code: def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, k=7):
      super().__init__()
      inner_dim = dim_head * heads
      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, k, stride=k, bias=False
      )
  
      self.dropout = nn.Dropout(dropout)
  
      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 GlobalAttention object from inherited base classes: nn with example: obj = GlobalAttention(dim, heads, dim_head, dropout, k)