code: def __init__(
      self, in_channels, out_channels, name=None, padding_same=True
  ):
      super(TransitionLayer, self).__init__()
  
      num_in = len(in_channels)
      num_out = len(out_channels)
      self.conv_bn_func_list = []
      for i in range(num_out):
          residual = None
          if i < num_in:
              if in_channels[i] != out_channels[i]:
                  residual = self.add_sublayer(
                      "transition_{}_layer_{}".format(name, i + 1),
                      layers.ConvBNReLU(
                          in_channels=in_channels[i],
                          out_channels=out_channels[i],
                          kernel_size=3,
                          padding=1 if not padding_same else "same",
                          bias_attr=False,
                      ),
                  )
          else:
              residual = self.add_sublayer(
                  "transition_{}_layer_{}".format(name, i + 1),
                  layers.ConvBNReLU(
                      in_channels=in_channels[-1],
                      out_channels=out_channels[i],
                      kernel_size=3,
                      stride=2,
                      padding=1 if not padding_same else "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
  import ppmatting
label_desc: Use image node_id 1 to create a new TransitionLayer object from inherited base classes: nn with example: obj = TransitionLayer(in_channels, out_channels, name, padding_same)