Different Behavior of tf.raw_ops.DivNoNan+tf.raw_ops.Asin with jit_compile=True
Hi @zoux1a ,

I have replicated the behaviour with jit_compile=True. With jit_compile=False and also by commenting out the code tf.config.run_functions_eagerly(False) the problem does not persist. Attached [gist](https://colab.research.google.com/gist/sushreebarsa/681ba89eaa46b5ed47781cb977dda47c/62247.ipynb) for reference. 
Thank you! However, we think the different behaviors between eager mode and `jit_compiled` should be corrected in the future. Will it be fixed? Hi @zoux1a ,

The difference in outputs is only minor precisional change which is expected with XLA due to internal fusions and conversions from `bfloat16` to `float32` etc. 

I have changed the `rtol` to `0.01` from `0.001` and executed the code 10 runs and it executed successfully. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/d970c2bd79dca7e44b3ab782dcf96f4d/62247_final.ipynb). 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/62247">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62247">No</a>

### 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.DivNoNan+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
import tensorflow as tf
import traceback

def replace_special_values(tensor):
    # Convert tensor to tf.float32 if it's not a supported dtype
    supported_dtypes = [tf.float16, tf.float32, tf.float64, tf.bfloat16]
    if tensor.dtype not in supported_dtypes:
        original_dtype = tensor.dtype
        tensor = tf.cast(tensor, tf.float32)
    else :
        original_dtype = None
    
    # Replace NaNs with zeros
    tensor = tf.where(tf.math.is_nan(tensor), tf.zeros_like(tensor), tensor)
    
    # Replace positive infinities with a large number (e.g., 1e30)
    tensor = tf.where(tf.math.is_inf(tensor), 100, tensor)
    
    # Replace negative infinities with a small number (e.g., -1e30)
    tensor = tf.where(tf.math.is_inf(tensor) & tf.math.less(tensor, 0), -100, tensor)
    
    # Convert tensor back to its original dtype
    if original_dtype is not None :
        tensor = tf.cast(tensor, original_dtype)
    return tensor

class Network(tf.Module):
    def __init__(self):
        super().__init__()

    @tf.function(jit_compile=True)
    def __call__(self, x):
      dic = {'ele': [[-921600.0, -499712.0, 638976.0, 344064.0]], 'size': [1, 4], 'dtype': tf.bfloat16}
      x = tf.raw_ops.DivNoNan(y=x, x=tf.constant(dic['ele'], dtype=tf.as_dtype(dic['dtype'])))       
      x = tf.raw_ops.Asin(x=x, )          
      return x

m = Network()
dic = {'ele': [[688128.0, -344064.0, -778240.0, -532480.0], [-466944.0, -888832.0, 843776.0, -172032.0], [-704512.0, -389120.0, -827392.0, -188416.0], [778240.0, 32768.0, 794624.0, 761856.0], [-122880.0, -110592.0, -811008.0, 139264.0], [-561152.0, 106496.0, -389120.0, -548864.0], [909312.0, -94208.0, -499712.0, 811008.0]], 'size': [7, 4], 'dtype': tf.bfloat16}

inp = {
    "x": tf.constant(dic['ele'], dtype=tf.as_dtype(dic['dtype'])),
    }

with tf.device('/CPU:0'):
    tf.config.run_functions_eagerly(True)
    no_op_res = m(**inp)
    tf.config.run_functions_eagerly(False)
    with tf.device('/CPU:0'):
        op_res = m(**inp)
    no_op_res = replace_special_values(no_op_res)
    op_res = replace_special_values(op_res)
    tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
```


### Relevant log output

```shell
File "/home/guihuan/LLM/results/tf-2/2023-10-22-20-21/test.py", line 53, in <module>
    tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), 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=float64), atol = tf.Tensor(0.001, shape=(), dtype=float64)'
b'x (shape=(7, 4) dtype=float64) = '
0.0, 0.0, -0.9609375, ...
b'y (shape=(7, 4) dtype=float64) = '
0.0, 0.0, -0.95703125, ...
```
