feat(logger): add SwanLabLogger for enhanced logging capabilities

[relic-yuexi]

What does this PR do?

This PR adds a new SwanLabLogger to PyTorch Lightning, providing integration with SwanLab (https://swanlab.cn) for enhanced experiment tracking and logging capabilities.

Key Features:

• Metrics Logging: Automatically log training/validation/test metrics during training
• Hyperparameter Tracking: Save model hyperparameters and configuration
• Media Logging Support: Log images, text, audio, and video files
• Model Checkpoint Logging: Optional automatic logging of model checkpoints
• Flexible Modes: Support for cloud, local, offline, and disabled modes
• Experiment Management: Create and manage SwanLab experiments with custom project names and descriptions

Implementation Details:

• Created src/lightning/pytorch/loggers/swanlab.py with full SwanLabLogger implementation
• Updated src/lightning/pytorch/loggers/__init__.py to export SwanLabLogger
• Follows existing Lightning logger patterns and conventions
• Compatible with swanlab>=0.3.0

Dependencies:

• Requires swanlab>=0.3.0 package (optional dependency)

Usage Example:

from lightning.pytorch.loggers import SwanLabLogger
from lightning.pytorch import Trainer

# Create logger
swanlab_logger = SwanLabLogger(
    project="my_project",
    experiment_name="experiment_1",
    description="My experiment description"
)

# Use with Trainer
trainer = Trainer(logger=swanlab_logger)

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📚 Documentation preview 📚: https://pytorch-lightning--21506.org.readthedocs.build/en/21506/