code: def __init__(self, block, num_layers, num_classes):
      super(ResNetMulti, self).__init__()
      self.weight_attr = paddle.ParamAttr(
          initializer=paddle.nn.initializer.Constant(value=1),
          learning_rate=0.1,
      )
      self.bias_attr = paddle.ParamAttr(
          initializer=paddle.nn.initializer.Constant(value=0)
      )
      self.weight_attr_conv = paddle.ParamAttr(
          initializer=paddle.nn.initializer.Normal(std=0.01)
      )
      self.inplanes = 64
      self.conv1 = nn.Conv2D(
          3,
          64,
          kernel_size=7,
          stride=2,
          padding=3,
          bias_attr=False,
          weight_attr=self.weight_attr_conv,
      )
      self.bn1 = nn.BatchNorm2D(
          64, bias_attr=self.bias_attr, weight_attr=self.weight_attr
      )
      self.relu = nn.ReLU()
      self.maxpool = nn.MaxPool2D(
          kernel_size=3, stride=2, padding=1, ceil_mode=True
      )
      self.layer1 = self._make_layer(block, 64, num_layers[0])
      self.layer2 = self._make_layer(
          block, 128, num_layers[1], stride=2
      )
      self.layer3 = self._make_layer(
          block, 256, num_layers[2], stride=1, dilation=2
      )
      self.layer4 = self._make_layer(
          block, 512, num_layers[3], stride=1, dilation=4
      )
      self.layer5 = self._make_pred_layer(
          ClassifierModule,
          1024,
          [6, 12, 18, 24],
          [6, 12, 18, 24],
          num_classes,
      )
      self.layer6 = self._make_pred_layer(
          ClassifierModule,
          2048,
          [6, 12, 18, 24],
          [6, 12, 18, 24],
          num_classes,
      )
  
      # for channels of four returned stages
      num_filters = [64, 128, 256, 512]
      self.feat_channels = [c * 4 for c in num_filters]
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new ResNetMulti object from inherited base classes: nn with example: obj = ResNetMulti(block, num_layers, num_classes)