class_docstr: The SFNetHead implementation.
  
  Args:
      inplane (int): Input channels of PPM module.
      num_class (int): The unique number of target classes.
      fpn_inplanes (list): The feature channels from backbone.
      fpn_dim (int, optional): The input channels of FAM module. Default: 256.
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: False.
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code: def __init__(
      self,
      inplane,
      num_class,
      fpn_inplanes,
      fpn_dim=256,
      enable_auxiliary_loss=False,
  ):
      super(SFNetHead, self).__init__()
      self.ppm = layers.PPModule(
          in_channels=inplane,
          out_channels=fpn_dim,
          bin_sizes=(1, 2, 3, 6),
          dim_reduction=True,
          align_corners=True,
      )
      self.enable_auxiliary_loss = enable_auxiliary_loss
      self.fpn_in = []
  
      for fpn_inplane in fpn_inplanes[:-1]:
          self.fpn_in.append(
              nn.Sequential(
                  nn.Conv2D(fpn_inplane, fpn_dim, 1),
                  layers.SyncBatchNorm(fpn_dim),
                  nn.ReLU(),
              )
          )
  
      self.fpn_in = nn.LayerList(self.fpn_in)
      self.fpn_out = []
      self.fpn_out_align = []
      self.dsn = []
      for i in range(len(fpn_inplanes) - 1):
          self.fpn_out.append(
              nn.Sequential(
                  layers.ConvBNReLU(
                      fpn_dim, fpn_dim, 3, bias_attr=False
                  )
              )
          )
          self.fpn_out_align.append(
              AlignedModule(inplane=fpn_dim, outplane=fpn_dim // 2)
          )
          if self.enable_auxiliary_loss:
              self.dsn.append(
                  nn.Sequential(
                      layers.AuxLayer(fpn_dim, fpn_dim, num_class)
                  )
              )
  
      self.fpn_out = nn.LayerList(self.fpn_out)
      self.fpn_out_align = nn.LayerList(self.fpn_out_align)
  
      if self.enable_auxiliary_loss:
          self.dsn = nn.LayerList(self.dsn)
  
      self.conv_last = nn.Sequential(
          layers.ConvBNReLU(
              len(fpn_inplanes) * fpn_dim, fpn_dim, 3, bias_attr=False
          ),
          nn.Conv2D(fpn_dim, num_class, kernel_size=1),
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new SFNetHead object from inherited base classes: nn with example: obj = SFNetHead(inplane, num_class, fpn_inplanes, fpn_dim, enable_auxiliary_loss)