code: def __init__(
      self,
      in_chs,
      out_chs,
      ks=1,
      stride=1,
      pad=0,
      dilation=1,
      groups=1,
      bn_weight_init=1,
  ):
      super().__init__()
      self.conv = nn.Conv2d(
          in_chs, out_chs, ks, stride, pad, dilation, groups, bias=False
      )
      self.bn = nn.BatchNorm2d(out_chs)
      torch.nn.init.constant_(self.bn.weight, bn_weight_init)
      torch.nn.init.constant_(self.bn.bias, 0)
  
imports: import itertools
  from collections import OrderedDict
  from typing import Dict
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SqueezeExcite, SelectAdaptivePool2d, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new ConvNorm object from inherited base classes: torch.nn with example: obj = ConvNorm(in_chs, out_chs, ks, stride, pad, dilation, groups, bn_weight_init)