code: def get_base_params(dataset_name, checkpoint_dir):
      params = {}
  
      # change name and gpuid later
      basic_params = {
          "dataset": dataset_name,
          "dataroot": "./data/" + dataset_name,
          "name": "",
          "gpu_ids": [0],
          "checkpoints_dir": checkpoint_dir,
          "verbose": False,
          "print_freq": 100,
      }
      params.update(basic_params)
  
      dataset_params = {
          "dataset_mode": "aligned",
          "direction": "BtoA",
          "num_threads": 4,
          "max_dataset_size": float("inf"),
          "preprocess": "resize_and_crop",
          "display_winsize": 256,
          "input_nc": 3,
          "output_nc": 3,
      }
      params.update(dataset_params)
  
      model_params = {
          "model": "pix2pix",
          # 'ngf': 64,
          # 'ndf': 64,
          # 'netD': 'basic',
          # 'netG': 'unet_256',
          "n_layers_D": 3,
          # 'norm': 'batch',
          # 'gan_mode': 'lsgan',
          # 'init_type': 'normal',
          "init_gain": 0.02,
          "no_dropout": False,
      }
      params.update(model_params)
  
      train_params = {
          "phase": "train",
          "isTrain": True,
          "serial_batches": False,
          "load_size": 286,
          "crop_size": 256,
          "no_flip": False,
          # 'batch_size': 1,
          # 'beta1': 0.5,
          "pool_size": 0,
          # 'lr_policy': 'linear',
          "lr_decay_iters": 50,
          #'lr': 0.0002,
          # 'lambda_L1': 100,
          "epoch_count": 1,
          # 'n_epochs': 10,           # 100
          # 'n_epochs_decay': 0,      # 100
          "continue_train": False,
      }
      train_params.update(params)
  
      test_params = {
          "phase": "test",
          "isTrain": False,
          "load_iter": -1,
          "epoch": "latest",
          "load_size": 256,
          "crop_size": 256,
          # 'batch_size': 1,
          "serial_batches": True,
          "no_flip": True,
          "eval": True,
      }
      test_params.update(params)
  
      return train_params, test_params
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_base_params(dataset_name, checkpoint_dir) and returns: train_params,  test_params