class_docstr: The PSPNetHead 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 Pyramid Pooling Module (PPModule).
          Usually backbone consists of four downsampling stage, and return an output of
          each stage. If we set it as (2, 3) in ResNet, that means taking feature map of the third
          stage (res4b22) in backbone, and feature map of the fourth stage (res5c) as input of PPModule.
      backbone_channels (tuple): The same length with "backbone_indices". It indicates the channels of corresponding index.
      pp_out_channels (int): The output channels after Pyramid Pooling Module.
      bin_sizes (tuple): The out size of pooled feature maps.
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
      align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
          is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.
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code: def forward(
      self, feat_list: List[paddle.Tensor]
  ) -> List[paddle.Tensor]:
      logit_list = []
      x = feat_list[self.backbone_indices[1]]
      x = self.psp_module(x)
      x = self.dropout(x)
      logit = self.conv(x)
      logit_list.append(logit)
  
      if self.enable_auxiliary_loss:
          auxiliary_feat = feat_list[self.backbone_indices[0]]
          auxiliary_logit = self.auxlayer(auxiliary_feat)
          logit_list.append(auxiliary_logit)
  
      return logit_list
  
file_path: PaddleHub/modules/image/semantic_segmentation/pspnet_resnet50_voc/module.py
imports: import os
  from typing import Union, List, Tuple
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  from pspnet_resnet50_voc.resnet import ResNet50_vd
  import pspnet_resnet50_voc.layers
label_desc: Use image node_id 2 for calling the PSPNetHead obj's underlying member method code with example usage: obj.forward(feat_list) and returns: logit_list