code: def __init__(self, dim, kernel_size=3):
      super().__init__()
      assert (kernel_size % 2) == 1
      padding = kernel_size // 2
  
      self.net = nn.Sequential(
          nn.LayerNorm(dim),
          Rearrange("b h w c -> b c h w"),
          nn.Conv2d(dim, dim, kernel_size, padding=padding, groups=dim),
          nn.BatchNorm2d(dim),
          nn.GELU(),
          nn.Conv2d(dim, dim, kernel_size, padding=padding, groups=dim),
          Rearrange("b c h w -> b h w c"),
      )
  
imports: from random import randrange
  import torch
  from torch import nn, einsum
  from torch.nn import Module, ModuleList
  import torch.nn.functional
  from einops import rearrange, repeat, pack, unpack
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 1 to create a new LocalPatchInteraction object from inherited base classes: Module with example: obj = LocalPatchInteraction(dim, kernel_size)