code: def __init__(
      self, in_channels, out_channels, align_corners, use_deconv=False
  ):
      super().__init__()
  
      self.align_corners = align_corners
  
      self.use_deconv = use_deconv
      if self.use_deconv:
          self.deconv = nn.Conv2DTranspose(
              in_channels,
              out_channels // 2,
              kernel_size=2,
              stride=2,
              padding=0,
          )
          in_channels = in_channels + out_channels // 2
      else:
          in_channels *= 2
  
      self.double_conv = nn.Sequential(
          layers.ConvBNReLU(in_channels, out_channels, 3),
          layers.ConvBNReLU(out_channels, out_channels, 3),
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg import utils
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
label_desc: Use image node_id 1 to create a new UpSampling object from inherited base classes: nn with example: obj = UpSampling(in_channels, out_channels, align_corners, use_deconv)