code: def __init__(
      self,
      levels,
      channels,
      output_stride=32,
      num_classes=1000,
      in_chans=3,
      global_pool="avg",
      cardinality=1,
      base_width=64,
      block=DlaBottle2neck,
      shortcut_root=False,
      drop_rate=0.0,
  ):
      super(DLA, self).__init__()
      self.channels = channels
      self.num_classes = num_classes
      self.cardinality = cardinality
      self.base_width = base_width
      assert output_stride == 32  # FIXME support dilation
  
      self.base_layer = nn.Sequential(
          nn.Conv2d(
              in_chans,
              channels[0],
              kernel_size=7,
              stride=1,
              padding=3,
              bias=False,
          ),
          nn.BatchNorm2d(channels[0]),
          nn.ReLU(inplace=True),
      )
      self.level0 = self._make_conv_level(
          channels[0], channels[0], levels[0]
      )
      self.level1 = self._make_conv_level(
          channels[0], channels[1], levels[1], stride=2
      )
      cargs = dict(
          cardinality=cardinality,
          base_width=base_width,
          root_shortcut=shortcut_root,
      )
      self.level2 = DlaTree(
          levels[2],
          block,
          channels[1],
          channels[2],
          2,
          level_root=False,
          **cargs,
      )
      self.level3 = DlaTree(
          levels[3],
          block,
          channels[2],
          channels[3],
          2,
          level_root=True,
          **cargs,
      )
      self.level4 = DlaTree(
          levels[4],
          block,
          channels[3],
          channels[4],
          2,
          level_root=True,
          **cargs,
      )
      self.level5 = DlaTree(
          levels[5],
          block,
          channels[4],
          channels[5],
          2,
          level_root=True,
          **cargs,
      )
      self.feature_info = [
          dict(
              num_chs=channels[0], reduction=1, module="level0"
          ),  # rare to have a meaningful stride 1 level
          dict(num_chs=channels[1], reduction=2, module="level1"),
          dict(num_chs=channels[2], reduction=4, module="level2"),
          dict(num_chs=channels[3], reduction=8, module="level3"),
          dict(num_chs=channels[4], reduction=16, module="level4"),
          dict(num_chs=channels[5], reduction=32, module="level5"),
      ]
  
      self.num_features = channels[-1]
      self.global_pool, self.head_drop, self.fc = create_classifier(
          self.num_features,
          self.num_classes,
          pool_type=global_pool,
          use_conv=True,
          drop_rate=drop_rate,
      )
      self.flatten = nn.Flatten(1) if global_pool else nn.Identity()
  
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
              m.weight.data.normal_(0, math.sqrt(2.0 / n))
          elif isinstance(m, nn.BatchNorm2d):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
  
imports: import math
  from typing import List, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import create_classifier
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new DLA object from inherited base classes: nn with example: obj = DLA(levels, channels, output_stride, num_classes, in_chans, global_pool, cardinality, base_width, block, shortcut_root, drop_rate)