src/transformers/modeling_utils.py/PreTrainedModel/retrieve_modules_from_names
class PreTrainedModel:    def retrieve_modules_from_names(self, names, add_prefix=False, remove_prefix=False):
        module_keys = set([".".join(key.split(".")[:-1]) for key in names])

        # torch.nn.ParameterList is a special case where two parameter keywords
        # are appended to the module name, *e.g.* bert.special_embeddings.0
        module_keys = module_keys.union(set([".".join(key.split(".")[:-2]) for key in names if key[-1].isdigit()]))

        retrieved_modules = []
        # retrieve all modules that has at least one missing weight name
        for name, module in self.named_modules():
            if remove_prefix:
                name = ".".join(name.split(".")[1:]) if name.startswith(self.base_model_prefix) else name
            elif add_prefix:
                name = ".".join([self.base_model_prefix, name]) if len(name) > 0 else self.base_model_prefix

            if name in module_keys:
                retrieved_modules.append(module)

        return retrieved_modules