@ptrblck Thank you for your reply. Just to clarify, for instance the densenet model has a couple of dense blocks, I believe that there are actually 4 of them. Given an input x I would like to get the output from denseblock1, denseblock2, …, denseblock4. Now as you said these blocks provide outputs of different shapes [B,C,H,W]. I would like to just fix the ‘C’ number of output channels for each of these blocks. Now why or where I would use such a thing. For instance I want to experiment with mask-rcnn and use the densenet as feature extractor but the mask-rcnn model expects 4 inputs a.k.a 4 tensors of shapes [B,C,H,W] and each of them requires a fixed number of channels otherwise I get a mismatch tensor error.