code: def __init__(
      self,
      inChans,
      outChans,
      nConvs,
      elu,
      dropout=False,
      dropout2=False,
      upsample_stride_size=(2, 2, 2),
      kernel=(2, 2, 2),
  ):
      super(UpTransition, self).__init__()
      """
          1. Add dropout to input and skip input optionally (generalization)
          2. Use Conv3DTranspose to upsample (upsample)
          3. concate the upsampled and skipx (multi-leval feature fusion)
          4. Add nConvs convs and residually add with result of step(residual + nonlinearity)
          """
      self.up_conv = nn.Conv3DTranspose(
          inChans,
          outChans // 2,
          kernel_size=kernel,
          stride=upsample_stride_size,
      )
  
      self.bn1 = nn.BatchNorm3D(outChans // 2)
      self.relu1 = nn.ELU() if elu else nn.PReLU(outChans // 2)
      self.relu2 = nn.ELU() if elu else nn.PReLU(outChans)
      self.if_dropout = dropout
      self.if_dropout2 = dropout2
      self.dropout1 = nn.Dropout3D()
      self.dropout2 = nn.Dropout3D()
      self.ops = _make_nConv(outChans, nConvs, elu)
  
imports: import os
  import sys
  import paddle
  import paddle.nn
  import numpy
  from inference.ops import DistMaps3D, ScaleLayer, BatchImageNormalize3D
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new UpTransition object from inherited base classes: nn with example: obj = UpTransition(inChans, outChans, nConvs, elu, dropout, dropout2, upsample_stride_size, kernel)