Fix format

src/MOI_wrapper.jl

@@ -19,11 +19,8 @@ const _SetWithDotProd = MOI.SetWithDotProducts{
     MOI.TriangleVectorization{MOI.LowRankMatrix{Cdouble}},
 }
 
-const SupportedSets = Union{
-    MOI.Nonnegatives,
-    MOI.PositiveSemidefiniteConeTriangle,
-    _SetWithDotProd,
-}
+const SupportedSets =
+    Union{MOI.Nonnegatives,MOI.PositiveSemidefiniteConeTriangle,_SetWithDotProd}
 
 mutable struct Optimizer <: MOI.AbstractOptimizer
     objective_constant::Float64
@@ -263,7 +260,9 @@ function _fill!(
         blk, i, j = model.varmap[t.variable.value]
         _fill_until(model, abs(blk), entptr, type, length(ent))
         if type[end] == Cchar('l')
-            error("Can either have one dot product variable or several normal variables in the same constraint")
+            error(
+                "Can either have one dot product variable or several normal variables in the same constraint",
+            )
         end
         if blk < 0
             type[end] = Cchar('l')

test/test_simple.jl

@@ -4,7 +4,10 @@ import Random
 import MathOptInterface as MOI
 
 # This is `test_conic_PositiveSemidefiniteConeTriangle_VectorOfVariables`
-@testset "Solve simple with sdplrlib with $file" for file in ["simple_sparse.jl", "simple_lowrank.jl"]
+@testset "Solve simple with sdplrlib with $file" for file in [
+    "simple_sparse.jl",
+    "simple_lowrank.jl",
+]
     include(file)
     # The `925` seed is taken from SDPLR's `main.c`
     Random.seed!(925)
@@ -43,9 +46,11 @@ end
 function simple_lowrank_model()
     model = SDPLR.Optimizer()
     A = MOI.LowRankMatrix(
-        [-1/4, 1/4],
-        [-1.0 1.0
-          1.0 1.0]
+        [-1 / 4, 1 / 4],
+        [
+            -1.0 1.0
+            1.0 1.0
+        ],
     )
     X, _ = MOI.add_constrained_variables(
         model,
@@ -76,7 +81,8 @@ function simple_test(model, X, c)
     @test σ ≈ sigma
 end
 
-@testset "MOI wrapper for $f" for f in [simple_sparse_model, simple_lowrank_model]
+@testset "MOI wrapper for $f" for f in
+                                  [simple_sparse_model, simple_lowrank_model]
     model, X, c = f()
     MOI.optimize!(model)
     simple_test(model, X, c)