code: def _pp_module(
      x: paddle.Tensor, psp_size: List[int]
  ) -> paddle.Tensor:
      n, c, h, w = x.shape
      priors = []
      for size in psp_size:
          feat = F.adaptive_avg_pool2d(x, size)
          feat = paddle.reshape(feat, shape=(0, c, -1))
          priors.append(feat)
      center = paddle.concat(priors, axis=-1)
      return center
  
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_cityscapes.resnet import ResNet50_vd
  import ann_resnet50_cityscapes.layers
label_desc: Use image node_id 1 for calling a global function with example usage: _pp_module(x, psp_size) and returns: center