tf.GradientTape throws internal error: RET_CHECK failure
@trickiwoo 
I was able to reproduce the issue on Colab using tf-nightly but I was able to execute the given code on Colab using TF v2.11. tf-nightly which is unstable, Could you please try on the latest stable version which is TF v2.11 and find the gist of [TF v2.11](https://colab.research.google.com/gist/tiruk007/0c1f6cef9de20d9ad88e1d367da00c6d/59582.ipynb) and [tf-nightly](https://colab.research.google.com/gist/tiruk007/a55d40d9a3a0232ab6426ad1de08113e/59582_nightly.ipynb) for reference . Let us know if it helps.

Thank you! This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.
 Closing as stale. Please reopen if you'd like to work on this further.
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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

2.13.0.dev20230204

### Custom Code

Yes

### OS Platform and Distribution

Ubuntu 20.04.4 LTS

### 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?

```shell
tf.GradientTape throws internal error: RET_CHECK failure
```


### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import numpy as np

# Create a simple Keras model.
with tf.device('gpu'):
    x = [-1, 0, 1, 2, 3, 4]
    y = [-3, -1, 1, 3, 5, 7]
    model = tf.keras.models.Sequential(
        [tf.keras.layers.Dense(units=1, input_shape=[1])])
    model.compile(optimizer='sgd', loss='mean_squared_error')
    model.fit(x, y, epochs=500)
```


### Relevant log output

```shell
Node: 'StatefulPartitionedCall_1'
RET_CHECK failure (tensorflow/compiler/xla/service/gpu/gpu_compiler.cc:618) dnn != nullptr 
         [[{{node StatefulPartitionedCall_1}}]] [Op:__inference_train_function_437]
```
</details>