Multi-input/output ONNX model quantization and inference Hi everyone,I'm integrating a single-object tracking model with the Voyager SDK and hitting a wall with multi-input quantization.Onnx Model opset : 17 Inputs:- template: [1, 3, 112, 112]- online_template: [1, 3, 112, 112]- search: [1, 3, 224, 224]I have calibration data ready as .bin files for each input.The problem: compiler.quantize() doesn't accept multi-input calibration data.Things I've tried:- Yielding a dict: {'template': arr, 'online_template': arr, 'search': arr} → "Failed to get input shape from calibration dataset"- Yielding a list: [template, online_template, search] → "'list' object has no attribute 'shape'"- Yielding a tuple → same error- transform_fn returning a list → same errorI found the September 2025 post where it was mentioned multi-input isn't supported and the concat+identity conv workaround. However, my inputs have different spatial sizes (112x112 vs 224x224) which makes that workaround complex.Two questions:1. Has multi-input quantization been added in v1.5.3
