content: import torch
  
  from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
  
  
  class nnUNetTrainer_250epochs_NoMirroring(nnUNetTrainer):
      def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, 
                   device: torch.device = torch.device('cuda')):
          super().__init__(plans, configuration, fold, dataset_json, device)
          self.num_epochs = 250
  
      def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self):
          rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \
              super().configure_rotation_dummyDA_mirroring_and_inital_patch_size()
          mirror_axes = None
          self.inference_allowed_mirroring_axes = None
          return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes
  
  
  class nnUNetTrainer_2000epochs_NoMirroring(nnUNetTrainer):
      def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, 
                   device: torch.device = torch.device('cuda')):
          super().__init__(plans, configuration, fold, dataset_json, device)
          self.num_epochs = 2000
  
      def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self):
          rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \
              super().configure_rotation_dummyDA_mirroring_and_inital_patch_size()
          mirror_axes = None
          self.inference_allowed_mirroring_axes = None
          return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes
  
      
  class nnUNetTrainer_4000epochs_NoMirroring(nnUNetTrainer):
      def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, 
                   device: torch.device = torch.device('cuda')):
          super().__init__(plans, configuration, fold, dataset_json, device)
          self.num_epochs = 4000
  
      def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self):
          rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \
              super().configure_rotation_dummyDA_mirroring_and_inital_patch_size()
          mirror_axes = None
          self.inference_allowed_mirroring_axes = None
          return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes
  
  
  class nnUNetTrainer_8000epochs_NoMirroring(nnUNetTrainer):
      def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, 
                   device: torch.device = torch.device('cuda')):
          super().__init__(plans, configuration, fold, dataset_json, device)
          self.num_epochs = 8000
  
      def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self):
          rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \
              super().configure_rotation_dummyDA_mirroring_and_inital_patch_size()
          mirror_axes = None
          self.inference_allowed_mirroring_axes = None
          return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes
  
  