Make sure first example in Custom Layers docs uses type parameter (#2415

)

* Make sure first example uses type parameter

* Move performance explanation up and expand a bit.

* tweak

docs/src/models/advanced.md

@@ -7,8 +7,8 @@ Here we will try and describe usage of some more advanced features that Flux pro
 Here is a basic example of a custom model. It simply adds the input to the result from the neural network.
 
 ```julia
-struct CustomModel
-  chain::Chain
+struct CustomModel{T <: Chain} # Parameter to avoid type instability
+  chain::T
 end
 
 function (m::CustomModel)(x)
@@ -21,6 +21,7 @@ end
 # Call @layer to allow for training. Described below in more detail.
 Flux.@layer CustomModel
 ```
+Notice that we parameterized the type of the `chain` field. This is necessary for fast Julia code, so that that struct field can be given a concrete type. `Chain`s have a type parameter fully specifying the types of the layers they contain. By using a type parameter, we are freeing Julia to determine the correct concrete type, so that we do not need to specify the full, possibly quite long, type ourselves.
 
 You can then use the model like:
 
@@ -140,7 +141,7 @@ end
 # allow Join(op, m1, m2, ...) as a constructor
 Join(combine, paths...) = Join(combine, paths)
 ```
-Notice that we parameterized the type of the `paths` field. This is necessary for fast Julia code; in general, `T` might be a `Tuple` or `Vector`, but we don't need to pay attention to what it specifically is. The same goes for the `combine` field.
+Notice again that we parameterized the type of the `combine` and `paths` fields. In addition to the performance considerations of concrete types, this allows either field to be `Vector`s, `Tuple`s, or one of each - we don't need to pay attention to which.
 
 The next step is to use [`Flux.@layer`](@ref) to make our struct behave like a Flux layer. This is important so that calling `Flux.setup` on a `Join` maps over the underlying trainable arrays on each path.
 ```julia