code: def __init__(
      self,
      stride_pre,
      in_channel,
      stride_cur,
      out_channels,
      align_corners=False,
      name=None,
  ):
      super(TransitionLayer, self).__init__()
      self.align_corners = align_corners
      num_out = len(out_channels)
      if num_out != len(stride_cur):
          raise ValueError(
              "The length of `out_channels` does not equal to the length of `stride_cur`".format(
                  num_out, len(stride_cur)
              )
          )
      self.conv_bn_func_list = []
      for i in range(num_out):
          residual = None
          if stride_cur[i] == stride_pre:
              if in_channel != out_channels[i]:
                  residual = self.add_sublayer(
                      "transition_{}_layer_{}".format(name, i + 1),
                      layers.ConvBNReLU(
                          in_channels=in_channel,
                          out_channels=out_channels[i],
                          kernel_size=3,
                          padding="same",
                          bias_attr=False,
                      ),
                  )
          elif stride_cur[i] > stride_pre:
              residual = self.add_sublayer(
                  "transition_{}_layer_{}".format(name, i + 1),
                  layers.ConvBNReLU(
                      in_channels=in_channel,
                      out_channels=out_channels[i],
                      kernel_size=3,
                      stride=2,
                      padding="same",
                      bias_attr=False,
                  ),
              )
          else:
              residual = self.add_sublayer(
                  "transition_{}_layer_{}".format(name, i + 1),
                  layers.ConvBNReLU(
                      in_channels=in_channel,
                      out_channels=out_channels[i],
                      kernel_size=1,
                      stride=1,
                      padding="same",
                      bias_attr=False,
                  ),
              )
          self.conv_bn_func_list.append(residual)
  
imports: import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new TransitionLayer object from inherited base classes: nn with example: obj = TransitionLayer(stride_pre, in_channel, stride_cur, out_channels, align_corners, name)