Inconsistency in XLA Compiled Model Involving `tf.nn.softmax, tf.cast, tf.math.reduce_sum` and Additional Concat Output
@tilakrayal Hi, have you reproduced this behavior? @sachinprasadhs,
I was able to reproduce the issue on tensorflow v2.14, v2.15 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/83b3500341f22fd9e35b5efe23936a21/untitled1609.ipynb). It is not expected to get the desired matching results when you **Altering Output Structure**, the behavior which you are observing is intended and it is not a bug. 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/62539">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/62539">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

_No response_

### GPU model and memory

_No response_

### Current behavior?

I have identified a inconsistency issue in TensorFlow when running a model under XLA compilation. The model uses a combination of `tf.nn.softmax`, `tf.cast`, and `tf.math.reduce_sum` operations, and the inconsistency arises when an additional `tf.concat` output node is included.

### Troubleshooting Conducted

To isolate the cause of this issue, the following tests were conducted:

1. **Removing Individual Operations**: We methodically removed each operation (`tf.nn.softmax`, `tf.transpose`, `tf.math.reduce_sum`, and `tf.cast`) from the model. In each case, this alteration prevented the inconsistency error, suggesting that the combination of these operations contributes to the issue.

2. **Altering Output Structure**: Removing the `tf.concat` nodes from Model2's return values also resolved the inconsistency, indicating that the manner in which outputs are structured and concatenated is a factor.

3. Run the model on **gpu** device.
I hope these findings will assist in identifying and resolving the root cause of this inconsistency issue in TensorFlow's XLA compilation.


### 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):
        # Forward pass logic using TensorFlow operations
        # inp: [28, 60, 1] : float64
        softmax = tf.nn.softmax(inp, axis=0)
        trans = tf.transpose(softmax, perm=[0, 2, 1])
        reduce_sum = tf.math.reduce_sum(trans, axis=0)
        cast = tf.cast(reduce_sum, dtype=tf.int32)
        return reduce_sum, cast,

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [28, 60, 1] : float64
        softmax = tf.nn.softmax(inp, axis=0)
        trans = tf.transpose(softmax, perm=[0, 2, 1])
        concat = tf.concat([trans, trans], axis=1)
        reduce_sum = tf.math.reduce_sum(trans, axis=0)
        cast = tf.cast(reduce_sum, dtype=tf.int32)
        return reduce_sum, cast, concat,

inputs = [
tf.random.uniform(shape=[20, 30, 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: 9 / 30 (30%)
Max absolute difference: 1
Max relative difference: 1.
 x: array([[1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,
        1, 1, 1, 1, 1, 0, 1, 0]], dtype=int32)
 y: array([[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0,
        0, 1, 1, 0, 1, 1, 1, 0]], dtype=int32)
```
