Cannot rrain the model using the TensorFlow tensors:
Hi @Bitten-bidbax ,

The error is due to the reason that here both `X_train_tensor` and `y_train_tensor` are empty Tensors only.You havent created any dataset here.

The same error can be replicated by me with the following code where I have taken empty list for both `x` and `y` and converted them into tensors and then used for training with `model.fit()`.

```
x=[]
x=tf.convert_to_tensor(x, dtype=tf.float32)
y=[]
y=tf.convert_to_tensor(y, dtype=tf.float32)
history = model.fit(x, y, epochs=50,)
```

Please refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/482b0afd48a884102d5fda9e33470da9/60651.ipynb).

As per the documentation of model.fit() , when input data is empty it will raise the Value error. You may refer the attached source [here](https://www.tensorflow.org/api_docs/python/tf/keras/Model#compile:~:text=tf.function.-,ValueError,what%20the%20model%20expects%20or%20when%20the%20input%20data%20is%20empty.,-get_compile).
  Are you satisfied with the resolution of your issue?
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<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

### Have you reproduced the bug with TF nightly?

Yes

### Source

source

### Tensorflow Version

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?

A bug happened! Cannot Train the model using the TensorFlow tensors.

### Standalone code to reproduce the issue

```shell
from keras.layers import LSTM, Dense
from keras.models import Sequential


# Define the sliding window size
window_size = 7
num_timesteps = 7
num_features = 6

# Create input-output pairs using the sliding window technique
X_train = []
y_train = []
for i in range(len(X_train) - window_size):
    X_train.append(X_train[i:i+window_size])
    y_train.append(y_train[i+window_size])

X_test = []
y_test = []
for i in range(len(X_test) - window_size):
    X_test.append(X_test[i:i+window_size])
    y_test.append(y_test[i+window_size])

# Convert the lists to numpy arrays
X_train = np.array(X_train)
y_train = np.array(y_train)
X_test = np.array(X_test)
y_test = np.array(y_test)


tf.config.run_functions_eagerly(True)

model = Sequential()
model.add(LSTM(64, input_shape=(window_size, num_features)))
model.add(Dense(3))
model.compile(loss='mean_squared_error', optimizer='adam')

X_train_tensor = tf.convert_to_tensor(X_train, dtype=tf.float32)
y_train_tensor = tf.convert_to_tensor(y_train, dtype=tf.float32)

history = model.fit(X_train_tensor, y_train_tensor, epochs=50, batch_size=32, validation_data=(X_test, y_test))
```


### Relevant log output

```shell
ValueError                                Traceback (most recent call last)
Cell In[54], line 1
----> 1 history = model.fit(X_train_tensor, y_train_tensor, epochs=50, batch_size=32, validation_data=(X_test, y_test))

File ~\anaconda3\Anaconda\lib\site-packages\keras\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 ~\anaconda3\Anaconda\lib\site-packages\keras\engine\training.py:1697, in Model.fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1695 logs = tf_utils.sync_to_numpy_or_python_type(logs)
   1696 if logs is None:
-> 1697     raise ValueError(
   1698         "Unexpected result of `train_function` "
   1699         "(Empty logs). Please use "
   1700         "`Model.compile(..., run_eagerly=True)`, or "
   1701         "`tf.config.run_functions_eagerly(True)` for more "
   1702         "information of where went wrong, or file a "
   1703         "issue/bug to `tf.keras`."
   1704     )
   1705 # Override with model metrics instead of last step logs
   1706 logs = self._validate_and_get_metrics_result(logs)

ValueError: Unexpected result of `train_function` (Empty logs). Please use `Model.compile(..., run_eagerly=True)`, or `tf.config.run_functions_eagerly(True)` for more information of where went wrong, or file a issue/bug to `tf.keras`.
```
</details>