review: address CarolinePascal feedback

- name the three modules everywhere (plan / interjections / vqa) instead
  of module_1/2/3 — config classes, config fields, executor params,
  staging keys and phase names now carry the module name
- rename examples/annotation -> examples/annotations; add the Apache
  header to run_hf_job.py
- drop the unused GeneralVqaModule._generate_one
- remove "PR 1" references from comments/docstrings
- frames.py: rely on the always-defined LeRobotDatasetMetadata.camera_keys
- executor.py: read/write meta/info.json via load_info / write_info
- reader.py: load meta/tasks.parquet via io_utils.load_tasks
- make --push_to_hub a bool; push the annotated dataset back to --repo_id
- move the on-disk test dataset builder into tests/fixtures
  (build_annotation_dataset); run_e2e_smoke reuses it
- clarify in the docs that the vqa module grounds each pair on a single
  frame (K = per-tick anchor count)
- hoist stdlib dynamic imports to module scope

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

pyproject.toml

@@ -205,7 +205,7 @@ peft = ["lerobot[transformers-dep]", "lerobot[peft-dep]"]
 # on Linux, with a transformers fallback elsewhere; openai is the default
 # backend and talks to any OpenAI-compatible server (``vllm serve`` /
 # ``transformers serve`` / hosted endpoints). Distributed execution is
-# delegated to Hugging Face Jobs (see examples/annotation/run_hf_job.py).
+# delegated to Hugging Face Jobs (see examples/annotations/run_hf_job.py).
 annotations = [
     "lerobot[dataset]",
     "lerobot[transformers-dep]",