Surely we don't need the double loop

test/sampler.jl

@@ -34,22 +34,20 @@
 
     @testset "init" begin
         @testset "$(model.f)" for model in DynamicPPL.TestUtils.DEMO_MODELS
-            @testset "$(model.f)" for model in DynamicPPL.TestUtils.DEMO_MODELS
-                N = 1000
-                chain_init = sample(model, SampleFromUniform(), N; progress=false)
-
-                for vn in keys(first(chain_init))
-                    if AbstractPPL.subsumes(@varname(s), vn)
-                        # `s ~ InverseGamma(2, 3)` and its unconstrained value will be sampled from Unif[-2,2].
-                        dist = InverseGamma(2, 3)
-                        b = DynamicPPL.link_transform(dist)
-                        @test mean(mean(b(vi[vn])) for vi in chain_init) ≈ 0 atol = 0.11
-                    elseif AbstractPPL.subsumes(@varname(m), vn)
-                        # `m ~ Normal(0, sqrt(s))` and its constrained value is the same.
-                        @test mean(mean(vi[vn]) for vi in chain_init) ≈ 0 atol = 0.11
-                    else
-                        error("Unknown variable name: $vn")
-                    end
+            N = 1000
+            chain_init = sample(model, SampleFromUniform(), N; progress=false)
+
+            for vn in keys(first(chain_init))
+                if AbstractPPL.subsumes(@varname(s), vn)
+                    # `s ~ InverseGamma(2, 3)` and its unconstrained value will be sampled from Unif[-2,2].
+                    dist = InverseGamma(2, 3)
+                    b = DynamicPPL.link_transform(dist)
+                    @test mean(mean(b(vi[vn])) for vi in chain_init) ≈ 0 atol = 0.11
+                elseif AbstractPPL.subsumes(@varname(m), vn)
+                    # `m ~ Normal(0, sqrt(s))` and its constrained value is the same.
+                    @test mean(mean(vi[vn]) for vi in chain_init) ≈ 0 atol = 0.11
+                else
+                    error("Unknown variable name: $vn")
                 end
             end
         end