class_docstr: The PFPNHead 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 FPN module. Default: 512.
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: False.
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code: def forward(self, conv_out):
      last_out = self.lateral_convs[-1](conv_out[-1])
      f = last_out
      fpn_feature_list = [last_out]
      for i in reversed(range(len(conv_out) - 1)):
          conv_x = conv_out[i]
          conv_x = self.lateral_convs[i](conv_x)
          prev_shape = paddle.shape(conv_x)[2:]
          f = conv_x + F.interpolate(
              f, prev_shape, mode="bilinear", align_corners=True
          )
          fpn_feature_list.append(self.fpn_out[i](f))
  
      output_size = paddle.shape(fpn_feature_list[-1])[2:]
  
      x = self.scale_heads[0](fpn_feature_list[-1])
      for index in range(len(self.scale_heads) - 2, 0, -1):
          x = x + F.interpolate(
              self.scale_heads[index](fpn_feature_list[index]),
              size=output_size,
              mode="bilinear",
              align_corners=self.align_corners,
          )
      x = self.cls_seg(x)
      if self.enable_auxiliary_loss:
          dsn = self.dsn(conv_out[2])
          return [x, dsn]
      else:
          return [x]
  
file_path: PaddleSeg/paddleseg/models/pfpnnet.py
imports: import numpy
  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 3 for calling the PFPNHead obj's underlying member method code with example usage: obj.forward(conv_out) and returns: list, list