code: def forward(self, x):  # x (B,N,C)
      B, N, _ = x.shape
  
      # Normalization
      x = self.norm(x)
  
      qkv = self.qkv(x)
      # (B, N, num_heads, d)
      q, k, v = qkv.reshape((B, N, self.num_heads, -1)).split(
          [self.key_dim, self.key_dim, self.d], axis=3
      )
      # (B, num_heads, N, d)
      q = q.transpose((0, 2, 1, 3))
      k = k.transpose((0, 2, 1, 3))
      v = v.transpose((0, 2, 1, 3))
  
      # only for infer
      attn = (q @ k.transpose((0, 1, 3, 2))) * self.scale + self.ab
      attn = F.softmax(attn, axis=-1)
      x = (attn @ v).transpose((0, 2, 1, 3)).reshape((B, N, self.dh))
      x = self.proj(x)
      return x
  
imports: import itertools
  import paddle
  from paddle import nn
  from paddle.nn import functional
  from .common import LayerNorm2d
label_desc: Use image node_id 3 for calling the Attention obj's underlying member method code with example usage: obj.forward(x) and returns: x