code: def forward(self, x: Tensor) -> Tensor:
      # using post-norm
      N, C, H, W = x.shape
      x = x.permute(2, 3, 0, 1).flatten(
          0, 1
      )  # (N, C, H, W) -> (H * W, N, C)
  
      # NOTE: cache pos_emb as buffer
      pos_emb = self.build_2d_sincos_pos_emb(
          W, H, C, device=x.device, dtype=x.dtype
      )
      q = k = x + pos_emb
  
      attn, _ = self.self_attn(q, k, x, need_weights=True)
      x = self.norm1(x + self.dropout1(attn))
      x = self.norm2(x + self.dropout2(self.ffn(x)))
  
      x = x.view(H, W, N, C).permute(
          2, 3, 0, 1
      )  # (H * W, N, C) -> (N, C, H, W)
      return x
  
imports: from __future__ import annotations
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.nn.utils.fusion import fuse_conv_bn_weights
  from kornia.contrib.models.common import ConvNormAct
  from kornia.core import Module, Tensor, concatenate, pad
label_desc: Use image node_id 2 for calling the AIFI obj's underlying member method code with example usage: obj.forward(x) and returns: x