[Code scan] Honor documented fparam/aparam shorthand in dpmodel DeepEval

This issue comes from a Codex global scan of `deepmodeling/deepmd-kit` at commit `73de44b1f94471b2e3bdb6b11f57b34d7bc791bb`.

## Problem

The dpmodel `DeepEval` backend documents broadcastable `fparam` and `aparam` input forms, but `_eval_model()` reshapes them directly to full multi-frame shapes.

The backend docstring says a single `dim_fparam` vector applies to all frames, and that `aparam` may be provided as `natoms x dim_aparam` or `dim_aparam` shorthand:

https://github.com/deepmodeling/deepmd-kit/blob/73de44b1f94471b2e3bdb6b11f57b34d7bc791bb/deepmd/dpmodel/infer/deep_eval.py#L209-L219

`_eval_model()` instead reshapes `fparam` directly to `(nframes, dim_fparam)`:

https://github.com/deepmodeling/deepmd-kit/blob/73de44b1f94471b2e3bdb6b11f57b34d7bc791bb/deepmd/dpmodel/infer/deep_eval.py#L349-L352

It also reshapes `aparam` directly to `(nframes, natoms, dim_aparam)`:

https://github.com/deepmodeling/deepmd-kit/blob/73de44b1f94471b2e3bdb6b11f57b34d7bc791bb/deepmd/dpmodel/infer/deep_eval.py#L353-L356

For `nframes > 1`, the documented shorthand arrays have too few elements for those reshapes and are rejected instead of being tiled.

The higher-level inference wrapper already implements this tiling contract:

https://github.com/deepmodeling/deepmd-kit/blob/73de44b1f94471b2e3bdb6b11f57b34d7bc791bb/deepmd/infer/deep_eval.py#L854-L875

## Impact

Users calling the dpmodel backend directly can pass parameter arrays that the API explicitly documents as valid, only to get reshape failures for multi-frame inference.

## Suggested fix

Normalize `fparam` and `aparam` in the dpmodel backend using the same size checks and tiling behavior as the higher-level inference wrapper. Add regressions for multi-frame dpmodel inference with `fparam.shape == (dim_fparam,)`, `aparam.shape == (natoms, dim_aparam)`, and `aparam.shape == (dim_aparam,)`.

Start in deepmd/dpmodel/infer/deep_eval.py at _eval_model() and compare its fparam/aparam handling with the tiling behavior in deepmd/infer/deep_eval.py. Add regression coverage for multi-frame inputs using the three documented shorthand shapes, and verify that dpmodel inference accepts them without reshape failures.

Written by the indexing model from the issue text.