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

I have replicated the reported behaviour with colab using TF v2.14, 2.15, and TF-nightly. Please find the [gist](https://colab.research.google.com/gist/Venkat6871/13a22e87001605bcd1ab51c8d6547366/62222_tf-2-14-2-15-nightly-v.ipynb) here for reference.

Thank you! Hi @zoux1a ,

The difference is results with `jit_compile=True` is due to the fact that with XLA there would be internal casting to `float32`, whereas without XLA it would be in `bfloat16` itself and hence there will be difference in precision of the output.

It's only precisional differences but not an error. The assertion is success with `atol=0.001 and rtol=0.1 `

However if you change the inputs dtype to `float32` or `float16`, then assertion is success with `atol=0.001 and rtol=0.001 ` also.

Please refer to attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/88c932f256cb95d170ecd3528cc68f4e/62222_gpu_r2.ipynb) for same. 

Thanks





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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.Cos+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 GPU device.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import traceback

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

    @tf.function(jit_compile=True)
    def __call__(self, x):
      
      x = tf.raw_ops.Cos(x=x, )        
      x = tf.raw_ops.Asin(x=x, )        
      return x

m = Network()
inp = {
    "x": tf.random.uniform([10,9], dtype=tf.bfloat16),
}

with tf.device('/GPU:0'):
    tf.config.run_functions_eagerly(True)
    no_op_res = m(**inp)
    tf.config.run_functions_eagerly(False)
    with tf.device('/GPU:0'):
        op_res = m(**inp)

    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 27, 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=(10, 9) dtype=float64) = '
1.484375, 0.93359375, 0.7734375, ...
b'y (shape=(10, 9) dtype=float64) = '
1.484375, 0.9375, 0.7734375, ...
```
