code: def __init__(self, in_channels, out_channels):
      super().__init__()
  
      assert len(in_channels) == 4
      assert isinstance(out_channels, int)
      self.out_channels = out_channels
  
      blocks_out_channels = [out_channels] + [
          min(out_channels * 2**i, 256) for i in range(4)
      ]
      blocks_in_channels = (
          [blocks_out_channels[1]]
          + [
              in_channels[i] + blocks_out_channels[i + 2]
              for i in range(3)
          ]
          + [in_channels[3]]
      )
  
      self.up4 = nn.Conv2DTranspose(
          blocks_in_channels[4],
          blocks_out_channels[4],
          kernel_size=4,
          stride=2,
          padding=1,
      )
      self.up_block3 = UpBlock(
          blocks_in_channels[3], blocks_out_channels[3]
      )
      self.up_block2 = UpBlock(
          blocks_in_channels[2], blocks_out_channels[2]
      )
      self.up_block1 = UpBlock(
          blocks_in_channels[1], blocks_out_channels[1]
      )
      self.up_block0 = UpBlock(
          blocks_in_channels[0], blocks_out_channels[0]
      )
  
file_path: PaddleOCR/ppocr/modeling/necks/fpn_unet.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new FPN_UNet object from inherited base classes: nn with example: obj = FPN_UNet(in_channels, out_channels)