

def grad(self, inputs, grads):
    (g_output,) = grads
    (x, y) = inputs[:2]
    idx_list = g_output[2:]
    gx = g_output
    gy = theano.tensor.advanced_subtensor1(g_output, *idx_list)
    return ([gx, gy] + ([DisconnectedType()()] * len(idx_list)))

dataset/ETHPy150Open rizar/attention-lvcsr/libs/Theano/theano/sparse/basic.py ConstructSparseFromList.grad/VarMisuse@35/40 `inputs`->`g_output`