code: def forward(self, x, H, W):
      x_shape = paddle.shape(x)
      B, N = x_shape[0], x_shape[1]
      C = self.dim
  
      q = (
          self.q(x)
          .reshape([B, N, self.num_heads, C // self.num_heads])
          .transpose([0, 2, 1, 3])
      )
  
      if self.sr_ratio > 1:
          x_ = x.transpose([0, 2, 1]).reshape([B, C, H, W])
          x_ = self.sr(x_).reshape([B, C, -1]).transpose([0, 2, 1])
          x_ = self.norm(x_)
          kv = (
              self.kv(x_)
              .reshape([B, -1, 2, self.num_heads, C // self.num_heads])
              .transpose([2, 0, 3, 1, 4])
          )
      else:
          kv = (
              self.kv(x)
              .reshape([B, -1, 2, self.num_heads, C // self.num_heads])
              .transpose([2, 0, 3, 1, 4])
          )
      k, v = kv[0], kv[1]
  
      attn = (q @ k.transpose([0, 1, 3, 2])) * self.scale
      attn = F.softmax(attn, axis=-1)
      attn = self.attn_drop(attn)
  
      x = (attn @ v).transpose([0, 2, 1, 3]).reshape([B, N, C])
      x = self.proj(x)
      x = self.proj_drop(x)
  
      return x
  
imports: import math
  from functools import partial
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import paddle.nn.initializer
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
  from paddleseg.models.backbones.transformer_utils import *
label_desc: Use image node_id 3 for calling the Attention obj's underlying member method code with example usage: obj.forward(x, H, W) and returns: x