code: def __init__(
      self,
      spacial_dim: int,
      embed_dim: int,
      num_heads: int,
      output_dim: int = None,
  ):
      super().__init__()
  
      self.positional_embedding = paddle.create_parameter(
          (spacial_dim**2 + 1, embed_dim), dtype="float32"
      )
  
      self.q_proj = nn.Linear(embed_dim, embed_dim, bias_attr=True)
      self.k_proj = nn.Linear(embed_dim, embed_dim, bias_attr=True)
      self.v_proj = nn.Linear(embed_dim, embed_dim, bias_attr=True)
      self.c_proj = nn.Linear(
          embed_dim, output_dim or embed_dim, bias_attr=True
      )
      self.num_heads = num_heads
  
      self.head_dim = embed_dim // num_heads
      assert (
          self.head_dim * num_heads == embed_dim
      ), "embed_dim must be divisible by num_heads"
  
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 1 to create a new AttentionPool2d object from inherited base classes: nn with example: obj = AttentionPool2d(spacial_dim, embed_dim, num_heads, output_dim)