content: experiment_name = "PC_Gel_Nathan_Q1"  # configuration name
  
  #[processing_unit]
  base_hardware = "PC"   # Where the program is run [EDDIE/PC]
  
  #[batch_file]
  core = "CPU"    # Which processor is used [GPU/CPU]
  pe = 1          # How many parallel environments (cores) needed
  memory = 64     # Required memory per core in GBytes
  
  #[data]
  n_channels = 1
  batch_size = 4  # Batch size for dataloader
  num_workers = 1 # parallel threads to use to speed up data processing
  img_scale = 0.5 # TODO: is this necessary to be 0.5?
  validation = 10 # % of the data that is used as validation (0-100)
  base_dir = 'C:/2022_Summer_Intern/Gel_Images_UNet_Test/Models'
  dir_img = 'C:/2022_Summer_Intern/Gel_Images_UNet_Test/Images_Q1'
  dir_mask = 'C:/2022_Summer_Intern/Gel_Images_UNet_Test/Masks_Q1'
  
  #[model]
  name = 'UNet'   # model architecture name
  lr = 1e-5       # learning rate
  classes = 2     # Number of possible segmentation classes
  bilinear = false # Use bilinear upsampling within down/up blocks in unet network
  
  #[training]
  epochs = 10     # Number of epochs to run
  amp = false     # Use mixed precision TODO: change variable name to something more descriptive
  load = false    # Load model from a .pth file (Bool/Path)
  optimizer_type = 'adam' # Use adam optimizer
  scheduler = false  # No scheduler is used
  save_checkpoint = true # Whether model checkpoints are saved