Different Behavior of tf.raw_ops.SquareDifference+tf.raw_ops.Cos with jit_compile=True
@sachinprasadhs,
I was able to reproduce the issue on tensorflow v2.14 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/12239f809d8cd60d8810b1fc5c529ef0/untitled1489.ipynb). I observe similar result in both jit_compile = True and jit_compile = False, since when compiling with JIT takes different code path and involves fusion operation and casting dtype to specific dtype, based on all these conditions, you can't expect the same result with JIT and without JIT.
Also when you are involving random number in the operation, it will give different result for each run.
Attaching the Gist [here](https://colab.sandbox.google.com/gist/sachinprasadhs/a67c9e106a49ae29ece79a0359c416e4/tf-raw_ops-squaredifference-tf-raw_ops-cos.ipynb) for comparison. Thanks! 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/62266">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62266">No</a>

### 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.SquareDifference+tf.raw_ops.Cos** 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

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

    @tf.function(jit_compile=True)
    def __call__(self, x):
      real_part = tf.random.normal([1], dtype=tf.float32)
      imag_part = tf.random.normal([1], dtype=tf.float32)
      tensor = tf.complex(real_part, imag_part)
      tensor = tf.cast(tensor,dtype=tf.complex64)    
      x = tf.raw_ops.SquaredDifference(y=x, x=tensor)        
      x = tf.raw_ops.Cos(x=x, )        
      return x

m = Network()
real_part = tf.random.normal([8, 2], dtype=tf.float32)
imag_part = tf.random.normal([8, 2], dtype=tf.float32)
tensor = tf.complex(real_part, imag_part)
tensor = tf.cast(tensor,dtype=tf.complex64)   
inp = {
    "x": tensor,
    }

with tf.device('/CPU:0'):
    tf.config.run_functions_eagerly(True)
    no_op_res = m(**inp)
    tf.config.run_functions_eagerly(False)
    with tf.device('/CPU: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 34, 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=(8, 2) dtype=float64) = '
-0.9023030400276184, -0.8812462687492371, -0.6324461102485657, ...
b'y (shape=(8, 2) dtype=float64) = '
-0.8898392915725708, 0.49911314249038696, -0.15750333666801453, ...
(night) guihuan@heyuan2:~$
```
