diff --git a/src/extras/spectral_normalization.jl b/src/extras/spectral_normalization.jl
index 7d4e036..09f929d 100644
--- a/src/extras/spectral_normalization.jl
+++ b/src/extras/spectral_normalization.jl
@@ -1,13 +1,13 @@
 # Power iteration algorithm for computing the spectral norm
 function power_iteration!(W, u, n_iterations)
-  v = nothing
-  for i = 1:n_iterations
-    Wu = W' * u
-    v = Wu ./ (norm(Wu) + eps(Float32))
-    Wv = W * v
-    u .= Wv ./ (norm(Wv) + eps(Float32))
-  end
-  u, v
+    v = nothing
+    for i=1:n_iterations
+        Wu = W' * u
+        v = Wu ./ (norm(Wu) + eps(Float32))
+        Wv = W * v
+        u .= Wv ./ (norm(Wv) + eps(Float32))
+    end
+    u, v
 end
 
 # Compute the maximum singular value
@@ -15,15 +15,15 @@ msv(u, v, W) = u' * W * v
 
 
 ## Dense Layer with Spectral Normalization
-struct DenseSN{F,S<:AbstractMatrix,B,I<:Int,VV<:AbstractArray}
+struct DenseSN{F,S<:AbstractMatrix, B, I<:Int, VV<:AbstractArray}
   weight::S
   bias::B
   σ::F
   n_iterations::I # Number of power iterations for computing max singular value
   u::VV # Left vector for power iteration
-  function DenseSN(W::M, bias=true, σ::F=identity, n_iterations::I=1, u::VV=randn(Float32, size(W, 1), 1)) where {M<:AbstractMatrix,F,I<:Int,VV<:AbstractArray}
-    b = Flux.create_bias(W, bias, size(W, 1))
-    new{F,M,typeof(b),I,VV}(W, b, σ, n_iterations, u)
+  function DenseSN(W::M, bias=true, σ::F=identity, n_iterations::I=1, u::VV=randn(Float32, size(W,1), 1)) where {M<:AbstractMatrix, F, I<:Int, VV<:AbstractArray}
+    b = Flux.create_bias(W, bias, size(W,1))
+    new{F,M,typeof(b), I, VV}(W, b, σ, n_iterations, u)
   end
 end
 
@@ -38,10 +38,10 @@ Flux.trainable(a::DenseSN) = (a.weight, a.bias)
 function (a::DenseSN)(x::AbstractVecOrMat)
   W, b, σ = a.weight, a.bias, a.σ
   u, v = ignore_derivatives(() -> power_iteration!(W, a.u, a.n_iterations))
-  σ.((W ./ msv(u, v, W)) * x .+ b)
+  σ.((W ./ msv(u, v, W))*x .+ b)
 end
 
-(a::DenseSN)(x::AbstractArray) = reshape(a(reshape(x, size(x, 1), :)), :, size(x)[2:end]...)
+(a::DenseSN)(x::AbstractArray) = reshape(a(reshape(x, size(x,1), :)), :, size(x)[2:end]...)
 
 function Base.show(io::IO, l::DenseSN)
   print(io, "DenseSN(", size(l.weight, 2), ", ", size(l.weight, 1))
@@ -51,7 +51,7 @@ end
 
 
 ## Convluational layer with Spectral Normalization
-struct ConvSN{N,M,F,A,V,I<:Int,VV<:AbstractArray}
+struct ConvSN{N,M,F,A,V, I<:Int, VV<:AbstractArray}
   σ::F
   weight::A
   bias::V
@@ -62,19 +62,19 @@ struct ConvSN{N,M,F,A,V,I<:Int,VV<:AbstractArray}
   u::VV # Left vector for power iteration
 end
 
-function ConvSN(w::AbstractArray{T,N}, b::Union{Bool,AbstractVector{T}}, σ=identity;
-            stride=1, pad=0, dilation=1, n_iterations=1) where {T,N}
-  stride = Flux.expand(Val(N - 2), stride)
-  dilation = Flux.expand(Val(N - 2), dilation)
+function ConvSN(w::AbstractArray{T,N}, b::Union{Bool, AbstractVector{T}}, σ = identity;
+              stride = 1, pad = 0, dilation = 1, n_iterations = 1) where {T,N}
+  stride = Flux.expand(Val(N-2), stride)
+  dilation = Flux.expand(Val(N-2), dilation)
   pad = Flux.calc_padding(Conv, pad, size(w)[1:N-2], dilation, stride)
   u = randn(Float32, size(to2D(w), 1), 1)
   return ConvSN(σ, w, b, stride, pad, dilation, n_iterations, u)
 end
 
-function ConvSN(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ=identity;
-            init=Flux.glorot_uniform, stride=1, pad=0, dilation=1,
-            weight=Flux.convfilter(k, ch, init=init), bias=false, n_iterations=1) where {N}
-  ConvSN(weight, bias, σ, stride=stride, pad=pad, dilation=dilation, n_iterations=n_iterations)
+function ConvSN(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
+            init = Flux.glorot_uniform,  stride = 1, pad = 0, dilation = 1,
+            weight = Flux.convfilter(k, ch, init = init), bias = false, n_iterations = 1) where N
+  ConvSN(weight, bias, σ, stride = stride, pad = pad, dilation = dilation, n_iterations = n_iterations)
 end
 
 Flux.@functor ConvSN
@@ -82,7 +82,7 @@ Flux.@functor ConvSN
 Flux.trainable(a::ConvSN) = (a.weight, a.bias)
 
 function (c::ConvSN)(x::AbstractArray)
-  σ, b = c.σ, reshape(c.bias, ntuple(_ -> 1, length(c.stride))..., :, 1)
+  σ, b = c.σ, reshape(c.bias, ntuple(_->1, length(c.stride))..., :, 1)
   cdims = DenseConvDims(x, c.weight; stride=c.stride, padding=c.pad, dilation=c.dilation)
   u, v = ignore_derivatives(() -> power_iteration!(to2D(c.weight), c.u, c.n_iterations))
   σ.(conv(x, c.weight ./ msv(u, v, to2D(c.weight)), cdims) .+ b)
@@ -90,7 +90,7 @@ end
 
 function Base.show(io::IO, l::ConvSN)
   print(io, "ConvSN(", size(l.weight)[1:ndims(l.weight)-2])
-  print(io, ", ", size(l.weight, ndims(l.weight) - 1), "=>", size(l.weight, ndims(l.weight)))
+  print(io, ", ", size(l.weight, ndims(l.weight)-1), "=>", size(l.weight, ndims(l.weight)))
   l.σ == identity || print(io, ", ", l.σ)
   print(io, ")")
 end
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