class_docstr: Self-Attention Block for APNB module.
  
  Args:
      in_channels (int): The input channels of APNB module.
      out_channels (int): The out channels of APNB module.
      key_channels (int): The key channels in self-attention block.
      value_channels (int): The value channels in self-attention block.
      scale (int, optional): Pooling size. Default: 1.
      psp_size (tuple, optional): The out size of pooled feature maps. Default: (1, 3, 6, 8).
code: def forward(self, x: paddle.Tensor) -> paddle.Tensor:
      batch_size, _, h, w = x.shape
      if self.scale > 1:
          x = self.pool(x)
  
      value = self.f_value(x)
      value = _pp_module(value, self.psp_size)
      value = paddle.transpose(value, perm=(0, 2, 1))
  
      query = self.f_query(x)
      query = paddle.reshape(query, shape=(0, self.key_channels, -1))
      query = paddle.transpose(query, perm=(0, 2, 1))
  
      key = self.f_key(x)
      key = _pp_module(key, self.psp_size)
  
      sim_map = paddle.matmul(query, key)
      sim_map = (self.key_channels**-0.5) * sim_map
      sim_map = F.softmax(sim_map, axis=-1)
  
      context = paddle.matmul(sim_map, value)
      context = paddle.transpose(context, perm=(0, 2, 1))
  
      x_shape = paddle.shape(x)
      context = paddle.reshape(
          context,
          shape=[0, self.value_channels, x_shape[2], x_shape[3]],
      )
      context = self.W(context)
  
      return context
  
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 ann_resnet50_voc.resnet import ResNet50_vd
  import ann_resnet50_voc.layers
label_desc: Use image node_id 2 for calling the SelfAttentionBlock_APNB obj's underlying member method code with example usage: obj.forward(x) and returns: context