multiple inputs pytorch model convert to coreml

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model have four images inputs, like [3, 224, 224], [3, 224, 224], [3, 224, 224], [3, 16, 16]
input_types = [ct.ImageType(name="F", shape=(224, 224, 3), bias=image_bias, scale=image_scale),
ct.ImageType(name="L", shape=(224, 224, 3), bias=image_bias, scale=image_scale),
ct.ImageType(name="R", shape=(224, 224, 3), bias=image_bias, scale=image_scale),
ct.ImageType(name="K", shape=(16, 16, 1), bias=image_bias, scale=image_scale)]

model is saved by torch.jit.save named as X, and load by torch.jit.load

when i use model = ct.convert(torch_model, inputs=input_types) get error:
ValueError: input_shape (length 0), kernel_shape (length 2), strides (length 2), dilations (length 2), and custom_pad (length 4) divided by two must all be the same length

Start by reproducing the failure at ct.convert(torch_model, inputs=input_types) with the four ct.ImageType inputs, using torch.jit.load on the model saved by torch.jit.save. Inspect the conversion traceback and input definitions; done means the failure is resolved or the supported input configuration and limitation are documented.

Written by the indexing model from the issue text.