Different Behavior of tf.raw_ops.Zeta+tf.raw_ops.Square with jit_compile=True
Hi @zoux1a ,

I have replicated the reported behaviour with jit_compile=True and with jit_compile = False as well. It seems not related to jit_compile issue. I attached a [gist](https://colab.research.google.com/gist/Venkat6871/72c9cb33e4d68a1e50dfe2db70e1682f/62254_gpu_2-14-nightly-v.ipynb) for your reference.

Thank you! Hi @zoux1a ,

`tf.math.is_inf` will return True for both `+inf` and `-inf` also. Numpy has separate APIs(`np. isposinf, np.isneginf`) for this but in TF it seems don't have these. Hence it is difficult to identify +ve and -ve inf with TF APIs.

Hence the code you tested results in unexpected behaviour and can't guarantee same results.  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/62254">Yes</a>
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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

_No response_

### GPU model and memory

_No response_

### Current behavior?

When the tf.raw_ops.Zeta+tf.raw_ops.Square 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):
      tensor = tf.random.normal([9, 9, 7, 10, 1], dtype=tf.float32)
      x = tf.raw_ops.Zeta(q=x, x=tensor)        
      x = tf.raw_ops.Square(x=x, )        
      return x

is_valid = True
inf = float('inf')
m = Network()
tensor = tf.random.normal([7, 10, 8], dtype=tf.float32)
inp = {
    "x": tensor,
}

with tf.device('/GPU:0'):
    tf.config.run_functions_eagerly(True)
    no_op_res = m(**inp)
    tf.config.run_functions_eagerly(False)
    with tf.device('/GPU: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 54, 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=(9, 9, 7, 10, 8) dtype=float64) = '
0.0, 0.0, 0.0, ...
b'y (shape=(9, 9, 7, 10, 8) dtype=float64) = '
0.0, 0.0, 0.0, ...
```
