code: def __init__(
      self,
      in_chans=3,
      num_classes=10,
      depths=[1, 1],  # noqa: B006
      dims=[2, 4],  # noqa: B006
      drop_path_rate=0.0,
      layer_scale_init_value=1e-6,
      head_init_scale=1.0,
  ):
      super().__init__()
  
      self.downsample_layers = nn.ModuleList()
      stem = DownSampling(in_chans, dims[0], 4, norm_first=False)
      self.downsample_layers.append(stem)
      for i in range(len(dims) - 1):
          downsample_layer = DownSampling(
              dims[i], dims[i + 1], 2, norm_first=True
          )
          self.downsample_layers.append(downsample_layer)
  
      self.stages = nn.ModuleList()
      dp_rates = [
          x.item()
          for x in torch.linspace(0, drop_path_rate, sum(depths))
      ]
      cur = 0
      for i in range(len(dims)):
          stage = nn.Sequential(
              *[
                  Block(
                      dim=dims[i],
                      drop_path=dp_rates[cur + j],
                      layer_scale_init_value=layer_scale_init_value,
                  )
                  for j in range(depths[i])
              ]
          )
          self.stages.append(stage)
          cur += depths[i]
  
      self.head = nn.Linear(dims[-1], num_classes)
      self.apply(init_weights)
  
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 ConvNeXt object from inherited base classes: nn with example: obj = ConvNeXt(in_chans, num_classes, depths, dims, drop_path_rate, layer_scale_init_value, head_init_scale)