src/transformers/modeling_utils.py/PreTrainedModel/__init__
class PreTrainedModel:    def __init__(self, config: PretrainedConfig, *inputs, **kwargs):
        super().__init__()
        if not isinstance(config, PretrainedConfig):
            raise ValueError(
                f"Parameter config in `{self.__class__.__name__}(config)` should be an instance of class "
                "`PretrainedConfig`. To create a model from a pretrained model use "
                f"`model = {self.__class__.__name__}.from_pretrained(PRETRAINED_MODEL_NAME)`"
            )
        # Save config and origin of the pretrained weights if given in model
        self.config = config
        self.name_or_path = config.name_or_path
        self.warnings_issued = {}
        self.generation_config = GenerationConfig.from_model_config(config) if self.can_generate() else None