does pluggable custom device support distributed runtime?
@Hoonly Pluggable custom devices in TensorFlow generally do not directly support distributed runtime on their own. Distributed runtime in TensorFlow refers to running computations across multiple machines or devices for tasks that are too large for a single machine. Could you please refer to [this](https://www.tensorflow.org/api_docs/python/tf/distribute/Strategy) guide to explore TF distributed strategies?
Please upgrade to the latest TF version as the older ones are not actively supported.

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

Support

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

No

### Source

source

### TensorFlow version

tf 2.6.5

### Custom code

Yes

### OS platform and distribution

Ubuntu1804

### Mobile device

_No response_

### Python version

3.8

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

how to support distributed training with pluggable device

### Standalone code to reproduce the issue

```shell
None
```


### Relevant log output

_No response_