code: def learnable_parameters(self):
      layer_features_BN = []
      layer_features = []
      layer_aspp = []
      layer_projection = []
      layer_decoder = []
      layer_other = []
      model_para = list(self.named_parameters())
      for name, para in model_para:
          if "xception" in name:
              if (
                  "bn" in name
                  or "downsample.1.weight" in name
                  or "downsample.1.bias" in name
              ):
                  layer_features_BN.append(para)
              else:
                  layer_features.append(para)
                  # print (name)
          elif "aspp" in name:
              layer_aspp.append(para)
          elif "projection" in name:
              layer_projection.append(para)
          elif "decode" in name:
              layer_decoder.append(para)
          elif "global" not in name:
              layer_other.append(para)
      return (
          layer_features_BN,
          layer_features,
          layer_aspp,
          layer_projection,
          layer_decoder,
          layer_other,
      )
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from collections import OrderedDict
  import torch.utils.model_zoo
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 6 for calling the DeepLabv3_plus obj's underlying member method code with example usage: obj.learnable_parameters() and returns: layer_features_BN,  layer_features,  layer_aspp,  layer_projection,  layer_decoder,  layer_other