tensorflow.tf concurrency issue
The error you're encountering is an InvalidArgumentError that originates from TensorFlow. This error typically occurs when TensorFlow operations are performed on tensors with incompatible shapes. In your case, you are attempting to concatenate tensors using tf.concat, and TensorFlow has detected that the shapes of the input tensors are not compatible for concatenation. > The error you're encountering is an InvalidArgumentError that originates from TensorFlow. This error typically occurs when TensorFlow operations are performed on tensors with incompatible shapes. In your case, you are attempting to concatenate tensors using tf.concat, and TensorFlow has detected that the shapes of the input tensors are not compatible for concatenation.

Yes, I understand. 
**The question is why the incompatible shapes error only happens in multi-thread environment?** It looks like one thread just set first array's shape and another thread set second array's shape in tf.concat that causes the incompatible shape error. Any idea? @bugzyz This issue seems to be fixed in the latest TF version 2.13, please find the gist [here](https://colab.research.google.com/gist/sushreebarsa/bf6f7a9bc929f0e4933d5e64ab14a282/61889.ipynb) and confirm the same?
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?
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### Issue type

Bug

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

No

### Source

source

### TensorFlow version

unknown 2.11.0 (from nvcr.io/nvidia/tensorflow:23.03-tf2-py3))

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04.6 LTS

### Mobile device

_No response_

### Python version

3.8.10

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

cuda_12.1.r12.1/compiler.32415258_0

### GPU model and memory

_No response_

### Current behavior?

We found that running tensorflow.concat operation will have concurrency issue only in the Nvidia tensorflow image. We also tried with other image but the issue cannot reproduce.

We expect that tensorflow.concat work fine for multi-thread environment. But it's not. Is it expected? 
FYI, adding a lock for concat can avoid such issue. What's the best practice?

Our environment:
GPU: A100
image: nvcr.io/nvidia/tensorflow:23.03-tf2-py3
driver: NVIDIA-SMI 525.125.06   Driver Version: 525.125.06   CUDA Version: 12.0



### Standalone code to reproduce the issue

```shell
import tensorflow as tf
from concurrent.futures import ThreadPoolExecutor, as_completed

client_num = 10
repeat = 10000
executor = ThreadPoolExecutor(max_workers=client_num)
future_2_input_shapes = {}

# import threading
# lock = threading.Lock()

test_cases = [
    [[1, 2] * 31, [2, 3]],
    [[1, 2, 8, 90] * 31, [2, 3, 3, 1]],
    [[[7, 4, 3], [8, 4, 3]], [[2, 10, 3], [15, 11, 3]] * 63],
]


def do_concat(t1, t2):
    # with lock:
    #     return tf.concat([t1, t2], 0)
    return tf.concat([t1, t2], 0)


print("creating task")
for _ in range(repeat):
    for test_case in test_cases:
        a = tf.constant(test_case[0])
        b = tf.constant(test_case[1])
        future = executor.submit(do_concat, a, b)
        future_2_input_shapes[future] = a.shape, b.shape

print("waiting task")
count = 0
for future in as_completed(future_2_input_shapes.keys()):
    print(f"{count}: {future_2_input_shapes[future]}")
    data = future.result()
    count = count + 1




```
```


### Relevant log output

```shell
...
17086: (TensorShape([62]), TensorShape([2]))
17087: (TensorShape([2, 3]), TensorShape([126, 3]))
17088: (TensorShape([124]), TensorShape([4]))
17089: (TensorShape([124]), TensorShape([4]))
17090: (TensorShape([2, 3]), TensorShape([126, 3]))
17091: (TensorShape([124]), TensorShape([4]))
17092: (TensorShape([2, 3]), TensorShape([126, 3]))
Traceback (most recent call last):
  File "x.py", line 37, in <module>
    data = future.result()
  File "/usr/lib/python3.8/concurrent/futures/_base.py", line 437, in result
    return self.__get_result()
  File "/usr/lib/python3.8/concurrent/futures/_base.py", line 389, in __get_result
    raise self._exception
  File "/usr/lib/python3.8/concurrent/futures/thread.py", line 57, in run
    result = self.fn(*self.args, **self.kwargs)
  File "x.py", line 22, in do_concat
    return tf.concat([t1, t2], 0)
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 7215, in raise_from_not_ok_status
    raise core._status_to_exception(e) from None  # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__ConcatV2_N_2_device_/job:localhost/replica:0/task:0/device:GPU:0}} ConcatOp : Ranks of all input tensors should match: shape[0] = [124] vs. shape[1] = [126,3] [Op:ConcatV2] name: concat
```
