code: def __init__(
      self, hidden_size, decoder_channels, n_skip, skip_channels
  ):
      super().__init__()
      head_channels = 512
      self.conv_more = Conv2DReLU(
          hidden_size,
          head_channels,
          kernel_size=3,
          padding=1,
          use_batchnorm=True,
      )
      decoder_channels = decoder_channels
      in_channels = [head_channels] + list(decoder_channels[:-1])
      out_channels = decoder_channels
      self.n_skip = n_skip
      if n_skip != 0:
          skip_channels = skip_channels
          for i in range(
              4 - n_skip
          ):  # re-select the skip channels according to n_skip
              skip_channels[3 - i] = 0
  
      else:
          skip_channels = [0, 0, 0, 0]
  
      blocks = [
          DecoderBlock(in_ch, out_ch, sk_ch)
          for in_ch, out_ch, sk_ch in zip(
              in_channels, out_channels, skip_channels
          )
      ]
      self.blocks = nn.LayerList(blocks)
  
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 DecoderCup object from inherited base classes: nn with example: obj = DecoderCup(hidden_size, decoder_channels, n_skip, skip_channels)