def parallel_mode(self):
        """
        The current mode used for parallelism if multiple GPUs/TPU cores are available. One of:

        - `ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU).
        - `ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses `torch.nn.DataParallel`).
        - `ParallelMode.DISTRIBUTED`: several GPUs, each having its own process (uses
          `torch.nn.DistributedDataParallel`).
        - `ParallelMode.TPU`: several TPU cores.
        """
        requires_backends(self, ["torch"])
        if is_torch_tpu_available():
            return ParallelMode.TPU
        elif is_sagemaker_mp_enabled():
            return ParallelMode.SAGEMAKER_MODEL_PARALLEL
        elif is_sagemaker_dp_enabled():
            return ParallelMode.SAGEMAKER_DATA_PARALLEL
        elif (
            self.distributed_state is not None and self.distributed_state.distributed_type != DistributedType.NO
        ) or (self.distributed_state is None and self.local_rank != -1):
            return ParallelMode.DISTRIBUTED
        elif self.n_gpu > 1:
            return ParallelMode.NOT_DISTRIBUTED
        else:
            return ParallelMode.NOT_PARALLEL

        The current mode used for parallelism if multiple GPUs/TPU cores are available. One of:

        - `ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU).
        - `ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses `torch.nn.DataParallel`).
        - `ParallelMode.DISTRIBUTED`: several GPUs, each having its own process (uses
          `torch.nn.DistributedDataParallel`).
        - `ParallelMode.TPU`: several TPU cores.
        
dpkd/transformers/src/transformers/training_args.py