Different Behavior of tf.raw_ops.Pow with jit_compile=True
Hi @zoux1a ,

I have replicated the reported behaviour with` jit_compile=True`.One interesting observation is that if I rerun the same code multiple times some times the assertion is success. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/1712342e4e59190ba824ea5968593e86/62294_gpu.ipynb) for reference.  @zoux1a ,

Could you please verify the behaviour I mentioned above? Hi @SuryanarayanaY Su

I have attempted to run this program a total of 15 times, and on each occasion, I encountered the same bug. Hi @zoux1a ,

I noticed two issues here. 

1. You are taking power to large number which eventually overflows since the output is not representable in int32 range
2. Inside the call function you are generating the value for y, which in each calls generates different values which makes the output different for obvious reason.

I have done the changes the dtype to int64 and also shifted y to outside the call function to ensure same inputs. I ran 10 experiments and printed the reduce_sum difference of outputs using:

 `print(tf.reduce_sum(no_op_res)-tf.reduce_sum(op_res))` 

which outputs `0` in every run. This indicates this is not an issue with any TF code.

Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/e429d7b42b664cc4d5788a5ebe06355c/62294_final.ipynb).
 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.Pow** 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 **GPU** device.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import traceback

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

    @tf.function(jit_compile=True)
    def __call__(self, x):
      
      x = tf.raw_ops.Pow(x=x, y=tf.random.uniform([4, 1], minval=0, maxval=1000000, dtype=tf.int32))        
      return x

m = Network()
inp = {
    "x": tf.random.uniform([], minval=-1000000, maxval=1000000, dtype=tf.int32),
}

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)

    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 26, 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=(4, 1) dtype=float64) = '
-249281983.0, 1935278665.0, -1676834287.0, ...
b'y (shape=(4, 1) dtype=float64) = '
-1839635735.0, -50435503.0, 1024727513.0, ...
```
