Different Behavior of tf.raw_ops.SqrtGrad with jit_compile=True
Hi, **@zoux1a**!
I was able to replicate this issue with jit_compile=True and jit_compile=False too.Here I attached a [gist](https://colab.research.google.com/gist/Venkat6871/e9db4fb9d907d226d962d2a2f85a76dd/62263_gpu-2-14-nightly.ipynb) here.

Thank you! Seems to be issue with tf.function as the replicable with and without `jit_compile`.
### 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

_No response_

### 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.SqrtGrad** 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):
      real_part = tf.random.normal([], dtype=tf.float64)
      imag_part = tf.random.normal([], dtype=tf.float64)
      tensor = tf.complex(real_part, imag_part)
      tensor = tf.cast(tensor,dtype=tf.complex128)
      x = tf.raw_ops.SqrtGrad(y=x, dy=tensor)        
      return x

m = Network()
real_part = tf.random.normal([], dtype=tf.float64)
imag_part = tf.random.normal([], dtype=tf.float64)
tensor = tf.complex(real_part, imag_part)
tensor = tf.cast(tensor,dtype=tf.complex128)
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)

    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 33, 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=() dtype=float64) = '
-0.006697387971180855
b'y (shape=() dtype=float64) = '
0.07167101474792367
```
