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.
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","."]
code: def __init__(
      self, num_classes, in_channels, align_corners, data_format="NCHW"
  ):
      super(Decoder, self).__init__()
  
      self.data_format = data_format
      self.conv_bn_relu1 = layers.ConvBNReLU(
          in_channels=in_channels,
          out_channels=48,
          kernel_size=1,
          data_format=data_format,
      )
  
      self.conv_bn_relu2 = layers.SeparableConvBNReLU(
          in_channels=304,
          out_channels=256,
          kernel_size=3,
          padding=1,
          data_format=data_format,
      )
      self.conv_bn_relu3 = layers.SeparableConvBNReLU(
          in_channels=256,
          out_channels=256,
          kernel_size=3,
          padding=1,
          data_format=data_format,
      )
      self.conv = nn.Conv2D(
          in_channels=256,
          out_channels=num_classes,
          kernel_size=1,
          data_format=data_format,
      )
  
      self.align_corners = align_corners
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
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, data_format)