code: def __init__(
      self, dim, key_dim, num_heads=8, attn_ratio=4, resolution=(14, 14)
  ):
      super().__init__()
      assert isinstance(resolution, tuple) and len(resolution) == 2
      self.num_heads = num_heads
      self.scale = key_dim**-0.5
      self.key_dim = key_dim
      self.nh_kd = nh_kd = key_dim * num_heads
      self.d = int(attn_ratio * key_dim)
      self.dh = int(attn_ratio * key_dim) * num_heads
      self.attn_ratio = attn_ratio
      h = self.dh + nh_kd * 2
  
      self.norm = nn.LayerNorm(dim)
      self.qkv = nn.Linear(dim, h)
      self.proj = nn.Linear(self.dh, dim)
  
      points = list(
          itertools.product(range(resolution[0]), range(resolution[1]))
      )
      N = len(points)
      attention_offsets = {}
      idxs = []
      for p1 in points:
          for p2 in points:
              offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
              if offset not in attention_offsets:
                  attention_offsets[offset] = len(attention_offsets)
              idxs.append(attention_offsets[offset])
      self.attention_biases = self.create_parameter(
          shape=(num_heads, len(attention_offsets)),
          dtype="float32",
          default_initializer=nn.initializer.Constant(0.0),
      )
      self.register_buffer(
          "attention_bias_idxs",
          paddle.to_tensor(idxs, dtype=paddle.int64).reshape((N, N)),
          persistable=False,
      )
  
imports: import itertools
  import paddle
  from paddle import nn
  from paddle.nn import functional
  from .common import LayerNorm2d
label_desc: Use image node_id 1 to create a new Attention object from inherited base classes: nn with example: obj = Attention(dim, key_dim, num_heads, attn_ratio, resolution)