<p>In <a href="https://github.com/mlpack/mlpack/pull/757#discussion_r74183802">src/mlpack/methods/ann/layer/concat_layer.hpp</a>:</p>
<pre style='color:#555'>> +
> +namespace mlpack {
> +namespace ann /** Artificial Neural Network. */ {
> +/**
> + * @tparam JoinLayers Contains all layer modules that need be concatenated.
> + */
> +template <typename JoinLayers,
> + typename InputDataType = arma::cube,
> + typename OutputDataType = arma::cube>
> +class ConcatLayer
> +{
> + public:
> + ConcatLayer(const size_t numLayers,
> + JoinLayers &&layers) :
> + numLayers(numLayers),
> + layers(std::forward<JoinLayers>(layers))
</pre>
<p>Your solution tell the compiler "JoinLayers must be rvalue", that means</p>
<pre><code>SomeLayer layers;
ConcatLayer<SomeLayer> ConcatLayer(layers);
</code></pre>
<p>Do not work, because layers is an lvalue in this case, but the constructor only accept rvalue.</p>
<p>If you want to perform perfect forwarding by std::forward(which means, if it is rvalue, cast it to rvalue, if it is lvalue, cast it lvalue), you need to write it as</p>
<pre><code>template<typename JoinLayersType>
ConcatLayer(const size_t numLayers,
JoinLayersType &&layers) ;
</code></pre>
<p>In this case, the compiler will treat JoinLayers&& as "<a href="https://isocpp.org/blog/2012/11/universal-references-in-c11-scott-meyers">universal reference</a>", so perfect forwarding work under this case.</p>
<p>You may ask, but I need the JoinLayersType == JoinLayers, use can use static_assert to add this restriction work</p>
<pre><code>using RawJLT = typename std::decay<JoinLayersType >::type;
static_assert(std::is_same<RawJLT , JoinLayers>::value, "type of JoinLayerType must equal to JoinLayers");
</code></pre>
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