add gray_box_device option to docstring

ext/MathOptAIPythonCallExt.jl

@@ -18,6 +18,8 @@ import MathOptAI
         config::Dict = Dict{Any,Any}(),
         reduced_space::Bool = false,
         gray_box::Bool = false,
+        gray_box_hessian::Bool = false,
+        gray_box_device::String = "cpu",
     )
 
 Add a trained neural network from PyTorch via PythonCall.jl to `model`.
@@ -41,6 +43,8 @@ Add a trained neural network from PyTorch via PythonCall.jl to `model`.
    nonlinear operator, with gradients provided by `torch.func.jacrev`.
  * `gray_box_hessian`: if `true`, the gray box additionally computes the Hessian
    of the output using `torch.func.hessian`.
+ * `gray_box_device`: device used to construct PyTorch tensors, e.g. `"cuda"`
+   to run on an Nvidia GPU.
 """
 function MathOptAI.add_predictor(
     model::JuMP.AbstractModel,
@@ -63,6 +67,7 @@ end
         config::Dict = Dict{Any,Any}(),
         gray_box::Bool = false,
         gray_box_hessian::Bool = false,
+        gray_box_device::String = "cpu",
     )
 
 Convert a trained neural network from PyTorch via PythonCall.jl to a
@@ -87,13 +92,15 @@ Convert a trained neural network from PyTorch via PythonCall.jl to a
    nonlinear operator, with gradients provided by `torch.func.jacrev`.
  * `gray_box_hessian`: if `true`, the gray box additionally computes the Hessian
    of the output using `torch.func.hessian`.
+ * `gray_box_device`: device used to construct PyTorch tensors, e.g. `"cuda"`
+   to run on an Nvidia GPU.
 """
 function MathOptAI.build_predictor(
     predictor::MathOptAI.PytorchModel;
     config::Dict = Dict{Any,Any}(),
     gray_box::Bool = false,
     gray_box_hessian::Bool = false,
-    gray_box_device = "cpu",
+    gray_box_device::String = "cpu",
 )
     if gray_box
         if !isempty(config)