docs: make docstrings have TYPEDSIGNATURES

src/DeepBSDE.jl

@@ -59,7 +59,7 @@ end
 DeepBSDE(u0, σᵀ∇u; opt = Flux.Optimise.Adam(0.1)) = DeepBSDE(u0, σᵀ∇u, opt)
 
 """
-$(SIGNATURES)
+$(TYPEDSIGNATURES)
 
 Returns a `PIDESolution` object.
 
@@ -73,6 +73,8 @@ Returns a `PIDESolution` object.
     [DifferentialEquations.jl doc](https://diffeq.sciml.ai/stable/solvers/sde_solve/).
 - `limits`: if `true`, upper and lower limits will be calculated, based on 
     [Deep Primal-Dual algorithm for BSDEs](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3071506).
+- `maxiters`: The number of training epochs. Defaults to `300`
+- `trajectories`: The number of trajectories simulated for training. Defaults to `100`
 - Extra keyword arguments passed to `solve` will be further passed to the SDE solver.
 """
 function DiffEqBase.solve(prob::ParabolicPDEProblem,

src/DeepBSDE_Han.jl

@@ -1,4 +1,15 @@
 # called whenever sdealg is not specified.
+"""
+$(TYPEDSIGNATURES)
+
+Returns a `PIDESolution` object. 
+
+# Arguments: 
+- `maxiters`: The number of training epochs. Defaults to `300`
+- `trajectories`: The number of trajectories simulated for training. Defaults to `100`
+
+To use [SDE Algorithms](https://diffeq.sciml.ai/stable/solvers/sde_solve/) use [`DeepBSDE`](@ref)
+"""
 function DiffEqBase.solve(prob::ParabolicPDEProblem,
         alg::DeepBSDE;
         dt,

src/DeepSplitting.jl

@@ -51,7 +51,7 @@ function DeepSplitting(nn;
 end
 
 """
-$(SIGNATURES)
+$(TYPEDSIGNATURES)
 
 Returns a `PIDESolution` object.
 

src/MLP.jl

@@ -21,7 +21,7 @@ end
 MLP(; M = 4, L = 4, K = 10, mc_sample = NoSampling()) = MLP(M, L, K, mc_sample)
 
 """
-$(SIGNATURES)
+$(TYPEDSIGNATURES)
 
 Returns a `PIDESolution` object.
 

src/NNStopping.jl

@@ -54,6 +54,22 @@ function (model::NNStoppingModelArray)(X, G)
     broadcast((x, m) -> m(x), eachslice(XG, dims = 2)[2:end], model.ms)
 end
 
+"""
+$(TYPEDSIGNATURES)
+
+Returns a NamedTuple with `payoff` and `stopping_time`
+
+Arguments: 
+- `sdealg`: a SDE solver from [DifferentialEquations.jl](https://diffeq.sciml.ai/stable/solvers/sde_solve/). 
+    If not provided, the plain vanilla [DeepBSDE](https://arxiv.org/abs/1707.02568) method will be applied.
+    If provided, the SDE associated with the PDE problem will be solved relying on 
+    methods from DifferentialEquations.jl, using [Ensemble solves](https://diffeq.sciml.ai/stable/features/ensemble/) 
+    via `sdealg`. Check the available `sdealg` on the 
+    [DifferentialEquations.jl doc](https://diffeq.sciml.ai/stable/solvers/sde_solve/).
+- `maxiters`: The number of training epochs. Defaults to `300`
+- `trajectories`: The number of trajectories simulated for training. Defaults to `100`
+- Extra keyword arguments passed to `solve` will be further passed to the SDE solver.
+"""
 function DiffEqBase.solve(prob::ParabolicPDEProblem,
         pdealg::NNStopping,
         sdealg;