code: def __init__(
      self, dim, attn_type, window_size, dim_head=32, dropout=0.0
  ):
      super().__init__()
      assert attn_type in {
          "short",
          "long",
      }, "attention type must be one of local or distant"
      heads = dim // dim_head
      self.heads = heads
      self.scale = dim_head**-0.5
      inner_dim = dim_head * heads
  
      self.attn_type = attn_type
      self.window_size = window_size
  
      self.norm = LayerNorm(dim)
  
      self.dropout = nn.Dropout(dropout)
  
      self.to_qkv = nn.Conv2d(dim, inner_dim * 3, 1, bias=False)
      self.to_out = nn.Conv2d(inner_dim, dim, 1)
  
      # positions
  
      self.dpb = DynamicPositionBias(dim // 4)
  
      # calculate and store indices for retrieving bias
  
      pos = torch.arange(window_size)
      grid = torch.stack(torch.meshgrid(pos, pos, indexing="ij"))
      grid = rearrange(grid, "c i j -> (i j) c")
      rel_pos = grid[:, None] - grid[None, :]
      rel_pos += window_size - 1
      rel_pos_indices = (
          rel_pos * torch.tensor([2 * window_size - 1, 1])
      ).sum(dim=-1)
  
      self.register_buffer(
          "rel_pos_indices", rel_pos_indices, persistent=False
      )
  
imports: import torch
  from torch import nn, einsum
  from einops import rearrange
  from einops.layers.torch import Rearrange, Reduce
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new Attention object from inherited base classes: nn with example: obj = Attention(dim, attn_type, window_size, dim_head, dropout)