Inconsistency Behavior in XLA Compiled Model with `tf.greater` and `tf.round` Followed by Multiple Extra Outputs
Hi @GwiHwan-Go ,

The issue was replicated in TF2.14v and with GPU runtime. On CPU it works fine with both Tf2.14 and 2.15 versions.

Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/2775daae9eeb131e0d2be8514fbde315/62528.ipynb) for reference.

 Hi @GwiHwan-Go ,

It seems the difference is only precision related as the assertion became success if I change rtol to 0.01 from 0.001 . Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/a6ad45993f6b3bd9395e31fbe6c138fc/62533_r1.ipynb) for reference.

As mentioned [here](https://github.com/tensorflow/tensorflow/issues/62287#issuecomment-1809045878) the XLA will undergo intermediate fusion and conversions to `float32`  it is expected some precisonal differences with XLA. I understand there exist numerical precision problem in Deep-Learning Compiler.
As it is expected behavior as you mentioned, I'm closing the issue. 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/62533">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/62533">No</a>

### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.15.0

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04.3 LTS

### Mobile device

_No response_

### Python version

3.10.0

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

cuda : 12.2 / cudnn 8.9.04

### GPU model and memory

Tesla V100S-PCIE-32GB

### Current behavior?

I've encountered a bug in TensorFlow where the `tf.greate`r operation, when combined with tf.round and followed by the addition of two extra nodes, triggers inconsistent results. 
This behavior is only seen on **gpu**.

Furthermore, our experiments indicate that:

1. Removing one of the additional output nodes resolves the inconsistency.
2. Using `tf.less` in place of `tf.greater` does not cause the same issue. 
3. Omitting the `tf.round` operation from the models prevents the error.

We hope that the details of our experiments will assist you in pinpointing the root cause of this erratic behavior.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import numpy as np
class Model1(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        trans = tf.transpose(inp, perm=[0, 1, 3, 2])
        round = tf.round(trans)
        greater = tf.greater(tf.reverse(round, axis=[0, 2]), round)
        logical_and = tf.logical_and(greater, greater)
        return greater, logical_and, 

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        trans = tf.transpose(inp, perm=[0, 1, 3, 2])
        round = tf.round(trans)
        greater = tf.greater(tf.reverse(round, axis=[0, 2]), round)
        logical_and = tf.logical_and(greater, greater)
        return greater, logical_and, trans,

inputs = [
tf.random.uniform(shape=[15, 1, 50, 35], dtype=tf.float64),
]
model1 = Model1()
model2 = Model2()
device = "gpu"
with tf.device(device):
    tf.config.run_functions_eagerly(True)
    out1 = model1(*inputs)
    out2 = model2(*inputs)
    print(f'=========eager_output(version:{tf.__version__})================')
    try :
        for i in range(min(len(out1),len(out2))):
            np.testing.assert_allclose(out1[i].numpy(), out2[i].numpy(), rtol=0.001, atol=0.001, err_msg=f'at checking {i}th')
        print("XLA_eager does not trigger assertion")
    except AssertionError as e:
        print("XLA_eager triggers assertion")
        print(e)
    tf.config.run_functions_eagerly(False)
    out1 = model1(*inputs)
    out2 = model2(*inputs)
    print(f'=========compiled_output(version:{tf.__version__})================')
    try :
        for i in range(min(len(out1),len(out2))):
            np.testing.assert_allclose(out1[i].numpy(), out2[i].numpy(), rtol=0.001, atol=0.001, err_msg=f'at checking {i}th')
        print("XLA_complie does not trigger assertion")
    except AssertionError as e:
        print("XLA_complie triggers assertion")
        print(e)
```


### Relevant log output

```shell
=========compiled_output(version:2.15.0)================
XLA_complie triggers assertion

=========eager_output(version:2.15.0)================
XLA_eager does not trigger assertion
Not equal to tolerance rtol=0.001, atol=0.001
at checking 0th
Mismatched elements: 6550 / 26250 (25%)
 x: array([[[[False, False, False, ..., False, False, False],
         [False, False, False, ..., False, False, False],
         [False, False, False, ..., False, False, False],...
 y: array([[[[False, False, False, ..., False, False, False],
         [False,  True,  True, ..., False, False, False],
         [False, False,  True, ...,  True, False, False],...
```
