Integrate SwanLab for offline/online experiment tracking and local visualization (#36433)

* add swanlab integration

* feat(integrate): add SwanLab as an optional experiment tracking tool in transformers

- Integrated SwanLab into the transformers library as an alternative for experiment tracking.
- Users can now log training metrics, hyperparameters, and other experiment details to SwanLab by setting `report_to="swanlab"` in the `TrainingArguments`.
- Added necessary dependencies and documentation for SwanLab integration.

* Fix the spelling error of SwanLabCallback in callback.md

* Apply suggestions from code review

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

* Fix typo in comment

* Fix typo in comment

* Fix typos and update comments

* fix annotation

* chore: opt some comments

---------

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: AAssets <20010618@qq.com>
Co-authored-by: ZeYi Lin <944270057@qq.com>
Co-authored-by: KAAANG <79990647+SAKURA-CAT@users.noreply.github.com>

docs/source/en/main_classes/callback.md

@@ -45,6 +45,7 @@ By default, `TrainingArguments.report_to` is set to `"all"`, so a [`Trainer`] wi
 - [`~integrations.DagsHubCallback`] if [dagshub](https://dagshub.com/) is installed.
 - [`~integrations.FlyteCallback`] if [flyte](https://flyte.org/) is installed.
 - [`~integrations.DVCLiveCallback`] if [dvclive](https://dvc.org/doc/dvclive) is installed.
+- [`~integrations.SwanLabCallback`] if [swanlab](http://swanlab.cn/) is installed.
 
 If a package is installed but you don't wish to use the accompanying integration, you can change `TrainingArguments.report_to` to a list of just those integrations you want to use (e.g. `["azure_ml", "wandb"]`). 
 
@@ -92,6 +93,9 @@ Here is the list of the available [`TrainerCallback`] in the library:
 [[autodoc]] integrations.DVCLiveCallback
     - setup
 
+[[autodoc]] integrations.SwanLabCallback
+    - setup
+
 ## TrainerCallback
 
 [[autodoc]] TrainerCallback