code: def __init__(
      self, dim, heads=8, dim_head=64, dropout=0.0, patch_size=7
  ):
      super().__init__()
      inner_dim = dim_head * heads
      self.patch_size = patch_size
      self.heads = heads
      self.scale = dim_head**-0.5
  
      self.norm = LayerNorm(dim)
      self.to_q = nn.Conv2d(dim, inner_dim, 1, bias=False)
      self.to_kv = nn.Conv2d(dim, inner_dim * 2, 1, bias=False)
  
      self.to_out = nn.Sequential(
          nn.Conv2d(inner_dim, dim, 1), nn.Dropout(dropout)
      )
  
imports: import torch
  from torch import nn, einsum
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 1 to create a new LocalAttention object from inherited base classes: nn with example: obj = LocalAttention(dim, heads, dim_head, dropout, patch_size)