Different behaviors of raw_ops.Sigmoid can be observed when jitcompiled=true.
Hi @zoux1a ,

I have replicated the reported behaviour. With jit_compile=True the results seems not in the precision level of `rtol=0.001`. If we change `rtol=0.01` then the results are same.

One more observation the behaviour seems from `eager = False`. With eager execution it works fine with given tolerances also.

Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/3481f0de94a04b050985455924192170/62206-gpu.ipynb). I believe the results in eager mode and XLA-compiled mode should be consistent. Will this bug be fixed in the future? Hi @zoux1a , For the Sigmoid Op, `bfloat16` dtype is not supported for XLA-GPU setup as per this [source](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md#:~:text=int64%2Cuint32%2Cuint64%7D-,Sigmoid,-T%3D%7Bcomplex64%2Cdouble).

Could you please cross check and confirm. 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/62206">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/62206">No</a>
 Hi @GwiHwan-Go ,

I have tested the code with Tf2.14v and the difference is related to precision only which happens due to XLA internal fusions and conversions and XLA uses FP32 precision by default.

To check that , I have printed the `reduce_sum` of results which are same for both which is `448` for an experiment. This indicates the results are same but only precision differences with XLA which is expected.

Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/1a600f6d0fa72d18b5e8e8de710d6642/62206_final.ipynb).

Thanks!
### 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?

In TensorFlow, enabling jitcompiled results in inconsistent behavior of raw_ops.Sigmoid.

### 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.Sigmoid(x=x, )        
      return x

m = Network()
inp = {
    "x": tf.random.uniform([10,9,8], 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
Traceback (most recent call last):
  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, 8) dtype=float64) = '
0.65234375, 0.5078125, 0.56640625, ...
b'y (shape=(10, 9, 8) dtype=float64) = '
0.59765625, 0.03125, 0.26953125, ...
```
