code: def __init__(
      self,
      spacial_dim: int,
      embed_dim: int,
      num_heads: int,
      output_dim: int = None,
  ):
      super().__init__()
      self.positional_embedding = nn.Parameter(
          torch.randn(spacial_dim**2 + 1, embed_dim)
          / embed_dim**0.5
      )
      self.k_proj = nn.Linear(embed_dim, embed_dim)
      self.q_proj = nn.Linear(embed_dim, embed_dim)
      self.v_proj = nn.Linear(embed_dim, embed_dim)
      self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
      self.num_heads = num_heads
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
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)