Hi,
I couldn’t understand from the documentation what the narrow method does on a tensor, the example doesn’t make sense to me 
More specifically, I’m trying to understand what’s going in this code (from https://github.com/pytorch/examples/tree/master/word_language_model 75):
def batchify(data, bsz):
    # Work out how cleanly we can divide the dataset into bsz parts.
    nbatch = data.size(0) // bsz
    # Trim off any extra elements that wouldn't cleanly fit (remainders).
    data = data.narrow(0, 0, nbatch * bsz)
    # Evenly divide the data across the bsz batches.
    data = data.view(bsz, -1).t().contiguous()
    return data.to(device)

Thanks!