Inconsistent Outputs from `tf.nn.conv2d_transpose` on GPU
@GwiHwan-Go,
I was able to replicate the reported behaviour with TF v2.13, TF v2.14 and tf-nightly..
Currently we are investigating the issue & will deep dive into the issue and provide the root-cause for the same. Thank you! > @GwiHwan-Go, I was able to replicate the reported behaviour with TF v2.13, TF v2.14 and tf-nightly.. Currently we are investigating the issue & will deep dive into the issue and provide the root-cause for the same. Thank you!

Thank you for your response! Please don't hesitate to request any additional information if needed.
### 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

### 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?

I've encountered an issue where `tf.nn.conv2d_transpose` produces inconsistent outputs on a GPU device across different runs, despite using the same input and operator. This inconsistency is seen **both** on xla compiled mode and eager mode. 


### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import numpy as np

class Model1(tf.keras.Model):
    def __init__(self):
        super().__init__()

    @tf.function(jit_compile=True)
    def __call__(self, inp):
        testout = tf.nn.conv2d_transpose(inp, inp, output_shape=[26, 57, 32, 1], strides=1, padding="VALID")
        return testout

inputs = [
tf.random.uniform(shape=[26, 32, 1, 9], dtype=tf.float16),
]
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__})================')
    print(tf.sysconfig.get_build_info()["cuda_version"])
    print(tf.sysconfig.get_build_info()["cudnn_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)
```


### Relevant log output

```shell
=========eager_output(version:2.15.0)================
12.2
8
XLA_eager triggers assertion

Not equal to tolerance rtol=0.001, atol=0.001
at checking 0th
Mismatched elements: 178 / 1824 (9.76%)
Max absolute difference: 0.125
Max relative difference: 0.002499
 x: array([[[3.156],
        [2.463],
        [1.905],...
 y: array([[[3.156],
        [2.463],
        [1.905],...
2023-11-26 16:06:46.480802: I external/local_xla/xla/service/service.cc:168] XLA service 0x557fce97e690 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-11-26 16:06:46.480861: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): Tesla V100S-PCIE-32GB, Compute Capability 7.0
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1701014806.982871   61794 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=0.001
at checking 0th
Mismatched elements: 133 / 1824 (7.29%)
Max absolute difference: 0.125
Max relative difference: 0.002611
 x: array([[[3.156],
        [2.463],
        [1.905],...
 y: array([[[3.156],
        [2.463],
        [1.905],...
```
