Inconsistent Outputs from `tf.linalg.eigh` in XLA-Compiled Model
@sachinprasadhs I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/93603cff7886c43d8b1f17b2d7f37906/62461.ipynb). Please have a look at this issue. 
Thank you! Hi, 

Since the code you have mentioned has random number generation, the outputs can not be guaranteed for each run.
I have tried by setting seed and enabling op determinism and still produces random results.
```
tf.random.set_seed(42)
tf.config.experimental.enable_op_determinism() 
``` 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. Hi @sachinprasadhs,
Is this issue expected behavior? I believe the random behavior is not directly related to the issue. Your matrices are not hermitian (`A.conjugate().transpose() != A`), so the results are garbage, even in the eager case.  We don't check this precondition since checking is just as expensive as the operation itself.  Behavior is therefore undefined. 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/62461">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/62461">No</a>

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.13.0

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04.3

### Mobile device

_No response_

### Python version

3.10.12

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I've encountered an issue where `tf.linalg.eigh`, when used in an XLA-compiled TensorFlow model, produces different outputs for the same input on consecutive runs.


### Standalone code to reproduce the issue

```python
import tensorflow as tf
import numpy as np

class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()

    @tf.function(jit_compile=True)
    def __call__(self, v4_0):
        v0_0, _ = tf.linalg.eigh(v4_0)
        return v0_0

inputs = [
    tf.complex(tf.random.uniform([1, 48, 1, 1], dtype=tf.float32), 
               tf.random.uniform([1, 48, 1, 1], dtype=tf.float32)),
]

model1 = Model1()
with tf.device('cpu'):
    # Test in eager execution mode
    tf.config.run_functions_eagerly(True)
    out1 = model1(*inputs)
    out2 = model1(*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=0.01, 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)

    # Test in compiled mode
    tf.config.run_functions_eagerly(False)
    out1 = model1(*inputs)
    out2 = model1(*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=0.01, err_msg=f'at checking {i}th')
        print("XLA_compile does not trigger assertion")
    except AssertionError as e:
        print("XLA_compile triggers assertion")
        print(e)
```


### Relevant log output

```shell
Skipping registering GPU devices...
=========eager_output(version:2.13.0)================
XLA_eager does not trigger assertion
2023-11-23 07:38:29.271178: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x561bb6eca560 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2023-11-23 07:38:29.271197: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2023-11-23 07:38:29.278907: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
=========compiled_output(version:2.13.0)================
XLA_complie triggers assertion

Not equal to tolerance rtol=0.01, atol=0
at checking 0th
x and y nan location mismatch:
 x: array([[ 3.354893e-01+1.480364e-01j],
       [ 4.999712e-01+2.206396e-01j],
       [ 4.584086e-02+1.555587e-01j],...
 y: array([[ 3.354893e-01+1.480364e-01j],
       [ 4.999712e-01+2.206396e-01j],
       [ 4.584086e-02+1.555587e-01j],...
```
```
