class_docstr: The GCNetHead implementation.
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone_indices (tuple): Two values in the tuple indicate the indices of output of backbone.
          The first index will be taken as a deep-supervision feature in auxiliary layer;
          the second one will be taken as input of GlobalContextBlock.
      backbone_channels (tuple): The same length with "backbone_indices". It indicates the channels of corresponding index.
      gc_channels (int): The input channels to Global Context Block.
      ratio (float): It indicates the ratio of attention channels and gc_channels.
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
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code: def __init__(
      self,
      num_classes,
      backbone_indices,
      backbone_channels,
      gc_channels,
      ratio,
      enable_auxiliary_loss=True,
  ):
      super().__init__()
  
      in_channels = backbone_channels[1]
      self.conv_bn_relu1 = layers.ConvBNReLU(
          in_channels=in_channels,
          out_channels=gc_channels,
          kernel_size=3,
          padding=1,
      )
  
      self.gc_block = GlobalContextBlock(
          gc_channels=gc_channels, in_channels=gc_channels, ratio=ratio
      )
  
      self.conv_bn_relu2 = layers.ConvBNReLU(
          in_channels=gc_channels,
          out_channels=gc_channels,
          kernel_size=3,
          padding=1,
      )
  
      self.conv_bn_relu3 = layers.ConvBNReLU(
          in_channels=in_channels + gc_channels,
          out_channels=gc_channels,
          kernel_size=3,
          padding=1,
      )
  
      self.dropout = nn.Dropout(p=0.1)
  
      self.conv = nn.Conv2D(
          in_channels=gc_channels,
          out_channels=num_classes,
          kernel_size=1,
      )
  
      if enable_auxiliary_loss:
          self.auxlayer = layers.AuxLayer(
              in_channels=backbone_channels[0],
              inter_channels=backbone_channels[0] // 4,
              out_channels=num_classes,
          )
  
      self.backbone_indices = backbone_indices
      self.enable_auxiliary_loss = enable_auxiliary_loss
  
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 GCNetHead object from inherited base classes: nn with example: obj = GCNetHead(num_classes, backbone_indices, backbone_channels, gc_channels, ratio, enable_auxiliary_loss)