Fix for sparse-to-sparse matrix broadcast

[kshyatt]

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diff --git a/src/host/sparse.jl b/src/host/sparse.jl
index 349e1f1..2335368 100644
--- a/src/host/sparse.jl
+++ b/src/host/sparse.jl
@@ -902,12 +902,12 @@ function Broadcast.copy(bc::Broadcasted{<:Union{GPUSparseVecStyle,GPUSparseMatSt
         args   = (bc.f, output, offsets, bc.args...)
         kernel = sparse_to_sparse_broadcast_kernel(get_backend(bc.args[first(sparse_args)]))
         ndrange = if sparse_typ <: AbstractGPUSparseVector
-                    output.nnz
-                  elseif sparse_typ <: AbstractGPUSparseMatrixCSC
-                    size(output, 2)
-                  else
-                     size(output, 1)
-                  end
+            output.nnz
+        elseif sparse_typ <: AbstractGPUSparseMatrixCSC
+            size(output, 2)
+        else
+            size(output, 1)
+        end
     else
         args   = sparse_typ <: AbstractGPUSparseVector ? (sparse_typ, bc.f, output, offsets, bc.args...) :
                                                          (sparse_typ, bc.f, output, bc.args...)
diff --git a/test/testsuite/sparse.jl b/test/testsuite/sparse.jl
index 3e0d92f..fd1dbad 100644
--- a/test/testsuite/sparse.jl
+++ b/test/testsuite/sparse.jl
@@ -285,13 +285,13 @@ function broadcasting_matrix(AT, eltypes)
             @test z == SparseMatrixCSC(dz)
 
             # create a matrix with nnz < leading_dim
-            x = spdiagm(m, m, 2=>rand(ET, m - 2))
+            x = spdiagm(m, m, 2 => rand(ET, m - 2))
             dx = AT(x)
             y = ET(3) * x
             dy = ET(3) * dx
             @test y == SparseMatrixCSC(dy)
 
-            x = spdiagm(m, m, -2=>rand(ET, m - 2))
+            x = spdiagm(m, m, -2 => rand(ET, m - 2))
             dx = AT(x)
             y = ET(3) * x
             dy = ET(3) * dx