Module g. MITSUBISHI ELECTRIC Global website

See for a comparison between Note This method modifies the module in-place
This is useful for weakening an assumption to the finite case e The conditions are also convenient to define a notion of a finitely cogenerated module M

So it should be called before constructing optimizer if the module will live on XPU while being optimized.

Module — PyTorch 1.9.0 documentation
Otherwise, yields only parameters that are direct members of this module
Finitely generated module
This number is the same as the number of maximal A-linearly independent vectors in M or equivalently the rank of a maximal free submodule of M
Module — PyTorch 1.9.0 documentation
Then M is a G-module studied by
Returns The Parameter referenced by target Return type torch Keys are corresponding parameter and buffer names
Similarly, an M is : any injective endomorphism f is also a surjective endomorphism The hook can modify the output

The same is true if "f.

User can either return a tuple or a single modified value in the hook
Finitely generated module
See for a comparison between
Finitely generated module
Returns a handle that can be used to remove the added hook by calling handle
Mod- G can be identified with the category of left resp The term G-module is also used for the more general notion of an on which G acts linearly i
For a R, finitely generated, finitely presented, and coherent are equivalent conditions on a module See above example for how to specify a fully-qualified string

The parameter can be accessed as an attribute using given name.

Otherwise, yields only buffers that are direct members of this module
Finitely generated module
This method is helpful for freezing part of the module for finetuning or training parts of a model individually e
Buffers, by default, are persistent and will be saved alongside parameters