TensorFlow Lite Inference Crash with `tf.reverse(x, axis=[])`
Hi @pkgoogle 
I have reproduced the issue in both tf-nightly and stable version(tf 2.15) as well. The session has crashed in both versions. Here is the [gist](https://colab.research.google.com/gist/LakshmiKalaKadali/5806e0f8dd5a92e648da714728c91408/-62679.ipynb). Please look into the issue.
Thank You
 I am able to replicate w/ the same gist, I should note that tf.reverse(x, axis=[]) is effectively a non-op. @ganler, Is that what you intended? I.e. you are saying reverse no axes. Yes the usage leading to a crash is reverse of no axes and it is a non-op. I am reporting it as a bug here as it unexpectedly crashed the Python program leading to inconveniences in an automated model generation pipeline. Maybe it is better to just eliminate the non-op and move on instead of crash without errors. :) No worries, just wanted to ensure you are able to continue w/ your work while we investigate this case, @nutsiepully can you please take a look? Thanks. Hi @ganler , if you are able to access a linux system you may be able to resolve your issue by using [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch), you can find more information here: [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/).

I have actually created a simple script for converting your model here:

```
import torch
import torch.nn as nn
import ai_edge_torch

class Foo(nn.Module):
    def __init__(self):
        super(Foo, self).__init__()

    def forward(self, x):
        return torch.flip(x, dims=[])

foo = Foo()

sample_input = (torch.randn(1),)

# Convert the model using AI Edge Torch
edge_model = ai_edge_torch.convert(foo.eval(), sample_input)

# Export the model to TFLite format
edge_model.export('foo_model.tflite')
```

If you want to, you can actually try visualizing the result in [model-explorer](https://github.com/google-ai-edge/model-explorer) as well.

Please try them out and let us know if this resolves your issue. If you still need further help, feel free to open a new issue at the respective repo.
### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): ubuntu 22
- TensorFlow installation (pip package or built from source): `pip install tf-nightly` where python is 3.9
- TensorFlow library (version, if pip package or github SHA, if built from source):

```
pip show tf-nightly                                           
Name: tf-nightly
Version: 2.16.0.dev20231221
...
```

### 2. Code

Colab link: https://colab.research.google.com/drive/1gAsclHMWEf9in0wkF-y1nIbbFrh1m11V?usp=sharing

```python
import tensorflow as tf

tf.reverse(tf.ones((1,), dtype=tf.float32), [])  # no problem

class Foo(tf.Module):
    @tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.float32)])
    def reverse(self, x):
        #    works fine if axis = [0]
        #    crashes if axis = []
        return tf.reverse(x, axis=[])

foo = Foo()
converter = tf.lite.TFLiteConverter.from_concrete_functions(
    funcs=[foo.reverse.get_concrete_function()],
    trackable_obj=foo,
)

tflite_model = converter.convert()
interpreter = tf.lite.Interpreter(model_content=tflite_model)
# crash
interpreter.get_signature_runner()(x=tf.ones((1,), dtype=tf.float32))
```

<img width="1028" alt="image" src="https://github.com/tensorflow/tensorflow/assets/38074777/33de223a-d703-4f6d-a43e-953d1593b958">

### 3. Failure after conversion

Converted model crash at inference and the model is fully valid.