code: def __init__(
      self, block, layers, num_classes, avg=False, aux=False, **_
  ):
      self.inplanes = 128
      super(GALDNet, self).__init__()
      self.aux = aux
      self.conv1 = nn.Sequential(
          conv3x3(3, 64, stride=2),
          BatchNorm2d(64),
          nn.ReLU(inplace=True),
          conv3x3(64, 64),
          BatchNorm2d(64),
          nn.ReLU(inplace=True),
          conv3x3(64, 128),
      )
      self.bn1 = BatchNorm2d(self.inplanes)
      self.relu = nn.ReLU(inplace=False)
      self.maxpool = nn.MaxPool2d(
          kernel_size=3, stride=2, padding=1, ceil_mode=True
      )
      self.layer1 = self._make_layer(block, 64, layers[0])
      self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
      self.layer3 = self._make_layer(
          block, 256, layers[2], stride=1, dilation=2
      )
      self.layer4 = self._make_layer(
          block,
          512,
          layers[3],
          stride=1,
          dilation=4,
          multi_grid=(1, 2, 4),
      )
  
      self.head = GALDHead(2048, 512, num_classes=num_classes)
      self.dsn = nn.Sequential(
          nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1),
          BatchNorm2d(512),
          nn.ReLU(),
          nn.Dropout2d(0.1),
          nn.Conv2d(
              512,
              num_classes,
              kernel_size=1,
              stride=1,
              padding=0,
              bias=True,
          ),
      )
  
imports: import torch.nn
  import torch
  import torch.nn.functional
  from torch.nn import BatchNorm2d
label_desc: Use image node_id 1 to create a new GALDNet object from inherited base classes: nn with example: obj = GALDNet(block, layers, num_classes, avg, aux)