: <tensorflow_hub.keras_layer.KerasLayer object at 0x7a4ac7e30f40> (of type <class 'tensorflow_hub.keras_layer.KerasLayer'>)
```
