Different Behavior of tf.raw_ops.Asin with jit_compile=True
Hi, @zoux1a! 
I was able to replicate the issue reported here with JIT compilation enabled (jit_compile=True) but didn't face any issue with (jit_compile=False) and after commenting out  tf.config.run_functions_eagerly(False). Please find the attached [gist](https://colab.research.google.com/gist/sushreebarsa/2e59f4d886ee959c228bdf3dd4d51a9d/asin.ipynb#scrollTo=f45-JnMr2Vw7). Thank you! Hi, @sushreebarsa 
However, we think the different behaviors between `eagermode` and `jit_compile=True` mode should be corrected in the future. What is your opinion on this matter? The code is failing with the condition of `rtol` and `atol=0.001` when` jit_compile = True` and does not fail when  `jit_compile = False`.
 The difference in behavior is not a bug rather it is when compiling with `JIT` it takes different code path and involves `fusion` operation and casting `dtype` to specific `dtype`, based on all these conditions, you can't expect the same result with `JIT` and without `JIT`.
Attaching the Gist [here](https://colab.sandbox.google.com/gist/sachinprasadhs/4463346fb30ddbdbe0669e7852b58a26/tf-raw_ops-asin.ipynb) for comparison. Thanks! This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62277">Yes</a>
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### Issue type

Bug

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

No

### Source

source

### TensorFlow version

2.14.0

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04.3 LTS (x86_64)

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

11.8

### GPU model and memory

GPU 0: NVIDIA GeForce RTX 2070 GPU 1: NVIDIA GeForce RTX 2070 GPU 2: NVIDIA GeForce RTX 2070 GPU 3: NVIDIA GeForce RTX 2070

### Current behavior?

When the **tf.raw_ops.Asin** operation is invoked within a tf.function with JIT compilation enabled (**jit_compile=True**), it produces different results compared to the same operation called without JIT compilation. This inconsistency is observed when the code is executed on a **CPU** device.

### Standalone code to reproduce the issue

```shell
i can reproduce this issue on colab: https://colab.research.google.com/drive/1htnUG-v-3av_ZAQg8KPm05aSfTnKwi2j?usp=sharing
```


### Relevant log output

```shell
File "/home/guihuan/LLM/results/tf-2/2023-10-22-20-21/test.py", line 27, in <module>
    tf.debugging.assert_near(no_op_res, op_res, atol=0.001, rtol=0.001)
  File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/ops/control_flow_assert.py", line 102, in Assert
    raise errors.InvalidArgumentError(
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b''
b'x and y not equal to tolerance rtol = tf.Tensor(0.001, shape=(), dtype=float32), atol = tf.Tensor(0.001, shape=(), dtype=float32)'
b'x (shape=(6, 9, 7, 2, 1) dtype=complex64) = '
(-0.35167462-13.58743j), (-0.35001186-13.258179j), (-0.9673807-14.284173j), ...
b'y (shape=(6, 9, 7, 2, 1) dtype=complex64) = '
(-0.33401474-13.561536j), (-0.35795882-13.256111j), (-1.0240958-14.297717j), ...
```
