Different reference order may cause other modules to be unavailable, e.g. xgboost, sklearn.
@h2222,
The error occurred because you are trying to run the **32-bit Python on a 64-bit OS** which was incompatible. When I tried to execute the mentioned code with the compatible on the latest tensorflow `v2.13`, it was executed without any issues.

Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/cbdf08a73f6d4ca7b249d89dfc287e3a/untitled1237.ipynb) and please try with the right infra which helps to execute the code smoothly. Thank you! 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/61240">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/61240">No</a>

### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.11.0

### Custom code

Yes

### OS platform and distribution

centos 7.6

### Mobile device

_No response_

### Python version

python 3.10.11

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

cuda=12.1

### GPU model and memory

32510MiB

### Current behavior?

I was trying to import xgboost(or sklearn) and tensorflow modules at same time, but when I  imported modules by different order, it just return me error message that I can not handle it, I don't whether it a bug or some issues that can be fixed by myself?  and I also search something resource, which said that it was a bug caused by glibc : https://sourceware.org/bugzilla/show_bug.cgi?id=17090. and then I was trying to reintstall glibc on my server, unfortunately, the plan finally failed and now I am just trying to rebuild my whole environment by rollbacking to previous mirror backup, sad.

### Standalone code to reproduce the issue

```shell
import numpy as np

## bad import order
import tensorflow as tf
from xgboost import XGBClassifier ## order # or import sklearn, may report different error messages.

## good import order
# from xgboost import XGBClassifier
# import tensorflow as tf

hparams = {
    'booster':'gbtree',
    'objective': 'binary:logistic',
    'eval_metric': 'aucpr',
    'max_depth': 10,
    'gamma': 4,
    'lambda':0.001,
    'subsample':0.7,
    'colsample_bytree':0.8,
    'colsample_bylevel':0.8,
    'colsample_bynode': 0.8,
    'min_child_weight':20,
    'eta': 0.03,
    'seed': 42,
    'nthread':15,
    'tree_method':'gpu_hist',
    'n_estimators': 350
}
estimator = XGBClassifier(**hparams)
X_train = np.random.rand(10000, 10)
y_train = np.random.randint(0, 2, (10000, 1))
X_eval = np.random.rand(1000, 10)
y_eval = np.random.randint(0, 2, (1000, 1))

estimator.fit(X_train, y_train, eval_set=[(X_train, y_train),(X_eval, y_eval)])
```


### Relevant log output

```shell
2023-07-11 15:57:01.594620: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Traceback (most recent call last):
  File "/home/haojiaxiang/projects/test/test.py", line 6, in <module>
    from xgboost import XGBClassifier ## order # or import sklearn, may report different error messages.
  File "/home/haojiaxiang/miniconda3/envs/gms/lib/python3.10/site-packages/xgboost/__init__.py", line 7, in <module>
    from . import collective, dask, rabit
  File "/home/haojiaxiang/miniconda3/envs/gms/lib/python3.10/site-packages/xgboost/collective.py", line 12, in <module>
    from .core import _LIB, _check_call, c_str, py_str, from_pystr_to_cstr
  File "/home/haojiaxiang/miniconda3/envs/gms/lib/python3.10/site-packages/xgboost/core.py", line 264, in <module>
    _LIB = _load_lib()
  File "/home/haojiaxiang/miniconda3/envs/gms/lib/python3.10/site-packages/xgboost/core.py", line 216, in _load_lib
    raise XGBoostError(
xgboost.core.XGBoostError: 
XGBoost Library (libxgboost.so) could not be loaded.
Likely causes:
  * OpenMP runtime is not installed
    - vcomp140.dll or libgomp-1.dll for Windows
    - libomp.dylib for Mac OSX
    - libgomp.so for Linux and other UNIX-like OSes
    Mac OSX users: Run `brew install libomp` to install OpenMP runtime.

  * You are running 32-bit Python on a 64-bit OS

Error message(s): ['dlopen: cannot load any more object with static TLS']
```
