…ng (#790)

## Summary

timm checkpoints load through transformers'' generic `TimmWrapper`
(`model_type="timm_wrapper"`) and previously failed in **every** `winml`
command with *"Cannot detect task: config has no ''architectures''
field"*. Two gaps:

1. **Task/class detection** — timm repos load as `TimmWrapperConfig`
with `architectures=None`, so auto-detection could not resolve a task or
class.
2. **OnnxConfig location** — Optimum registers timm''s config
(`TimmDefaultOnnxConfig`) only under `library_name="timm"`, but every
`winml` lookup defaults to `transformers`.

`timm_wrapper` is transformers'' generic bridge for the whole timm
library — not a model architecture — so it is resolved at the **shared
resolution layer**, not as a per-model config. Only the library is
recorded; the task is derived from Optimum.

## Changes (no `models/hf/` entry)

- **`loader/task.py`** — `WRAPPED_LIBRARY_MODEL_TYPES` (`model_type ->
optimum_library`) + `resolve_optimum_library()`. When a config has no
`architectures`, `_detect_task_and_class_from_config` derives the task
from Optimum''s task list for the library
(`get_supported_tasks("timm_wrapper", "timm")` ->
`["image-classification"]`) and the class from
`get_model_class_for_task` (generic `AutoModelForImageClassification`,
which transformers dispatches to `TimmWrapper` at load). The task is not
hardcoded; the branch imports `optimum.exporters.onnx.model_configs`
first to populate Optimum''s registry (scoped so normal model loading
never pays for it).
- **`export/io.py`** — `_get_onnx_config` routes the library via
`resolve_optimum_library`, so `timm_wrapper` resolves Optimum''s
`TimmDefaultOnnxConfig` from every call site
(config/build/export/inspect) with no `--library` flag.
- **`commands/inspect.py`** + **`inspect/resolver.py`** — route both the
CLI inspect path and the public `inspect_model` path the same way:
library routing for the OnnxConfig lookup, plus wrapped-library task
detection so the task is not mislabeled.
- Tests: `resolve_optimum_library` + wrapped-library architectures
fallback with task derivation (loader); timm library routing for
`resolve_io_specs` / `_get_onnx_config` (export); public inspect path
`detect_task` / `resolve_exporter` for timm (inspect).

## Validation

**Functional (end-to-end)** on a timm image-classification model:

| Command | Before | After |
|---|---|---|
| `winml config` | exit 2 — *no ''architectures'' field* |
task=image-classification, 1 input |
| `winml export` | exit 2 — same | `model.onnx` (pixel_values to logits)
|
| `winml inspect` | exit 1 — same | `AutoModelForImageClassification` +
`TimmDefaultOnnxConfig`, full I/O table |

`config` -> `export` -> `optimize` -> `model.onnx` validated end-to-end
for multiple timm CNN classifiers. Also resolves on a timm ViT backbone
(`num_labels=0`) -> task=image-classification, matching Optimum''s own
`infer_task_from_model`, so it generalizes across timm architectures
(CNN + ViT).

**No impact on existing models** — scanned all 439 entries / 401 unique
models in `scripts/e2e_eval/testsets/models_all.json`: **0** are
`timm_wrapper` (by JSON metadata and by loaded config; 330 loadable).
Since `timm_wrapper` is the only trigger of the new branch, no existing
model changes behavior. (71 fail to load a config — custom/GGUF/tabular
types that fail at `AutoConfig` regardless; 7 have empty `architectures`
but are not timm — a pre-existing "Cannot detect task", identical before
and after the PR.)

**No overhead for normal (non-timm) models** — `winml config` on a
standard non-timm model: this branch vs base, min ~12.6s vs ~12.5s
(within run-to-run noise). Non-timm configs have `architectures`, so
they skip the new branch; the only added cost is one dict lookup.

**Unit tests** — `tests/unit/loader` + `tests/unit/export` +
`tests/unit/inspect`: green.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Yi Ren <reny@microsoft.com>