ext/torch/tensor.cpp in torch-rb-0.8.3 vs ext/torch/tensor.cpp in torch-rb-0.9.0

@@ -105,11 +105,11 @@
     "_backward(Tensor? gradient=None, bool? retain_graph=None, bool create_graph=False)"
   });
   ParsedArgs<4> parsed_args;
   auto _r = parser.parse(self_, argc, argv, parsed_args);
   // _backward(Tensor self, Tensor[] inputs, Tensor? gradient=None, bool? retain_graph=None, bool create_graph=False) -> ()
-  auto dispatch__backward = [](const Tensor & self, TensorList inputs, const OptionalTensor & gradient, c10::optional<bool> retain_graph, bool create_graph) -> void {
+  auto dispatch__backward = [](const Tensor & self, TensorList inputs, const c10::optional<at::Tensor> & gradient, c10::optional<bool> retain_graph, bool create_graph) -> void {
     // in future, release GVL
     self._backward(inputs, gradient, retain_graph, create_graph);
   };
   dispatch__backward(self, {}, _r.optionalTensor(0), _r.toBoolOptional(1), _r.toBool(2));
   RETURN_NIL
@@ -123,16 +123,16 @@
   THPVariableClass = rb_cTensor.value();
 
   rb_define_method(rb_cTensor, "backward", (VALUE (*)(...)) tensor__backward, -1);
 
   rb_cTensor
-    .define_method("cuda?", &torch::Tensor::is_cuda)
-    .define_method("sparse?", &torch::Tensor::is_sparse)
-    .define_method("quantized?", &torch::Tensor::is_quantized)
-    .define_method("dim", &torch::Tensor::dim)
-    .define_method("numel", &torch::Tensor::numel)
-    .define_method("element_size", &torch::Tensor::element_size)
-    .define_method("requires_grad", &torch::Tensor::requires_grad)
+    .define_method("cuda?", [](Tensor& self) { return self.is_cuda(); })
+    .define_method("sparse?", [](Tensor& self) { return self.is_sparse(); })
+    .define_method("quantized?", [](Tensor& self) { return self.is_quantized(); })
+    .define_method("dim", [](Tensor& self) { return self.dim(); })
+    .define_method("numel", [](Tensor& self) { return self.numel(); })
+    .define_method("element_size", [](Tensor& self) { return self.element_size(); })
+    .define_method("requires_grad", [](Tensor& self) { return self.requires_grad(); })
     .define_method(
       "_size",
       [](Tensor& self, int64_t dim) {
         return self.size(dim);
       })