Unable to concatenate Keras Tensor and Eager Tensor using tf.keras.layers.Concatenate
@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/5551526739f9cda1b55893287fc16a3d/61925.ipynb). Thank you! Hi @Prashant-THRSL ,

Apologies for the delayed response.The issue got fixed with keras3 and tf-keras 2.15v. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/ceac0d7b22dba33b40b3a46be5eecf07/61925.ipynb).

Since Keras3 is now multi backend supporting and for TF specific you need to install tf-keras package.
 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?

No

### Source

source

### TensorFlow version

2.13

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.8

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I want to pad the Keras Tensor **without using tf.pad or tf.concat**.
My approach is to create an empty tensor with zeros and concatenate the last layer with the Keras Tensor.

I have successfully saved the model. But when **I try to load the model, I am getting errors**, I have attached code to recreate the error and logs as well.

The expected output is concatenated Keras tensor on the last axis.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
from tensorflow.keras.models import Model, save_model, load_model
from tensorflow.keras.layers import Input, Concatenate, Conv2D, ReLU

# Define the input shape
input_shape = (128, 128, 3)  # Replace with your input dimensions

# Create the input layer
input_layer = Input(shape=input_shape)

# Create empty channels
empty_channels = tf.zeros((input_shape[0], input_shape[1], 4))

# Expand the dimensions of empty_channels to match input_shape
empty_channels = tf.expand_dims(empty_channels, axis=0)

# Concatenate the input with the empty channels
concatenated = Concatenate(axis=-1)([input_layer, empty_channels])

# Create the model
model = Model(inputs=input_layer, outputs=concatenated)

# Display model summary
model.summary()

# Save the model to a file
model.save("concatenated_model.h5")

print("Model has saved.")
# Now, to load the saved model
loaded_model = load_model("concatenated_model.h5")
print("Model has loaded.")
```


### Relevant log output

```shell
python3 testPython.py                                                               1 ✘ 
2023-09-20 13:14:52.746990: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-09-20 13:14:52.777548: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, 128, 128, 3)]     0         
                                                                 
 concatenate (Concatenate)   (1, 128, 128, 7)          0         
                                                                 
=================================================================
Total params: 0 (0.00 Byte)
Trainable params: 0 (0.00 Byte)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
/home/hitech/.local/lib/python3.8/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
Model has saved.
Traceback (most recent call last):
  File "testPython.py", line 33, in <module>
    loaded_model = load_model("concatenated_model.h5")
  File "/home/hitech/.local/lib/python3.8/site-packages/keras/src/saving/saving_api.py", line 238, in load_model
    return legacy_sm_saving_lib.load_model(
  File "/home/hitech/.local/lib/python3.8/site-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/home/hitech/.local/lib/python3.8/site-packages/keras/src/layers/merging/concatenate.py", line 119, in build
    raise ValueError(err_msg)
ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concatenation axis. Received:
input_shape=[(None, 128, 128, 3), [[[[(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], [(), (), (), ()], .....
```
