### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

tf2.17

### Custom code

Yes

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Version
TensorFlow Version: 2.x (based on error patterns)
CUDA Environment: GPU-enabled system
Error Type: CUDA resource handle corruption
Python Version: 3.x
Current Behavior
Error Description
The application crashes with a core dump when executing 3D convolution operations with large tensor dimensions. The failure occurs during GPU-to-CPU memory copy operation with invalid resource handles.
Error Log tf2.10
2025-11-21 11:07:08.127020: E tensorflow/stream_executor/stream.cc:320] Error recording event in stream: Error recording CUDA event: CUDA_ERROR_INVALID_HANDLE: invalid resource handle; not marking stream as bad, as the Event object may be at fault. Monitor for further errors.
2025-11-21 11:07:08.127072: F tensorflow/core/common_runtime/gpu/gpu_util.cc:303] GPU->CPU Memcpy failed
Aborted (core dumped)

### tf2.17
RuntimeError: Bad StatusOr access: INTERNAL: failed initializing StreamExecutor for CUDA device ordinal 0: INTERNAL: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 51041271808

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
input_data = tf.random.uniform([10, 10, 
    10, 10, 10], minval=0,
    maxval=1, dtype=tf.float32)
filters_data = tf.random.uniform([3, 3, 3, 3, 5], minval=0, maxval=1, dtype
    =tf.float32)
strides_data = [1, 2, 2, 2, 1]
padding_data = 'SAME'
data_format_data = 'NDHWC'
dilations_data = [1, 1, 1, 1, 1]
name_data = 'conv3d_op'
output = tf.nn.conv3d(input=input_data, filters=filters_data, strides=
    strides_data, padding=padding_data, data_format=data_format_data,
    dilations=dilations_data, name=name_data)
```

### Relevant log output

```shell

```