def resume_or_load(self, resume=True):
        """
        If `resume==True` and `cfg.OUTPUT_DIR` contains the last checkpoint (defined by
        a `last_checkpoint` file), resume from the file. Resuming means loading all
        available states (eg. optimizer and scheduler) and update iteration counter
        from the checkpoint. ``cfg.MODEL.WEIGHTS`` will not be used.
        Otherwise, this is considered as an independent training. The method will load model
        weights from the file `cfg.MODEL.WEIGHTS` (but will not load other states) and start
        from iteration 0.
        Args:
            resume (bool): whether to do resume or not
        """
        # The checkpoint stores the training iteration that just finished, thus we start
        # at the next iteration (or iter zero if there's no checkpoint).
        checkpoint = self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume)

        if resume and self.checkpointer.has_checkpoint():
            self.start_epoch = checkpoint.get("epoch", -1) + 1
            # The checkpoint stores the training iteration that just finished, thus we start
            # at the next iteration (or iter zero if there's no checkpoint).

        If `resume==True` and `cfg.OUTPUT_DIR` contains the last checkpoint (defined by
        a `last_checkpoint` file), resume from the file. Resuming means loading all
        available states (eg. optimizer and scheduler) and update iteration counter
        from the checkpoint. ``cfg.MODEL.WEIGHTS`` will not be used.
        Otherwise, this is considered as an independent training. The method will load model
        weights from the file `cfg.MODEL.WEIGHTS` (but will not load other states) and start
        from iteration 0.
        Args:
            resume (bool): whether to do resume or not
        
fastreid/engine/defaults.py