Output Discrepancy in XLA Compiled Models with `tf.divide, tf.math.reduce_prod, and tf.cos` Involving Extra Node
Hi @GwiHwan-Go ,

I have replicated the reported difference in results with `jit_compile=True` and with `jit_compile=False` the results are same . Attaching [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/7e02216d3d9fd52c8252992f77078237/62514.ipynb) for reference.
### 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

### 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, a model involving `tf.divide`, `tf.math.reduce_prod`, and `tf.cos`, along with an additional concat node returning, exhibits different outputs under XLA compilation compared to a simpler model without the extra node. 
You can reproduce this issue using the [Colab notebook](https://colab.research.google.com/drive/19bN4XvDdfPLiXDbWakcgxfA4aBVIBreG?usp=sharing) I have prepared.

### Additional Context
In our efforts to understand and locate the root cause of this issue, we have conducted several tests and experiments. 

Removing an Operator: If any one of the operators (tf.divide, tf.math.reduce_prod, tf.cos) is removed from either model, the discrepancy under XLA compilation is no longer triggered.

Removing `transposed_concat` or replacing with `concat` from Model2's Return: Omitting or replacing the output `transposed_concat` will not trigger this error.


### Standalone code to reproduce the issue

```python
from typing import Dict
import tensorflow as tf
import pickle
import os
import numpy as np

params = [
tf.random.uniform(shape=[49, 9, 1], dtype=tf.float32),
]
class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()
        # Tensor objects (with comments for shapes)
        self.p0 = tf.constant(params[0]) # [49, 9, 1] float32

    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [7, 5, 49, 1, 1] : float32
        div = tf.divide(inp, self.p0)
        transposed_div = tf.transpose(div, perm=[0, 1, 3, 2, 4])
        red = tf.math.reduce_prod(transposed_div, axis=2)
        cos = tf.cos(red)
        return red, cos,

class Model2(tf.keras.Model):
    def __init__(self):
        super().__init__()
        # Tensor objects (with comments for shapes)
        self.p0 = tf.constant(params[0]) # [49, 9, 1] float32

        # Layers or other Keras model objects

    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [7, 5, 49, 1, 1] : float32
        div = tf.divide(inp, self.p0)
        transposed_div = tf.transpose(div, perm=[0, 1, 3, 2, 4])
        concat = tf.concat([transposed_div, transposed_div], axis=2)
        transposed_concat = tf.transpose(concat, perm=[1, 0, 2, 3, 4])
        red = tf.math.reduce_prod(transposed_div, axis=2)
        cos = tf.cos(red)
        return red, cos, transposed_concat

inputs = [
tf.random.uniform(shape=[7, 5, 49, 1, 1], dtype=tf.float32),
]
model1 = Model1()
model2 = Model2()
device = "cpu"
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
=========eager_output(version:2.15.0)================
XLA_eager does not trigger assertion
=========compiled_output(version:2.15.0)================
XLA_complie triggers assertion

Not equal to tolerance rtol=0.001, atol=0.001
at checking 1th
Mismatched elements: 77 / 1715 (4.49%)
Max absolute difference: 0.85310125
Max relative difference: 32.919815
 x: array([[[[-0.067188],
         [ 0.995112],
         [ 0.119131],...
 y: array([[[[-0.067309],
         [ 0.995112],
         [ 0.119131],...
=========RUNNING WITH PICKLE FILES=========== # on AMD CPU 
=========eager_output(version:2.15.0)================
XLA_eager does not trigger assertion
=========compiled_output(version:2.15.0)================
XLA_complie triggers assertion

Not equal to tolerance rtol=0.001, atol=0.001
at checking 1th
Mismatched elements: 1080 / 1715 (63%)
Max absolute difference: 1.9984823
Max relative difference: 360.84747
 x: array([[[[ 0.942858],
         [-0.681655],
         [-0.800721],...
 y: array([[[[ 0.738128],
         [-0.681655],
         [-0.800721],...
```
