code: def forward(self, x):
      B, H, W, _ = x.shape
      N = H * W
      x = x.reshape(B, N, -1)
      qkv = (
          self.qkv(x)
          .view(B, N, 3, self.num_heads, -1)
          .permute(2, 0, 3, 1, 4)
      )
      # qkv with shape (3, B, nHead, H * W, C)
      q, k, v = qkv.reshape(3, B * self.num_heads, N, -1).unbind(0)
      # q, k, v with shape (B * nHead, H * W, C)
      q, k = self.q_norm(q), self.k_norm(k)
  
      if self.use_rel_pos:
          attn_bias = get_decomposed_rel_pos_bias(
              q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)
          )
      else:
          attn_bias = None
          if self.rope is not None:
              rope = self.rope.get_embed()
              q = apply_rot_embed_cat(q, rope).type_as(v)
              k = apply_rot_embed_cat(k, rope).type_as(v)
  
      if self.fused_attn:
          x = torch.nn.functional.scaled_dot_product_attention(
              q,
              k,
              v,
              attn_mask=attn_bias,
              dropout_p=self.attn_drop.p if self.training else 0.0,
          )
      else:
          q = q * self.scale
          attn = q @ k.transpose(-2, -1)
          if attn_bias is not None:
              attn = attn + attn_bias
          attn = attn.softmax(dim=-1)
          attn = self.attn_drop(attn)
          x = attn @ v
  
      x = (
          x.view(B, self.num_heads, N, -1)
          .transpose(1, 2)
          .reshape(B, N, -1)
      )
      x = self.proj(x)
      x = x.view(B, H, W, -1)
      return x
  
imports: import logging
  from functools import partial
  from typing import Callable, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch.jit import Final
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, PatchDropout, LayerNorm2d, ClassifierHead, NormMlpClassifierHead, Format, resample_abs_pos_embed_nhwc, RotaryEmbeddingCat, apply_rot_embed_cat, to_2tuple, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import generate_default_cfgs, register_model
  from ._features_fx import register_notrace_function
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.forward(x) and returns: x