refactor(policies): clean MolmoAct2 to follow EO1/TOPReward patterns (#3724)

Align the MolmoAct2 implementation with lerobot codebase conventions:

- Rename hf_model/ to molmoact2_hf_model/
- Slim config: move all I/O and runtime logic to modeling
- Remove blanket  from 8 vendored files, fix 66 lint issues
- Deduplicate _hf_token() and _resolve_checkpoint_location()
- Make huggingface_hub imports lazy
- Remove custom MolmoAct2CosineDecayWithWarmupSchedulerConfig, use base class
- Extract 13 static/classmethods from MolmoAct2Policy to free functions
- Replace print() with logger in vendored action_tokenizer
- Add module docstrings, class docstring, and key method docstrings
- Add module-level loggers to modeling and processor
- Fix docs: pip to uv install, deduplicate README symlink
- Remove shebangs from all files

docs/source/molmoact2.mdx

@@ -17,7 +17,7 @@ the paper, see [allenai/molmoact2](https://github.com/allenai/molmoact2).
 Install LeRobot with the MolmoAct2 optional dependencies:
 
 ```bash
-pip install -e ".[molmoact2]"
+uv sync --locked --extra molmoact2
 ```
 
 To run the models in this repository, you need an NVIDIA GPU. The measurements
@@ -46,8 +46,8 @@ The repo has been tested with Ubuntu 22.04.
 
 To use MolmoAct2 in a LeRobot training config, set:
 
-```python
-policy.type=molmoact2
+```bash
+--policy.type=molmoact2
 ```
 
 ## Training