content: experiment_name = "UNDEFINED_MODEL"  # configuration name
  base_dir = 'UNDEFINED'
  
  [processing]
  base_hardware = "UNDEFINED"   # Where the program is run [EDDIE/PC]
  device = "cpu"    # Which processor is used [GPU/CPU]
  pe = 1          # How many parallel environments (cores) needed
  
  [data]
  n_channels = 1
  batch_size = 1  # Batch size for dataloader
  num_workers = 4 # parallel threads to use to speed up data processing
  val_percent = 10 # % of the data that is used as validation (0-100)
  dir_train_mask = 'UNDEFINED'
  dir_train_img = 'UNDEFINED'
  dir_val_img = 'UNDEFINED'
  dir_val_mask = 'UNDEFINED'
  split_training_dataset = false
  apply_augmentations = false
  padding = true
  individual_padding = false
  weak_augmentations = false
  minmax_norm = false
  
  [model]
  model_name = 'UNDEFINED'   # model architecture name
  classes = 2     # Number of possible segmentation classes
  in_channels = 1 # Number of input channels
  [training]
  loss = ['dice', 'crossentropy']
  loss_component_weighting = [1, 1]
  class_loss_weighting = false
  class_loss_weight_damper = [1.0, 1.0]
  lr = 1e-5       # learning rate
  epochs = 10     # Number of epochs to run
  grad_scaler = false     # Use mixed precision for faster training
  load_checkpoint = false    # Load model from a .pth file (Bool/Path)
  optimizer_type = 'adam' # Use adam optimizer
  scheduler_type = false  # No scheduler is used
  save_checkpoint = true # Whether model checkpoints are saved
  checkpoint_frequency = 1 # How often checkpoints are saved (per epoch)
  wandb_track = true # Whether to track training with wandb
  model_cleanup_metric = 'Dice Score' # use validation dice score to clean up by default
  restart_wandb = false # Whether to restart wandb run if continuing from a checkpoint
  