Inconsistency in XLA Compiled Models with Distributed Multiplications, `tf.abs`, and `tf.clip_by_value`
Hi @GwiHwan-Go ,

I have replicated the reported behaviour with colab using TF v2.14, 2.15 and nightly. Please find the [gist](https://colab.research.google.com/gist/Venkat6871/9300b2e3055a14d51db78647887cb7d0/62517_2-15_2-14_nightly-v.ipynb) here for reference.

Thank you! Hi,

This precision loss is due to the different order of multiplication and involving mutiple `tf.multiply `operations will accumulate the precession error. This does not show any unexpected behavior in the implementation.

```
        add = tf.add(inp2, inp1)
        mul = tf.multiply(add, add)
        abs = tf.abs(mul)
```

vs 

```
        add = tf.add(inp1, inp2)
        mul1_1 = tf.multiply(inp1, add)
        mul1_2 = tf.multiply(inp2, add)
        mul = tf.add(mul1_1, mul1_2)
        abs = tf.abs(mul)
```
 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. Since it is not an implementation bug, I will close this 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/62517">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/62517">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

_No response_

### GPU model and memory

_No response_

### Current behavior?

In TensorFlow 2.15.0, I've encountered an inconsistency where models involving distributed multiplications combined with `tf.abs` and `tf.clip_by_value` yield different results under XLA compilation. This behavior only seen on **cpu.**
You can reproduce this bug on [colab](https://colab.research.google.com/drive/1i4Et5UB6fEsAQi-E8FNVa7OH1UUqR8WZ?usp=sharing)


### Standalone code to reproduce the issue

```python
## Model overview
## refer colab link to reproduce the error
class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.p0 = tf.constant(params[0]) # [] int8
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2):
        add = tf.add(inp2, inp1)
        mul = tf.multiply(add, add)
        abs = tf.abs(mul)
        print(abs)
        clipped = tf.clip_by_value(abs, -1, 1)
        return abs, clipped,

class Model2(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.p0 = tf.constant(params[0]) # [] int8
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2):
        add = tf.add(inp1, inp2)
        mul1_1 = tf.multiply(inp1, add)
        mul1_2 = tf.multiply(inp2, add)
        mul = tf.add(mul1_1, mul1_2)
        abs = tf.abs(mul)
        clipped = tf.clip_by_value(abs, -1, 1)
        return abs, clipped ,#, v4_0,
```


### Relevant log output

```shell
# On colab
=========RUNNING WITH PICKLE FILES===========
=========eager_output(version:2.14.0, cpu:Intel(R) Xeon(R) CPU @ 2.20GHz)================
XLA_eager does not trigger assertion
=========compiled_output(version:2.14.0, cpu:Intel(R) Xeon(R) CPU @ 2.20GHz)================
XLA_complie triggers assertion

Not equal to tolerance rtol=0.001, atol=0.001
at checking 0th
Mismatched elements: 1 / 1 (100%)
Max absolute difference: 18
Max relative difference: 0.1512605
 x: array(-119, dtype=int8)
 y: array(119, dtype=int8)

Not equal to tolerance rtol=0.001, atol=0.001
at checking 0th
Mismatched elements: 1 / 1 (100%)
Max absolute difference: 18
Max relative difference: 0.1512605
 x: array(-119, dtype=int8)
 y: array(119, dtype=int8)
```
