got NaN when invoking tf.experimental.numpy.arcsin() and the input not over range
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### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

tf 2.13.0

### 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 behavior?

got NaN when invoking tf.experimental.numpy.arcsin()
and the input not over range

### Standalone code to reproduce the issue

```shell
import tensorflow as tf  with tf.device('cpu'):   input_data = tf.constant([[0.        , 0.99455555, 0.5230071 , 0.        , 0.72101855,         0.5321333 , 0.7079337 , 0.        , 0.        , 0.02282775],        [0.        , 0.49104163, 0.6195026 , 0.        , 0.8540474 ,         0.9993153 , 2.0230498 , 0.        , 0.        , 0.0270395 ],        [0.        , 0.3878699 , 0.48934025, 0.        , 0.6746053 ,         0.3691354 , 0.597991  , 0.        , 0.        , 0.02135829],        [0.        , 0.44674954, 0.5636233 , 0.        , 0.7770121 ,         0.7287762 , 0.8405701 , 0.        , 0.        , 0.02460053]])    def nu_arcsin(input_data):       return tf.experimental.numpy.arcsin(           input_data)   output = nu_arcsin(input_data)   print(output)  with tf.device('gpu'):   input_data = tf.constant([[0.        , 0.99455555, 0.5230071 , 0.        , 0.72101855,         0.5321333 , 0.7079337 , 0.        , 0.        , 0.02282775],        [0.        , 0.49104163, 0.6195026 , 0.        , 0.8540474 ,         0.9993153 , 2.0230498 , 0.        , 0.        , 0.0270395 ],        [0.        , 0.3878699 , 0.48934025, 0.        , 0.6746053 ,         0.3691354 , 0.597991  , 0.        , 0.        , 0.02135829],        [0.        , 0.44674954, 0.5636233 , 0.        , 0.7770121 ,         0.7287762 , 0.8405701 , 0.        , 0.        , 0.02460053]])   def nu_arcsin(input_data):     return tf.experimental.numpy.arcsin(       input_data)   output = nu_arcsin(input_data)   print(output)
```


### Relevant log output

_No response_