code: def forward(self, x: Tensor) -> Tensor:
      B, N, _ = x.shape
      x = self.norm(x)
      qkv = self.qkv(x)
      qkv = qkv.view(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
      q, k, v = qkv.split([self.key_dim, self.key_dim, self.d], dim=3)
  
      bias = (
          self.attention_biases[:, self.attention_bias_idxs]
          if self.training
          else self.ab
      )
      attn = (q @ k.transpose(-2, -1)) * self.scale + bias
  
      attn = attn.softmax(dim=-1)
      x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)
      x = self.proj(x)
      return x
  
imports: from __future__ import annotations
  import itertools
  import warnings
  from typing import Any, Optional
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.utils import checkpoint
  from kornia.contrib.models.common import DropPath, LayerNorm2d, window_partition, window_unpartition
  from kornia.core import Module, Tensor
  from kornia.core.check import KORNIA_CHECK
label_desc: Use image node_id 4 for calling the Attention obj's underlying member method code with example usage: obj.forward(x) and returns: x