code: def __init__(self, num_classes, abn=InPlaceABNSync, trained=True):
      super().__init__()
      self.inplanes = 128
      self.is_trained = trained
  
      self.conv1 = conv3x3(3, 64, stride=2)
      self.bn1 = abn(64)
      self.relu1 = nn.ReLU(inplace=False)
      self.conv2 = conv3x3(64, 64)
      self.bn2 = abn(64)
      self.relu2 = nn.ReLU(inplace=False)
      self.conv3 = conv3x3(64, 128)
      self.bn3 = abn(128)
      self.relu3 = nn.ReLU(inplace=False)
      self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
      self.layers = [3, 4, 23, 3]
      self.abn = abn
      strides = [1, 2, 1, 1]
      dilations = [1, 1, 1, 2]
  
      self.layer1 = self._make_layer(
          Bottleneck,
          64,
          self.layers[0],
          stride=strides[0],
          dilation=dilations[0],
      )
      self.layer2 = self._make_layer(
          Bottleneck,
          128,
          self.layers[1],
          stride=strides[1],
          dilation=dilations[1],
      )
      self.layer3 = self._make_layer(
          Bottleneck,
          256,
          self.layers[2],
          stride=strides[2],
          dilation=dilations[2],
      )
      self.layer4 = self._make_layer(
          Bottleneck,
          512,
          self.layers[3],
          stride=strides[3],
          dilation=dilations[3],
          multi_grid=(1, 1, 1),
      )
      # Context Aware
      self.context = DDualGCNHead(2048, 512, abn)
      self.layer6 = Parsing(512, 256, num_classes, abn)
      # edge
      if self.is_trained:
          self.edge_layer = Edges(abn, out_fea=num_classes)
  
file_path: insightface/parsing/dml_csr/networks/dml_csr.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import torch.nn
  from torch.nn import functional
  from inplace_abn import InPlaceABNSync
  from .modules.ddgcn import DDualGCNHead
  from .modules.parsing import Parsing
  from .modules.edges import Edges
  from .modules.util import Bottleneck
label_desc: Use image node_id 1 to create a new DML_CSR object from inherited base classes: nn with example: obj = DML_CSR(num_classes, abn, trained)