code: def __init__(self, in_channels, out_channels, kernel_size, shortcut):
      super(DlaRoot, self).__init__()
      self.conv = nn.Conv2d(
          in_channels,
          out_channels,
          1,
          stride=1,
          bias=False,
          padding=(kernel_size - 1) // 2,
      )
      self.bn = nn.BatchNorm2d(out_channels)
      self.relu = nn.ReLU(inplace=True)
      self.shortcut = shortcut
  
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 DlaRoot object from inherited base classes: nn with example: obj = DlaRoot(in_channels, out_channels, kernel_size, shortcut)