code: def load(self, filename):
          
          if os.path.isfile(filename):
              checkpoint = torch.load(filename, map_location={'cuda:0': 'cpu'})
              if self.distributed:
                  self.model.module.load_state_dict(checkpoint['state_dict'])
              else:
                  self.model.load_state_dict(checkpoint['state_dict'])
              self.fp_optimizer.initialize_model(self.model)
              self.optimizer.load_state_dict(checkpoint['optimizer'])
              self.scheduler.load_state_dict(checkpoint['scheduler'])
              self.epoch = checkpoint['epoch']
              self.loss = checkpoint['loss']
              logging.info(f'Loaded checkpoint {filename} (epoch {self.epoch})')
          else:
              logging.error(f'Invalid checkpoint: {filename}')
docs: Loads checkpoint from filename.
  
          :param filename: path to the checkpoint file