code: def forward(self, x, rel_pos_bias=None):
      x_shape = paddle.shape(x)
      N, C = x_shape[1], x_shape[2]
  
      qkv_bias = None
      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)
  
      qkv = qkv.reshape(
          (-1, N, 3, self.num_heads, C // self.num_heads)
      ).transpose((2, 0, 3, 1, 4))
      q, k, v = qkv[0], qkv[1], qkv[2]
      attn = (q.matmul(k.transpose((0, 1, 3, 2)))) * self.scale
  
      if self.relative_position_bias_table is not None:
          relative_position_bias = self.relative_position_bias_table[
              self.relative_position_index.reshape([-1])
          ].reshape(
              [
                  self.window_size[0] * self.window_size[1] + 1,
                  self.window_size[0] * self.window_size[1] + 1,
                  -1,
              ]
          )  # Wh*Ww,Wh*Ww,nH
          relative_position_bias = relative_position_bias.transpose(
              (2, 0, 1)
          )  # .contiguous()  # nH, Wh*Ww, Wh*Ww
          attn = attn + relative_position_bias.unsqueeze(0)
      if rel_pos_bias is not None:
          attn = attn + rel_pos_bias
  
      attn = nn.functional.softmax(attn, axis=-1)
      attn = self.attn_drop(attn)
  
      x = (attn.matmul(v)).transpose((0, 2, 1, 3)).reshape((-1, N, C))
      x = self.proj(x)
      x = self.proj_drop(x)
      return x
  
imports: import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  from paddle.nn.initializer import Constant
  from ppdet.modeling.shape_spec import ShapeSpec
  from ppdet.core.workspace import register, serializable
  from .transformer_utils import zeros_, DropPath, Identity
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.forward(x, rel_pos_bias) and returns: x