docs(train): document stat_file, EMA shadow model, and LMDB datasets

Parent issue: #5917 (checklist items 10, 12, 16)

## Scope

Three training-side features registered in the schema (`deepmd/utils/argcheck.py`) but lacking prose:

1. **`stat_file` / `stat_file_mode`** (`2b3bb05a7`, `feat(tf): support training stat_file`; implementation `deepmd/tf/model/stat_file.py`; schema at `argcheck.py:5300-5313,5348-5357`): persist data statistics so later trainings skip recomputation. `stat_file` selects a `.h5`/`.hdf5` HDF5 file or (otherwise) a directory of NumPy binaries; `stat_file_mode` is PyTorch-only (`update` = create/write missing stats; `read` = require an existing complete cache, read-only, safe to share one HDF5 file across training processes). Nothing in `doc/train/` mentions them.
2. **EMA shadow model** (`57f870ff4`, PyTorch): `enable_ema`, `ema_decay`, `ema_ckpt_keep` (`argcheck.py:5264-5278,5425+`). Only a passing `ema_ckpt_keep` mention exists inside the `ckpt_keep_ratio` description at `doc/train/training-advanced.md:107`.
3. **LMDB training datasets** (`565f4beec` pt/dpmodel dataloader, `54f42d90c` pt_expt, `9d6381602` max/filter mode, `a3f548d7b` full-validation lmdb): `deepmd/dpmodel/utils/lmdb_data.py` (pure Python lmdb/msgpack reader), `deepmd/pt_expt/utils/lmdb_dataset.py`. Not documented as a training data format in `doc/data/` or `doc/train/` (only as a dpa-adapt conversion target at `doc/dpa_adapt/input_formats.md:105`).

## Implementation plan

### 1+2. `doc/train/training-advanced.md`

- Add a "Data statistics cache (`stat_file`)" subsection:
- Purpose (avoid re-running neighbor/statistics passes), `stat_file` path semantics (HDF5 vs npy directory by extension), backend support (TensorFlow and PyTorch — verify TF support from `deepmd/tf/model/stat_file.py`), and a minimal JSON example under the `training` section;
- `stat_file_mode` (`update` default / `read`), PyTorch-only, parallel-training use case.
- Add an "Exponential moving average (EMA) of parameters" subsection:
- `enable_ema` (PyTorch only), `ema_decay` (document default from argcheck), the `_ema`-suffixed periodic checkpoints, `ema_ckpt_keep` retention, interaction with `ckpt_keep_ratio`; how to consume an EMA checkpoint (verify whether freeze/test can take it directly from `deepmd/pt/entrypoints`).

### 3. LMDB datasets — `doc/data/system.md` (+ pointer from `doc/train/training.md`)

Read `deepmd/dpmodel/utils/lmdb_data.py`, `deepmd/pt_expt/utils/lmdb_dataset.py`, and how `training_data.systems` entries are dispatched (e.g. `deepmd/pt/data_utils/`, `deepmd/utils/data_system.py`) before writing.

- In `doc/data/system.md`, add LMDB to the supported data system formats:
- which backends support it (PyTorch, PyTorch-Exportable, DP — verify; note TF does not if that is the case);
- on-disk layout/metadata expectations as implemented (msgpack-serialized entries, metadata keys, type map storage — keep it concise and accurate);
- how to point `training_data.systems` / `validation_data.systems` at an LMDB directory (verify path detection logic — suffix or directory marker);
- how to produce LMDB datasets (link `doc/dpa_adapt/input_formats.md` conversion route).
- In `doc/train/training.md`, add one sentence near the DataSystem summary paragraph noting LMDB systems are also accepted on supported backends (cross-ref the data docs).

## Constraints

- Verify defaults and backend support from code (argcheck entries and dataloader dispatch) — several of these are backend-restricted; state restrictions explicitly.
- Keep JSON snippets minimal and valid (`check-json` runs in pre-commit only for repo files, not docs, but stay valid).

## Verification

- `grep -n "stat_file" doc/train/training-advanced.md` and `grep -n -i "ema" doc/train/training-advanced.md` show new subsections.
- `grep -n -i "lmdb" doc/data/system.md doc/train/training.md` shows the new format docs.

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Parent issue: #5917

Coding agent: opencode
opencode version: 1.18.9
Model: ustc/k3
Reasoning effort: max