How to use Triton server "ensemble model" with 1:N input/output to create patches from large image?

I am trying to feed a very large image into Triton server. I need to divide the input image into patches and feed the patches one by one into a tensorflow model. The image has a variable size, so the number of patches N is variable for each call.

I think a Triton ensemble model that calls the following steps would do the job:

1. A python model (pre-process) to create the patches
2. The segmentation model
3. Finally another python model (post-process) to merge the output patches into a big output mask

However, for this, I would have to write a config. pbtxt file with 1:N and N:1 relation, meaning the ensemble scheduler needs to call the 2nd step multiple times and the 3rd once with the aggregated output.

Is this possible, or do I need to use some other technique?

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