Inconsistencies in XLA Compiled Models Using `raw_ops.LRN` and `tf.math.reduce_min` with Extra Outpu
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/f1eb68d213b7fbfe9e01f22b768c6ac6/62531_2-14_2-15-nightly-v.ipynb) here for reference.

Thank you! Hi,

Providing an extra output will not result in matching outputs when comparing.
This behavior is expected. 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/62535">Yes</a>
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### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.15

### 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 discovered an inconsistency in TensorFlow's XLA compilation for models that utilize `raw_ops.LRN` combined with `tf.math.reduce_min`. The issue results in inconsistent outputs when XLA compilation is enabled. Through rigorous testing, the following observations were made:

1. **Removing tf.math.reduce_min** from the model (and replacing it with an operation like tf.squeeze(axis=3)) prevents the error from occurring.
2.  **Removing the transpose output** from the model's return outputs also avoids triggering the inconsistency.
3. **Directly returning** the result of `tf.math.reduce_min` prevents the error too.

Interestingly, while random input values can trigger this inconsistency, we've identified that specific input tensors exacerbate the discrepancy in outputs. 
To assist in resolving this issue, we've prepared a [Google Colab notebook](https://colab.research.google.com/drive/18RR5wfk_DGrtkgRsaKZGkebH-IS20e4H?usp=sharing) that robustly reproduces the problem.

This Colab notebook includes a specific set of inputs that consistently trigger the observed inconsistency, providing a reliable test case for debugging purposes.
However, since I cannot access the V100 GPU on colab, I was unable to reproduce the error on colab. Please let me know if you cannot reproduce the error on V100 GPU.

### Standalone code to reproduce the issue

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

class Model1(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        cos = tf.cos(inp)
        transpose = tf.transpose(cos, perm=[4, 1, 2, 3, 0])
        reduce_min = tf.math.reduce_min(transpose, axis=2)
        lrn = tf.raw_ops.LRN(input=reduce_min, depth_radius=1, bias=62.98211185437273, alpha=22.83989611654185, beta=0.9124946866870809)
        return lrn,

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        cos = tf.cos(inp)
        transpose = tf.transpose(cos, perm=[4, 1, 2, 3, 0])
        reduce_min = tf.math.reduce_min(transpose, axis=2) # tf.squeeze(transpose, axis=3)
        lrn = tf.raw_ops.LRN(input=reduce_min, depth_radius=1, bias=62.98211185437273, alpha=22.83989611654185, beta=0.9124946866870809)
        return lrn, transpose,

inputs = [
tf.random.uniform(shape=[21, 27, 10, 1, 1], dtype=tf.float32),
]
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
=========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 0th
Mismatched elements: 21 / 567 (3.7%)
Max absolute difference: 0.00186182
Max relative difference: 0.14022177
 x: array([[[[0.01146 , 0.010013, 0.010936, 0.009743, 0.01017 , 0.010607,
          0.01057 , 0.010013, 0.010744, 0.00963 , 0.010538, 0.010292,
          0.009591, 0.010573, 0.01096 , 0.009638, 0.010813, 0.010375,...
 y: array([[[[0.011634, 0.009475, 0.011923, 0.009137, 0.010123, 0.01081 ,
          0.010774, 0.009508, 0.011514, 0.008942, 0.010976, 0.010411,
          0.009055, 0.010694, 0.011764, 0.008721, 0.011516, 0.01012 ,...
=========RUNNING WITH PICKLE FILES===========
=========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 0th
Mismatched elements: 101 / 567 (17.8%)
Max absolute difference: 0.00307472
Max relative difference: 0.5682127
 x: array([[[[-0.002965, -0.01124 , -0.011004, -0.011653, -0.011652,
          -0.011463, -0.008849, -0.011621, -0.011612, -0.011653,
          -0.011243, -0.011639, -0.010928, -0.011649, -0.011555,...
 y: array([[[[-0.002476, -0.013035, -0.009902, -0.012564, -0.012114,
          -0.012089, -0.006702, -0.01318 , -0.01141 , -0.012575,
          -0.009897, -0.013295, -0.009197, -0.01293 , -0.010948,...
```
