Repository: tensorflow
Issue: The warning "The structure of `inputs` doesn't match the expected structure" when training a functional model
Labels: type:bug,comp:keras,TF 2.13

### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

v2.13.1-0-gf841394b1b7 2.13.1 (Nightly: v1.12.1-119104-gf8fd6f53fa3 2.19.0-dev20241204)

### Custom code

Yes

### OS platform and distribution

Windows 11 23H2 22631.4460

### Mobile device

Windows 11 23H2 22631.4460

### Python version

3.10

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

When the model is functional, not Sequential, the warning has occured:

```
Epoch 1/5
<path-to-python>\lib\site-packages\keras\src\models\functional.py:225: UserWarning: The structure of `inputs` doesn't match the expected structure: ['keras_tensor']. Received: the structure of inputs=*
  warnings.warn(
```

Yes, the warning message has interrupted on parenthesis. When I've run the same code in Nightly, the warning message is:

```
Epoch 1/5
<path-to-python>\lib\site-packages\keras\src\models\functional.py:237: UserWarning: The structure of `inputs` doesn't match the expected structure.
Expected: ['keras_tensor']
Received: inputs=Tensor(shape=(None, 10))
  warnings.warn(msg)
```

After the warning, the training continues normally, but because of this warning, I can't be sure that the model works as I expect.

I've traced the source and found that in `Lib\site-packages\keras\src\tree\optree_impl.py` on line 95 comparasion of expected and actual structure failed. Now I place the traced variables here:

```
>>> a
<tf.Tensor 'data:0' shape=(None, 10) dtype=float64>
>>> b
[<KerasTensor shape=(None, 10), dtype=float32, sparse=False, name=keras_tensor>]
>>> a_structure
PyTreeSpec(*, NoneIsLeaf)
>>> b_structure
PyTreeSpec([*], NoneIsLeaf)
```

The data passed to the `fit` function fully corresponds to the [documentation](https://keras.io/api/models/model_training_apis/#fit-method). The warning appears independently of whether I use numpy array or PyDataset as dataset of `fit` function.



### Standalone code to reproduce the issue

```shell
from keras.models import Model
from keras.layers import Dense, Input, Flatten, Concatenate
from keras import utils
import numpy as np
import tensorflow as tf

class SamplesSet(utils.PyDataset):
    
    def __init__(self, batch_size, **kwargs):
        super().__init__(**kwargs)
        self.batch_size = batch_size
        
    def __len__(self):
        return 1
    
    def __getitem__(self, idx):
        x1 = np.random.uniform(size=10*self.batch_size).reshape((self.batch_size, 10))
        y = np.arange(self.batch_size)
        return x1, y
    
train = SamplesSet(100)
x1_train = np.random.uniform(size=10*100).reshape((100, 10))
y_train = np.arange(100)

input1 = Input(shape=(10,))
l1 = Dense(1)(input1)
d2 = Dense(1, activation='sigmoid')(l1)
model = Model(inputs=[input1], outputs=[d2])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())

history = model.fit(x1_train, y_train, epochs=5, verbose=1)
# In the all cases below warning occures too
# history = Model.fit(train, epochs=5, verbose=1) 
# ret = model.predict(np.arange(10)[np.newaxis,:])
# ret = model.predict(tf.constant([[0,1,2,3,4,5,6,7,8,9]]))
```


### Relevant log output

_No response_