format

Author: Vaibhavdixit02

NEWS.md

@@ -1,5 +1,5 @@
 # v4 Breaking changes
 
-The main change in this breaking release has been the way mini-batching is handled. The data argument in the solve call and the implicit iteration of that in the callback has been removed, 
-the stochastic solvers (Optimisers.jl and Sophia) now handle it explicitly. You would now pass in a DataLoader to OptimziationProblem as the second argument to the objective etc (p) if you 
+The main change in this breaking release has been the way mini-batching is handled. The data argument in the solve call and the implicit iteration of that in the callback has been removed,
+the stochastic solvers (Optimisers.jl and Sophia) now handle it explicitly. You would now pass in a DataLoader to OptimziationProblem as the second argument to the objective etc (p) if you
 want to do minibatching, else for full batch just pass in the full data.

lib/OptimizationOptimisers/src/OptimizationOptimisers.jl

@@ -93,7 +93,8 @@ function SciMLBase.__solve(cache::OptimizationCache{
                     cache.f.grad(G, θ)
                     x = cache.f(θ)
                 end
-                opt_state = Optimization.OptimizationState(iter = i + (epoch-1)*length(data),
+                opt_state = Optimization.OptimizationState(
+                    iter = i + (epoch - 1) * length(data),
                     u = θ,
                     objective = x[1],
                     grad = G,

lib/OptimizationOptimisers/test/runtests.jl

@@ -70,7 +70,8 @@ using Zygote
 end
 
 @testset "Minibatching" begin
-    using Optimization, OptimizationOptimisers, Lux, Zygote, MLUtils, Random, ComponentArrays
+    using Optimization, OptimizationOptimisers, Lux, Zygote, MLUtils, Random,
+          ComponentArrays
 
     x = rand(10000)
     y = sin.(x)