code: def __init__(
      self,
      in_chs,
      out_chs,
      norm_layer=LayerNorm2d,
  ):
      super().__init__()
      self.in_chs = in_chs
      self.out_chs = out_chs
  
      self.norm = norm_layer(in_chs)
      self.conv = nn.Conv2d(
          in_chs,
          out_chs,
          kernel_size=2,
          stride=2,
          padding=0,
      )
  
imports: from functools import partial
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, trunc_normal_, Mlp, LayerNorm2d, get_norm_layer, use_fused_attn
  from timm.layers import NormMlpClassifierHead, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._manipulate import checkpoint_seq
  from ._registry import generate_default_cfgs, register_model
label_desc: Use image node_id 1 to create a new Downsample object from inherited base classes: nn with example: obj = Downsample(in_chs, out_chs, norm_layer)