class_docstr: Decoder module of DeepLabV3P model
  
  Args:
      num_classes (int): The number of classes.
      in_channels (int): The number of input channels in decoder module.
      align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.
class_docstr_tok: ["Decoder","module","of","DeepLabV3P","model","Args",":","num_classes","(","int",")",":","The","number","of","classes",".","in_channels","(","int",")",":","The","number","of","input","channels","in","decoder","module",".","align_corners","(","bool",")",":","An","argument","of","F.interpolate",".","It","should","be","set","to","False","when","the","output","size","of","feature","is","even",",","e.g",".","1024x512",",","otherwise","it","is","True",",","e.g",".","769x769","."]
code: def __init__(
      self, num_classes: int, in_channels: int, align_corners: bool
  ):
      super(Decoder, self).__init__()
  
      self.conv_bn_relu1 = L.ConvBNReLU(
          in_channels=in_channels, out_channels=48, kernel_size=1
      )
  
      self.conv_bn_relu2 = L.SeparableConvBNReLU(
          in_channels=304, out_channels=256, kernel_size=3, padding=1
      )
      self.conv_bn_relu3 = L.SeparableConvBNReLU(
          in_channels=256, out_channels=256, kernel_size=3, padding=1
      )
      self.conv = nn.Conv2D(
          in_channels=256, out_channels=num_classes, kernel_size=1
      )
  
      self.align_corners = align_corners
  
imports: import os
  from typing import Union, List, Tuple
  import paddle
  from paddle import nn
  import paddle.nn.functional
  import numpy
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  from deeplabv3p_resnet50_cityscapes.resnet import ResNet50_vd
  import deeplabv3p_resnet50_cityscapes.layers
label_desc: Use image node_id 1 to create a new Decoder object from inherited base classes: nn with example: obj = Decoder(num_classes, in_channels, align_corners)