code: def add_det_unet_configs(self):
      self.learning_rate = [1e-4] * self.num_epochs
  
      # aggregation from pixel perdiction to object scores (connected component). One of ['max', 'median']
      self.aggregation_operation = "max"
  
      # max number of roi candidates to identify per image (slice in 2D, volume in 3D)
      self.n_roi_candidates = 3 if self.dim == 2 else 8
  
      # loss mode: either weighted cross entropy ('wce'), batch-wise dice loss ('dice), or the sum of both ('dice_wce')
      self.seg_loss_mode = "dice_wce"
  
      # if <1, false positive predictions in foreground are penalized less.
      self.fp_dice_weight = 1 if self.dim == 2 else 1
  
      self.wce_weights = [1, 1, 1]
      self.detection_min_confidence = self.min_det_thresh
  
      # if 'True', loss distinguishes all classes, else only foreground vs. background (class agnostic).
      self.class_specific_seg_flag = True
      self.num_seg_classes = 3 if self.class_specific_seg_flag else 2
      self.head_classes = self.num_seg_classes
  
file_path: medicaldetectiontoolkit/experiments/toy_exp/configs.py
imports: import sys
  import os
  import numpy
  from default_configs import DefaultConfigs
label_desc: Use image node_id 2 for calling the configs obj's underlying member method code with example usage: obj.add_det_unet_configs() without return types