diff --git a/src/api/instancenorm.jl b/src/api/instancenorm.jl
index 58db6e63..15878552 100644
--- a/src/api/instancenorm.jl
+++ b/src/api/instancenorm.jl
@@ -43,8 +43,8 @@ end
 
 function instancenorm(x::AbstractArray, γ::Optional{<:AbstractVector},
         β::Optional{<:AbstractVector}, rμ::Optional{<:AbstractVector},
-        rσ²::Optional{<:AbstractVector}, training::TrainingType,
-        σ::F=identity, momentum::Real=0.1f0, epsilon::Real=default_epsilon(x)) where {F}
+        rσ²::Optional{<:AbstractVector}, training::TrainingType, σ::F=identity,
+        momentum::Optional{<:Real}=0.1f0, epsilon::Real=default_epsilon(x)) where {F}
     assert_valid_instancenorm_arguments(x)
 
     y, rμₙ, rσ²ₙ = instancenorm_impl(
diff --git a/src/impl/normalization.jl b/src/impl/normalization.jl
index 83d82d2c..9afc4cde 100644
--- a/src/impl/normalization.jl
+++ b/src/impl/normalization.jl
@@ -132,10 +132,10 @@ CRC.@non_differentiable get_norm_reshape_dims(::Any...)
 
 # Entry Points
 ## InstanceNorm
-function instancenorm(x::AbstractArray{xT, N}, rμ::Optional{<:AbstractVector},
-        rσ²::Optional{<:AbstractVector}, γ::Optional{<:AbstractVector},
-        β::Optional{<:AbstractVector}, training::StaticBool,
-        momentum, epsilon, act::F) where {xT, N, F}
+function instancenorm(x::AbstractArray{xT, N}, γ::Optional{<:AbstractVector},
+        β::Optional{<:AbstractVector}, rμ::Optional{<:AbstractVector},
+        rσ²::Optional{<:AbstractVector}, training::StaticBool,
+        act::F, momentum, epsilon) where {xT, N, F}
     y, rμₙ, rσ²ₙ = normalization(
         x, rμ, rσ², γ, β, instancenorm_reduce_dims(x), training, momentum, epsilon, act)
     return y, safe_vec(rμₙ), safe_vec(rσ²ₙ)
diff --git a/test/normalization/instancenorm_tests.jl b/test/normalization/instancenorm_tests.jl
index 4e12c197..848b25ba 100644
--- a/test/normalization/instancenorm_tests.jl
+++ b/test/normalization/instancenorm_tests.jl
@@ -53,9 +53,10 @@ function run_instancenorm_testing(gen_f, T, sz, training, act, aType, mode, ongp
     @jet instancenorm(x, scale, bias, rm, rv, training, act, T(0.1), epsilon)
 
     if anonact !== act && is_training(training)
-        lfn = (x, sc, b, rm, rv, act, ϵ) -> sum(first(instancenorm(
-            x, sc, b, rm, rv, Val(true), act, T(0.1), ϵ)))
-        @test @inferred(Zygote.gradient(lfn, x, scale, bias, rm, rv, act, epsilon)) isa Any
+        lfn = (x, sc, b, rm, rv, act, m, ϵ) -> sum(first(instancenorm(
+            x, sc, b, rm, rv, Val(true), act, m, ϵ)))
+        @test @inferred(Zygote.gradient(
+            lfn, x, scale, bias, rm, rv, act, T(0.1), epsilon)) isa Any
     end
 
     @test y isa aType{T, length(sz)}