[pull] master from deepmodeling:master - #265

## Summary

Enable eight Ruff rules that have safe, automatic fixes in the
repository's pinned Ruff v0.15.18:

- `PIE808`, `PT022`, `RET502`, `SIM910`, `TD006`
- `PLR1711`, `PLR1733`, `PLR2044`

## Intentional follow-up

This PR deliberately changes only `pyproject.toml`; it does **not**
include the 128 existing code fixes. The configured pre-commit.ci
autofix should apply those fixes to this PR, which provides an
end-to-end confirmation that the automated repair is clean.

`COM812` is intentionally excluded because `ruff-format` is already
enabled and Ruff recommends against combining the formatter with this
trailing-comma rule. Rules whose fixes are classified as unsafe in Ruff
v0.15.18 are also excluded.

## Validation

- `git diff --check`
- `ruff v0.15.18 check --output-format json .` reports exactly 128
intentional diagnostics from the eight newly enabled rules; no `--fix`
was run.

Authored by OpenClaw (model: custom-chat-jinzhezeng-group/gpt-5.6-terra)


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **Bug Fixes**
- Improved attribute assignment handling to fully control
weight/bias-related updates during tracing.
- Ensured neighborhood-distance resolution is initialized from available
runtime/tensor sources and saved consistently.
- Improved test isolation by clearing leaked device contexts before
tests.

- **Refactor**
- Simplified option lookups (optional fields/seed/precision mappings)
and equivalent loop-bound expressions across models/kernels.

- **Chores**
- Expanded linting rules and removed stray placeholder
comments/formatting artifacts across docs and code.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

## What

- Add an `edge_norm` descriptor option to control normalization on
feature branches that vanish at the cutoff, with matching PyTorch and
DPModel implementations.
- When disabled, remove hidden radial-MLP RMSNorm, bypass
environment-seed FiLM and cross-focus competition norms, and use a unit
variance floor for post-SO(2) residual scaling.
- Serialize the new option and its nested module settings, expose it
through input validation, and enable the cutoff-smooth mode in the DPA4
water example.
- Add cutoff-smoothness, serialization, configuration-propagation, and
PyTorch/DPModel parity coverage.

## Why

DPA4 radial features carry a C3 cutoff envelope and should decay to zero
at `rcut`. Per-edge RMSNorm divides out that envelope until the feature
variance reaches the norm epsilon floor, which can introduce a narrow,
non-physical curvature and force feature just inside the cutoff. The
same small-signal amplification applies to other normalization sites
driven by cutoff-vanishing inputs.

The new mode preserves the cutoff envelope: FiLM modulation returns
smoothly to identity, cross-focus weights approach uniform, and
post-SO(2) residual messages remain linear near zero.

## Impact

- `edge_norm=True` remains the default, so existing configurations
retain their current behavior.
- Setting `edge_norm=False` opts into the recommended cutoff-smooth
behavior.
- PyTorch and DPModel serialization and numerical behavior remain
aligned in both modes.
- Normalization on persistent node states and FFN branches is unchanged.

## Checks

- Added a near-cutoff radial sweep regression that verifies the
curvature spike is suppressed when radial RMSNorm is disabled.
- Added structure and serialization round-trip coverage for both
normalization modes.
- Added block- and descriptor-level PyTorch/DPModel parity coverage for
standard and unit-floor post-SO(2) scaling.
- Tests were run separately by the author; they were not rerun in this
checkout per request.

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **New Features**
* Added `edge_norm` for DPA4 and SeZM descriptors to control
edge-dependent normalization behavior.
* Added `radial_norm` for radial embeddings and `focus_norm` for SO(2)
focus competition normalization.
  * Added `so2_post_norm_eps` to tune SO(2 post-normalization stability.
* Updated model save/restore so these normalization settings are
preserved.
* **Documentation / Examples**
  * Updated the water DPA4 example input to set `edge_norm` explicitly.
* **Tests**
* Added tests covering the new switches (including serialization) and
parity/consistency across implementations.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->