ext/torch/nn.cpp in torch-rb-0.13.0 vs ext/torch/nn.cpp in torch-rb-0.13.1

@@ -12,15 +12,13 @@
     Parameter(Tensor&& t) : torch::autograd::Variable(t) { }
 };
 
 void init_nn(Rice::Module& m) {
   auto rb_mNN = Rice::define_module_under(m, "NN");
-  rb_mNN.add_handler<torch::Error>(handle_error);
   add_nn_functions(rb_mNN);
 
   Rice::define_module_under(rb_mNN, "Init")
-    .add_handler<torch::Error>(handle_error)
     .define_singleton_function(
       "_calculate_gain",
       [](NonlinearityType nonlinearity, double param) {
         return torch::nn::init::calculate_gain(nonlinearity, param);
       })
@@ -89,10 +87,9 @@
       [](Tensor tensor, double sparsity, double std) {
         return torch::nn::init::sparse_(tensor, sparsity, std);
       });
 
   Rice::define_class_under<Parameter, torch::Tensor>(rb_mNN, "Parameter")
-    .add_handler<torch::Error>(handle_error)
     .define_method(
       "grad",
       [](Parameter& self) {
         auto grad = self.grad();
         return grad.defined() ? Object(Rice::detail::To_Ruby<torch::Tensor>().convert(grad)) : Nil;