code: def __init__(
      self,
      level,
      block,
      ch_in,
      ch_out,
      stride=1,
      level_root=False,
      root_dim=0,
      root_kernel_size=1,
      root_residual=False,
  ):
      super(Tree, self).__init__()
      if root_dim == 0:
          root_dim = 2 * ch_out
      if level_root:
          root_dim += ch_in
      if level == 1:
          self.tree1 = block(ch_in, ch_out, stride)
          self.tree2 = block(ch_out, ch_out, 1)
      else:
          self.tree1 = Tree(
              level - 1,
              block,
              ch_in,
              ch_out,
              stride,
              root_dim=0,
              root_kernel_size=root_kernel_size,
              root_residual=root_residual,
          )
          self.tree2 = Tree(
              level - 1,
              block,
              ch_out,
              ch_out,
              1,
              root_dim=root_dim + ch_out,
              root_kernel_size=root_kernel_size,
              root_residual=root_residual,
          )
  
      if level == 1:
          self.root = Root(
              root_dim, ch_out, root_kernel_size, root_residual
          )
      self.level_root = level_root
      self.root_dim = root_dim
      self.downsample = None
      self.project = None
      self.level = level
      if stride > 1:
          self.downsample = nn.MaxPool2D(stride, stride=stride)
      if ch_in != ch_out:
          self.project = ConvNormLayer(
              ch_in,
              ch_out,
              filter_size=1,
              stride=1,
              bias_on=False,
              norm_decay=None,
          )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register, serializable
  from ppdet.modeling.layers import ConvNormLayer
  from ..shape_spec import ShapeSpec
label_desc: Use image node_id 1 to create a new Tree object from inherited base classes: nn with example: obj = Tree(level, block, ch_in, ch_out, stride, level_root, root_dim, root_kernel_size, root_residual)