Decouple heavy plugins from core to optimize for serverless deployment

[google-labs-jules]

Context & Rationale

The primary goal of this PR is to transform the eegprep core package into a lightweight orchestrator. Currently, mandatory dependencies on machine learning frameworks like PyTorch and CUDA-related libraries result in multi-gigabyte installation sizes. This overhead creates significant friction for serverless deployments, causing slow cold starts and exceeding resource limits for headless scaling.

By moving ICLabel and ASR (clean_rawdata) into independent distribution units, we significantly reduce the default footprint while maintaining a modular architecture that allows researchers to opt-in to heavy dependencies as needed.

Key Changes

1. Dependency & Package Refactoring

• Removed heavy dependencies: Excised torch from the core optional-dependencies to prevent automatic downloads of ML frameworks during a standard install.
• Plugin Decoupling: Deleted src/eegprep/plugins/ICLabel and src/eegprep/plugins/clean_rawdata. These are now intended to be hosted in separate repositories.
• Parity Maintenance: Migrated essential shared utilities (e.g., rand_permutation in runica.py) to the core module to ensure base functionality remains intact without the plugins.

2. Plugin Discovery & Runtime Loading

• Entry Point Integration: Implemented standardized entry point discovery. The core library now scans the environment at runtime to register externally installed plugins.
• Lazy Loading: Updated menu_actions.py and cli/commands/transforms.py to load plugin-specific logic only when requested, ensuring the headless CLI boot time remains sub-second.

3. Improved User Experience

• Graceful Degradation: Updated __init__.py with a custom __getattr__ implementation. If a user attempts to call legacy functions like pop_iclabel without the extension installed, they receive a descriptive RuntimeError with specific installation instructions:

  pip install eegprep-iclabel

• "All-in-one" Support: Maintained the [all] install extra in pyproject.toml to support the existing comprehensive installation workflow for local researchers.

Success Metrics

• Footprint Reduction: A clean installation of eegprep no longer triggers PyTorch or CUDA downloads.
• Performance: Headless agents initialize significantly faster on standard serverless platforms.
• Compatibility: Verified that the CLI successfully identifies and loads plugins when they are present in the environment via entry points.

Verification Results

• All test_extensions and test_plugin_menu suites passed.
• Verified that core scientific operations (MNE/SciPy dependent) function correctly in an environment devoid of the decoupled plugins.