code: def __init__(
      self,
      levels,
      channels,
      classes=1000,
      block=BasicBlock,
      momentum=0.9,
      norm_layer=BatchNorm,
      norm_kwargs=None,
      residual_root=False,
      linear_root=False,
      use_feature=False,
      **kwargs,
  ):
      super(DLA, self).__init__(**kwargs)
      if norm_kwargs is None:
          norm_kwargs = {}
      norm_kwargs["momentum"] = momentum
      self._use_feature = use_feature
      self.channels = channels
      self.base_layer = nn.HybridSequential("base")
      self.base_layer.add(
          nn.Conv2D(
              in_channels=3,
              channels=channels[0],
              kernel_size=7,
              strides=1,
              padding=3,
              use_bias=False,
          )
      )
      self.base_layer.add(
          norm_layer(in_channels=channels[0], **norm_kwargs)
      )
      self.base_layer.add(nn.Activation("relu"))
  
      self.level0 = self._make_conv_level(
          channels[0], channels[0], levels[0], norm_layer, norm_kwargs
      )
      self.level1 = self._make_conv_level(
          channels[0],
          channels[1],
          levels[1],
          norm_layer,
          norm_kwargs,
          stride=2,
      )
      self.level2 = Tree(
          levels[2],
          block,
          channels[1],
          channels[2],
          2,
          level_root=False,
          root_residual=residual_root,
          norm_layer=norm_layer,
          norm_kwargs=norm_kwargs,
          prefix="level2_",
      )
      self.level3 = Tree(
          levels[3],
          block,
          channels[2],
          channels[3],
          2,
          level_root=True,
          root_residual=residual_root,
          norm_layer=norm_layer,
          norm_kwargs=norm_kwargs,
          prefix="level3_",
      )
      self.level4 = Tree(
          levels[4],
          block,
          channels[3],
          channels[4],
          2,
          level_root=True,
          root_residual=residual_root,
          norm_layer=norm_layer,
          norm_kwargs=norm_kwargs,
          prefix="level4_",
      )
      self.level5 = Tree(
          levels[5],
          block,
          channels[4],
          channels[5],
          2,
          level_root=True,
          root_residual=residual_root,
          norm_layer=norm_layer,
          norm_kwargs=norm_kwargs,
          prefix="level5_",
      )
  
      if not self._use_feature:
          self.global_avg_pool = nn.GlobalAvgPool2D()
          self.fc = nn.Dense(units=classes)
  
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 DLA object from inherited base classes: HybridBlock with example: obj = DLA(levels, channels, classes, block, momentum, norm_layer, norm_kwargs, residual_root, linear_root, use_feature)