Feat/module no grad

[nathanielsimard]

The ability to dynamically set the gradient requirements for modules has been added. Previously, it was assumed that every parameter required gradients, which resulted in resetting the graph after each operation at the module level. This was problematic because we might want to execute operations on modules while still tracking operations. To address this, the fork function has been added to modules, which indicates when we want to send a module to another device with an empty graph.

The detach function has been removed from Module because it can lead to mistakes. Instead of using to_device().detach().require_grad(), which would override previous set_require_grad(false), we can call fork, which preserves gradient settings set when creating a module.

The from_inner function has been removed from ADModule. A static method cannot keep track of dynamic gradient configurations. Instead, the function inner(self) has been changed to inner(&self). Additionally, inner may be renamed to valid since it highlights the purpose of the method and is familiar to PyTorch users.