code: def __init__(self, in_channels, out_channels, block, layers):
      super(ResNetBase, self).__init__()
  
      self.out_channels_block = [
          int(out_channels / 4),
          int(out_channels / 2),
          out_channels,
          out_channels,
      ]
  
      self.inplanes = int(out_channels / 8)
      self.conv0_1 = nn.Conv2D(
          in_channels,
          int(out_channels / 16),
          kernel_size=3,
          stride=1,
          padding=1,
          bias_attr=False,
      )
      self.bn0_1 = nn.BatchNorm(int(out_channels / 16))
      self.conv0_2 = nn.Conv2D(
          int(out_channels / 16),
          self.inplanes,
          kernel_size=3,
          stride=1,
          padding=1,
          bias_attr=False,
      )
      self.bn0_2 = nn.BatchNorm(self.inplanes)
      self.relu = nn.ReLU()
  
      self.maxpool1 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
      self.layer1 = self._make_layer(
          block, self.out_channels_block[0], layers[0]
      )
      self.conv1 = nn.Conv2D(
          self.out_channels_block[0],
          self.out_channels_block[0],
          kernel_size=3,
          stride=1,
          padding=1,
          bias_attr=False,
      )
      self.bn1 = nn.BatchNorm(self.out_channels_block[0])
  
      self.maxpool2 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
      self.layer2 = self._make_layer(
          block, self.out_channels_block[1], layers[1], stride=1
      )
      self.conv2 = nn.Conv2D(
          self.out_channels_block[1],
          self.out_channels_block[1],
          kernel_size=3,
          stride=1,
          padding=1,
          bias_attr=False,
      )
      self.bn2 = nn.BatchNorm(self.out_channels_block[1])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal, KaimingNormal
label_desc: Use image node_id 1 to create a new ResNetBase object from inherited base classes: nn with example: obj = ResNetBase(in_channels, out_channels, block, layers)