found the bug, fixed

test/rules.jl

@@ -201,11 +201,13 @@ end
   end
 end
 
-@testset verbose=true "with complex numebers: Flux#1776" begin
+@testset "with complex numebers: Flux#1776" begin
   empty!(LOG)
-  @testset "$(name(f(1e-2)))" for f in [
-              ADAM, RMSProp, RADAM, OADAM, ADAGrad, ADADelta, NADAM, AdaBelief,
-              Descent, Momentum, Nesterov, ADAMW, # not in Flux PR
+  @testset "$(name(opt))" for opt in [
+              # The Flux PR had 1e-2 for all. But ADADelta(ρ) needs ρ≈0.9 not small. And it helps to make ε not too small too:
+              ADAM(1e-2), RMSProp(1e-2), RADAM(1e-2), OADAM(1e-2), ADAGrad(1e-2), ADADelta(0.9, 1e-5), NADAM(1e-2), AdaBelief(1e-2),
+              # These weren't in Flux PR:
+              Descent(1e-2), Momentum(1e-2), Nesterov(1e-2), ADAMW(1e-2), 
               ]
     # Our "model" is just a complex number
     model = (w = zeros(ComplexF64, 1),)
@@ -218,25 +220,23 @@ end
       # on older Flux builds that don't have a fixed `mse()`
       return sum(abs2.(m.w * x .- conj(x)))
     end
+    @test loss(model) ≈ 2.0
 
-    opt = f(1e-2)
     state = Optimisers.setup(opt, model)
 
     # Train for 10 iterations, enforcing that loss is monotonically decreasing
     last_loss = Inf
     for idx in 1:10
       grads = loggradient(opt)(loss, model)
       state, model = Optimisers.update!(state, model, grads...)
-      if opt isa Union{Momentum, Nesterov} && idx > 8  # these are very flat at the end
-        @test_skip loss(model) < last_loss
-      else
-        @test loss(model) < last_loss
-      end
+      opt isa Union{Momentum, Nesterov} && idx > 8 && continue  # these are very flat at the end
+      @test loss(model) < last_loss
       last_loss = loss(model)
     end
+    @test loss(model) < 1.9
 
     # Repeat with StaticArrays
-    static_model = (w = SA[1.0 + 0im],)
+    static_model = (w = SA[0.0 + 0im],)
     static_state = Optimisers.setup(opt, static_model)
     function static_loss(m)
       x = hcat(SA[1.0 + im])
@@ -246,13 +246,11 @@ end
     for idx in 1:10
       grads = gradient(static_loss, static_model)
       static_state, static_model = Optimisers.update!(static_state, static_model, grads...)
-      if opt isa Union{Momentum, Nesterov} && idx > 8
-        @test_skip loss(static_model) < last_loss
-      else
-        @test loss(static_model) < last_loss
-      end
-      last_loss = loss(static_model)
+      opt isa Union{Momentum, Nesterov} && idx > 8 && continue
+      @test static_loss(static_model) < last_loss
+      last_loss = static_loss(static_model)
     end
+    @test static_loss(static_model) < 1.9 
   end
 end
 
@@ -264,7 +262,7 @@ _plot!(:OADAM)
 
 _plot!(:RMSProp) 
 _plot!(:NADAM)
-_plot!(:ADADelta)  # barely declines
+_plot!(:ADADelta)
 
 _plot(:Momentum)
 _plot!(:Nesterov)  # very flat at the end