code: def forward(self, x):
      qkv = (
          self.qkv(x)
          .reshape((0, -1, 3, self.num_heads, self.head_dim))
          .transpose((2, 0, 3, 1, 4))
      )
      q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
  
      attn = q.matmul(k.transpose((0, 1, 3, 2)))
      if self.mixer == "Local":
          attn += self.mask
      attn = nn.functional.softmax(attn, axis=-1)
      attn = self.attn_drop(attn)
  
      x = (
          (attn.matmul(v))
          .transpose((0, 2, 1, 3))
          .reshape((0, -1, self.dim))
      )
      x = self.proj(x)
      x = self.proj_drop(x)
      return x
  
imports: from paddle import ParamAttr
  from paddle.nn.initializer import KaimingNormal
  import numpy
  import paddle
  import paddle.nn
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal
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