Help: Missing Class in PyTorch Residuals Module

[ibm]

Help: Missing Class in PyTorch Residuals Module

[Joao-L-S-Almeida]

Give a look, @TarikKaanKoc, in the commit ef2bc63 of the branch fix/pytorch-residuals-module, apparently it is a solution.
In addition to the preference for PyTorch, I think the factory function which was being used was not the best solution, since we cannot guarantee the entire source code can be reused with different backends as JAX and Apache MXNet, just changing the importing source.

[Joao-L-S-Almeida]

Hi. Let me know if I understand properly. Have you flawlessly build the docs locally, but it is not being updated in RTD ?

[TarikKaanKoc]

Hi. Let me know if I understand properly. Have you flawlessly build the docs locally, but it is not being updated in RTD ?

Hi, I will check now. I'm sorry I wasn't available because of the earthquake.

[TarikKaanKoc]

Difficulty documenting class within function

When attempting to generate documentation with Sphinx, I am having trouble documenting a class that is defined within a function. The current setup is as follows:

def SymbolicOperator(
    expressions: list = None,
    input_vars: list = None,
    output_vars: list = None,
    function: callable = None,
    gradient: callable = None,
    keys: str = None,
    inputs_key: str = None,
    processing: str = "serial",
    device: str = "cpu",
    engine: str = "torch",
    auxiliary_expressions: list = None,
    constants: dict = None,
) -> object:
    """
    Constructs a tensor operator using symbolic expressions using PyTorch or any other supported engine.

    Args:
        expressions (list, optional): List of expressions representing the operator. Defaults to None.
        input_vars (list, optional): List of input variables. Defaults to None.
        output_vars (list, optional): List of output variables. Defaults to None.
        function (callable, optional): A callable that represents the evaluation of the tensor operator. Defaults to None.
        gradient (callable, optional): A callable that represents the gradient evaluation of the tensor operator. Defaults to None.
        keys (str, optional): The keys for accessing the inputs in the input dictionary. Defaults to None.
        inputs_key (str, optional): The key for accessing the input data in the input dictionary. Defaults to None.
        processing (str, optional): The processing method to be used during the evaluation of the tensor operator. Defaults to 'serial'.
        device (str, optional): The device to be used during the evaluation of the tensor operator. Can be either 'cpu' or 'gpu'. Defaults to 'cpu'.
        engine (str, optional): The symbolic computation engine to be used. Currently supported engines are 'torch' and 'numpy'. Defaults to 'torch'.
        auxiliary_expressions (list, optional): Additional expressions that are needed to evaluate the tensor operator. Defaults to None.
        constants (dict, optional): Constants that are required during the evaluation of the tensor operator. Defaults to None.

    Returns:
        object: An instance of the SymbolicOperatorClass.
    """

    class SymbolicOperatorClass(torch.nn.Module if engine == "torch" else object):

Difficulty documenting a class defined within a function

I am having trouble documenting a class that is defined within a function when generating documentation with Sphinx. To properly document a class with Sphinx, it should be defined outside the function and then its documentation should be written in a separate documentation block. This way, Sphinx can generate separate documentation for both the function and the class.

@Joao-L-S-Almeida

[TarikKaanKoc]

How about we fix this or leave it as it is? @Joao-L-S-Almeida

[Joao-L-S-Almeida]

Hi. @TarikKaanKoc. This implementation uses a factory function to return the class SymbolicOperator in order to support the usage of others backends than PyTorch. However, as we are currently more interested in using PyTorch as foundation (and, certainly, there are better ways to support multiple engines) I will make it a simple class in the branch associated with this issue.