diff --git a/src/logdensityfunction.jl b/src/logdensityfunction.jl
index 7c7438c9f..84305feb2 100644
--- a/src/logdensityfunction.jl
+++ b/src/logdensityfunction.jl
@@ -131,42 +131,44 @@ struct LogDensityFunction{
     adtype::AD
     "(internal use only) gradient preparation object for the model"
     prep::Union{Nothing,DI.GradientPrep}
+end
 
-    function LogDensityFunction(
-        model::Model,
-        getlogdensity::Function=getlogjoint_internal,
-        varinfo::AbstractVarInfo=ldf_default_varinfo(model, getlogdensity);
-        adtype::Union{ADTypes.AbstractADType,Nothing}=nothing,
-    )
-        if adtype === nothing
-            prep = nothing
+function LogDensityFunction(
+    model::Model,
+    getlogdensity::Function=getlogjoint_internal,
+    varinfo::AbstractVarInfo=ldf_default_varinfo(model, getlogdensity);
+    adtype::Union{ADTypes.AbstractADType,Nothing}=nothing,
+)
+    if adtype === nothing
+        prep = nothing
+    else
+        # Make backend-specific tweaks to the adtype
+        adtype = tweak_adtype(adtype, model, varinfo)
+        # Check whether it is supported
+        is_supported(adtype) ||
+            @warn "The AD backend $adtype is not officially supported by DynamicPPL. Gradient calculations may still work, but compatibility is not guaranteed."
+        # Get a set of dummy params to use for prep
+        x = [val for val in varinfo[:]]
+        if use_closure(adtype)
+            prep = DI.prepare_gradient(
+                LogDensityAt(model, getlogdensity, varinfo), adtype, x
+            )
         else
-            # Make backend-specific tweaks to the adtype
-            adtype = tweak_adtype(adtype, model, varinfo)
-            # Check whether it is supported
-            is_supported(adtype) ||
-                @warn "The AD backend $adtype is not officially supported by DynamicPPL. Gradient calculations may still work, but compatibility is not guaranteed."
-            # Get a set of dummy params to use for prep
-            x = [val for val in varinfo[:]]
-            if use_closure(adtype)
-                prep = DI.prepare_gradient(
-                    LogDensityAt(model, getlogdensity, varinfo), adtype, x
-                )
-            else
-                prep = DI.prepare_gradient(
-                    logdensity_at,
-                    adtype,
-                    x,
-                    DI.Constant(model),
-                    DI.Constant(getlogdensity),
-                    DI.Constant(varinfo),
-                )
-            end
+            prep = DI.prepare_gradient(
+                logdensity_at,
+                adtype,
+                x,
+                DI.Constant(model),
+                DI.Constant(getlogdensity),
+                DI.Constant(varinfo),
+            )
         end
-        return new{typeof(model),typeof(getlogdensity),typeof(varinfo),typeof(adtype)}(
-            model, getlogdensity, varinfo, adtype, prep
-        )
     end
+    return LogDensityFunction{
+        typeof(model),typeof(getlogdensity),typeof(varinfo),typeof(adtype)
+    }(
+        model, getlogdensity, varinfo, adtype, prep
+    )
 end
 
 """