Format

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: penelopeysm

test/ad.jl

@@ -12,7 +12,9 @@
             θ = convert(Vector{Float64}, varinfo[:])
             # Calculate reference logp + gradient of logp using ForwardDiff
             default_adtype = ADTypes.AutoForwardDiff(; chunksize=0)
-            ref_logp, ref_grad = LogDensityProblems.logdensity_and_gradient(f, θ, default_adtype)
+            ref_logp, ref_grad = LogDensityProblems.logdensity_and_gradient(
+                f, θ, default_adtype
+            )
 
             @testset "$adtype" for adtype in [
                 ADTypes.AutoReverseDiff(; compile=false),
@@ -47,7 +49,7 @@
             x = Vector{T}(undef, TT)
             x[1] = α
             for t in 2:TT
-                x[t] = x[t-1] + η[t-1] * τ
+                x[t] = x[t - 1] + η[t - 1] * τ
             end
             # measurement model
             y ~ MvNormal(x, σ^2 * I)
@@ -68,6 +70,8 @@
         spl = Sampler(MyEmptyAlg())
         vi = VarInfo(model)
         ldf = DynamicPPL.LogDensityFunction(vi, model, SamplingContext(spl))
-        @test LogDensityProblems.logdensity_and_gradient(ldf, vi[:], AutoReverseDiff(; compile=true)) isa Any
+        @test LogDensityProblems.logdensity_and_gradient(
+            ldf, vi[:], AutoReverseDiff(; compile=true)
+        ) isa Any
     end
 end