Different Behavior of tf.raw_ops.Zeta with jit_compile=True
![image](https://github.com/tensorflow/tensorflow/assets/120479977/c5f8c38d-df65-42de-b1f5-15b414381983)

I'm trying to recreate the issue, but the code i copied from your issue is not creating any inconsistency. or if it is then i am not able to figure out the inconsistency. 
thank you for your time! @zoux1a Inside the call function you are generating the value for tensor, which in each calls generates different random values because XLA currently ignores TF seeds to random operations which makes the output different for obvious reason. Please refer [known](https://www.tensorflow.org/xla/known_issues#random_number_generation_ignores_tf_seed) issues from XLA section.

Please have a look at this modified [gist](https://colab.research.google.com/gist/sushreebarsa/cb6eb80f33f04716a56da117e291a439/62246.ipynb) for reference.
Thank you! 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?

No

### Source

source

### TensorFlow version

2.14.0

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04.3 LTS (x86_64)

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

11.8

### GPU model and memory

GPU 0: NVIDIA GeForce RTX 2070 GPU 1: NVIDIA GeForce RTX 2070 GPU 2: NVIDIA GeForce RTX 2070 GPU 3: NVIDIA GeForce RTX 2070

### Current behavior?

When the **tf.raw_ops.Zeta** operation is invoked within a tf.function with JIT compilation enabled (**jit_compile=True**), it produces different results compared to the same operation called without JIT compilation. This inconsistency is observed when the code is executed on a **CPU** device.



### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import traceback

def replace_special_values(tensor):
    # Convert tensor to tf.float32 if it's not a supported dtype
    supported_dtypes = [tf.float16, tf.float32, tf.float64, tf.bfloat16]
    if tensor.dtype not in supported_dtypes:
        original_dtype = tensor.dtype
        tensor = tf.cast(tensor, tf.float32)
    else :
        original_dtype = None
    
    # Replace NaNs with zeros
    tensor = tf.where(tf.math.is_nan(tensor), tf.zeros_like(tensor), tensor)
    
    # Replace positive infinities with a large number (e.g., 1e30)
    tensor = tf.where(tf.math.is_inf(tensor), 100, tensor)
    
    # Replace negative infinities with a small number (e.g., -1e30)
    tensor = tf.where(tf.math.is_inf(tensor) & tf.math.less(tensor, 0), -100, tensor)
    
    # Convert tensor back to its original dtype
    if original_dtype is not None :
        tensor = tf.cast(tensor, original_dtype)
    return tensor

class Network(tf.Module):
    def __init__(self):
        super().__init__()

    @tf.function(jit_compile=True)
    def __call__(self, x):
      random_tensor = tf.random.normal([2, 1, 10], dtype=tf.float32)
      x = tf.raw_ops.Zeta(q=x, x=random_tensor)             
      return x

m = Network()
random_tensor = tf.random.normal([10, 1, 5, 10], dtype=tf.float32)
inp = {
    "x": random_tensor,
    }

with tf.device('/CPU:0'):
    tf.config.run_functions_eagerly(True)
    no_op_res = m(**inp)
    tf.config.run_functions_eagerly(False)
    with tf.device('/CPU:0'):
        op_res = m(**inp)
    no_op_res = replace_special_values(no_op_res)
    op_res = replace_special_values(op_res)
    tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
```


### Relevant log output

```shell
File "/home/guihuan/LLM/results/tf-2/2023-10-22-20-21/test.py", line 51, in <module>
    tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
  File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/ops/control_flow_assert.py", line 102, in Assert
    raise errors.InvalidArgumentError(
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b''
b'x and y not equal to tolerance rtol = tf.Tensor(0.001, shape=(), dtype=float64), atol = tf.Tensor(0.001, shape=(), dtype=float64)'
b'x (shape=(10, 2, 5, 10) dtype=float64) = '
0.0, 0.0, 494.08197021484375, ...
b'y (shape=(10, 2, 5, 10) dtype=float64) = '
0.0, 11.413576126098633, 0.0, ...
```
