code: def forward(self, x):
      x = x.reshape(
          (x.shape[0], x.shape[1], x.shape[2] * x.shape[3])
      ).transpose(
          (2, 0, 1)
      )  # NCHW -> (HW)NC
      max_len, batch_size, emb_dim = x.shape
      head_dim = self.head_dim
      x = paddle.concat(
          [paddle.mean(x, axis=0, keepdim=True), x], axis=0
      )
      x = x + paddle.unsqueeze(self.positional_embedding, 1)
      out = multi_head_attention_forward(
          x,
          self.num_heads,
          self.q_proj,
          self.k_proj,
          self.v_proj,
          self.c_proj,
      )
  
      return out[0]
  
imports: from typing import Optional
  import paddle
  import paddle.nn
  from paddle import Tensor
  from paddle.nn import functional
  from paddle.nn import Linear
label_desc: Use image node_id 2 for calling the AttentionPool2d obj's underlying member method code with example usage: obj.forward(x) and returns: out