code: def __init__(
      self,
      levels,
      block,
      in_channels,
      out_channels,
      stride=1,
      level_root=False,
      root_dim=0,
      root_kernel_size=1,
      dilation=1,
      root_residual=False,
      norm_layer=BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(Tree, self).__init__(**kwargs)
      if norm_kwargs is None:
          norm_kwargs = {}
      if root_dim == 0:
          root_dim = 2 * out_channels
      if level_root:
          root_dim = root_dim + in_channels
      with self.name_scope():
          self.downsample = nn.HybridSequential()
          self.project = nn.HybridSequential()
          if levels == 1:
              self.tree1 = block(
                  in_channels,
                  out_channels,
                  stride,
                  dilation=dilation,
                  norm_layer=norm_layer,
                  norm_kwargs=norm_kwargs,
                  prefix="block_tree_1_",
              )
              self.tree2 = block(
                  out_channels,
                  out_channels,
                  1,
                  dilation=dilation,
                  norm_layer=norm_layer,
                  norm_kwargs=norm_kwargs,
                  prefix="block_tree_2_",
              )
              if in_channels != out_channels:
                  self.project.add(
                      *[
                          nn.Conv2D(
                              in_channels=in_channels,
                              channels=out_channels,
                              kernel_size=1,
                              strides=1,
                              use_bias=False,
                              prefix="proj_conv0_",
                          ),
                          norm_layer(
                              in_channels=out_channels,
                              prefix="proj_bn0_",
                              **norm_kwargs,
                          ),
                      ]
                  )
          else:
              self.tree1 = Tree(
                  levels - 1,
                  block,
                  in_channels,
                  out_channels,
                  stride,
                  root_dim=0,
                  root_kernel_size=root_kernel_size,
                  dilation=dilation,
                  root_residual=root_residual,
                  norm_layer=norm_layer,
                  norm_kwargs=norm_kwargs,
                  prefix="tree_1_",
              )
              self.tree2 = Tree(
                  levels - 1,
                  block,
                  out_channels,
                  out_channels,
                  root_dim=root_dim + out_channels,
                  root_kernel_size=root_kernel_size,
                  dilation=dilation,
                  root_residual=root_residual,
                  norm_layer=norm_layer,
                  norm_kwargs=norm_kwargs,
                  prefix="tree_2_",
              )
          if levels == 1:
              self.root = Root(
                  root_dim,
                  out_channels,
                  root_kernel_size,
                  root_residual,
                  norm_layer=norm_layer,
                  norm_kwargs=norm_kwargs,
                  prefix="root_",
              )
          self.level_root = level_root
          self.root_dim = root_dim
          self.levels = levels
          if stride > 1:
              self.downsample.add(
                  nn.MaxPool2D(stride, strides=stride, prefix="maxpool")
              )
  
imports: from __future__ import division
  import os
  import mxnet
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
label_desc: Use image node_id 1 to create a new Tree object from inherited base classes: HybridBlock with example: obj = Tree(levels, block, in_channels, out_channels, stride, level_root, root_dim, root_kernel_size, dilation, root_residual, norm_layer, norm_kwargs)