Inconsistency in XLA Compiled Model with `tf.minimum` and Additional Outputs on GPU
@GwiHwan-Go,
The code you have mentioned has random number generation, the outputs cannot be guaranteed for each run.
I have tried by setting seed and enabling op determinism and it still produces random results.

```
tf.random.set_seed(42)
tf.config.experimental.enable_op_determinism() 
``` Hi @tilakrayal,

I don't believe that the problem you mentioned is influencing this issue. This is because we generated random inputs before feeding them to the models and ensured that the same inputs were given to both models. Additionally, the fact that eager mode does not trigger this behavior seems to support this perspective. @Gwihwan-Go,
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! @tilakrayal, could you explain which APIs in the code above are used to generate a tensor? They include tf.transpose, tf.split(trans, 2, axis=0), tf.cast(trans, dtype=tf.int64), slice, and tf.minimum. Hi,

Providing additional outputs does not result in matching outputs. 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?
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### 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?

An inconsistency arises in a TensorFlow model under XLA compilation when using `tf.minimum` along with an additional output nodes. This inconsistency does not occur when either the `tf.minimum` or the extra output node is removed.

### Testing and Observations

   - Removing the `tf.transpose` operation or one instance of `tf.minimum` prevents the inconsistency, suggesting their involvement in the issue.
   - Intriguingly, removing either the second `tf.minimum` (referred to as `min2`) or the outputs of `tf.split` also resolves the inconsistency. This observation indicates that the specific combination and arrangement of these operations contribute to the problem.

This error is seen on specific input tensor. Please refer this [colab notebook](https://colab.research.google.com/drive/1P58ohVZEf_fxr_kndlD9l5sM6bKEAf1e?usp=sharing). 
Please reproduce the bug with the V100 GPU.


### Standalone code to reproduce the issue

```shell
class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()
        # Tensor objects (with comments for shapes)


        # Layers or other Keras model objects

    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [17, 10, 42] : complex128
        trans = tf.transpose(inp, perm=[2, 1, 0])
        cast = tf.cast(trans, dtype=tf.int64)
        sliced = cast[(slice(None, None, None), slice(-1, 9223372036854775807, 1), slice(None, None, None))]
        min1 = tf.minimum(cast, sliced)
        min2 = tf.minimum(min1, min1)
        return min1, min2,

class Model2(tf.keras.Model):
    def __init__(self):
        super().__init__()
        # Tensor objects (with comments for shapes)


        # Layers or other Keras model objects

    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [17, 10, 42] : complex128
        trans = tf.transpose(inp, perm=[2, 1, 0])
        v6_0, v6_1 = tf.split(trans, 2, axis=0)
        cast = tf.cast(trans, dtype=tf.int64)
        sliced = cast[(slice(None, None, None), slice(-1, 9223372036854775807, 1), slice(None, None, None))]
        min1 = tf.minimum(cast, sliced)
        min2 = tf.minimum(min1, min1)
        return min1, min2, v6_0, v6_1
```


### Relevant log output

```shell
=========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: 3261 / 7140 (45.7%)
Max absolute difference: 197
Max relative difference: 93.
 x: array([[[-25,  35, -17, ..., -87,  44,  12],
        [-75, -40,  90, ...,  57, -40,  13],
        [-31,  16, -95, ..., -82, -65,  14],...
 y: array([[[-25, -94, -94, ..., -87, -49,  -2],
        [-75, -94, -94, ...,  13, -49,  -2],
        [-31, -94, -95, ..., -82, -65,  -2],...
```
