code: def __init__(self, dim, groups=32, **kwargs):
      super().__init__()
      self.group_norm = nn.GroupNorm(groups, dim)
      self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1)
      self.out = nn.Conv2d(dim, dim, kernel_size=1)
  
imports: import math
  import kornia
  import torch
  import torch.nn.functional
  from einops import rearrange
  from torch import nn
label_desc: Use image node_id 1 to create a new SelfAttention object from inherited base classes: nn with example: obj = SelfAttention(dim, groups)