Inconsistency in XLA Compiled Model with Extra Transpose and Argmax Output Nodes
@sachinprasadhs I was able to replicate the issue on both [gpu](https://colab.research.google.com/gist/sushreebarsa/b9173e2ec49832a9e87f78e54da9780d/62521.ipynb) and [cpu](https://colab.research.google.com/gist/sushreebarsa/dc437db57486f1fb8afe56b7f923869e/62521.ipynb).
Please find the attached gists here. Thank you! When you try to compare with additional nodes output to assert_allclose, it gives mismatch and it is working as expected when you remove those nodes as you have mentioned. 

```python
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: [37, 1, 1, 15, 36] : int8
        mul = tf.multiply(inp, inp)
        abs = tf.abs(mul)
        reduce_max = tf.math.reduce_max(abs, axis=2)
        return reduce_max

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [37, 1, 1, 15, 36] : int8
        trans1 = tf.transpose(inp, perm=[1, 0, 2, 3, 4])
        trans_mul = tf.multiply(trans1, trans1)
        mul = tf.transpose(trans_mul, perm=[1, 0, 2, 3, 4])
        abs = tf.abs(mul)
        reduce_max = tf.math.reduce_max(abs, axis=2)
        argmax = tf.math.argmax(mul, axis=4)
        return reduce_max # removing eitheor trans1 or argmax will not trigger error!

inputs = [
tf.cast(tf.random.uniform(shape=[37, 1, 1, 15, 36], minval=-128, maxval=128, dtype=tf.int32), tf.int8),
]
model1 = Model1()
model2 = Model2()
device = "cpu" # "gpu" also trigger the error
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)
``` 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/62521">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/62521">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?

In TensorFlow 2.15.0, I've found that a XLA compiled model returning an extra two nodes produces inconsistent outputs, compared to a simpler model. This inconsistency is observed on both **CPU and GPU devices**. Interestingly, removing one of the extra output nodes from the complex model resolves the discrepancy.


### Standalone code to reproduce the issue

```python
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: [37, 1, 1, 15, 36] : int8
        mul = tf.multiply(inp, inp)
        abs = tf.abs(mul)
        reduce_max = tf.math.reduce_max(abs, axis=2)
        return reduce_max, 

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [37, 1, 1, 15, 36] : int8
        trans1 = tf.transpose(inp, perm=[1, 0, 2, 3, 4])
        trans_mul = tf.multiply(trans1, trans1)
        mul = tf.transpose(trans_mul, perm=[1, 0, 2, 3, 4])
        abs = tf.abs(mul)
        reduce_max = tf.math.reduce_max(abs, axis=2)
        argmax = tf.math.argmax(mul, axis=4)
        return reduce_max, trans1, argmax, # removing eitheor trans1 or argmax will not trigger error!

inputs = [
tf.cast(tf.random.uniform(shape=[37, 1, 1, 15, 36], minval=-128, maxval=128, dtype=tf.int32), tf.int8),
]
model1 = Model1()
model2 = Model2()
device = "cpu" # "gpu" also trigger the error
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
2023-12-01 06:50:46.483140: I external/local_xla/xla/service/service.cc:168] XLA service 0x55c0d1fa47c0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
=========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: 8812 / 19980 (44.1%)
Max absolute difference: 126
Max relative difference: 2.
 x: array([[[[  73,  105,  121, ..., -112,  -15,  -87],
         [ 121,  -79,   89, ...,  100, -111,  100],
         [  89,   68,    9, ...,  -47,  -31,  -87],...
 y: array([[[[ 73, 105, 121, ..., 112,  15,  87],
         [121,  79,  89, ..., 100, 111, 100],
         [ 89,  68,   9, ...,  47,  31,  87],...
```
