code: def __init__(self, inplanes, interplanes, num_classes):
      super(GALDHead, self).__init__()
      self.conva = nn.Sequential(
          nn.Conv2d(inplanes, interplanes, 3, padding=1, bias=False),
          BatchNorm2d(interplanes),
          nn.ReLU(interplanes),
      )
      self.a2block = GALDBlock(interplanes, interplanes // 2)
      self.convb = nn.Sequential(
          nn.Conv2d(interplanes, interplanes, 3, padding=1, bias=False),
          BatchNorm2d(interplanes),
          nn.ReLU(interplanes),
      )
  
      self.bottleneck = nn.Sequential(
          nn.Conv2d(
              inplanes + interplanes,
              interplanes,
              kernel_size=3,
              padding=1,
              dilation=1,
              bias=False,
          ),
          BatchNorm2d(interplanes),
          nn.ReLU(interplanes),
          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 GALDHead object from inherited base classes: nn with example: obj = GALDHead(inplanes, interplanes, num_classes)