code: def __init__(self, dim, drop_path=0.0, layer_scale_init_value=1e-6):
      super().__init__()
      self.dwconv = nn.Conv2d(
          dim, dim, kernel_size=7, padding=3, groups=dim
      )  # depthwise conv
      self.norm = LayerNorm(
          dim, eps=1e-6, data_format=torch.contiguous_format
      )
      self.pwconv1 = nn.Conv2d(
          dim, 4 * dim, kernel_size=1, stride=1
      )  # nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers
      self.act = nn.GELU()
      self.pwconv2 = nn.Conv2d(
          4 * dim, dim, kernel_size=1, stride=1
      )  # nn.Linear(4 * dim, dim)
      self.gamma = (
          nn.Parameter(
              layer_scale_init_value * torch.ones((dim, 1, 1)),
              requires_grad=True,
          )
          if layer_scale_init_value > 0
          else None
      )
      self.drop_path = nn.Identity()
  
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 Block object from inherited base classes: nn with example: obj = Block(dim, drop_path, layer_scale_init_value)