 Inconsistency with Extra Transpose Node in  XLA Compiled Models Using `tf.squeeze` and `tf.abs`
@sachinprasadhs I was able to replicate the issue reported here for [cpu](https://colab.research.google.com/gist/sushreebarsa/91ee69d4fd7c3cade0239f4ea0d21e6d/62515.ipynb) and [gpu](https://colab.research.google.com/gist/sushreebarsa/a7f796a67b3e71e5059d5cae430cf3b6/62515.ipynb). Thank you! Hi, 

Could you please confirm why you're returning `trans_mul` along with `abs` in the `Model2`, where as `Model1` returns only `abs`.  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. Hello @sachinprasadhs,
We were considering the importance of examining the intermediate values in the layer and thought it might be a crucial aspect to look into. Additionally, we think that returning extra output should not impact the model's original return values. Hi, 

Providing extra node to the output does not yield in matching results when comparing.
This behavior is expected behavior.  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/62515">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/62515">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

_No response_

### Mobile device

_No response_

### Python version

_No response_

### 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, I've encountered a inconsistency where a TensorFlow model using `tf.squeeze` and `tf.abs` with an additional transpose node output compiled with XLA.
This behavior occurs on both **GPU and CPU**.

### Additional Context

In our efforts to understand and locate the root cause of this issue, we have conducted several tests and experiments. Through this process, we've identified specific conditions under which the discrepancy does not occur :

1. If any one of the operators (tf.squeeze(along with reduced_dim input), tf.abs) is removed from either model, the discrepancy under XLA compilation is no longer triggered.
2. Removing `trans_mul` output from Model2's Return values will not trigger this issue.
3. Changing Input Tensor Data Type: Altering the data type of the input tensor to a type other than int8 also prevents the occurrence of this discrepancy.

### 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: [1, 55, 3, 27] : int8
        squeeze = tf.squeeze(inp, axis=0)
        mul = tf.multiply(squeeze, squeeze)
        abs = tf.abs(mul)
        return abs,

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        # Forward pass logic using TensorFlow operations
        # inp: [1, 55, 3, 27] : int8
        squeeze = tf.squeeze(inp, axis=0)
        transposed = tf.transpose(squeeze, perm=[1, 0, 2])
        trans_mul = tf.multiply(transposed, transposed)
        mul = tf.transpose(trans_mul, perm=[1, 0, 2])
        abs = tf.abs(mul)
        return abs, trans_mul,

inputs = [
tf.cast(tf.random.uniform(shape=[1, 55, 3, 27], minval=-128, maxval=128, dtype=tf.int32), tf.int8),
]
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 0th
Mismatched elements: 1980 / 4455 (44.4%)
Max absolute difference: 126
Max relative difference: 2.
 x: array([[[  16,  100,  -79, ...,   73,   36,  -28],
        [   0,  -71,   16, ...,   41,   16,   25],
        [  49,  -31,  113, ...,   64,  -60,  -15]],...
 y: array([[[ 16, 100,  79, ...,  73,  36,  28],
        [  0,  71,  16, ...,  41,  16,  25],
        [ 49,  31, 113, ...,  64,  60,  15]],...
```
