I have a set of data which I’ve used to generate an image, which I then iterate through convolution layers and then ultimately flatten the features into one layer so it becomes a size: [1, 11552].
I now want to connect that layer to be included in the training for each sample in a batch of say [4000, 310].  I know that if I copied that layer into a size [4000, 11552] I could then use torch.cat() to combine the features, however that seems needlessly wasteful (Though I may be missing something important).
Is there a way to connect that layer to each sample from the larger batch without having to duplicate the data?  So that during training I would effectively be training a batch of size [4000, 11862]?