code: def forward(self, x):
      b, c, h, w = x.shape
  
      avg_pool = self.pool(x)
  
      h_pos_encoding = (
          self.h_scale(avg_pool) * self.h_position_encoder[:, :, :h, :]
      )
      w_pos_encoding = (
          self.w_scale(avg_pool) * self.w_position_encoder[:, :, :, :w]
      )
  
      out = x + h_pos_encoding + w_pos_encoding
  
      out = self.dropout(out)
  
      return out
  
imports: import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr, reshape, transpose
  from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
  from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
  from paddle.nn.initializer import KaimingNormal, Uniform, Constant
label_desc: Use image node_id 4 for calling the Adaptive2DPositionalEncoding obj's underlying member method code with example usage: obj.forward(x) and returns: out