code: def nsum0(lx):
    
    lx = numpy.asarray(lx)
    base = lx.max()
    x = numpy.exp(lx - base)
    ssum = x.sum(0)
    result = ssum / ssum.sum()
  
    conventional = (numpy.exp(lx).sum(0) / numpy.exp(lx).sum())
    assert similar(result, conventional)
  
    return result
docs: Accepts log-values as input, exponentiates them, sums down the rows
    (first dimension), normalizes and returns the result.
    Handles underflow by rescaling so that the largest values is exactly 1.0.