Repository: tensorflow
Issue: tf.keras.Model.predict passing x=tf.keras.utils.Sequence causing exceptions  ValueError: Data is expected to be in format `x`, `(x,)`, `(x, y)`, or `(x, y, sample_weight)`
Labels: stat:awaiting tensorflower,type:bug,comp:keras,TF 2.12

<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

### Have you reproduced the bug with TF nightly?

No

### Source

source

### Tensorflow Version

tf 2.12.0

### Custom Code

Yes

### OS Platform and Distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/Compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current Behaviour?

```Python
tf_model.predict(test_generator)
```

will cause exceptions

```Python
ValueError: in user code:

    File "/DATA/home/yuehc/.local/lib/python3.8/site-packages/keras/engine/training.py", line 2169, in predict_function  *
        return step_function(self, iterator)
    File "/DATA/home/yuehc/.local/lib/python3.8/site-packages/keras/engine/training.py", line 2155, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/DATA/home/yuehc/.local/lib/python3.8/site-packages/keras/engine/training.py", line 2143, in run_step  **
        outputs = model.predict_step(data)
    File "/DATA/home/yuehc/.local/lib/python3.8/site-packages/keras/engine/training.py", line 2110, in predict_step
        x, _, _ = data_adapter.unpack_x_y_sample_weight(data)
    File "/DATA/home/yuehc/.local/lib/python3.8/site-packages/keras/engine/data_adapter.py", line 1775, in unpack_x_y_sample_weight
        raise ValueError(error_msg)

    ValueError: Data is expected to be in format `x`, `(x,)`, `(x, y)`, or `(x, y, sample_weight)`, found: (<tf.Tensor 'IteratorGetNext:0' shape=(None, None, None) dtype=float32>, <tf.Tensor 'IteratorGetNext:1' shape=(None, None, None) dtype=float32>, <tf.Tensor 'IteratorGetNext:2' shape=(None, None, None) dtype=float32>, <tf.Tensor 'IteratorGetNext:3' shape=(None, None, None) dtype=float32>, <tf.Tensor 'IteratorGetNext:4' shape=(None, None, None) dtype=float32>)
```

The codes works well back in tf 2.4.0 and should still be working per release notes and the latest documentations.

the `test_generator` is a sub-class of `tf.keras.utils.Sequence` which works normally in tf 2.4.0

The return format for `__getitem__` is a tuple with a List element that indicate multiple inputs of the model
```Python
def __getitem__(self, i) -> Tuple[List[numpy.array]]:
    ...
```

For an example, see below
![image](https://github.com/tensorflow/tensorflow/assets/45007045/b8948609-693e-4918-8f1b-990fb1fa8bc8)
![image](https://github.com/tensorflow/tensorflow/assets/45007045/0c41d2ce-7460-405b-867a-103e6a794cd2)
![image](https://github.com/tensorflow/tensorflow/assets/45007045/51b80f7a-9c39-4ea4-93ee-7812c9765fa7)

I assume some behavors behind the `predict` interface are changed after the 2.4.0 to 2.12.0 upgrades.

Any idea how to fix it? Thx in advance.

### Standalone code to reproduce the issue

```Python
import tensorflow as tf
import math
import numpy as np
from tensorflow.keras.models import Model
from tensorflow.keras.layers import LSTM, Dense, Input

class BasicGenerator(tf.keras.utils.Sequence):

    def __init__(self, x, y=None, w=None, *, obj=None, batch_size=256, **kwargs):
        """
        a simple example of data generator by utilizing tf.keras.utils.Sequence

        Parameters
        ----------
        x: List[np.array], input data, the array size: (n_samples, n_features)

        y: np.arrya, input label, size: (n_samples, n_labels)

        w: sample weights, default None

        batch_size: batch_size, default 256
        """
        super(BasicGenerator, self).__init__()

        # basic params
        self.x = x
        self.y = y
        self.w = w
        self.batch_size = batch_size
        
    def __len__(self):
        return math.ceil(self.x[0].shape[0] / self.batch_size)

    def __getitem__(self, index):
        
        res = ()

        b_x = [_x[index * self.batch_size:(index + 1) * self.batch_size] for _x in self.x]
        res += (b_x,)

        if self.y is not None:
            b_y = self.y[index * self.batch_size:(index + 1) * self.batch_size]
            res += (b_y,)

        if self.w is not None:
            b_w = self.w[index * self.batch_size:(index + 1) * self.batch_size]
            res += (b_w,)
        
        return res

# Define the model
input1 = Input(shape=(3, 1))
input2 = Input(shape=(3, 1))

lstm1 = LSTM(50, activation='relu')(input1)
lstm2 = LSTM(50, activation='relu')(input2)

concat = tf.keras.layers.concatenate([lstm1, lstm2])
output = Dense(1)(concat)

model = Model(inputs=[input1, input2], outputs=output)
model.compile(optimizer='adam', loss='mse')

# Generate some fake data
n_samples = 10000
X_1 = np.random.rand(n_samples, 3, 1)
X_2 = np.random.rand(n_samples, 3, 1)
y = np.random.rand(n_samples, 1)

# generator sub-classing from utils.Sequence
train_generator = BasicGenerator(x=[X_1, X_2], y=y)
test_generator = BasicGenerator(x=[X_1, X_2])

# fit
model.fit(train_generator)

# the problem occurs
model.predict(
    test_generator,
    verbose=1,
    max_queue_size=30,
    workers=1,
    use_multiprocessing=False
)
```


### Relevant log output

```Python
40/40 [==============================] - 7s 14ms/step - loss: 0.1985
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-13-82ba4c04abb5> in <module>
     76 
     77 # the problem occurs
---> 78 model.predict(
     79     test_generator,
     80     verbose=1,

/DATA1/anaconda3/envs/py38tf2.4/lib/python3.8/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     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

/DATA1/anaconda3/envs/py38tf2.4/lib/python3.8/site-packages/keras/engine/training.py in tf__predict_function(iterator)
     13                 try:
     14                     do_return = True
---> 15                     retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
     16                 except:
     17                     do_return = False

ValueError: in user code:

    File "/DATA1/anaconda3/envs/py38tf2.4/lib/python3.8/site-packages/keras/engine/training.py", line 2169, in predict_function  *
        return step_function(self, iterator)
    File "/DATA1/anaconda3/envs/py38tf2.4/lib/python3.8/site-packages/keras/engine/training.py", line 2155, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/DATA1/anaconda3/envs/py38tf2.4/lib/python3.8/site-packages/keras/engine/training.py", line 2143, in run_step  **
        outputs = model.predict_step(data)
    File "/DATA1/anaconda3/envs/py38tf2.4/lib/python3.8/site-packages/keras/engine/training.py", line 2111, in predict_step
        return self(x, training=False)
    File "/DATA1/anaconda3/envs/py38tf2.4/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
        raise e.with_traceback(filtered_tb) from None
    File "/DATA1/anaconda3/envs/py38tf2.4/lib/python3.8/site-packages/keras/engine/input_spec.py", line 219, in assert_input_compatibility
        raise ValueError(

    ValueError: Layer "model_3" expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, None, None) dtype=float32>]
```
</details>