docstr: 1. double the output channel and downsample the input using down_conv(the kernel size can be changed)
  2. add dropout by option
  3. add nConvs layer to add linearity and add with original downsample one
docstr_tok: ["1.","double","the","output","channel","and","downsample","the","input","using","down_conv","(","the","kernel","size","can","be","changed",")","2.","add","dropout","by","option","3.","add","nConvs","layer","to","add","linearity","and","add","with","original","downsample","one"]
code: def __init__(
      self,
      inChans,
      nConvs,
      elu,
      dropout=False,
      downsample_stride=(2, 2, 2),
      kernel=(2, 2, 2),
  ):
      """
      1. double the output channel and downsample the input using down_conv(the kernel size can be changed)
      2. add dropout by option
      3. add nConvs layer to add linearity and add with original downsample one
      """
      super(DownTransition, self).__init__()
      outChans = 2 * inChans
      self.if_dropout = dropout
      self.down_conv = nn.Conv3D(
          inChans,
          outChans,
          kernel_size=kernel,
          stride=downsample_stride,
      )
      self.bn1 = nn.BatchNorm3D(outChans)
      self.relu1 = nn.ELU() if elu else nn.PReLU(outChans)
      self.relu2 = nn.ELU() if elu else nn.PReLU(outChans)
      self.dropout = nn.Dropout3D()
      self.ops = _make_nConv(outChans, nConvs, elu)
  
imports: import os
  import sys
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from medicalseg.cvlibs import manager
  from medicalseg.utils import utils
label_desc: Use image node_id 1 to create a new DownTransition object from inherited base classes: nn with example: obj = DownTransition(inChans, nConvs, elu, dropout, downsample_stride, kernel)