feat: add VisdomXGBLogger with autolog for xgboost training

[Jayantparashar10]

Description

Adds VisdomXGBLogger, an XGBoost TrainingCallback that plots train/eval metrics to Visdom as boosting progresses, with an autolog() classmethod that patches xgb.train and every XGBModel subclass fit() for zero-config logging.

Motivation and Context

There was no way to visualize XGBoost training runs in Visdom without manually attaching a TrainingCallback and wiring up viz.line() calls yourself. This adds opt-in auto-logging behind a single autolog() call, with no changes required to model, train(), or fit() code

How Has This Been Tested?

Ran example/train_xgboost_example.py covering both the functional xgb.train and sklearn XGBClassifier.fit APIs, xgb.cv tuple-metric case, and repeated autolog() calls, against visdom.server instance.

Screenshots (if appropriate):

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Code refactor or cleanup (changes to existing code for improved readability or performance)

Checklist:

• I adapted the version number under py/visdom/VERSION according to Semantic Versioning
• My code follows the code style of this project.
• My change requires a change to the documentation.
• I have updated the documentation accordingly.

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[tonypzy]

How do you think of adding XGBoost unit tests?

[Jayantparashar10]

How do you think of adding XGBoost unit tests?

Will make a separate PR for addition of unit test for each loggers.