code: def forward(self, x):
      b, c, h, w = x.size()
      x = self.group_norm(x)
      q, k, v = tuple(
          t.view(b, c, h * w)
          for t in self.qkv(x).chunk(chunks=3, dim=1)
      )
      attn_matrix = (
          torch.bmm(k.permute(0, 2, 1), q) / math.sqrt(c)
      ).softmax(dim=-2)
      out = torch.bmm(v, attn_matrix).view(b, c, h, w)
      return self.out(out)
  
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 2 for calling the SelfAttention obj's underlying member method code with example usage: obj.forward(x) and returns: self