code: def __init__(
      self, dim, heads=8, dim_head=32, dropout=0.0, window_size=7
  ):
      super().__init__()
      self.heads = heads
      self.scale = dim_head**-0.5
      self.window_size = window_size
      inner_dim = dim_head * heads
  
      self.norm = ChanLayerNorm(dim)
  
      self.attend = nn.Sequential(
          nn.Softmax(dim=-1), nn.Dropout(dropout)
      )
  
      self.to_qkv = nn.Conv1d(dim, inner_dim * 3, 1, bias=False)
  
      # window tokens
  
      self.window_tokens = nn.Parameter(torch.randn(dim))
  
      # prenorm and non-linearity for window tokens
      # then projection to queries and keys for window tokens
  
      self.window_tokens_to_qk = nn.Sequential(
          nn.LayerNorm(dim_head),
          nn.GELU(),
          Rearrange("b h n c -> b (h c) n"),
          nn.Conv1d(inner_dim, inner_dim * 2, 1),
          Rearrange("b (h c) n -> b h n c", h=heads),
      )
  
      # window attention
  
      self.window_attend = nn.Sequential(
          nn.Softmax(dim=-1), nn.Dropout(dropout)
      )
  
      self.to_out = nn.Sequential(
          nn.Conv2d(inner_dim, dim, 1), nn.Dropout(dropout)
      )
  
imports: from functools import partial
  import torch
  from torch import nn, einsum
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange, Reduce
label_desc: Use image node_id 1 to create a new DSSA object from inherited base classes: nn with example: obj = DSSA(dim, heads, dim_head, dropout, window_size)