Add ViLT (dandelin/vilt-b32-finetuned-vqa) visual-question-answering support

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Summary

Adds first-class support for ViLT under the visual-question-answering task, validated on dandelin/vilt-b32-finetuned-vqa.

ViLT has no vendor optimum coverage, and its stock ViltEmbeddings.visual_embed is fundamentally not ONNX-traceable (Python iteration over tensor shapes, torch.multinomial, per-row nonzero() loops). Eager works because the loops resolve concretely; tracing fails. This PR therefore ships:

1. A from-scratch ViltVqaOnnxConfig(OnnxConfig) registered via @register_onnx_overwrite("vilt", "visual-question-answering").
2. A _ViltVisualEmbedPatcher(ModelPatcher) that swaps visual_embed for a static-shape replacement using nn.functional.interpolate(spatial_pos, size=(H, W), mode='bilinear', align_corners=True) and a synthesized all-ones token mask.
3. Pinned static H/W on pixel_values; pixel_mask is intentionally omitted from the export signature since the patched path doesn't read it (leaving it in would create a dead graph input).
4. Recipe + README row + model class mapping wired into models/hf/__init__.py.

Files changed

File	Kind
src/winml/modelkit/models/hf/vilt.py	NEW (190 LOC)
src/winml/modelkit/models/hf/__init__.py	+3 (wiring)
examples/recipes/dandelin_vilt-b32-finetuned-vqa/visual-question-answering_config.json	NEW
examples/recipes/README.md	+1 row

Validation (dandelin/vilt-b32-finetuned-vqa @ CPU fp32)

Gate	Result
L0 build	✅ Build complete in 62.9s (Export 29.8s, Optimize 32.2s); 449.2 MB optimized ONNX
L1 perf	✅ mean=67.49 ms, p50=65.83 ms, p90=76.52 ms, throughput=14.82 samples/sec, std=5.92 ms (20 iters, warmup 3)
L2 numerics (PT vs ORT)	✅ cos=1.000000, max_abs_diff=4.2e-5, top class match (3129-way head)
Patched-vs-original PT parity	✅ cos=1.000000, max_abs_diff=1.2e-5, same argmax
L3 dataset eval	⏭ skipped (no default VQA dataset wired)

Notes for reviewers

• Inputs declared: input_ids, attention_mask, token_type_ids (dynamic batch_size/sequence_length), pixel_values (only batch_size dynamic, H/W=384 static).
• Output: logits (3129-way), batch_size dynamic.
• Opset 17, fp32, CPU/auto resolution.
• Recipe value_range for mask-of-ones inputs must be [1, 2] not [0, 1] because randint high is exclusive — relevant if anyone re-derives this recipe.