code: def __init__(self, cf, logger):
      super(net, self).__init__()
      self.cf = cf
      self.logger = logger
      backbone = utils.import_module("bbone", cf.backbone_path)
      conv = mutils.NDConvGenerator(cf.dim)
  
      # set operate_stride1=True to generate a unet-like FPN.)
      self.fpn = backbone.FPN(cf, conv, operate_stride1=True).cuda()
      self.conv_final = conv(
          cf.end_filts,
          cf.num_seg_classes,
          ks=1,
          pad=0,
          norm=cf.norm,
          relu=None,
      )
  
      if self.cf.weight_init is not None:
          logger.info(
              "using pytorch weight init of type {}".format(
                  self.cf.weight_init
              )
          )
          mutils.initialize_weights(self)
      else:
          logger.info("using default pytorch weight init")
  
file_path: medicaldetectiontoolkit/models/detection_unet.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from scipy.ndimage.measurements import label
  import numpy
  import utils.exp_utils
  import utils.model_utils
label_desc: Use image node_id 1 to create a new net object from inherited base classes: nn with example: obj = net(cf, logger)