Inconsistency in XLA Compiled Models with `tf.experimental.numpy.triu` and `tf.math.reduce_min` with Extra Concat Output
@Gwihwan-Go,
Hi,

Providing additional outputs does not result in matching outputs. This behavior is expected. Inside the call function you are generating the value for tensor, which in each calls generates different random values because XLA currently ignores TF seeds to random operations which makes the output different for obvious reason. Please refer [known](https://www.tensorflow.org/xla/known_issues#random_number_generation_ignores_tf_seed) issues from XLA section. Also Providing additional outputs does not result in matching outputs. This behavior is expected. Thank you! 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/62534">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/62534">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 inconsistent behavior in XLA compiled models when using `tf.experimental.numpy.triu` combined with `tf.math.reduce_min`, and an additional concatenation node is returned from the model. This issue seems to be similar to #62533, particularly regarding the conditions that trigger the error—specifically, the data type(only int8 input tensor will trigger this error) involved and the dataflow of the models.
This behavior is only seen on **gpu.**







### 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):
        triu = tf.experimental.numpy.triu(inp, k=0)
        reduce_min = tf.math.reduce_min(triu, axis=0)
        return reduce_min, 

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [57, 22] : int8
        triu = tf.experimental.numpy.triu(inp, k=0)
        trans = tf.transpose(triu, perm=[1, 0])
        concat = tf.concat([trans, trans], axis=0)
        reduce_min = tf.math.reduce_min(triu, axis=0)
        return reduce_min, concat,

inputs = [
tf.cast(tf.random.uniform(shape=[57, 22], minval=-128, maxval=128, dtype=tf.int32), tf.int8),
]
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 / 22 (95.5%)
Max absolute difference: 127
Max relative difference: 1.
 x: array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
      dtype=int8)
 y: array([ -92,    0, -124,  -38,  -61,  -30, -112, -118,  -75, -120, -127,
       -112,  -86,  -96, -126, -127, -120, -125,  -75, -122, -109, -125],
      dtype=int8)
```
