 Different Behavior of tf.raw_ops.Exp+tf.raw_ops.Square with jit_compile=True
@zoux1a,
I tried to execute the mentioned code on both [CPU](https://colab.research.google.com/gist/tilakrayal/418333a8757368178be2491ccbd4164d/untitled1482.ipynb) and [GPU](https://colab.research.google.com/gist/tilakrayal/1a746f8bfde6d33247e108f8094cd007/untitled1481.ipynb) with tensorflow v2.14 and observed that the error outputs are similar with both the cases. Kindly find the gist. Thank you! @tilakrayal ,
Yes, we have also tried running the code on both CPU and GPU and observed  similar error outputs. Thanks for your information. @zoux1a,
 This is likely due to fusion, where the intermediate result may be computed and kept in float32 in the case of jit-compilation, whereas without fusion it would cast to bfloat16 between the ops and produce a less precise answer. Still, both are correct. 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?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62253">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

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.Exp+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
i can replicate this issue on 
colab: https://colab.research.google.com/drive/1otYLopKrwLbSNl5iwNrk05ofy59WLYP3?usp=sharing
```


### Relevant log output

```shell
File "/home/guihuan/LLM/results/tf-2/2023-10-22-20-21/test.py", line 53, 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, 9) dtype=float64) = '
0.0, 0.0, 0.0, ...
b'y (shape=(10, 9) dtype=float64) = '
-0.0, -0.0, 100.0, ...
```
