code: def __init__(
      self,
      levels,
      block,
      in_channels,
      out_channels,
      stride=1,
      dilation=1,
      cardinality=1,
      base_width=64,
      level_root=False,
      root_dim=0,
      root_kernel_size=1,
      root_shortcut=False,
  ):
      super(DlaTree, self).__init__()
      if root_dim == 0:
          root_dim = 2 * out_channels
      if level_root:
          root_dim += in_channels
      self.downsample = (
          nn.MaxPool2d(stride, stride=stride)
          if stride > 1
          else nn.Identity()
      )
      self.project = nn.Identity()
      cargs = dict(
          dilation=dilation,
          cardinality=cardinality,
          base_width=base_width,
      )
      if levels == 1:
          self.tree1 = block(in_channels, out_channels, stride, **cargs)
          self.tree2 = block(out_channels, out_channels, 1, **cargs)
          if in_channels != out_channels:
              # NOTE the official impl/weights have  project layers in levels > 1 case that are never
              # used, I've moved the project layer here to avoid wasted params but old checkpoints will
              # need strict=False while loading.
              self.project = nn.Sequential(
                  nn.Conv2d(
                      in_channels,
                      out_channels,
                      kernel_size=1,
                      stride=1,
                      bias=False,
                  ),
                  nn.BatchNorm2d(out_channels),
              )
          self.root = DlaRoot(
              root_dim, out_channels, root_kernel_size, root_shortcut
          )
      else:
          cargs.update(
              dict(
                  root_kernel_size=root_kernel_size,
                  root_shortcut=root_shortcut,
              )
          )
          self.tree1 = DlaTree(
              levels - 1,
              block,
              in_channels,
              out_channels,
              stride,
              root_dim=0,
              **cargs,
          )
          self.tree2 = DlaTree(
              levels - 1,
              block,
              out_channels,
              out_channels,
              root_dim=root_dim + out_channels,
              **cargs,
          )
          self.root = None
      self.level_root = level_root
      self.root_dim = root_dim
      self.levels = levels
  
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 DlaTree object from inherited base classes: nn with example: obj = DlaTree(levels, block, in_channels, out_channels, stride, dilation, cardinality, base_width, level_root, root_dim, root_kernel_size, root_shortcut)