code: def __init__(
      self,
      in_chs,
      out_chs,
      stride=1,
      dilation=1,
      first_dilation=None,
      preact=True,
      conv_layer=None,
      norm_layer=None,
  ):
      super(DownsampleConv, self).__init__()
      self.conv = conv_layer(in_chs, out_chs, 1, stride=stride)
      self.norm = (
          nn.Identity()
          if preact
          else norm_layer(out_chs, apply_act=False)
      )
  
imports: from collections import OrderedDict
  from functools import partial
  import torch
  import torch.nn
  from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import GroupNormAct, BatchNormAct2d, EvoNorm2dS0, FilterResponseNormTlu2d, ClassifierHead, DropPath, AvgPool2dSame, create_pool2d, StdConv2d, create_conv2d, get_act_layer, get_norm_act_layer, make_divisible
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq, named_apply, adapt_input_conv
  from ._registry import generate_default_cfgs, register_model, register_model_deprecations
label_desc: Use image node_id 1 to create a new DownsampleConv object from inherited base classes: nn with example: obj = DownsampleConv(in_chs, out_chs, stride, dilation, first_dilation, preact, conv_layer, norm_layer)