code: def forward(
      self,
      x,
      shortcut: Optional[torch.Tensor] = None,
      children: Optional[List[torch.Tensor]] = None,
  ):
      if children is None:
          children = []
      bottom = self.downsample(x)
      shortcut = self.project(bottom)
      if self.level_root:
          children.append(bottom)
      x1 = self.tree1(x, shortcut)
      if self.root is not None:  # levels == 1
          x2 = self.tree2(x1)
          x = self.root([x2, x1] + children)
      else:
          children.append(x1)
          x = self.tree2(x1, None, children)
      return x
  
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 2 for calling the DlaTree obj's underlying member method code with example usage: obj.forward(x, shortcut, children) and returns: x