code: def __init__(
      self, dim_in=3, dim_out=2, down_scale=4, norm_first=False
  ):
      super().__init__()
      self.norm_first = norm_first
      if norm_first:
          self.norm = LayerNorm(
              dim_in, eps=1e-6, data_format=torch.contiguous_format
          )
          self.conv = nn.Conv2d(
              dim_in, dim_out, kernel_size=down_scale, stride=down_scale
          )
      else:
          self.conv = nn.Conv2d(
              dim_in, dim_out, kernel_size=down_scale, stride=down_scale
          )
          self.norm = LayerNorm(
              dim_out, eps=1e-6, data_format=torch.contiguous_format
          )
  
imports: import os
  import time
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  from torch.distributed._tensor import DeviceMesh, distribute_module, distribute_tensor, Replicate, Shard
label_desc: Use image node_id 1 to create a new DownSampling object from inherited base classes: nn with example: obj = DownSampling(dim_in, dim_out, down_scale, norm_first)