code: def __init__(
      self,
      in_channels,
      out_channels,
      skip_channels=0,
      use_batchnorm=True,
  ):
      super().__init__()
      self.conv1 = Conv2DReLU(
          in_channels + skip_channels,
          out_channels,
          kernel_size=3,
          padding=1,
          use_batchnorm=use_batchnorm,
      )
      self.conv2 = Conv2DReLU(
          out_channels,
          out_channels,
          kernel_size=3,
          padding=1,
          use_batchnorm=use_batchnorm,
      )
      self.up = nn.UpsamplingBilinear2D(scale_factor=2)
  
imports: import copy
  import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from medicalseg.cvlibs import manager
  from medicalseg.utils import load_pretrained_model
label_desc: Use image node_id 1 to create a new DecoderBlock object from inherited base classes: nn with example: obj = DecoderBlock(in_channels, out_channels, skip_channels, use_batchnorm)