Output Inconsistency in XLA Compilation with Order Swapping of `tf.multiply` on GPU
The code you have mentioned has random number generation, the outputs cannot be guaranteed for each run.
I have tried by setting seed and enabling op determinism and it still produces random results.

```
tf.random.set_seed(42)
tf.config.experimental.enable_op_determinism() 
``` Hi @tilakrayal,

I don't believe that the problem you mentioned is influencing this issue. This is because we generated random inputs before feeding them to the models and ensured that the same inputs were given to both models. Additionally, the fact that eager mode does not trigger this behavior seems to support this perspective. Change in order of operation will take the different code path and the results are expected.
This is not considered as 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/62546">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/62546">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?

We have identified a significant inconsistency in TensorFlow's XLA compilation when the order of operands in `tf.multiply` is swapped. 
This behavior is only seen on **gpu.**

### Additional Testing Conducted

To further investigate the issue, we conducted the following tests:

1. **Altering `tf.transpose` and `tf.multiply`**: Removing either `tf.transpose` or the first instance of `tf.multiply` prevents the inconsistency, suggesting their involvement in the issue.

2. **Replacing `tf.squeeze`**: Substituting `tf.squeeze` with `tf.math.reduce_min` also triggers the error. This finding implies that the root cause of the inconsistency may not lie within the `tf.squeeze` API.

We hope this detailed report will assist in identifying and resolving the root cause of this inconsistency in TensorFlow's XLA compilation process.


### Standalone code to reproduce the issue

```shell
from typing import Dict
import tensorflow as tf
import pickle
import os
import numpy as np

class Model1(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2):
        trans = tf.transpose(inp1, perm=[1, 0])
        gather = tf.gather(trans, tf.clip_by_value(inp2, 0, 63), axis=0)
        squeeze = tf.squeeze(gather, axis=1) # replace with tf.math.reduce_min also trigger the error
        mul1 = tf.multiply(squeeze, squeeze)
        mul2 = tf.multiply(trans, mul1)
        return mul2,

class Model2(tf.keras.Model):
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2):
        # Forward pass logic using TensorFlow operations
        # inp1: [64, 64] : complex128
        # inp2: [1, 1, 64] : int32
        trans = tf.transpose(inp1, perm=[1, 0])
        gather = tf.gather(trans, tf.clip_by_value(inp2, 0, 63), axis=0)
        squeeze = tf.squeeze(gather, axis=1) # replace with tf.math.reduce_min also trigger the error
        mul1 = tf.multiply(squeeze, squeeze)
        mul2 = tf.multiply(mul1, trans)
        return mul2, 

inputs = [
tf.random.uniform([64, 64], dtype=tf.float64),
tf.random.uniform(shape=[1, 1, 64], minval=-100, maxval=100, dtype=tf.int32),
]
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
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1701613506.470278 2323572 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
=========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: 3916 / 4096 (95.6%)
Max absolute difference: 0.99773916
Max relative difference: 8.1213542e+09
 x: array([[[9.954084e-04, 1.219345e-02, 2.660004e-02, ..., 1.034067e-01,
         2.044861e-01, 2.751211e-01],
        [8.196792e-01, 1.095101e-04, 3.054382e-01, ..., 3.612774e-02,...
 y: array([[[0.002754, 0.195666, 0.044557, ..., 0.013043, 0.305706,
         0.210448],
        [0.386147, 0.014669, 0.420922, ..., 0.0958  , 0.017486,...
```
