Inconsistency in XLA Compiled Model with Additional `tf.math.ceil` and` tf.transpose` Outputs
@GwiHwan-Go I was not able to replicate the issue on both [cpu](https://colab.research.google.com/gist/sushreebarsa/05ee5336d2a118927a9e8c8f42dbb6d1/62532.ipynb) and [gpu](https://colab.research.google.com/gist/sushreebarsa/585f1b475a6e6f6bd72ba2e7ad44713a/62532.ipynb) using colab. Please have a look at these gist and let us know if I am missing something here?
Thank you! Hi @sushreebarsa , have you tried this with V100 GPU? @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. 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/62532">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/62532">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 encountereda bug in TensorFlow where a model with additional `tf.math.ceil` and `tf.transpose` outputs yields inconsistent results under XLA compilation. This error is only seen on **gpu**.

After a series of experiments to locate the root cause of this behavior, we found that (1) altering the outputs of Model2 (either by adding more output tensors or removing one of the current output tensors), or (2) removing the casting operation (along with changing the input tensor data type), or (3) eliminating one of the operators within the model2, does not trigger this error. 
These findings suggest that the inconsistency may be linked to the specific combination of operations and outputs in the XLA-compiled execution path. 
We hope these insights will assist you in pinpointing the root cause of the bug.


### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import numpy as np

densenet = tf.keras.layers.Dense(units=1, dtype=tf.float32, autocast=False)
class Model1(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        cast = tf.cast(inp, dtype=tf.float32)
        dense = densenet(cast)
        add = tf.add(dense, cast)
        reduce_min = tf.math.reduce_min(add, axis=1)
        return reduce_min,

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        cast = tf.cast(inp, dtype=tf.float32)
        ceil = tf.math.ceil(cast)
        dense = densenet(cast)
        add = tf.add(cast, dense)
        trans1 = tf.transpose(add, perm=[1, 0])
        reduce_min = tf.math.reduce_min(add, axis=1)
        return reduce_min, ceil, trans1,
shape = [45, 29]
inputs = [
tf.complex(tf.random.uniform(shape, dtype=tf.float64), tf.random.uniform(shape, dtype=tf.float64)),
]
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: 45 / 45 (100%)
Max absolute difference: 1.0970391
Max relative difference: 2398.6377
 x: array([ 0.178823,  0.136228,  0.895783,  1.074548,  0.868067,  0.623781,
        1.129867,  0.734073,  0.032901, -0.044131,  0.242464,  0.834015,
        0.091566,  0.332864, -0.192198,  0.392632, -0.049305,  0.347389,...
 y: array([7.386290e-02, 1.035314e-02, 7.214887e-02, 2.470637e-02,
       2.454510e-03, 2.196119e-02, 1.195706e-01, 3.828454e-04,
       4.757595e-02, 1.569487e-02, 1.624023e-03, 1.525528e-01,...
```
