add docsting to train!

src/optimise/train.jl

@@ -4,10 +4,19 @@ using Flux.Tracker: back!
 tocb(f) = f
 tocb(fs::AbstractVector) = () -> foreach(call, fs)
 
+"""
+    train!(loss, data, opt; cb = () -> ())
+
+For each datapoint `d` in `data` computes the gradient of `loss(d...)` through
+backpropagation and calls the optimizer `opt` and the callback `cb`
+(i.e. `opt()` and `cb()`).
+
+Multiple callbacks can be passed to `cb` as an array.
+"""
 function train!(loss, data, opt; cb = () -> ())
   cb = tocb(cb)
-  @progress for x in data
-    l = loss(x...)
+  @progress for d in data
+    l = loss(d...)
     isinf(l.data[]) && error("Loss is Inf")
     isnan(l.data[]) && error("Loss is NaN")
     back!(l)