content: import subprocess
  
  import torch
  from batchgenerators.utilities.file_and_folder_operations import save_json, join, isfile, load_json
  
  from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
  from torch import distributed as dist
  
  
  class nnUNetTrainerBenchmark_5epochs(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)
          assert self.fold == 0, "It makes absolutely no sense to specify a certain fold. Stick with 0 so that we can parse the results."
          self.disable_checkpointing = True
          self.num_epochs = 5
          assert torch.cuda.is_available(), "This only works on GPU"
          self.crashed_with_runtime_error = False
  
      def perform_actual_validation(self, save_probabilities: bool = False):
          pass
  
      def save_checkpoint(self, filename: str) -> None:
          # do not trust people to remember that self.disable_checkpointing must be True for this trainer
          pass
  
      def run_training(self):
          try:
              super().run_training()
          except RuntimeError:
              self.crashed_with_runtime_error = True
              self.on_train_end()
  
      def on_train_end(self):
          super().on_train_end()
  
          if not self.is_ddp or self.local_rank == 0:
              torch_version = torch.__version__
              cudnn_version = torch.backends.cudnn.version()
              gpu_name = torch.cuda.get_device_name()
              if self.crashed_with_runtime_error:
                  fastest_epoch = 'Not enough VRAM!'
              else:
                  epoch_times = [i - j for i, j in zip(self.logger.my_fantastic_logging['epoch_end_timestamps'],
                                                       self.logger.my_fantastic_logging['epoch_start_timestamps'])]
                  fastest_epoch = min(epoch_times)
  
              if self.is_ddp:
                  num_gpus = dist.get_world_size()
              else:
                  num_gpus = 1
  
              benchmark_result_file = join(self.output_folder, 'benchmark_result.json')
              if isfile(benchmark_result_file):
                  old_results = load_json(benchmark_result_file)
              else:
                  old_results = {}
              # generate some unique key
              hostname = subprocess.getoutput('hostname')
              my_key = f"{hostname}__{cudnn_version}__{torch_version.replace(' ', '')}__{gpu_name.replace(' ', '')}__num_gpus_{num_gpus}"
              old_results[my_key] = {
                  'torch_version': torch_version,
                  'cudnn_version': cudnn_version,
                  'gpu_name': gpu_name,
                  'fastest_epoch': fastest_epoch,
                  'num_gpus': num_gpus,
                  'hostname': hostname
              }
              save_json(old_results,
                        join(self.output_folder, 'benchmark_result.json'))
  