code: def __init__(
      self, in_channels, out_channels, kernel_size=3, upsampling=1
  ):
      conv2d = nn.Conv2D(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          padding=kernel_size // 2,
      )
      upsampling = (
          nn.UpsamplingBilinear2D(scale_factor=upsampling)
          if upsampling > 1
          else nn.Identity()
      )
      super().__init__(conv2d, upsampling)
  
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 SegmentationHead object from inherited base classes: nn with example: obj = SegmentationHead(in_channels, out_channels, kernel_size, upsampling)