Inconsistency in XLA Compiled Model Output for `tf.nn.conv2d` Combined with `tf.tan` on GPU
This is another code that trigger the same bug : 
```python
from typing import Dict
import tensorflow as tf
import pickle
import os
import numpy as np

###updated at 23.12.06
p0 = tf.random.uniform(shape=[6, 21, 59, 6], dtype=tf.float32)
p1 = tf.random.uniform(shape=[1, 54, 6, 6], dtype=tf.float32)

class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.p0 = p0
        self.p1 = p1
###updated at 23.12.06
    @tf.function(jit_compile=True)
    def __call__(self, inp):
        conv2 = tf.nn.conv2d(self.p0, inp, strides=1, padding="SAME", dilations=(3, 3))
        _tan = tf.tan(conv2)
        return conv2, _tan

inputs = [
tf.random.uniform(shape=[18, 54, 6, 6], dtype=tf.float32),
]
model1 = Model1()
device = "gpu"
with tf.device(device):
    tf.config.run_functions_eagerly(True)
    out1 = model1(*inputs)
    out2 = model1(*inputs)
    print(f'=========eager_output(version:{tf.__version__})================')
    try :
        for i in range(min(len(out1),len(out2))):
            np.testing.assert_allclose(out1[i].numpy(), out2[i].numpy(), rtol=0.001, atol=0.001, err_msg=f'at checking {i}th')
        print("XLA_eager does not trigger assertion")
    except AssertionError as e:
        print("XLA_eager triggers assertion")
        print(e)
    tf.config.run_functions_eagerly(False)
    out1 = model1(*inputs)
    out2 = model1(*inputs)
    print(f'=========compiled_output(version:{tf.__version__})================')
    try :
        for i in range(min(len(out1),len(out2))):
            np.testing.assert_allclose(out1[i].numpy(), out2[i].numpy(), rtol=0.001, atol=0.001, err_msg=f'at checking {i}th')
        print("XLA_complie does not trigger assertion")
    except AssertionError as e:
        print("XLA_complie triggers assertion")
        print(e)
```
and the log output 
```
=========compiled_output(version:2.15.0)================
XLA_complie triggers assertion

Not equal to tolerance rtol=0.001, atol=0.001
at checking 1th
Mismatched elements: 89 / 44604 (0.2%)
Max absolute difference: 634.3164
Max relative difference: 0.1164573
 x: array([[[[-5.409578e-01, -2.587222e-01,  1.120472e+00,  1.204898e-01,
          -5.236940e+00, -7.960033e-01],
         [-6.590452e-02,  1.740580e+00,  1.766167e+00,  1.575795e+00,...
 y: array([[[[-5.409578e-01, -2.587222e-01,  1.120472e+00,  1.204898e-01,
          -5.236940e+00, -7.960033e-01],
         [-6.590452e-02,  1.740580e+00,  1.766167e+00,  1.575795e+00,...
``` @sachinprasadhs I was able to replicate this issue on colab, please find the gist [here](https://colab.research.google.com/gist/sushreebarsa/b21c94c48966c50c740215442c8cce0f/62485.ipynb). Thank you! Hi,

Since the code you have mentioned has random number generation, the outputs can not be guaranteed for each run.
I have tried by setting seed and enabling op determinism and still produces random results.

```
tf.random.set_seed(42)
tf.config.experimental.enable_op_determinism() 
``` Hi, @sachinprasadhs 
Thank you for pointing out the error in my code in **comment**. I'll update my comment accordingly. Additionally, there still exist bugs related to misbehavior in the XLA compiled model output when using tf.nn.conv2d combined with tf.tan on a GPU. 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/62485">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/62485">No</a>

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.15.0

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04.3 LTS

### Mobile device

_No response_

### Python version

3.10.0

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

cuda : 12.2 / cudnn 8.9.04

### GPU model and memory

Tesla V100S-PCIE-32GB

### Current behavior?

In TensorFlow 2.15.0, a inconsistency arises when using `tf.nn.conv2d` followed by `tf.tan` in an XLA compiled model. The output differs significantly when comparing the XLA compiled execution to the eager mode execution.


### Standalone code to reproduce the issue

```shell
from typing import Dict
import tensorflow as tf
import pickle
import os
import numpy as np

class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()
    @tf.function(jit_compile=True)
    def __call__(self, inp1, inp2):
        # inp1: [1, 8147, 1, 1] : float32
        # inp2: [1, 1, 8160, 1] : float32
        nnconv = tf.nn.conv2d(inp2, inp1, strides=1, padding="VALID", dilations=(2, 1))
        nnconvtan = tf.tan(nnconv)
        return nnconv, nnconvtan


inputs = [
tf.random.uniform(shape=[1, 8147, 1, 1], dtype=tf.float32),
tf.random.uniform(shape=[1, 1, 8160, 1], dtype=tf.float32),
]
model1 = Model1()
device = "gpu"
with tf.device(device):
    tf.config.run_functions_eagerly(True)
    out1 = model1(*inputs)
    out2 = model1(*inputs)
    print(f'=========eager_output(version:{tf.__version__})================')
    try :
        for i in range(min(len(out1),len(out2))):
            np.testing.assert_allclose(out1[i].numpy(), out2[i].numpy(), rtol=0.001, atol=0, err_msg=f'at checking {i}th')
        print("XLA_eager does not trigger assertion")
    except AssertionError as e:
        print("XLA_eager triggers assertion")
        print(e)
    tf.config.run_functions_eagerly(False)
    out1 = model1(*inputs)
    out2 = model1(*inputs)
    print(f'=========compiled_output(version:{tf.__version__})================')
    try :
        for i in range(min(len(out1),len(out2))):
            np.testing.assert_allclose(out1[i].numpy(), out2[i].numpy(), rtol=0.001, atol=0, err_msg=f'at checking {i}th')
        print("XLA_complie does not trigger assertion")
    except AssertionError as e:
        print("XLA_complie triggers assertion")
        print(e)
```


### Relevant log output

```shell
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-11-27 16:13:45.155776: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-11-27 16:13:51.062493: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 30946 MB memory:  -> device: 0, name: Tesla V100S-PCIE-32GB, pci bus id: 0000:01:00.0, compute capability: 7.0
2023-11-27 16:13:52.134495: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904
=========eager_output(version:2.15.0)================
XLA_eager does not trigger assertion
2023-11-27 16:13:54.695301: I external/local_xla/xla/service/service.cc:168] XLA service 0x561d3b24cf00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-11-27 16:13:54.695336: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): Tesla V100S-PCIE-32GB, Compute Capability 7.0
2023-11-27 16:13:54.695345: I external/local_xla/xla/service/service.cc:176]   StreamExecutor 
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1701101635.181623  741835 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
=========compiled_output(version:2.15.0)================
XLA_complie triggers assertion

Not equal to tolerance rtol=0.001, atol=0
at checking 1th
Mismatched elements: 2 / 14 (14.3%)
Max absolute difference: 0.0102663
Max relative difference: 0.00553954
 x: array([[[[-3.548327],
         [ 0.044403],
         [-2.706214],...
 y: array([[[[-3.548327],
         [ 0.044158],
         [-2.706214],...
```
