Made the most minimal version of the non-working l4casadi example

bioptim/examples/getting_started/temporary_non_working_example.py

@@ -0,0 +1,40 @@
+from casadi import Opti, MX, Function
+import l4casadi as l4c
+import torch
+
+
+class NeuralNetworkModel(torch.nn.Module):
+    def __init__(self, layer_node_count: tuple[int]):
+        super(NeuralNetworkModel, self).__init__()
+        layers = torch.nn.ModuleList()
+        layers.append(torch.nn.Linear(layer_node_count[0], layer_node_count[-1]))
+        self._forward_model = torch.nn.Sequential(*layers)
+        self.eval()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self._forward_model(x)
+
+
+def main():
+    opti = Opti()
+    nx = nu = 1
+    torch_model = NeuralNetworkModel(layer_node_count=(nu, nx))
+
+    # ---- decision variables ---------
+    x = opti.variable(nx, 1)  # state
+    u = opti.variable(nu, 1)  # control
+
+    # ---- dynamic constraints --------
+    x_sym = MX.sym("x", nx, 1)
+    u_sym = MX.sym("u", nu, 1)
+    forward_model = l4c.L4CasADi(torch_model, device="cpu")
+    f = Function("xdot", [x_sym, u_sym], [x_sym - forward_model(u_sym)])
+    opti.subject_to(f(x, u) == 0)  # Adding this line yields the error : jac_adj_i0_adj_o0 is not provided by L4CasADi.
+
+    # ---- solve NLP  ------
+    opti.solver("ipopt")
+    opti.solve()
+
+
+if __name__ == "__main__":
+    main()