class_docstr: The ANNHead implementation.
  
  It mainly consists of AFNB and APNB modules.
  
  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 low-level features; the second one will be
          taken as high-level features in AFNB module. Usually backbone consists of four
          downsampling stage, such as ResNet, and return an output of each stage. If it is (2, 3),
          it means taking feature map of the third stage and the fourth stage in backbone.
      backbone_channels (tuple): The same length with "backbone_indices". It indicates the channels of corresponding index.
      key_value_channels (int): The key and value channels of self-attention map in both AFNB and APNB modules.
      inter_channels (int): Both input and output channels of APNB modules.
      psp_size (tuple): The out size of pooled feature maps.
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
code: def forward(self, feat_list):
      logit_list = []
      low_level_x = feat_list[self.backbone_indices[0]]
      high_level_x = feat_list[self.backbone_indices[1]]
      x = self.fusion(low_level_x, high_level_x)
      x = self.context(x)
      logit = self.cls(x)
      logit_list.append(logit)
  
      if self.enable_auxiliary_loss:
          auxiliary_logit = self.auxlayer(low_level_x)
          logit_list.append(auxiliary_logit)
  
      return logit_list
  
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 2 for calling the ANNHead obj's underlying member method code with example usage: obj.forward(feat_list) and returns: logit_list