text: """
  """
  import traceback
  import importlib
  
  
  if __name__ == "__main__":
      try:
          try:
              # in cfg_models.py
              from torch_ecg.torch_ecg.model_configs import (
                  # cnn bankbone
                  vgg_block_basic, vgg_block_mish, vgg_block_swish,
                  vgg16, vgg16_leadwise,
                  resnet_block_basic, resnet_bottle_neck_B, resnet_bottle_neck_D,
                  resnet_block_basic_se, resnet_block_basic_gc,
                  resnet_bottle_neck_se, resnet_bottle_neck_gc,
                  resnet_nature_comm, resnet_nature_comm_se, resnet_nature_comm_gc,
                  resnet_nature_comm_bottle_neck, resnet_nature_comm_bottle_neck_se,
                  resnetN, resnetNB, resnetNS, resnetNBS,
                  tresnetF, tresnetP, tresnetN, tresnetS, tresnetM,
                  multi_scopic_block,
                  multi_scopic, multi_scopic_leadwise,
                  densenet_leadwise,
                  xception_leadwise,
                  # lstm
                  lstm,
                  attention,
                  # mlp
                  linear,
                  # attn
                  non_local,
                  squeeze_excitation,
                  global_context,
                  # the whole model config
                  ECG_CRNN_CONFIG,
              )
  
              # in model.py
              from torch_ecg.torch_ecg.models.ecg_crnn import ECG_CRNN
  
              # in train.py
              from torch_ecg.torch_ecg.models.loss import BCEWithLogitsWithClassWeightLoss, AsymmetricLoss
              from torch_ecg.torch_ecg.utils.utils_nn import default_collate_fn as collate_fn
              from torch_ecg.torch_ecg.utils.misc import (
                  init_logger, get_date_str, dict_to_str, str2bool,
              )
              from torch_ecg.torch_ecg._preprocessors import PreprocManager
              from torch_ecg.torch_ecg.augmenters import AugmenterManager
              from torch_ecg.torch_ecg.utils.trainer import BaseTrainer
          except:
              print("import torch_ecg directly")
              # in cfg_models.py
              from torch_ecg.model_configs import (
                  # cnn bankbone
                  vgg_block_basic, vgg_block_mish, vgg_block_swish,
                  vgg16, vgg16_leadwise,
                  resnet_block_basic, resnet_bottle_neck_B, resnet_bottle_neck_D,
                  resnet_block_basic_se, resnet_block_basic_gc,
                  resnet_bottle_neck_se, resnet_bottle_neck_gc,
                  resnet_nature_comm, resnet_nature_comm_se, resnet_nature_comm_gc,
                  resnet_nature_comm_bottle_neck, resnet_nature_comm_bottle_neck_se,
                  resnetN, resnetNB, resnetNS, resnetNBS,
                  tresnetF, tresnetP, tresnetN, tresnetS, tresnetM,
                  multi_scopic_block,
                  multi_scopic, multi_scopic_leadwise,
                  densenet_leadwise,
                  xception_leadwise,
                  # lstm
                  lstm,
                  attention,
                  # mlp
                  linear,
                  # attn
                  non_local,
                  squeeze_excitation,
                  global_context,
                  # the whole model config
                  ECG_CRNN_CONFIG,
              )
  
              # in model.py
              from torch_ecg.models.ecg_crnn import ECG_CRNN
  
              # in train.py
              from torch_ecg.models.loss import BCEWithLogitsWithClassWeightLoss, AsymmetricLoss
              from torch_ecg.utils.utils_nn import default_collate_fn as collate_fn
              from torch_ecg.utils.misc import (
                  init_logger, get_date_str, dict_to_str, str2bool,
              )
              from torch_ecg._preprocessors import PreprocManager
              from torch_ecg.augmenters import AugmenterManager
              from torch_ecg.utils.trainer import BaseTrainer
  
          print("successfully import torch_ecg from the submodule!")
      except Exception as e:
          print("failed to import torch_ecg from the submodule!")
          traceback.print_exc()
  
      try:
          # in cfg.py
          from torch_ecg_bak.torch_ecg.model_configs import (
              # cnn bankbone
              vgg_block_basic, vgg_block_mish, vgg_block_swish,
              vgg16, vgg16_leadwise,
              resnet_block_basic, resnet_bottle_neck_B, resnet_bottle_neck_D,
              resnet_block_basic_se, resnet_block_basic_gc,
              resnet_bottle_neck_se, resnet_bottle_neck_gc,
              resnet_nature_comm, resnet_nature_comm_se, resnet_nature_comm_gc,
              resnet_nature_comm_bottle_neck, resnet_nature_comm_bottle_neck_se,
              resnetN, resnetNB, resnetNS, resnetNBS,
              tresnetF, tresnetP, tresnetN, tresnetS, tresnetM,
              multi_scopic_block,
              multi_scopic, multi_scopic_leadwise,
              densenet_leadwise,
              xception_leadwise,
              # lstm
              lstm,
              attention,
              # mlp
              linear,
              # attn
              non_local,
              squeeze_excitation,
              global_context,
              # the whole model config
              ECG_CRNN_CONFIG,
          )
  
          # in model.py
          from torch_ecg_bak.torch_ecg.models.ecg_crnn import ECG_CRNN
  
          # in train.py
          from torch_ecg_bak.torch_ecg.models.loss import BCEWithLogitsWithClassWeightLoss, AsymmetricLoss
          from torch_ecg_bak.torch_ecg.utils.utils_nn import default_collate_fn as collate_fn
          from torch_ecg_bak.torch_ecg.utils.misc import (
              init_logger, get_date_str, dict_to_str, str2bool,
          )
          from torch_ecg_bak.torch_ecg._preprocessors import PreprocManager
          from torch_ecg_bak.torch_ecg.augmenters import AugmenterManager
          from torch_ecg_bak.torch_ecg.utils.trainer import BaseTrainer
  
          print("successfully import torch_ecg from the backup folder!")
      except Exception as e:
          print("failed to import torch_ecg from the backup folder!")
          traceback.print_exc()
  
      import torch
      cuda_is_available = " " if torch.cuda.is_available() else " not "
      print(f"torch version == {torch.__version__}")
      print(f"cuda is{cuda_is_available}available")
  