class_docstr: The FAM module implementation.
  
  Args:
     inplane (int): Input channles of FAM module.
     outplane (int): Output channels of FAN module.
     kernel_size (int, optional): Kernel size of semantic flow convolution layer. Default: 3.
class_docstr_tok: ["The","FAM","module","implementation",".","Args",":","inplane","(","int",")",":","Input","channles","of","FAM","module",".","outplane","(","int",")",":","Output","channels","of","FAN","module",".","kernel_size","(","int",",","optional",")",":","Kernel","size","of","semantic","flow","convolution","layer",".","Default",":","3","."]
code: def __init__(self, inplane, outplane, kernel_size=3):
      super(AlignedModule, self).__init__()
      self.down_h = nn.Conv2D(inplane, outplane, 1, bias_attr=False)
      self.down_l = nn.Conv2D(inplane, outplane, 1, bias_attr=False)
      self.flow_make = nn.Conv2D(
          outplane * 2,
          2,
          kernel_size=kernel_size,
          padding=1,
          bias_attr=False,
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new AlignedModule object from inherited base classes: nn with example: obj = AlignedModule(inplane, outplane, kernel_size)