Different Behavior of tf.raw_ops.Cos+tf.raw_ops.LeakyRelu 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/4ea5571eb6f9f8b3ec85661da80ecfbf/untitled899.ipynb#scrollTo=cft5lNrsVcI2) 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 ,

For the Op Cos, XLA-GPU support for `dtype=bfloat16` is not available. Please refer the [source](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md) here. Maybe this is the reason for this behaviour. 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/62223">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/62223">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 `7456` 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/70570ced7f76175dcaecc2f363236010/62223_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?

When the tf.raw_ops.Cos+tf.raw_ops.LeakyRelu 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.LeakyRelu(features=x, alpha=9.456766920329814)        
      return x

m = Network()
inp = {
    "x": tf.random.normal([10,9,8,1,8,3,2], 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 29, 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, 1, 8, 3, 2) dtype=float64) = '
0.83984375, 0.96484375, -2.6875, ...
b'y (shape=(10, 9, 8, 1, 8, 3, 2) dtype=float64) = '
0.83984375, 0.96484375, -2.6875, ...
```
