code: def __init__(
      self, folder_with_cropped_data, preprocessed_output_folder
  ):
      os.makedirs(preprocessed_output_folder, exist_ok=True)
      self.folder_with_cropped_data = folder_with_cropped_data
      self.preprocessed_output_folder = preprocessed_output_folder
      self.list_of_cropped_npz_files = [
          join_paths(self.folder_with_cropped_data, file_name)
          for file_name in os.listdir(self.folder_with_cropped_data)
          if file_name.endswith(".npz")
          and os.path.isfile(
              join_paths(self.folder_with_cropped_data, file_name)
          )
      ]
      self.list_of_cropped_npz_files.sort()
  
      self.preprocessor_name = GenericPreprocessor
  
      assert os.path.isfile(
          join_paths(
              self.folder_with_cropped_data, "dataset_properties.pkl"
          )
      ), "folder_with_cropped_data must contain dataset_properties.pkl"
      with open(
          join_paths(
              self.folder_with_cropped_data, "dataset_properties.pkl"
          ),
          "rb",
      ) as f:
          self.dataset_properties = pickle.load(f)
  
      self.plans_per_stage = OrderedDict()
      self.plans = OrderedDict()
  
      self.transpose_forward = [0, 1, 2]
      self.transpose_backward = [0, 1, 2]
  
      self.unet_base_num_features = BASE_NUM_FEATURES_3D
      self.unet_max_num_filters = 320
      self.unet_max_numpool = 999
      self.unet_min_batch_size = 2
      self.unet_featuremap_min_edge_length = 4
  
      self.target_spacing_percentile = 50
      self.anisotropy_threshold = 3
      self.how_much_of_a_patient_must_the_network_see_at_stage0 = 4
      self.batch_size_covers_max_percent_of_dataset = 0.05
      self.conv_per_stage = 2
  
imports: import os
  import pickle
  import shutil
  from collections import OrderedDict
  from .image_crop import get_case_identifier_from_npz
  from .preprocessing import GenericPreprocessor, PreprocessorFor2D
  from .experiment_utils import *
  from .path_utils import join_paths
label_desc: Use image node_id 1 to create a new ExperimentPlanner object  with example: obj = ExperimentPlanner(folder_with_cropped_data, preprocessed_output_folder)