code: def forward(self, x):
      B, H, W, C = paddle.shape(x)
  
      if self.q_bias is not None:
          qkv_bias = paddle.concat(
              (self.q_bias, paddle.zeros_like(self.v_bias), self.v_bias)
          )
          qkv = F.linear(x, weight=self.qkv.weight, bias=qkv_bias)
      else:
          qkv = (
              self.qkv(x)
              .reshape([B, H * W, 3, self.num_heads, self.head_dim])
              .transpose([2, 0, 3, 1, 4])
              .reshape([3, B * self.num_heads, H * W, self.head_dim])
          )
  
      q, k, v = qkv[0], qkv[1], qkv[2]
      attn = q.matmul(k.transpose([0, 2, 1])) * self.scale
  
      if self.use_rel_pos:
          attn = self.add_decomposed_rel_pos(attn, q, H, W)
  
      attn = F.softmax(attn, axis=-1)
      attn = self.attn_drop(attn)
      x = (
          attn.matmul(v)
          .reshape([B, self.num_heads, H * W, self.head_dim])
          .transpose([0, 2, 1, 3])
          .reshape([B, H, W, C])
      )
      x = self.proj(x)
      return x
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  import math
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Constant, TruncatedNormal
  from ppdet.modeling.shape_spec import ShapeSpec
  from ppdet.core.workspace import register, serializable
  from .transformer_utils import zeros_, DropPath, Identity, window_partition, window_unpartition
  from ..initializer import linear_init_
label_desc: Use image node_id 5 for calling the Attention obj's underlying member method code with example usage: obj.forward(x) and returns: x