Fix shapes in the MarginRankingLoss docs #43131
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mrshenli
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Fixes #42884
I did some additional research and considering the first few lines of the docs (
Creates a criterion that measures the loss given inputs x1, x2, two 1D mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1) and the provided tests, this loss should be used primarily with 1-D tensors. More advanced users (that may use this loss in non-standard ways) can easily check the source and see that the definition accepts inputs/targets of arbitrary dimension as long as they match in shape or are broadcastable.