chore: fix formatting

docs/src/DeepSplitting.md

@@ -112,6 +112,8 @@ u(t_{n+1}, X_{T - t_{n+1}}) \approx \sum_{j=1}^{\text{batch\_size}} \left[ u(t_{
 ```
 
 !!! tip
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 In practice, if you have a non-local model, you need to provide the sampling method and the number $K$ of MC integration through the keywords `mc_sample` and `K`.
 `julia alg = DeepSplitting(nn, opt = opt, mc_sample = mc_sample, K = 1) `
 `mc_sample` can be whether `UniformSampling(a, b)` or ` NormalSampling(σ_sampling, shifted)`.

docs/src/problems.md

@@ -4,5 +4,7 @@ ParabolicPDEProblem
 ```
 
 !!! note
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 While choosing to define a PDE using `PIDEProblem`, note that the function being integrated `f` is a function of `f(x, y, v_x, v_y, ∇v_x, ∇v_y)` out of which `y` is the integrating variable and `x` is constant throughout the integration.
 If a PDE has no integral and the non linear term `f` is just evaluated as `f(x, v_x, ∇v_x)` then we suggest using `ParabolicPDEProblem`

docs/src/tutorials/nnstopping.md

@@ -39,6 +39,8 @@ prob = ParabolicPDEProblem(f, sigma, u0, tspan; payoff = g)
 ```
 
 !!! note
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 We provide the payoff function with a keyword argument `payoff`
 
 And now we define our models:
@@ -49,6 +51,8 @@ models = [Chain(Dense(d + 1, 32, tanh), BatchNorm(32, tanh), Dense(32, 1, sigmoi
 ```
 
 !!! note
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 The number of models should be equal to the time discritization.
 
 And finally we define our optimizer and algorithm, and call `solve`: