code: def __init__(self, F_g, F_l, F_out):
      super().__init__()
      self.W_g = nn.Sequential(
          nn.Conv2D(F_g, F_out, kernel_size=1, stride=1, padding=0),
          nn.BatchNorm2D(F_out),
      )
  
      self.W_x = nn.Sequential(
          nn.Conv2D(F_l, F_out, kernel_size=1, stride=1, padding=0),
          nn.BatchNorm2D(F_out),
      )
  
      self.psi = nn.Sequential(
          nn.Conv2D(F_out, 1, kernel_size=1, stride=1, padding=0),
          nn.BatchNorm2D(1),
          nn.Sigmoid(),
      )
  
      self.relu = nn.ReLU()
  
imports: import paddle
  import paddle.nn
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg import utils
  import numpy
label_desc: Use image node_id 1 to create a new AttentionBlock object from inherited base classes: nn with example: obj = AttentionBlock(F_g, F_l, F_out)