local LayerT = nerv.class('nerv.LayerT')

function LayerT:__init(id, global_conf, layer_conf)
    nerv.error_method_not_implemented()
end

function LayerT:init(batch_size, chunk_size)
    nerv.error_method_not_implemented()
end

function LayerT:update(bp_err, input, output, t)
    nerv.error_method_not_implemented()
end

function LayerT:propagate(input, output, t)
    nerv.error_method_not_implemented()
end

function LayerT:back_propagate(bp_err, next_bp_err, input, output, t)
    nerv.error_method_not_implemented()
end

function LayerT:check_dim_len(len_in, len_out)
    local expected_in = #self.dim_in
    local expected_out = #self.dim_out
    if len_in > 0 and expected_in ~= len_in then
        nerv.error("layer %s expects %d inputs, %d given",
                    self.id, len_in, expected_in)
    end
    if len_out > 0 and expected_out ~= len_out then
        nerv.error("layer %s expects %d outputs, %d given",
                    self.id, len_out, expected_out)
    end
end

LayerT.find_param = nerv.Layer.find_param

function LayerT:get_params()
    nerv.error_method_not_implemented()
end

function LayerT:get_dim()
    return self.dim_in, self.dim_out
end

nerv.include('sutil.lua')
nerv.include('tnn.lua')
nerv.include('layersT/softmax_ce_t.lua')
nerv.include('layersT/lstm_t.lua')
nerv.include('layersT/dropout_t.lua')
nerv.include('layer_dag_t.lua')