have the possibility to build object with a function instead of a class

[petermartigny]

When you want to experiment with someone else's code, you don't want to copy-paste their code.

If you want to use a class AwesomeClass from an awesome github repo, you can do:

from transfer_nlp.pluginf.config import register_plugin
from awesome_repo.module import AwesomeClass

register_plugin(AwesomeClass)

and then use it in your experiments.

However, when reusing complex objects, it might complicated to configure them.
An example is the pre-trained model from the pytorch-pretrained-bert repo, where you can build complex models with nice one-liners such as model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=4)

It's possible to encapsulate these into other classes and have Transfer NLP build them, but it can feel awkward and adds unnecessary complexity / lines of code compared to the initial one-liner.
An alternative is to build these objects with a method, in the previous example we would only write:

@register_function
def bert_classifier(bert_version: str='bert-base-uncased', num_labels: int=4):
    return BertForSequenceClassification.from_pretrained(pretrained_model_name_or_path=bert_version, num_labels=num_labels)

and we could use functions just as methods in the config loading.

[kireet]

could we also be able to avoid the wrapper function? is it possible to call the decorator directly with an existing function rather than using the @ syntax?

[petermartigny]

Yes definitely!