Same but for Differentiate subclasses

Author: ValerianRey

src/torchjd/autojac/_transform/_grad.py

@@ -34,7 +34,7 @@ def __init__(
     ):
         super().__init__(outputs, inputs, retain_graph, create_graph)
 
-    def _differentiate(self, grad_outputs: Sequence[Tensor]) -> tuple[Tensor, ...]:
+    def _differentiate(self, grad_outputs: Sequence[Tensor], /) -> tuple[Tensor, ...]:
         """
         Computes the gradient of each output element with respect to each input tensor, and applies
         the linear transformations represented by the grad_outputs to the results.

src/torchjd/autojac/_transform/_jac.py

@@ -42,7 +42,7 @@ def __init__(
         super().__init__(outputs, inputs, retain_graph, create_graph)
         self.chunk_size = chunk_size
 
-    def _differentiate(self, jac_outputs: Sequence[Tensor]) -> tuple[Tensor, ...]:
+    def _differentiate(self, jac_outputs: Sequence[Tensor], /) -> tuple[Tensor, ...]:
         """
         Computes the jacobian of each output with respect to each input, and applies the linear
         transformations represented by the jac_outputs to the results.