Output Discrepancy in TensorFlow XLA Compilation with Operand Order Swap in Addition with `tf.rev + tf.math.argmax`
@sachinprasadhs I was able to replicate this issue on colab, please find the gist [here](https://colab.research.google.com/gist/sushreebarsa/6f76c1eaa0f1d566c7cfea43d2459df7/62518.ipynb). 
Thank you! Hi, 

This behavior is expected, since the change in order of operation results in the different code path for numerical computations. Thanks!  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

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

In TensorFlow 2.15.0, a inconsistency arises when **swapping the order** of operands in an addition operation followed by `tf.math.argmax`, under XLA compilation. 
This error is seen both on **cpu and 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, inp1, inp2):
        mul = tf.matmul(inp2, inp1)
        rev = tf.reverse(mul, axis=[1])
        add = tf.add(rev, mul)
        argmax = tf.math.argmax(add, axis=1)
        return add, argmax,

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2):
        mul = tf.matmul(inp2, inp1)
        rev = tf.reverse(mul, axis=[1])
        add = tf.add(mul, rev)
        # add = tf.add(rev, mul) <- this will not trigger error
        argmax = tf.math.argmax(add, axis=1)
        return add, argmax,

inputs = [
tf.random.uniform(shape=[1, 18], dtype=tf.float32),
tf.random.uniform(shape=[34, 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
2023-11-30 17:52:36.159568: I external/local_xla/xla/service/service.cc:168] XLA service 0x55bc4ba863c0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2023-11-30 17:52:36.159586: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1701366756.171765 2872023 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
2023-11-30 17:52:36.171907: E external/local_xla/xla/stream_executor/stream_executor_internal.h:177] SetPriority unimplemented for this stream.
=========compiled_output(version:2.15.0)================
XLA_complie triggers assertion

Not equal to tolerance rtol=0.001, atol=0.001
at checking 1th # indicates that errors happen after applied tf.math.argmax
Mismatched elements: 11 / 34 (32.4%)
Max absolute difference: 3
Max relative difference: 0.42857143
 x: array([ 7,  7,  7,  7,  7,  7, 10,  7,  7,  7,  7,  7,  7,  7,  7,  7, 10,
        7,  7,  7,  7,  7,  7,  7,  7, 10, 10,  7,  7,  7,  7,  7,  7,  7])
 y: array([ 7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7, 10,  7, 10,  7,  7,  7,
       10, 10,  7,  7,  7,  7, 10,  7,  7,  7,  7, 10,  7,  7,  7, 10,  7])```
