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

I see it fails in both **jit_compile = True** and **jit_compile = False** on GPU, since it invloves random values as an input, results are not guaranteed to match in both the scenarios.
Here is the attached [Gist](https://colab.research.google.com/gist/tilakrayal/616308fba879567a81fe3e4ca91ec259/untitled.ipynb) for reference. 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. Hi @tilakrayal , please feel free to close this issue. thanks for your information. As mentioned above, closing the issue as it has been resolved. Thank you! 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/62279">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.Cos+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 **GPU** device.
The problem occurs when input Tensors pass through raw_ops.Cos and raw_ops.Square. With individual Ops there is no issue.

### 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.Cos(x=x, )        
      x = tf.raw_ops.Square(x=x, )        
      return x

m = Network()
inp = {
    "x": tf.random.normal([10, 9], dtype=tf.bfloat16),
    }

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 27, 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) = '
0.1396484375, 0.66015625, 0.064453125, ...
b'y (shape=(10, 9) dtype=float64) = '
0.1396484375, 0.65625, 0.06494140625, ...
```
