local DuplicateLayer = nerv.class('nerv.DuplicateLayer', 'nerv.Layer')

function DuplicateLayer:__init(id, global_conf, layer_conf)
    nerv.Layer.__init(self, id, global_conf, layer_conf)
    self:check_dim_len(1, -1)
    if #self.dim_out < 1 then
        nerv.error('no output specified')
    end
    for i = 1, #self.dim_out do
        if self.dim_out[i] ~= self.dim_in[1] then
            nerv.error('mismatching dimensions of outputs')
        end
    end
end

function DuplicateLayer:init()
end

function DuplicateLayer:batch_resize()
end

function DuplicateLayer:propagate(input, output)
    -- do nothing, use reference copy in nn/network.lua
end

function DuplicateLayer:back_propagate(bp_err, next_bp_err)
    next_bp_err[1]:copy_from(bp_err[1])
    for i = 2, #self.dim_out do
        next_bp_err[1]:add(next_bp_err[1], bp_err[i], 1.0, 1.0)
    end
end

function DuplicateLayer:update()
end

function DuplicateLayer:get_params()
    return nerv.ParamRepo({}, self.loc_type)
end